{"@type":"dcat:Catalog","describedBy":"https://project-open-data.cio.gov/v1.1/schema/catalog.json","conformsTo":"https://project-open-data.cio.gov/v1.1/schema","@context":"https://project-open-data.cio.gov/v1.1/schema/data.json","dataset":[{"identifier":"EBC9DB05EDEA5B0EE043065706812DF81","accessLevel":"public","contactPoint":{"hasEmail":"mailto:gretchen.greene@nist.gov","fn":"Gretchen Greene"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/EBC9DB05EDEA5B0EE043065706812DF81","description":"National Institute of Standards and Technology (NIST) public data inventory is a catalog of digital products generated from the NIST enterprise data inventory (EDI).   The catalog is dynamically updated in coordination with mission goals for the dissemination of information for discovery and access.   It includes digital products derived from multiple disciplines of scientific, engineering and technology areas of research and operation.  This inventory is provided as a data.json file format, based on the DCAT-US Schema v1.1 standard definition.","language":["en"],"title":"NIST Public Data Inventory","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/EBC9DB05EDEA5B0EE043065706812DF81/data.json","mediaType":"application/json"},{"downloadURL":"https://data.nist.gov/od/ds/EBC9DB05EDEA5B0EE043065706812DF81/data.json.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2025-07-09 11:35:58","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology","Information Technology:Data and informatics"],"keyword":["NIST","research data","DCAT","enterprise data inventory"]},{"identifier":"EBC9DB05EDEC5B0EE043065706812DF83","accessLevel":"public","references":["https://doi.org/10.3390/atoms8030056"],"contactPoint":{"hasEmail":"mailto:alexander.kramida@nist.gov","@type":"vcard:Contact","fn":"Alexander Kramida"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/EBC9DB05EDEC5B0EE043065706812DF83","description":"This database provides access and search capability for NIST critically evaluated data on atomic energy levels, wavelengths, and transition probabilities that are reasonably up-to-date. The Atomic Spectroscopy Data Center has carried out these critical compilations. The Data Center is located in the Physical Measurement Laboratory at the National Institute of Standards and Technology (NIST).","language":["en"],"title":"NIST Atomic Spectra Database - SRD 78","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4W30F","format":"text/html","description":"DOI Access to NIST Atomic Spectra Database - SRD 78","mediaType":"text/html","title":"DOI Access to NIST Atomic Spectra Database - SRD 78"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-11-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Spectroscopy","Standards:Reference data"],"keyword":["cesium","chlorine","chromium","cobalt","columbium","copernicium","copper","curium","darmstadtium","database","deuterium","doubly-charged","dubnium","dysprosium","einsteinium","element","energy levels","erbium","europium","fermium","flerovium","fluorine","francium","gadolinium","gallium","germanium","gold","ground states","hafnium","hassium","helium","holmium","hydrogen","indium","iodine","ionization potentials","ionization-energies","ionization-limits","ionized-atoms","iridium","iron","mendelevium","mercury","molybdenum","multiply-charged","neodymium","neon","neptunium","neutral","nickel","niobium","nitrogen","nobelium","osmium","oxygen","palladium","phosphorus","platinum","plutonium","polonium","potassium","praseodymium","promethium","protactinium","quadruply-charged","radium","rhenium","rhodium","roentgenium","rubidium","ruthenium","rutherfordium","samarium","scandium","seaborgium","selenium","silicium","silicon","silver","singly-charged","sodium","spectra","spectral lines","spectroscopy","spectrum","strontium","sulfur","sulphur","tantalum","technetium","tellurium","terbium","thallium","thorium","thulium","tin","titanium","transition probabilities","triply-charged","tritium","tungsten","ununbium","ununhexium","ununoctium","ununpentium","ununquadium","ununseptium","ununtrium","unununium","uranium","vanadium","wavelengths","xenon","ytterbium","yttrium","zinc","zirconium","krypton","kurchatovium","lawrencium","lanthanum","lead","lithium","livermorium","lutetium","magnesium","manganese","meitnerium","Atomic","Atomic Spectroscopy","Radon","actinium","aluminium","aluminum","americium","antimony","argon","arsenic","astatine","atom","atomic physics","atomic property","atomic-ions","barium","berkelium","beryllium","bismuth","bohrium","boron","bromine","cadmium","calcium","californium","carbon","cerium"]},{"identifier":"EBC9DB05EDED5B0EE043065706812DF84","accessLevel":"public","contactPoint":{"hasEmail":"mailto:chris.muzny@nist.gov","@type":"vcard:Contact","fn":"Chris Muzny"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://kinetics.nist.gov/kinetics/","description":"The NIST Chemical Kinetics Database includes essentially all reported kinetics results for thermal gas-phase chemical reactions. The database is designed to be searched for kinetics data based on the specific reactants involved, for reactions resulting in specified products, for all the reactions of a particular species, or for various combinations of these. In addition, the bibliography can be searched by author name or combination of names. The database contains in excess of 38,000 separate reaction records for over 11,700 distinct reactant pairs. These data have been abstracted from over 12,000 papers with literature coverage through early 2000. Rate constant records for a specified reaction are found by searching the Reaction Database. All rate constant records for that reaction are returned, with a link to 'Details' on that record. Each rate constant record contains the following information (as available): a) Reactants and, if defined, reaction products; b) Rate parameters: A, n, Ea/R, where k = A* (T/298)**n exp[-(Ea/R)/T], where T is the temperature in Kelvins; c) Uncertainty in A, n, and Ea/R, if reported; d) Temperature range of experiment or temperature range of validity of a review or theoretical paper; e) Pressure range and bulk gas of the experiment; f) Data type of the record (i.e., experimental, relative rate measurement, theoretical calculation, modeling result, etc.). If the result is a relative rate measurement, then the reaction to which the rate is relative is also given; g) Experimental procedure, including separate fields for the description of the apparatus, the time resolution of the experiment, and the excitation technique. A majority of contemporary chemical kinetics methods are represented. The Kinetics Database is being expanded to include other resources for the convenience of the users. Presently this includes direct links to the corresponding NIST WebBook page for all substances for which such a link is possible. This is indicated by underling and highlighting the species. The WebBook provides thermodynamic, spectral, and other data on the species. Note that the link to the WebBook is opened as a new frame in your browser.","language":["en"],"title":"NIST Chemical Kinetics Database","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4Z302","mediaType":"text/html","title":"DOI Access to NIST Chemical Kinetics Database"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-10-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Thermochemical properties"],"keyword":["NIST chemical kinetics database","arrhenius equation","chemical kinetics","chemical reaction","kinetics","kinetics database","rate coefficient","rate constant","reaction","reaction kinetics","reaction rate constant"]},{"identifier":"EBC9DB05EDEE5B0EE043065706812DF85","accessLevel":"public","references":["https://dx.doi.org/10.1021/je000236i"],"contactPoint":{"hasEmail":"mailto:peter.linstrom@nist.gov","@type":"vcard:Contact","fn":"Peter Linstrom"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://webbook.nist.gov/chemistry","description":"The NIST Chemistry WebBook provides users with easy access to chemical and physical property data for chemical species through the internet. The data provided in the site are from collections maintained by the NIST Standard Reference Data Program and outside contributors. Data in the WebBook system are organized by chemical species. The WebBook system allows users to search for chemical species by various means. Once the desired species has been identified, the system will display data for the species.  Data include thermochemical properties of species and reactions, thermophysical properties of species, and optical, electronic and mass spectra.","language":["en"],"title":"NIST Chemistry WebBook - SRD 69","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4D303","mediaType":"text/html"},{"accessURL":"https://dx.doi.org/10.18434/T4D303","format":"text/html","description":"a persistent identifier (DOI) to access NIST Chemistry WebBook, SRD 69","mediaType":"text/html","title":"DOI access for NIST Chemistry WebBook - SRD 69"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-06-21","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry: Chemical engineering and processing","Chemistry: Chemical thermodynamics and chemical properties","Standards: Reference data","Chemistry: Thermochemical properties","Chemistry: Analytical chemistry","Physics: Spectroscopy"],"keyword":["IR spectrum","InChI","InChIKey","UV Vis spectrum","boiling point","chemical data","chemical structure","enthalpy","entropy","heat capacity","heat of formation","ionization potential","mass spectrum","gas chromatography","retention index","thermochemical data","thermochemistry","thermodynamic data","vapor pressure","reaction thermochemistry","electronic and vibrational spectra","constants of diatomic molecules","ion energetics","appearance energy","electron affinity","proton affinity","gas basicity","thermophysical fluid property data","thermal conductivity","sound speed"]},{"identifier":"EBC9DB05EDEF5B0EE043065706812DF86","accessLevel":"public","contactPoint":{"hasEmail":"mailto:talapady.bhat@nist.gov","fn":"Talapady N. Bhat"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/EBC9DB05EDEF5B0EE043065706812DF86","description":"Chemical Block Layered Alignment of Substructure or Chem-BLAST uses a method for finding chemical compounds within a large collection. In this method, all chemical compounds are annotated in terms of standard chemical structural fragments. These fragments are then organized into a data tree based on their chemical substructures. Search engines have been developed to use this data tree. These search engines use the Chem-BLAST technique to search on the fragments and look for their chemical structural neighbors. The technique was originally developed in the context of the HIV Structural database to enable a query on inhibitors of HIV protease. (See http://xpdb.nist.gov/hivsdb/hivsdb.html.) Recently the method has been significantly improved to extend to the ligands found in the Protein Data Bank (PDB). (See http://xpdb.nist.gov/chemblast/pdb.html.)  The method establishes a tree-like relationship between the rings found in three-letter codes that denote ligands of the structures found in the PDB. Semantic Web relations are established between the structural scaffolds of the ligands and organizes them in an XML database utilizing the Web's Resource Description Framework (RDF). An Adobe Flex-based interface is used to present this information on the Web. Plans are under way to extend this work to non-ring type scaffolds as well. Chem-BLAST has also been extended to structures in PubChem. http://xpdb.nist.gov/chemblast/pdb.pl which includes several non-ring standard reused groups such as sulfates. Efforts are under way to use the underlying principles of Chem-BLAST to enable query on non-structural data, such as cell image data http://xpdb.nist.gov/cell/image.pl.","language":["en"],"title":"NIST Chem-BLAST Gateway for PDB","distribution":[{"accessURL":"http://dx.doi.org/10.18434/T4DS34","format":"text/html","description":"DOI Access to NIST Chem-BLAST Gateway for PDB","title":"DOI Access to NIST Chem-BLAST Gateway for PDB"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2013-04-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Thermodynamics","Information Technology:Data and informatics","Bioscience"],"keyword":["Cell-image-data","Chem-BLAST","Enabling-scientific-linked-data-by-automation","Federated-terms-building","Global-data-integration-challenge-solutions","Infrastructure-for-semantic-terminology","Latin-like-root-terminology-for-science","Machine-friendly-vocabulary","On-demand-ontology-nuggets","PDB-ligands","PubChem-structures","Re-used-nuggets-of-ontology","Re-used-scalable-terminology","Re-used-use-case-friendly-terminology","Rule-based-linking-of-data","Rule-based-structural-data-graphs","Rule-based-vocabulary-building","Sanskrit-like-root-terminology-for-science","Structural-resource-for-drug-design","Thermodynamic-data"]},{"identifier":"EBC9DB05EDF05B0EE043065706812DF87","accessLevel":"public","references":["https://dx.doi.org/10.1016/j.cossms.2013.10.001"],"contactPoint":{"hasEmail":"mailto:lucas.hale@nist.gov","@type":"vcard:Contact","fn":"Lucas Hale"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/EBC9DB05EDF05B0EE043065706812DF87","description":"This repository provides a source for interatomic potentials (force fields), related files, and evaluation tools to help researchers obtain interatomic models and judge their quality and applicability. Users are encouraged to download and use interatomic potentials, with proper acknowledgement, and developers are welcome to contribute potentials for inclusion. The files provided have been submitted or vetted by their developers and appropriate references are provided. All classes of potentials (e.g., MEAM, ADP, COMB, Reax, EAM, etc.) and materials are welcome. Interatomic potentials and/or related files are currently available for various metals, semiconductors, oxides, and carbon-containing systems.","language":["en"],"title":"NIST Interatomic Potentials Repository","distribution":[{"accessURL":"http://www.ctcms.nist.gov/potentials","mediaType":"text/html","title":"Interatomic Potentials Repository Project"},{"accessURL":"https://doi.org/10.18434/m37","format":"text/html","description":"DOI Access to NIST Interatomic Potentials Repository","title":"DOI Access to NIST Interatomic Potentials Repository"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-02-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Modeling and computational material science"],"keyword":["force fields","integrated computational materials engineering","interatomic potentials","materials genome initiative","materials modeling","materials science","molecular dynamics","molecular simulation","materials"]},{"identifier":"EBC9DB05EDF45B0EE043065706812DF811","accessLevel":"public","references":["http://wtt-pro.nist.gov/wtt-pro/help/toc.html"],"contactPoint":{"hasEmail":"mailto:ala.bazyleva@nist.gov","@type":"vcard:Contact","fn":"Ala Bazyleva"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/srd/nist-standard-reference-database-203-web-thermo-tables-wtt-professional-edition","description":"The Web Thermo Tables (WTT) are a collection of critically evaluated thermodynamic property data for pure compounds with a primary focus on organics. These data were generated through dynamic data analysis, as implemented in the NIST ThermoData Engine software package. Also included are some critically evaluated data from the historical TRC Thermodynamic Tables archive. The Professional Edition contains information on over 28,000 compounds. For a subset of the data focused on commonly-used pure compounds please see the Lite Edition (http://www.nist.gov/srd/nistwebsub2.cfm) .","language":["en"],"title":"NIST Web Thermo Tables (WTT) - Professional Edition - SRD 203","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4BS3H","format":"text/html","description":"DOI Access to Web Thermo Tables - Professional Edition - SRD 203","mediaType":"text/html","title":"DOI Access to Web Thermo Tables - Professional Edition - SRD 203"},{"accessURL":"https://wtt-pro.nist.gov/wtt-pro/","description":"Some information from the WTT-Pro database can be accessed without having a subscription.  These include properties provided for a given compound, ranges of validity, number of experimental data points.","title":"Complimentary Access to WTT-Pro Database"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Access to WTT - Professional Edition is subscription-based.For more information please visit http://www.nist.gov/srd/nistwebsub3.cfm","modified":"2012-05-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["NIST ThermoData Engine","critically-evaluated data","dynamic data evaluation","organic compounds - thermodynamics","thermophysical properties"]},{"identifier":"ECBCC1C130062ED9E04306570681B10712","accessLevel":"public","references":["https://srdata.nist.gov/xps/Intro","https://datascience.codata.org/articles/abstract/171/","https://dx.doi.org/10.1016/j.elspec.2011.12.001"],"contactPoint":{"hasEmail":"mailto:justin.gorham@nist.gov","@type":"vcard:Contact","fn":"Justin Gorham"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://srdata.nist.gov/xps/","description":"NIST X-ray Photoelectron Spectroscopy Database XPS contains over 33,000 data records that can be used for the identification of unknown lines, retrieval of data for selected elements (binding energy, Auger kinetic energy, chemical shift, and surface or interface core-level shift), retrieval of data for selected compounds (according to chemical name, selected groups of elements, or chemical classes), display of Wagner plots, and retrieval of data by scientific citation. For the newer data records, additional information is provided on the specimen material, the conditions of measurement, and the analysis of the data. Version 5.0 includes the addition of Digital Object Identifiers (DOI) to each of the citations.  Additionally, Version 5.0 has new features including chemical shift plots, custom-built components for displaying both formatted molecular formulas and formatted spectral lines, and spectral sorting functions of photoelectron lines and Auger Parameters.","language":["en"],"title":"NIST X-ray Photoelectron Spectroscopy Database - SRD 20","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4T88K","description":"DOI Access to NIST X-ray Photoelectron Spectroscopy Database","mediaType":"text/html","title":"DOI Access to NIST X-ray Photoelectron Spectroscopy Database"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2012-10-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://srdata.nist.gov/xps/DataDefinition","accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Auger electron","Auger electron spectroscopy","Auger kinetic energy","Auger parameter","ESCA","XPS","doublet separation","electron spectroscopy for chemical analysis","material database","photoelectron","photoelectron spectroscopy","photoemission","surface analysis","X-ray photoelectron spectroscopy","Binding energies","Chemical shifts","Wagner plots"]},{"identifier":"ECBCC1C130072ED9E04306570681B10713","accessLevel":"public","references":["https://www.nist.gov/system/files/documents/srd/SRD64UsersGuideV3-2.pdf"],"contactPoint":{"hasEmail":"mailto:angela.lee@nist.gov","@type":"vcard:Contact","fn":"Angela Y. Lee"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://srdata.nist.gov/srd64","description":"**** Note that this SRD is superseded by SRD 64 Version 4.0. ****The NIST Electron Elastic-Scattering Cross-Section Database provides values of differential elastic-scattering cross sections, total elastic-scattering cross sections, phase shifts, and transport cross sections in electron-atom scattering for elements with atomic numbers from 1 to 96 and for electron energies between 50 eV and 300 keV (in steps of 1 eV). Knowledge of elastic-scattering effects is important for the development of theoretical models for quantitative analysis by Auger-electron spectroscopy, X-ray photoelectron spectroscopy, electron microprobe analysis, and analytical electron microscopy. These data are also needed for modeling of electron transport in radiation dosimetry, electron-beam lithography, and interactions of ionizing radiation with matter. The database is designed to facilitate simulations of electron transport for these and similar applications in which electron energies from 50 eV to 300 keV are utilized.An analysis of available elastic-scattering cross-section data has been published [A. Jablonski, F. Salvat, and C. J. Powell, J. Phys. Chem. Ref. Data 33, 409 (2004)].","language":["en"],"title":"NIST Electron Elastic-Scattering Cross-Section Database - SRD 64 Version 3.2","distribution":[{"accessURL":"https://www-s.nist.gov/srd_online/index.cfm?fuseaction=home.main&productID=SRD64v3.2","description":"This database is free, but requires that a form be filled out in order to be able to download.","title":"Form to download the database"},{"accessURL":"https://dx.doi.org/10.18434/T4NK50","format":"text/html","description":"DOI Access to Electron Elastic-Scattering Cross-Section Database - SRD 64 Version 3.2","mediaType":"text/html","title":"DOI Access to Electron Elastic-Scattering Cross-Section Database - SRD 64 Version 3.2"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2012-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Auger electron spectroscopy","analytical electron microscopy","cross-section","elastic scattering","electron scattering","electron spectroscopy","electron transport","electron-probe microanalysis","surface analysis","x-ray photoelectron spectroscopy","x-ray spectroscopy"]},{"identifier":"ECBCC1C130082ED9E04306570681B10714","accessLevel":"public","references":["https://www.nist.gov/system/files/documents/srd/SRD71UsersGuideV1-2.pdf","https://dx.doi.org/10.1063/1.556035"],"contactPoint":{"hasEmail":"mailto:justin.gorham@nist.gov","@type":"vcard:Contact","fn":"Justin Gorham"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/srd/nist-standard-reference-database-71","description":"The NIST Electron Inelastic-Mean-Free-Path Database provides values of electron inelastic mean free paths (IMFPs) principally for use in surface analysis by Auger-electron spectroscopy and X-ray photoelectron spectroscopy. The database includes IMFPs calculated from experimental optical data and IMFPs measured by elastic-peak electron spectroscopy. If no calculated or measured IMFPs are available for a material of interest, values can be estimated from the predictive IMFP formulae of Tanuma et al. and of Gries. IMFPs are available for electron energies between 50 eV and 10,000 eV although most of the available data are for energies less than 2,000 eV. A critical review of calculated and measured IMFPs has been published [C. J. Powell and A. Jablonski, J. Phys. Chem. Ref. Data 28, 19 (1999)].","language":["en"],"title":"NIST Electron Inelastic-Mean-Free-Path Database - SRD 71","distribution":[{"accessURL":"https://www-s.nist.gov/srd_online/index.cfm?fuseaction=home.main&productID=SRD71V1.2","description":"This database is free, but requires that a form be filled out in order to be able to download.","title":"Form to download the database"},{"accessURL":"https://dx.doi.org/10.18434/T48C78","format":"text/html","description":"DOI Access to NIST Electron Inelastic-Mean-Free-Path Database - SRD 71","mediaType":"text/html","title":"DOI Access to NIST Electron Inelastic-Mean-Free-Path Database - SRD 71"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2010-12-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Auger electron","Auger electron spectroscopy","ESCA","X ray photoelectron spectroscopy","XPS","electron spectroscopy for chemical analysis","inelastic mean free path","material database","photoelectron","photoelectron spectroscopy","photoemission","surface analysis"]},{"identifier":"ECBCC1C130092ED9E04306570681B10715","accessLevel":"public","references":["https://www.nist.gov/system/files/documents/srd/SRD82UsersGuideV1-3.pdf","https://dx.doi.org/10.1116/1.3071947"],"contactPoint":{"hasEmail":"mailto:justin.gorham@nist.gov","@type":"vcard:Contact","fn":"Justin Gorham"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/srd/nist-standard-reference-database-82","description":"The NIST Electron Effective Attenuation Length Database provides values of electron effective attenuation lengths (EALs) in materials at user-selected electron energies between 50 eV and 2,000 eV. The database was designed mainly to provide EALs (to account for effects of elastic-electron scattering) for measurements of the thicknesses of overlayer films and, to a much lesser extent, for measurements of the depths of thin marker layers. EALs are calculated using an algorithm based on electron transport theory for measurement conditions specified by the user. A critical review on the EAL has been published [A. Jablonski and C. J. Powell, Surf. Science Reports 47, 33 (2002)], and simple practical expressions for the EAL, mean escape depth, and information depth are given in another paper by the same authors [J. Vac. Sci. Technol. A 27, 253 (2009)].","language":["en"],"title":"NIST Electron Effective-Attenuation-Length Database - SRD 82","distribution":[{"accessURL":"https://www-s.nist.gov/srd_online/index.cfm?fuseaction=home.main&productID=SRD82v1.3","description":"This database is free, but requires that a form be filled out in order to be able to download.","title":"Form to download the database"},{"accessURL":"https://dx.doi.org/10.18434/T4MK5P","format":"text/html","description":"DOI Access to NIST Electron Effective-Attenuation-Length Database - SRD 82","mediaType":"text/html","title":"DOI Access to NIST Electron Effective-Attenuation-Length Database - SRD 82"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2011-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Auger electron spectroscopy","ESCA","XPS","depth distribution function","effective attenuation length","electron spectroscopy for chemical analysis","material database","photoelectron","photoelectron spectroscopy","photoemission","surface analysis","x-ray photoelectron spectroscopy"]},{"identifier":"ECBCC1C1300A2ED9E04306570681B10716","accessLevel":"public","references":["https://www.nist.gov/document/sessa211usersguideaug2018pdf","https://dx.doi.org/10.1002/sia.2097"],"contactPoint":{"hasEmail":"mailto:justin.gorham@nist.gov","@type":"vcard:Contact","fn":"Justin Gorham"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/srd/nist-standard-reference-database-100","description":"The NIST Database for the Simulation of Electron Spectra for Surface Analysis (SESSA) can be used to simulate Auger-electron spectra and X-ray photoelectron spectra of nanostructures such as islands, lines, spheres, and layered spheres on surfaces. As for earlier versions, such simulations can be performed for multilayer films. Users can specify the compositions and dimensions of each material in the sample structure as well as the measurement configuration. The database contains extensive physical data needed for quantitative interpretations of observed spectra. A more detailed description of SESSA has been published [W. Smekal, W. S. M. Werner, and C. J. Powell Surf. Interface Anal. 37, 1059 (2005)].","language":["en"],"title":"NIST Database for the Simulation of Electron Spectra for Surface Analysis (SESSA) - SRD 100","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4C30S","format":"text/html","description":"DOI Access to NIST Database for the Simulation of Electron Spectra for Surface Analysis (SESSA) - SRD 100","mediaType":"text/html","title":"DOI Access to NIST Database for the Simulation of Electron Spectra for Surface Analysis (SESSA) - SRD 100"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2014-11-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Auger electron spectroscopy","Auger-electron","Auger-electron backscattering factors","XPS","cross sections","elastic scattering","electron scattering","electron transport","electron-impact ionization cross section","fluorescence yields","inelastic mean free paths","inelastic scattering","photoelectron lineshapes","photoionization asymmetry parameters","photoionization cross sections","surface analysis","transport cross sections","x-ray photoelectron spectroscopy"]},{"identifier":"ECBCC1C1300B2ED9E04306570681B10717","accessLevel":"public","references":["https://dx.doi.org/10.1016/j.susc.2010.07.030","https://www.nist.gov/system/files/documents/2017/06/27/srd_154_users_guide_version_1.1.pdf"],"contactPoint":{"hasEmail":"mailto:justin.gorham@nist.gov","@type":"vcard:Contact","fn":"Justin Gorham"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/srd/nist-standard-reference-database-154","description":"This database provides values of backscattering correction factors (BCF) of homogeneous materials for quantitative surface analyses by Auger electron spectroscopy. These BCFs are obtained from Monte Carlo simulations based on two models of electron transport in the material, a simplified model and an advanced model [A. Jablonski and C. J. Powell, Surf. Science 604, 1928 (2010)]. One assumption for the former model is that the primary-electron beam is unchanged, in intensity, energy or direction, within the information depth for Auger-electron emission. This assumption becomes progressively less useful as the primary energy becomes closer to the core-level ionization energy for the relevant Auger transition or for increasing angles of incidence of the primary electrons.","language":["en"],"title":"NIST NIST Backscattering-Correction-Factor Database for Auger Electron Spectroscopy - SRD 154","distribution":[{"accessURL":"https://www-s.nist.gov/srd_online/index.cfm?fuseaction=home.main&productID=SRD154","description":"This database is free, but requires that a form be filled out in order to be able to download.","title":"Form to download the database"},{"accessURL":"https://dx.doi.org/10.18434/T4JG6D","format":"text/html","description":"DOI Access to NIST Backscattering Correction Factor Database for Auger Electron Spectroscopy","mediaType":"text/html","title":"DOI Access to NIST Backscattering Correction Factor Database for Auger Electron Spectroscopy"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-07-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Auger electron spectroscopy","backscattering correction factor","surface analysis"]},{"identifier":"ECBCC1C130162ED9E04306570681B10728","accessLevel":"public","references":["https://www.nist.gov/document/09-0303-scimanicsdv1pdf"],"contactPoint":{"hasEmail":"mailto:igor.levin@nist.gov","@type":"vcard:Contact","fn":"Igor Levin"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/srd/nist-standard-reference-database-84","description":"The Inorganic Crystal Structure Database (ICSD) is produced cooperatively by the Fachinformationszentrum Karlsruhe (FIZ) and the National Institute of Standards and Technology (NIST). Components and devices used in a broad spectrum of technology sectors such as health care, communications, energy and electronics are manufactured from crystalline materials; the development of advanced crystalline materials requires accurate crystal-structure data. SRD 84 ICSD provides critically evaluated, comprehensive crystal-structure data and search software that enable phase identification by their characteristic diffraction patterns using X-rays, neutrons and electrons. SRD 84 ICSD contains full crystallographic and atomic-position information for more than 180,000 non-organic materials, including inorganics, ceramics, minerals, pure elements, metals and intermetallics, published in literature from 1913 through the present. ICSD is updated twice a year, with each update comprising about 2,000 to 10,000 new or re-evaluated entries. Data items include bibliographic information, compound designation such as chemical name, chemical formula, mineral name; and crystallographic parameters such as unit cell, space group, element symbol with numbering, oxidation state, multiplicity for Wyckoff position, x,y,z coordinates, site occupation, thermal parameters and reliability index R, among others. A free demonstration CD which includes a comprehensive user interface, FindIt, for a demo database is available upon request or can be downloaded from the website https://www.nist.gov/srd/nist-standard-reference-database-84.","language":["en"],"title":"FIZ/NIST Inorganic Crystal Structure Database (ICSD) - SRD 84","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4GW2D","mediaType":"text/html","title":"Home page for FIZ/NIST Inorganic Crystal Structure Database (ICSD)"},{"downloadURL":"https://www.nist.gov/sites/default/files/documents/srd/ICSD-Demo.zip","description":"This is a demonstration version of the  Inorganic Crystal Structure Database (ICSD).","mediaType":"application/zip","title":"ICSD-Demo.zip"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"This database consists of a one year subscription, with two releases per year, which must be renewed to continue use. For more information please visit http://nist.gov/srd/nist84.cfm","modified":"2015-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Rietveld profiles","X ray crystallography","X ray diffraction","X rays","XRD","absolute configurations","chemical structures","crystal data","crystal structures","crystallography","crystals","defect structures","diffraction","disorders","electron diffractions","electrons","identification","inorganic","inorganic structures","magnetic structures","materials","metals","mineral names","minerals","mixed crystal solid solutions","mixed crystals","modulated structures","neutron diffractions","neutrons","nuclear magnetic resonance","polytype structures","powder simulations","power","powers","prediction","simulations","single crystals","structure predictions","structures","synchroton radiations","twinned crystals","visualizations","x ray diffractions"]},{"identifier":"ECBCC1C130192ED9E04306570681B10731","accessLevel":"public","references":["https://www.nist.gov/system/files/documents/srd/Supertrapp.pdf"],"contactPoint":{"hasEmail":"mailto:marcia.huber@nist.gov","@type":"vcard:Contact","fn":"Marcia L. 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These results include: Equilibrium properties: density, compressibility factor, enthalpy, entropy, Cp, Cp/Cv, sound speed, Joule-Thomson coefficient Transport properties: viscosity, thermal conductivity Features: bubble and dew point pressure calculations bubble and dew point temperature calculations flash calculations ({T,P}, {T,S} and {P,H}) saturation properties for pure components tables of thermophysical properties with user selected dependent and independent variables ability to add new fluids using minimal data (user must supply at least Tc, Pc, Vc, MW and boiling point) ability to estimate properties of petroleum fractions characterized by an average normal boiling point and an API gravity choice of units FORTRAN source code for property calculations NOTE: Many of the fluids contained in SUPERTRAPP are also available in the REFPROP database. Please see www.nist.gov/srd/nist23.htm for additional details. The REFPROP database is recommended over SUPERTRAPP when REFPROP contains the fluids of interest.","language":["en"],"title":"NIST Thermophysical Properties of Hydrocarbon Mixtures Database- SRD 4","distribution":[{"accessURL":"http://dx.doi.org/10.18434/T4CC76","format":"text/html","description":"DOI Access to NIST Themophysical Properties of Hydrocarbon Mixtures Database - SRD 4","mediaType":"text/html","title":"DOI Access to NIST Themophysical Properties of Hydrocarbon Mixtures Database - SRD 4"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"This is a paid resource. Price depends on the number of copies purchased and whether or not the purchase is an upgrade. For more information on purchasing SRD 4 see: http://www.nist.gov/srd/nist4.cfm","modified":"2013-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Lennard Jones","butenes","chemical engineering","chemistry","equations of state","flash calculations","fluids","gaseous","gases","hydrocarbons","liquidus","mixtures","natural gases","petrochemicals","phase equilibria","physics","supercritical fluids","thermo chemistry","thermo physical","thermochemical data","thermodynamic data","thermodynamic properties","thermodynamics","thermophysics","transport properties","transport property data"]},{"identifier":"ECBCC1C1301B2ED9E04306570681B10733","accessLevel":"public","contactPoint":{"hasEmail":"mailto:s.seltzer@nist.gov","@type":"vcard:Contact","fn":"Stephen M. Seltzer"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/pml/xcom-photon-cross-sections-database","description":"A web database is provided which can be used to calculate photon cross sections for scattering, photoelectric absorption and pair production, as well as total attenuation coefficients, for any element, compound or mixture (Z <= 100) at energies from 1 keV to 100 GeV. XCOM provides two forms of output: (a) tables which correspond closely in format to existing tables in the literature; (b) graphical display of the tabular data.   Access to the database is provided through a web-based form located at: http://physics.nist.gov/PhysRefData/Xcom/html/xcom1.html.","language":["en"],"title":"NIST XCOM: Photon Cross Sections Database - SRD 8","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T48G6X","format":"text/html","description":"DOI Access to NIST XCOM: Photon Cross Sections Database - SRD 8","mediaType":"text/html","title":"DOI Access to NIST XCOM: Photon Cross Sections Database - SRD 8"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2010-11-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Radiation","Standards:Reference data"],"keyword":["X rays","Xrays","bremsstrahlung","electron pair productions","elements","energies","gamma rays","mixtures","nuclear pair productions","nuclear physics","photons","physics","total attenuation coefficients"]},{"identifier":"ECBCC1C1301C2ED9E04306570681B10734","accessLevel":"restricted public","references":["https://www.nist.gov/system/files/documents/srd/STEAM30-2.pdf","https://doi.org/10.6028/NIST.IR.5078"],"contactPoint":{"hasEmail":"mailto:allan.harvey@nist.gov","fn":"Allan H. Harvey"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/srd/nist-standard-reference-database-10","description":"The NIST/ASME Steam Properties Database is based upon the International Association for the Properties of Water and Steam (IAPWS) 1995 formulation for general and scientific use for the thermodynamic properties of water, this updated version provides water properties from the international standards over a wide range of conditions. Version 3.0 incorporates new IAPWS standards adopted in 2008 for the viscosity and for the melting and sublimation curves and in 2011 for the thermal conductivity. The thermophysical properties included in the STEAM Database are: temperature, Helmholtz energy, thermodynamic derivatives, pressure, Gibbs energy, density, fugacity, thermal conductivity, volume, isothermal compressibility, viscosity, quality, dielectric constant, enthalpy, volume expansivity, dielectric derivatives, internal energy, speed of sound, Debye-Hückel slopes, entropy, Joule-Thomson coefficient, refractive index, Cv, surface tension, Cp, Prandtl number, Kinematic viscosity, Ionization constant (pKw), Virial Coefficients, Exergy. Using a Windows® interface, the STEAM database generates tables and plots of property values at specified conditions corresponding to saturation conditions, a fixed property value (isotherm, isobar, etc.), or individual values of two independent variables. Vapor-liquid, vapor-solid, and liquid-solid saturation calculations with either temperature or pressure specified are available. The user can specify which properties to display and the units in which to enter and display data. Additional features include an interactive on-line help system, the ability to plot calculated points and a wide variety of thermodynamic diagrams, and the capability to copy and paste data to and from other applications.","language":["en"],"title":"NIST/ASME Steam Properties Database - SRD 10","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T46K5Z","format":"text/html","description":"DOI Access for NIST/ASME Steam Properties Database - SRD 10","mediaType":"text/html","title":"DOI Access for NIST/ASME Steam Properties Database - SRD 10"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"This is a paid resource. Price depends on number of copies ordered and whether it is an initial purchase or an upgrade. Links to purchase options are available at the dataset's webpage.","modified":"2013-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["chemical engineering","chemistry","dielectric","energies","equations of state","fluids","gas phases","liquid phases","phase equilibria","physics","properties of water","refractive index","refractivity","steam tables","supercritical","thermodynamic data","thermodynamic properties","thermodynamics","thermophysical","thermophysics","transport properties","transport property data"]},{"identifier":"ECBCC1C1301D2ED9E04306570681B10735","accessLevel":"public","references":["https://janaf.nist.gov/pdf/JANAF-FourthEd-1998-1Vol1-Intro.pdf"],"contactPoint":{"hasEmail":"mailto:thomas.allison@nist.gov","fn":"Thomas C. 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It contains tables of recommended temperature-dependent values for the standard enthalpy of formation, Gibbs (free) energy of formation, the logarithm of the equilibrium constant of formation, the heat capacity, entropy, enthalpy, and Gibbs energy function for 48 elements and many of their compounds.","language":["en"],"title":"NIST-JANAF Thermochemical Tables - SRD 13","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T42S31","format":"text/html","mediaType":"text/html","title":"DOI access to NIST-JANAF Thermochemical Tables - SRD 13"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2013-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data","Chemistry:Thermochemical properties"],"keyword":["thermochemical tables"]},{"identifier":"ECBCC1C130222ED9E04306570681B10740","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ian.bell@nist.gov","@type":"vcard:Contact","fn":"Ian Bell"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/ECBCC1C130222ED9E04306570681B10740","description":"**NOTICE: THIS VERSION OF REFPROP (9.1) has been superseded by Version 10: https://doi.org/10.18434/T4/1502528 **  NIST Standard Reference Data 23 contains revised data in a Windows version of the database, including 105 pure fluids and allowing mixtures of up to 20 components. The fluids include the environmentally acceptable HFCs, traditional HFCs and CFCs and 'natural' refrigerants like ammonia.  Version9.1  REFPROP 9.1 has replaced the NIST 12 and 14 databases.  Source code: The FORTRAN subroutines and associated fluid data files are provided for those wishing to access REFPROP calculations from their own applications;  Excel spreadsheets: A sample spreadsheet is included that demonstrates how the REFPROP DLL can be linked to Excel. Most properties that are available in the graphical interface can also be calculated in the spreadsheet.  System Requirements: PC running Windows 98, 2000, XP, Vista, Window 7 or similar operating system; 10.0 MB available hard disk space.","language":["en"],"title":"NIST Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) Version 9 - SRD 23","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4JS3C","format":"text/html","mediaType":"text/html","title":"DOI access for NIST Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) Version 9 - SRD 23"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"This is a paid resource. Pricing depends on number of copies purchased and whether or not this is an upgrade. Links to order forms can be found on the site: https://www.nist.gov/srd/refprop.","modified":"2013-04-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["AGA-8","Benedict Webb Rubin","CFCs","Excels","FORTRAN","GERG 2004","GERG 2008","Gibbs","Gruneisen","HCFCs","HFCs","Joule-Thomson","Lennard Jones","MBWR","Pitzer","Second cross virial coefficient","acentric factors","adiabatics","air","air conditioning","air conductivities","air densities","air viscosities","alternative refrigerants","azeotropes","binaries","binary mixtures","biodiesels","biofuels","boiling points","boilings","butenes","calorific values","chemical engineering","chemical potentials","chemicals","chemistry","chlorofluorocarbons","compressed natural gases","conductivity functions","cooling equipment","critical flow factors","criticals","cryogen","cryogenics","energies","equations of state","excess values","extended corresponding states","fatty acid methyl esters","fluids","fluorinated","freons","fugacity coefficients","fundamentals","gas and oil","gas phases","gaseous","gases","graphical interfaces","greenhouse gases","gross heating values","heats","heavy waters","hydrocarbons","hydrochlorofluorocarbons","hydrofluorocarbons","hydrogen fuel cells","interaction parameters","liquid equilibria","liquified natural gases","mechanical engineering","mixtures","modelings","molar masses","natural gases","net heating values","normal hydrogens","nuclear magnetic resonance","orthohydrogens","ozone depletions","parahydrogens","phase boundaries","phase diagrams","phase equilibria","physical","physics","pseudo pure fluids","pure fluids","reference states","refrigerants","refrigerations","saturated","siloxanes","software","sound speeds","specific heat inputs","states","substances","supercritical CO2","thermal","thermo chemistry","thermo physical","thermochemical data","thermodynamic data","thermodynamic properties","thermodynamics","thermophysical data","thermophysical properties","thermophysics","thermos","transport equations","transport properties","transport property data","triples","vapor","vapor compression cycles","water densities","water properties","water vapor pressures"]},{"identifier":"ECBCC1C130232ED9E04306570681B10741","accessLevel":"public","references":["https://srdata.nist.gov/CeramicDataPortal/manual"],"contactPoint":{"hasEmail":"mailto:angela.lee@nist.gov","fn":"Angela Lee"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://srdata.nist.gov/CeramicDataPortal/scd","description":"The NIST WWW Structural Ceramics Database (WebSCD) provides evaluated materials property data for a wide range of advanced ceramics known variously as structural ceramics, engineering ceramics, and fine ceramics. These materials tend to have low mass densities and high strengths and tend to be resistant to corrosion. These characteristics form the basis for applications of these materials in high-temperature, energy-efficient heat exchangers, advanced engine designs, bearings, wear resistant parts, and stable electronic substrates and electronic packaging.\n\nThe range of materials covers the major series of compounds derived from the ceramic oxide, carbide, nitride, boride, and oxynitride chemical families. The materials are described by specification and characterization information that includes processing details and chemical compositions. Physical characteristics such as density and crystal structure are given in numeric tables. All measured values are evaluated and supported by descriptions of the measurement methods, procedures, and conditions. In all cases, the sources of the data are fully documented in a detailed bibliography.\n","language":["en"],"title":"NIST Structural Ceramics Database - SRD 30","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4F30D","mediaType":"text/html","title":"NIST Structural Ceramics Database (SCD) Database"},{"accessURL":"https://doi.org/doi:10.18434/M31019"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-06-22","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Composition and structure","Mechanical properties","Thermal properties","Standards: Reference data","Materials : Ceramics"],"keyword":["advanced ceramics","borides","carbides","ceramics","diffusivities","energies","engineering ceramics","fine ceramics","material databases","materials properties","nitrides","oxides","oxynitrides","structural ceramics","tensile"]},{"identifier":"ECBCC1C130252ED9E04306570681B10743","accessLevel":"public","references":["http://www.nist.gov/srd/upload/35_204jcmp-revised.pdf"],"contactPoint":{"hasEmail":"mailto:stephen.stein@nist.gov","@type":"vcard:Contact","fn":"Stephen E. Stein"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/srd/nist-standard-reference-database-35","description":"This data collection contains 5,228 infrared spectra of different compounds along with chemical structures for most of them. Spectra are provided on a CD-ROM in the JCAMP-DX (Joint Committee for Atomic and Molecular Physical Data 'Data Exchange') format. Chemical structures are provided in the MOL-file format. The IR data originated from two sources, from the so-called 'EPA Vapor-Phase IR Library' and from NIST laboratories. The data have been sub-divided in two ways: 1) as concatenated JCAMP and SDF files (concatenated MOL-files) and 2) in individual files where each spectrum and structure is provided in a separate JCAMP and MOL file, using file names containing the CAS registry number of the compound.  Important Note: All spectra were measured in the gas phase by GC/IR (gas chromatography/infrared spectroscopy), hence concentrations in the IR cell are not known or estimable. Molar absorption coefficients are not reported. This data provides only relative absorption coefficients as a function of wavelength which can be used for identification, not quantification.","language":["en"],"title":"NIST/EPA Gas-Phase Infrared Database JCAMP Format - SRD 35","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T45K5N","format":"text/html","description":"DOI Access to NIST/EPA Gas-Phase Infrared Database JCAMP Format - SRD 35","mediaType":"text/html","title":"DOI Access to NIST/EPA Gas-Phase Infrared Database JCAMP Format - SRD 35"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"This is a paid resource. Pricing depends on number of copies purchased. For information on Distributor Agreements please contact data@nist.gov.","modified":"2004-12-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Spectroscopy","Standards:Reference data","Physics:Atomic, molecular, and quantum"],"keyword":["Joint Committee for Atomic and Molecular Physical Data","absorption spectra","chemical structures","gas phases","gases","infra red","infrared spectra","mass spectra","mass spectrometry","molecular spectroscopy","molecular vibrations","physics","spectra","spectroscopy","structural data","structures","vapor phase infrared"]},{"identifier":"ECBCC1C130262ED9E04306570681B10744","accessLevel":"public","contactPoint":{"hasEmail":"mailto:chris.muzny@nist.gov","fn":"Chris Muzny"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://kinetics.nist.gov/solution/","description":"The NDRL/NIST Solution Kinetics Database contains data on rate constants for solution-phase chemical reactions. The database is designed to be searched by reactants, products, solvents, or any combination of these. In addition, the bibliography may be searched by author name, title words, journal, page(s), and/or year. This is not the same database as the one at Notre Dame, although both databases share a common data source.","language":["en"],"title":"NDRL/NIST Solution Kinetics Database - SRD 40","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T41S3Q","format":"text/html","description":"DOI Access to NDRL/NIST Solution Kinetics Database on the Web","mediaType":"text/html","title":"DOI Access to NDRL/NIST Solution Kinetics Database on the Web"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2002-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Standards:Reference data"],"keyword":["aqueous solutions","chemical kinetics data","chemical reactions","energies","free radicals","kinetics","kinetics databases","radiation effects","rates of reaction"]},{"identifier":"ECBCC1C130292ED9E04306570681B10747","accessLevel":"public","references":["https://www.nist.gov/srd/nist-standard-reference-database-49"],"contactPoint":{"hasEmail":"mailto:piotr.domanski@nist.gov","@type":"vcard:Contact","fn":"Piotr A. 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Derived molecular properties, such as rotational constants, hyperfine structure constants, electric dipole moments, rotational g-factors and internuclear distances, are listed with one standard deviation uncertainties for all species.","language":["en"],"title":"NIST Diatomic Spectral Database - SRD 114","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4T59X","format":"text/html","description":"NIST Diatomic Spectral Database - SRD 114","mediaType":"text/html","title":"NIST Diatomic Spectral Database - SRD 114"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2005-11-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Atomic / molecular / quantum","Standards:Reference data","Chemistry:Analytical chemistry"],"keyword":["diatomic molecules","electric dipole moments","hyperfine structure","internuclear distances","microwave spectra","rotational constants","rotational spectra"]},{"identifier":"FDB59097466A5200E043065706813E54114","accessLevel":"public","references":["http://physics.nist.gov/PhysRefData/MolSpec/Hydro/Html/intro.html"],"contactPoint":{"hasEmail":"mailto:francis.lovas@nist.gov","@type":"vcard:Contact","fn":"Francis J. 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The derived molecular properties, such as rotational and centrifugal distortion constants, hyperfine structure constants, electric dipole moments, and rotational g-factors are listed.","language":["en"],"title":"NIST Hydrocarbon Spectral Database - SRD 115","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4PC70","format":"text/html","description":"DOI Access to NIST Hydrocarbon Spectral Database - SRD 115","mediaType":"text/html","title":"DOI Access to NIST Hydrocarbon Spectral Database - SRD 115"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2004-07-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Atomic / molecular / quantum","Standards:Reference data","Chemistry:Analytical chemistry"],"keyword":["electric dipole moments","hydrocarbons","hyperfine structure","internuclear distances","microwave spectra","rotational constants","rotational spectra"]},{"identifier":"FDB59097466B5200E043065706813E54115","accessLevel":"public","references":["http://physics.nist.gov/PhysRefData/Micro/Html/sec2.html"],"contactPoint":{"hasEmail":"mailto:francis.lovas@nist.gov","@type":"vcard:Contact","fn":"Francis J. Lovas"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/pml/observed-interstellar-molecular-microwave-transitions","description":"This database contains critically evaluated transition frequencies for the molecular transitions detected in interstellar and circumstellar clouds reported in the literature through mid-2008. The tabulated transition frequencies are recommended for reference in future astronomical observations in the centimeter and millimeter wavelength regions. The transition frequencies have been selected through a critical examination and analysis of the laboratory spectral data obtained from the literature. The information tabulated includes the species identity, transition frequency, uncertainty, and quantum state labels. For convenience, representative line antenna temperatures are listed for a typical astronomical source for each transition, and the references are cited for the laboratory and astronomical literature that have been employed.   2009 Revision","language":["en"],"title":"NIST Observed Interstellar Molecular Microwave Transitions Database - SRD 116","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4JP4Q","format":"text/html","description":"DOI Access to NIST Observed Interstellar Molecular Microwave Transitions Database - SRD 116","mediaType":"text/html","title":"DOI Access to NIST Observed Interstellar Molecular Microwave Transitions Database - SRD 116"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2009-09-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Atomic / molecular / quantum","Standards:Reference data","Chemistry:Analytical chemistry"],"keyword":["astronomy","atomic physics","circumstellar clouds","interstellar molecules","microwave spectroscopy","molecular clouds","molecular lines","physics","radio astronomy","rotational spectra"]},{"identifier":"FDB59097466C5200E043065706813E54116","accessLevel":"public","references":["http://physics.nist.gov/PhysRefData/MolSpec/Triatomic/Html/intro.html"],"contactPoint":{"hasEmail":"mailto:francis.lovas@nist.gov","@type":"vcard:Contact","fn":"Francis J. 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The derived molecular properties, such as rotational and centrifugal distortion constants, hyperfine structure constants, electric dipole moments, and rotational g-factors are listed with one standard deviation uncertainty for all values.","language":["en"],"title":"NIST Triatomic Spectral Database - SRD 117","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4DW2S","format":"text/html","description":"DOI Access to NIST Triatomic Spectral Database - SRD 117","mediaType":"text/html","title":"DOI Access to NIST Triatomic Spectral Database - SRD 117"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2003-07-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Atomic / molecular / quantum","Standards:Reference data","Chemistry:Analytical chemistry"],"keyword":["electric dipole moments","hyperfine structure","microwave spectra","molecular structure","rotational constants","rotational spectra","triatomic molecules"]},{"identifier":"FDB59097466D5200E043065706813E54117","accessLevel":"public","references":["https://www.nist.gov/pml/wavenumbers-calibration-ir-spectrometers/wavenumbers-calibration-ir-spectrometers-bibliography"],"contactPoint":{"hasEmail":"mailto:gerald.fraser@nist.gov","@type":"vcard:Contact","fn":"Gerald T. 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The vibrationally resolved photoelectron spectra are analyzed to generate relative vibrational transition amplitudes and the angular asymmetry parameters describing the various transitions observed.","language":["en"],"title":"NIST Photoionization of CO2 (ARPES) Database - SRD 119","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T45C7B","format":"text/html","description":"DOI Access to NIST Photoionization of CO2 (ARPES) Database - SRD 119","mediaType":"text/html","title":"DOI Access to NIST Photoionization of CO2 (ARPES) Database - SRD 119"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2007-06-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Analytical chemistry","Standards:Reference data","Atomic / molecular / quantum"],"keyword":["ARPES","X ray photoelectron spectroscopy","photoelectron spectroscopy","physics","spectra"]},{"identifier":"FDB59097466F5200E043065706813E54119","accessLevel":"public","contactPoint":{"hasEmail":"mailto:brian.zimmerman@nist.gov","@type":"vcard:Contact","fn":"Brian E. 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Stopping-power and range tables can be calculated for electrons in any user-specified material and for protons and helium ions in 74 materials.","language":["en"],"title":"NIST Stopping-Power & Range Tables for Electrons, Protons, and Helium Ions - SRD 124","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4NC7P","format":"text/html","description":"DOI Access to NIST Stopping-Power & Range Tables for Electrons, Protons, and Helium Ions - SRD 124","mediaType":"text/html","title":"DOI Access to NIST Stopping-Power & Range Tables for Electrons, Protons, and Helium Ions - SRD 124"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2005-08-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Atomic / molecular / quantum","Physics:Radiation","Standards:Reference data"],"keyword":["Barks correction","ICRUs","International Commission on Radiation Units and Measurements","air","alpha particles","atomic physics","block correction","bremsstrahlung","charged particles","collisions","electronic stopping powers","electrons","energies","nuclear physics","nuclear stopping powers","physics","projected ranges"]},{"identifier":"FDB5909746735200E043065706813E54123","accessLevel":"public","contactPoint":{"hasEmail":"mailto:s.seltzer@nist.gov","@type":"vcard:Contact","fn":"Stephen M. 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This updated bibliography now includes 580 non-duplicative references to available measured data, plus 42 references to critical evaluations and review articles.","language":["en"],"title":"NIST Bibliography of Photon Total Cross Section (Attenuation Coefficient) Measurements","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4HP4D","format":"text/html","description":"DOI Access to NIST Bibliography of Photon Total Cross Section (Attenuation Coefficient) Measurements","mediaType":"text/html","title":"DOI Access to NIST Bibliography of Photon Total Cross Section (Attenuation Coefficient) Measurements"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2003-06-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Atomic / molecular / quantum","Physics:Radiation"],"keyword":["bibliography","bremsstrahlung","gamma-ray","photon","x-ray"]},{"identifier":"FDB5909746755200E043065706813E54125","accessLevel":"public","contactPoint":{"hasEmail":"mailto:s.seltzer@nist.gov","@type":"vcard:Contact","fn":"Stephen M. 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Version 1.2","language":["en"],"title":"NIST X-Ray Transition Energies Database - SRD 128","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4859Z","format":"text/html","description":"DOI Access to NIST X-Ray Transition Energies Database - SRD 128","mediaType":"text/html","title":"DOI Access to NIST X-Ray Transition Energies Database - SRD 128"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2005-08-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data","Physics:Radiation","Physics:Spectroscopy"],"keyword":["Bearden database","X ray wavelengths","X rays","atomic spectroscopy","measured transitions","physics","transition energies"]},{"identifier":"FDB5909746775200E043065706813E54127","accessLevel":"public","contactPoint":{"hasEmail":"mailto:lisa.borsuk@nist.gov","@type":"vcard:Contact","fn":"Lisa Borsuk"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://strbase.nist.gov","description":"Short Tandem Repeat DNA Internet DataBase is intended to benefit research and application of short tandem repeat DNA markers for human identity testing. Facts and sequence information on each STR system, population data, commonly used multiplex STR systems, PCR primers and conditions, and a review of various technologies for analysis of STR alleles have been included.","language":["en"],"title":"NIST Short Tandem Repeat DNA Internet DataBase","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T44G6P","description":"DOI Access to NIST Short Tandem Repeat DNA Internet Database","mediaType":"text/html","title":"DOI Access to NIST Short Tandem Repeat DNA Internet Database"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-03-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Forensics:DNA and biological evidence"],"keyword":["DNAs","STR typings","bio technology","bioinformatics","biological","biology","forensic DNAs","human identity testings","short tandem repeats"]},{"identifier":"FDB5909746785200E043065706813E54128","accessLevel":"public","references":["http://bioinfo.nist.gov/hmpd/Description.html","http://bioinfo.nist.gov/hmpd/MITOXMLDOC.html"],"contactPoint":{"hasEmail":"mailto:talapady.bhat@nist.gov","@type":"vcard:Contact","fn":"Talapady N. 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NIST is measuring the thermophysical properties of the process gases, the 'surrogate' gases, and binary mixtures of process and carrier gases. The process gases are used in CVD and the surrogate gases are used to calibrate MFCs. The results will be disseminated in this data base providing the heat capacity, thermal conductivity, viscosity, and the virial coefficients for the virial equation of state providing the pressure-density-temperature relation for the process gases.","language":["en"],"title":"NIST Database of the Thermophysical Properties of Gases Used in the Semiconductor Industry","distribution":[{"accessURL":"https://doi.org/10.18434/t4r599","format":"text/html","description":"DOI Access to NIST Database of the Thermophysical Properties of Gases Used in the Semiconductor Industry","title":"DOI Access to NIST Database of the Thermophysical Properties of Gases Used in the Semiconductor Industry"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Electronics:Semiconductors","Chemistry:Thermochemical properties"],"keyword":["gases","semiconductors"]},{"identifier":"FDB59097467C5200E043065706813E54132","accessLevel":"public","references":["http://physics.nist.gov/PhysRefData/DFTdata/intro.html"],"contactPoint":{"hasEmail":"mailto:eric.shirley@nist.gov","fn":"Eric L. 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The Mallik Database contains observations from wells at the Mallik gas hydrate field in Canada.","language":["en"],"title":"NIST Clathrate Hydrate Physical Property Database - SRD 156","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4901H","format":"text/html","description":"DOI Access to NIST Clathrate Hydrate Physical Property Database - SRD 156","mediaType":"text/html","title":"DOI Access to NIST Clathrate Hydrate Physical Property Database - SRD 156"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2009-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Clathrate Hydrates"]},{"identifier":"FDB59097468A5200E043065706813E54146","accessLevel":"public","references":["https://dx.doi.org/10.1016/j.jqsrt.2005.05.008","https://dx.doi.org/10.1063/1.1824866"],"contactPoint":{"hasEmail":"mailto:yuri.ralchenko@nist.gov","@type":"vcard:Contact","fn":"Yuri Ralchenko"},"programCode":["006:052"],"@type":"dcat:Dataset","description":"This database contains benchmark results for simulation of plasma population kinetics and emission spectra. 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The observations were made in collaboration with the European Southern Observatory (ESO), in order to provide calibration reference data for new high-resolution Echelle spectrographs, such as the Cryogenic High-Resolution IR Echelle Spectrograph ([CRIRES]), ESO's new IR spectrograph at the Very Large Telescope in Chile.","language":["en"],"title":"NIST The Spectrum of Th-Ar Hollow Cathode Lamps - SRD 161","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4S01V","format":"text/html","description":"A persistent identifier to locate and access SRD 161- The Spectrum of Th-Ar Hollow Cathode Lamps in the 691 nm to 5804 nm region","title":"DOI access NIST The Spectrum of Th-Ar Hollow Cathode Lamps -  SRD 161"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2011-12-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data","Physics:Spectroscopy"],"keyword":["hollow cathode lamps","infra red","lamps","spectra"]},{"identifier":"FDB59097468F5200E043065706813E54151","accessLevel":"public","references":["https://dx.doi.org/10.1186/1471-2105-12-487"],"contactPoint":{"hasEmail":"mailto:talapady.bhat@nist.gov","@type":"vcard:Contact","fn":"Talapady N. 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End group and chair architecture information is available but not as a sortable field. There are entries for dendrimers, nanoparticles, polysaccharides, and homopolymer polypeptides, but the database has significantly less coverage in these areas. The database could be extended into these areas if there is significant community interest.   Chemical Abstracts Service (CAS) number are provided for all matrices having them. Links to the NIST Chemistry WebBook are available for all matrices having a WebBook entry. 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For more information please visit https://www.nist.gov/srd/nist-special-database-19.","modified":"2016-08-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Biometrics"],"keyword":["automated data capture","character recognition","characters","hand printed characters","hand prints","hand writing recognition","OCR","handprints","printed characters","recognition","software recognition"]},{"identifier":"FF429BC178608B3EE0431A570681E858207","accessLevel":"public","references":["https://s3.amazonaws.com/nist-srd/SD2/users_guide_sd2.pdf"],"contactPoint":{"hasEmail":"mailto:karen.marshall@nist.gov","fn":"Karen Marshall"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/FF429BC178608B3EE0431A570681E858207","description":"The documents in this database are 12 different tax forms from the IRS 1040 Package X for the year 1988. 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This collection consists of eight videos, totaling over two hours in length, selected from NIST's public domain archive of marketing, technical, and educational material. The characteristics of these videos include, but are not limited to, different levels of motion (static to fast moving objects), close-up figures (talking heads, moving arms, and moving hands), outdoor shots (laboratory, auditorium, and conference room environments), and various levels and quality of audio. In addition to the base data (titles below), pre- or post-production transcripts are included as reference data.It is our intent to gather feedback on the use of this collection, the need for additional base data, and further requirements for reference data (or \"truth\"). Please send email to dvr-info@nist.gov with questions, comments, or suggestions. Or, visit the Digital Video Retrieval web site at http://www.itl.nist.gov/iaui/894.02/projects/dv .Below is the title of each video included as base data on \"Digital Video 1\".NIST in 5 Minutes and 41 SecondsInformational tour of the agency and its efforts to promote economic growth by working with industry to develop and apply technology, measurements, and standards.Enhanced Aerial Lift ControllerDescribes how the controller may provide solutions to many jobs that cannot be addressed with existing commercial aerial lifts.Portsmouth Flexible Manufacturing WorkstationDescribes the Portsmouth Fastener Workstation, which makes accurate threaded fasteners for Navy ships.You Don't Have To Be There... Telepresence MicroscopyThe program shows how telepresence can provide the potential for remote, instantaneous, around-the-clock access to critical metrology services using the Internet (1998).A Decade of Business Excellence for AmericaHighlights the decade of excellence as seen through the Malcolm Baldrige National Quality Award.A Uniquely Rewarding ExperienceDescribes the advantages of becoming a Baldrige Quality Award examiner.Aircraft Hangar Fires: Fire Protection ImprovementsDescribes how NIST and the U.S. Navy conducted tests on sprinkler and heat detection systems in high bay aircraft hangars in Iceland and Hawaii.Engineer in SpacePublic lecture which describes a NIST engineer's adventure and research on two missions aboard the space shuttle Columbia.System Requirements: DVD-ROM drive for accessing the digitized video collection and a compatible MPEG decoder is needed to view the collection. Note: Not for use in set-top boxes. Reference data is best viewed using a browser which supports HTML 1.0.","language":["en"],"title":"NIST Digital Video 1 - NIST Special Database 26","distribution":[{"accessURL":"https://www.nist.gov/srd/nist-special-database-26","mediaType":"text/html","title":"NIST Digital Video Volume 1 - NIST Special Database 26"},{"accessURL":"https://doi.org/10.18434/T4V88W","format":"text/html","description":"DOI Access to NIST Digital Video 1 - NIST Special Database 26","title":"DOI Access to NIST Digital Video 1 - NIST Special Database 26"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"NIST Digital Video Volume 1 is available for a fee. For more information visit http://www.nist.gov/srd/nistsd26.cfm.","modified":"2000-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Video analytics","Information retrieval"],"keyword":["digital videos","test collections"]},{"identifier":"FF429BC178698B3EE0431A570681E858216","accessLevel":"public","references":["https://www.nist.gov/software-quality-group/national-software-reference-library-nsrl"],"contactPoint":{"hasEmail":"mailto:douglas.white@nist.gov","fn":"Douglas R. White"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/FF429BC178698B3EE0431A570681E858216","description":"The National Software Reference Library (NSRL)  collects software from various sources and incorporates file profiles computed from this software into a Reference Data Set (RDS) of information. The RDS can be used by law enforcement, government, and industry organizations to review files on a computer by matching file profiles in the RDS. This alleviates much of the effort involved in determining which files are important as evidence on computers or file systems that have been seized as part of criminal investigations. The RDS is a collection of digital signatures of known, traceable software applications. There are application hash values in the hash set which may be considered malicious, i.e. steganography tools and hacking scripts. There are no hash values of illicit data, i.e. child abuse images.","language":["en"],"title":"National Software Reference Library (NSRL) Reference Data Set (RDS) - NIST Special Database 28","distribution":[{"accessURL":"https://www.nist.gov/software-quality-group/national-software-reference-library-nsrl","format":"NSRL main page","title":"National Software Reference Library"},{"accessURL":"https://doi.org/10.18434/M3695G"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"This is a paid resource. It is an annual subscription with quarterly releases and can be ordered online at https://www.nist.gov/srd/nist-special-database-28","modified":"2021-12-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://www.nist.gov/sites/default/files/data-formats-of-the-nsrl-reference-data-set-16_0.pdf","accrualPeriodicity":"R/P3M","theme":["Forensics:Digital and multimedia evidence"],"keyword":["computer crimes","computer forensics","crimes","cyber crimes","Defense Computer Forensics Laboratory","Federal Bureau of Investigation","file profiles","finger print software","fingerprints","graphics","hash keeper","hashes","investigations","KFF","known file filters","law enforcement","National Institute of Justice","National Software Reference Library","OLES","profiles","reference data set","softwares","US Customs Services"]},{"identifier":"FF429BC1786D8B3EE0431A570681E858220","accessLevel":"public","references":["https://s3.amazonaws.com/nist-srd/SD6/SD06_users_guide.pdf"],"contactPoint":{"hasEmail":"mailto:karen.marshall@nist.gov","fn":"Karen Marshall"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/FF429BC1786D8B3EE0431A570681E858220","description":"The documents in this database are 12 different tax forms with the IRS 1040 Package X for the year 1988. These include Forms 1040, 2106, 2441, 4562, and 6251 together with Schedules A, B, C, D, E, F, and SE. Eight of these forms contain two pages or form faces; therefore, there are 20 different form faces represented in the database. The document images in this database appear to be real hand-printed forms prepared by individuals, but the images have been automatically derived and synthesized using a computer and contain no \"real\" tax data. There are 900 simulated tax submissions represented in the database averaging 6.22 form faces per submission.","language":["en"],"title":"NIST Structured Forms Reference Set of Binary Images II (SFRS2) - NIST Special Database 6","distribution":[{"accessURL":"https://doi.org/10.18434/M3D95B"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Human language technology"],"keyword":["ASCII references","binary image databases","character recognition","characters","forms identifications","forms recognition","ground truth","hand prints","hand printed characters","hand writing recognition","handprints","images","OCR","printed characters","recognition","software recognition","tax forms"]},{"identifier":"FF429BC178718B3EE0431A570681E858224","accessLevel":"public","references":["http://www.itl.nist.gov/div898/strd/general/howto.html","http://www.itl.nist.gov/div898/strd/general/faq.html"],"contactPoint":{"hasEmail":"mailto:william.guthrie@nist.gov","@type":"vcard:Contact","fn":"William F. Guthrie"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/FF429BC178718B3EE0431A570681E858224","description":"The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software.   Currently datasets and certified values are provided for assessing the accuracy of software for univariate statistics, linear regression, nonlinear regression, and analysis of variance. The collection includes both generated and 'real-world' data of varying levels of difficulty. Generated datasets are designed to challenge specific computations. These include the classic Wampler datasets for testing linear regression algorithms and the Simon & Lesage datasets for testing analysis of variance algorithms. Real-world data include challenging datasets such as the Longley data for linear regression, and more benign datasets such as the Daniel & Wood data for nonlinear regression. Certified values are 'best-available' solutions. The certification procedure is described in the web pages for each statistical method.   Datasets are ordered by level of difficulty (lower, average, and higher). Strictly speaking the level of difficulty of a dataset depends on the algorithm. These levels are merely provided as rough guidance for the user. Producing correct results on all datasets of higher difficulty does not imply that your software will pass all datasets of average or even lower difficulty. Similarly, producing correct results for all datasets in this collection does not imply that your software will do the same for your particular dataset. It will, however, provide some degree of assurance, in the sense that your package provides correct results for datasets known to yield incorrect results for some software.   The Statistical Reference Datasets is also supported by the Standard Reference Data Program.","language":["en"],"title":"NIST Statistical Reference Datasets - SRD 140","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T43G6C","format":"text/html","description":"DOI Access to NIST Statistical Reference Datasets - SRD 140","mediaType":"text/html","title":"DOI Access to NIST Statistical Reference Datasets - SRD 140"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2003-11-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["ANOVAs","Bayes theorems","Bayesian","Bayesian computations","Bayesian statistics","MCMC","algorithms","averages","benchmark data","benchmarks","computational accuracies","least squares","linear regressions","nonlinear regressions","numerical accuracies","numerical analysis","round off","rounding errors","roundings","software evaluations","standard deviations","statistical reference datasets","statistical software","summary statistics","variance analysis"]},{"identifier":"FF429BC178758B3EE0431A570681E858228","accessLevel":"public","references":["http://ws680.nist.gov/publication/get_pdf.cfm?pub_id=860382"],"contactPoint":{"hasEmail":"mailto:aron.newman@nist.gov","fn":"Aron Newman"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-228","description":"Dataset of 3-D microstructures obtained by x-ray microtomography at the ESRF facility in Grenoble, France. Materials investigated including hydrating cement paste, hydrating Plaster of Paris, and a typical building brick.","language":["en"],"title":"NIST Visible Cement Dataset","distribution":[{"accessURL":"http://visiblecement.nist.gov/cement.html","format":"Web site with links to data and analysis programs","description":"Cement pastes were prepared from Cement and Concrete Reference Laboratory (CCRL) cement 133, issued in June of 1999. Complete information on this cement can be found in the NIST Cement Images database. Cement pastes with w/c ratios between 0.3 and 0.45 were prepared and viewed after various hydration times.","title":"Cement Paste Data"},{"accessURL":"http://visiblecement.nist.gov/plaster.html","description":"Plaster of Paris was prepared from a commercial locally-available source with a water-to-solids mass ratio of 1.0. One sample of the dry powder was prepared and paste samples were viewed after various hydration times.","title":"Plaster of Paris Data"},{"accessURL":"http://visiblecement.nist.gov/brick.html","description":"Brick Data","title":"Brick Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2013-09-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization"],"keyword":["cement; Plaster of Paris; microstructure; brick"]},{"identifier":"FF429BC178768B3EE0431A570681E858229","accessLevel":"public","references":["http://www.nist.gov/srd/upload/NISTNSRDS1642014.pdf","https://dx.doi.org/10.1063/1.4832851"],"contactPoint":{"hasEmail":"mailto:justin.gorham@nist.gov","@type":"vcard:Contact","fn":"Justin Gorham"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/FF429BC178768B3EE0431A570681E858229","description":"The NIST Database of Cross Sections for Inner-Shell Ionization by Electron or Positron Impact provides cross sections for ionization of the K shell and of the L and M subshells of neutral atoms of the elements, from hydrogen to einsteinium, by electrons or positrons, for projectile energies from the ionization threshold to 1 GeV. These cross sections were calculated from a combination of the relativistic distorted-wave and the plane-wave Born approximations. Extensive comparisons have been made of the calculated cross sections for inner-shell ionization by electron impact with available experimental data that satisfied mutual-consistency checks. These comparisons showed that the overall root-mean-square deviation between measured and calculated cross sections was 10.9 % [X. Llovet, C. J. Powell, A. Jablonski, and F. Salvat, J. Phys. Chem. Ref. Data 43, 013102 (2014)].","language":["en"],"title":"NIST Database of Cross Sections for Inner-Shell Ionization by Electron or Positron Impact - SRD 164","distribution":[{"accessURL":"https://www-s.nist.gov/srd_online/index.cfm?fuseaction=home.main&productID=SRD164v1.0","description":"This database is free, but requires that a form be filled out in order to be able to download.","title":"Form to download the database"},{"accessURL":"https://dx.doi.org/10.18434/T4N881","format":"text/html","description":"DOI Access to NIST Database of Cross Sections for Inner-Shell Ionization by Electron or Positron Impact - SRD 164","mediaType":"text/html","title":"DOI Access to NIST Database of Cross Sections for Inner-Shell Ionization by Electron or Positron Impact - SRD 164"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2014-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Auger-electron spectroscopy","atomic physics","electron energy-loss spectroscopy","electron-probe microanalysis","inner-shell ionization cross sections","plasma physics","radiation physics","surface analysis","thin-film analysis","vacancy-production cross sections","x-ray emission cross sections"]},{"identifier":"FF429BC178778B3EE0431A570681E858230","accessLevel":"public","references":["http://pubs.acs.org/doi/abs/10.1021/ci050067b","http://trc.nist.gov/thermoplan/main/help.html"],"contactPoint":{"hasEmail":"mailto:ala.bazyleva@nist.gov","fn":"Ala Bazyleva"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/FF429BC178778B3EE0431A570681E858230","description":"This web application provides free and open access for the broader research community to the experimental planning utilities that are incorporated into ThermoData Engine (TDE) [J. Chem. Inf. Model. 2005, 45, 816-838]. TDE provides recommendations for the relative merit of a proposed measurement via assessment of the existing body of knowledge, including availability of experimental thermophysical property data, variable ranges studied, associated uncertainties, state of prediction methods, and parameters for deployment of prediction methods. The web applications provides utilities for the assessment of specific property measurements for pure and binary chemical systems, the broader data needs of pure systems, and recommendations for binary mixture measurements that could extend the current UNIFAC model.    The primary focus of this recommendation service is molecular organic compounds. Some common inorganic and organometallic compounds are included, but, in general, polymers, radicals, ions, salt and acid solutions, metals, metal oxides, and inter-metallics are not considered.   References NIST Standard Reference Data 103a and 103b.","language":["en"],"title":"NIST ThermoPlan - Experimental Planning and Coverage Evaluation Aid for Thermophysical Property Measurements - SRD 167","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4830V","format":"text/html","description":"DOI Access to NIST ThermoPlan - Experimental Planning and Coverage Evaluation Aid for Thermophysical Property Measurements","mediaType":"text/html","title":"DOI Access to NIST ThermoPlan - Experimental Planning and Coverage Evaluation Aid for Thermophysical Property Measurements"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2013-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Thermochemical properties","Physics:Thermodynamics"],"keyword":["OCR","automated character recognition","automated image recognition","character recognition","characters","complex document recognition","printed characters","software recognition","technical document images"]},{"identifier":"FF429BC178788B3EE0431A570681E858231","accessLevel":"public","references":["http://trc.nist.gov/thermolit/main/home.html#help"],"contactPoint":{"hasEmail":"mailto:chris.muzny@nist.gov","fn":"Chris Muzny"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/FF429BC178788B3EE0431A570681E858231","description":"This web application provides free and open access to literature information contained in the NIST SOURCE Data Archive, and provides an easy-to-use tool for generation of a NIST Literature Report in PDF format, as required by the Journals. The tool is intended to aid researchers and reviewers in determining relevant literature sources for a given experimental measurement; however, it is not intended to replace the comprehensive literature review required by all journals, and no guarantee is made regarding completeness of the information provided. For an analysis of the comparative impact a particular measurement may have, the ThermoPlan: Experimental Planning and Coverage Evaluation Aid for Thermophysical Property Measurements web tool (http://trc.nist.gov/thermoplan/) is recommended.","language":["en"],"title":"ThermoLit: NIST Literature Report Builder for Thermophysical and Thermochemical Property Measurements","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4VS35","format":"text/html","description":"DOI Acces to ThermoLit: NIST Literature Report Builder for Thermophysical and Thermochemical Property Measurements","mediaType":"text/html","title":"DOI Access to ThermoLit: NIST Literature Report Builder for Thermophysical and Thermochemical Property Measurements"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-02-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics","Information Technology"],"keyword":["OCR","automated character recognition","automated image recognition","character recognition","characters","complex document recognition","printed characters","software recognition","technical document images"]},{"identifier":"FF429BC178798B3EE0431A570681E858232","accessLevel":"public","contactPoint":{"hasEmail":"mailto:daniel.siderius@nist.gov","fn":"Daniel Siderius"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/FF429BC178798B3EE0431A570681E858232","description":"The Standard Reference Simulation Website is an ongoing project whose aim is to provide well-documented simulation results for a variety of systems and from various simulation techniques. The data include raw canonical potential energy, macrostate probability distributions, metadata explaining the simulation parameters and constraints, and thermophysical properties generated by processing the raw simulation output, including pressure, phase coexistence properties, self-diffusivity, and excess entropy. Thermodynamic properties and reference energy calculations are given on the website in tabular form. Data are generated from well-proven molecular simulation software with repeated simulation runs to prove repeatability and provide error estimates so that the data can serve as Standard Reference Data (http://www.nist.gov/srd/upload/SRDAct-2.pdf).","language":["en"],"title":"NIST Standard Reference Simulation Website - SRD 173","distribution":[{"accessURL":"https://www.nist.gov/programs-projects/nist-standard-reference-simulation-website","format":"HTML","description":"The Standard Reference Simulation Website is an ongoing project whose aim is to provide well-documented simulation results for a variety of systems and from various simulation techniques. The results contained here are usually generated in-house at NIST but, when certain criteria are satisfied, may also include results from provided from outside collaborators.","title":"NIST Standard Reference Simulation Website"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-09-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Chemical thermodynamics and chemical properties","Chemistry:Chemical engineering and processing","Chemistry:Theoretical chemistry and modeling","Physics:Atomic, molecular, and quantum","Chemistry:Thermochemical properties","Standards:Reference data","Physics:Thermodynamics","Materials:Materials characterization"],"keyword":["Molecular simulation","statistical mechanics","thermophysical properties","monte carlo","molecular dynamics","lennard-jones","trappe","spc","united atom","ewald"]},{"identifier":"FF429BC1787A8B3EE0431A570681E858233","accessLevel":"public","references":["http://pah.nist.gov/pdf/sp922.pdf","https://dx.doi.org/10.1021/acs.jpca.5b07908"],"contactPoint":{"hasEmail":"mailto:thomas.allison@nist.gov","fn":"Thomas Allison"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"http://pah.nist.gov/","description":"This website was designed to serve two purposes:   1) serve as a modern replacement for the original web site serving the content of NIST Special Publication 922: Polycyclic Aromatic Hydrocarbon Structure Index   2) serve thermochemical data derived in the publication 'First Principles Prediction of Enthalpies of Formation for Polycyclic Aromatic Hydrocarbons and Derivatives,' Thomas C. Allison and Donald R. Burgess Jr., manuscript in preparation.  Using procedures fully described in the publication referenced above, the enthalpy of formation has been derived and is available on this website in the temperature range 0 - 6000 K. These data are computed from the results of an energy-extrapolated B3LYP/cc-pVDZ optimization and frequency calculation. The enthalpy of formation is computed within the ideal gas, rigid rotor, harmonic oscillator approximation, and an empirical correction based on a chemical group scheme is applied. Please see the literature citation for details.","language":["en"],"title":"NIST Polycyclic Aromatic Hydrocarbon Structure Index - SRD 204","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4730J","mediaType":"text/html","title":"Home page for NIST Polycyclic Aromatic Hydrocarbon Structure Index"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2014-09-01","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry: Thermochemical properties","Standards: Reference data","Chemistry: Molecular characterization"],"keyword":["Polycyclic aromatic hydrocarbons PAHs thermochemistry chemical structures"]},{"identifier":"FF429BC1787B8B3EE0431A570681E858234","accessLevel":"public","contactPoint":{"hasEmail":"mailto:stephen.stein@nist.gov","fn":"Stephen E. Stein"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://chemdata.nist.gov/dokuwiki/doku.php?id=peptidew:start","description":"NIST peptide libraries are comprehensive, annotated mass spectral reference collections from various organisms and proteins useful for the rapid matching and identification of acquired MS/MS spectra. Spectra were produced by tandem mass spectrometers using liquid chromatographic separations followed by electrospray ionization. Unlike the NIST small molecule electron ionization library which contains one spectrum per molecular structure, there are several different modes of fragmentation (ion trap and ?beam-type? collision cells are currently the most commonly used fragmentation devices) that result in spectra with different, energy dependent, patterns. These result in multiple spectral libraries, distinguished by ionization mode, each of which may contain several spectra per peptide. Different libraries have also been assembled for iTRAQ-4 derivatized peptides and for phosphorylated peptides. Separating libraries by animal species reduces search time, although investigators may elect to include several species in their searches.","language":["en"],"title":"NIST Libraries of Peptide Fragmentation Mass Spectra Database - SRD 1c","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4ZK5S","format":"text/html","description":"DOI access to NIST Libraries of Peptide Fragmentation Mass Spectra Database - SRD 1c","mediaType":"text/html","title":"DOI Access to NIST Libraries of Peptide Fragmentation Mass Spectra Database - SRD 1c"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2014-04-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Proteomics","Standards:Reference data"],"keyword":["peptides","mass spectroscopy","proteomics","LC-MS"]},{"identifier":"FF429BC1787C8B3EE0431A570681E858235","accessLevel":"public","contactPoint":{"hasEmail":"mailto:mohr@nist.gov","@type":"vcard:Contact","fn":"Peter J. Mohr"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://physics.nist.gov/cuu/Constants/Citations/Search.html","description":"This bibliographic database contains citations for the most important theoretical and experimental publications relevant to the fundamental constants and closely related precision measurements published since the mid 1980s, but also includes many older papers of particular interest, dating back to the early 1800s.","language":["en"],"title":"NIST Fundamental Physical Constants Searchable Bibliography","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4S59M","format":"text/html","description":"DOI access to NIST Fundamental Physical Constants Searchable Bibliography","mediaType":"text/html","title":"DOI Access to NIST Fundamental Physical Constants Searchable Bibliography"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2014-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Magnetics","Physics:Atomic, molecular, and quantum","Physics:Electron physics","Physics:Nuclear physics"],"keyword":["bibliographic data","fundamental constants","least-squares adjustment"]},{"identifier":"FF429BC1787D8B3EE0431A570681E858236","accessLevel":"public","references":["https://adsorption.nist.gov/isodb/index.php#user-guide"],"contactPoint":{"hasEmail":"mailto:daniel.siderius@nist.gov","fn":"Daniel Siderius"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/FF429BC1787D8B3EE0431A570681E858236","description":"The NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials is a free, web-based catalog of adsorbent materials and measured adsorption properties of numerous materials obtained from article entries from the scientific literature. Search fields for the database include adsorbent material, adsorbate gas, experimental conditions (pressure, temperature), and bibliographic information (author, title, journal), and results from queries are provided as a list of articles matching the search parameters. The database also contains adsorption isotherms digitized from the cataloged articles, which can be compared visually online in the web application or exported for offline analysis.","language":["en"],"title":"NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials","distribution":[{"accessURL":"https://adsorption.nist.gov/isodb/index.php#apis","description":"A list of APIs that can be used to directly access the resource.","title":"APIs to access NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials"},{"accessURL":"https://dx.doi.org/10.18434/T43882","mediaType":"text/html","title":"DOI Access to NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-09-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials"],"keyword":["adsorbate","adsorbent","adsorption","isotherm","metal organic Framework","porous Material","surface science","Advanced Materials","Energy","Environment and Climate","Manufacturing"]},{"identifier":"FF429BC178808B3EE0431A570681E858239","accessLevel":"public","contactPoint":{"hasEmail":"mailto:vadim.okun@nist.gov","fn":"Vadim Okun"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://samate.nist.gov/SARD/","description":"This dataset provides the NIST Software Assurance Metrics And Tool Evaluation (SAMATE) Software Assurance Reference Dataset (SARD) - a set of programs with known security flaws. This will allow end users to evaluate tools and tool developers to test their methods.","language":["en"],"title":"NIST SAMATE Software Assurance Reference Dataset","distribution":[{"accessURL":"https://samate.nist.gov/SARD/","downloadURL":"https://samate.nist.gov/SARD/archive/sard_archive.zip","mediaType":"application/zip"},{"accessURL":"https://doi.org/10.18434/T4/1433084","format":"text/html","description":"DOI Access to \"NIST SAMATE Software Assurance Refrence Dataset\"","title":"DOI Access to \"NIST SAMATE Software Assurance Refrence Dataset\""}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-11-18","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Software testing","Software assurance"],"keyword":["test suite","software assurance","software testing","static analysis","security","weakness","bug","defect","flaw","vulnerability"]},{"identifier":"19A9D7193F868BDDE0531A57068151D2431","accessLevel":"public","contactPoint":{"hasEmail":"mailto:przemek.klosowski@nist.gov","fn":"Przemek Klosowski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.ncnr.nist.gov/pub/ncnrdata/","description":"Neutron scattering data from NCNR's thermal and cold neutron scattering instruments.","language":["en"],"title":"NIST Center for Neutron Research raw data archive","distribution":[{"accessURL":"https://doi.org/10.18434/t4201b","title":"DOI Access to NIST Center for Neutron Research raw data archive"},{"downloadURL":"https://ftp.ncnr.nist.gov/pub/ncnrdata/","format":"CSV file of raw data from the NIST Center for Neutron Research.","description":"Raw data from the NIST Center for Neutron Research.","mediaType":"text/csv","title":"NIST Center for Neutron Research Raw Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-06-29 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Neutron Research"],"keyword":["neutron research","neutron scattering","neutron diffraction","neutron reflectometry","small-angle neutron scattering","neutron interferometry","neutron spin-echo","polarized neutrons"]},{"identifier":"1E0F15DAAEFB84E4E0531A5706813DD8436","accessLevel":"public","contactPoint":{"hasEmail":"mailto:tanya.brewer@nist.gov","fn":"Tanya Brewer"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/1E0F15DAAEFB84E4E0531A5706813DD8436","description":"Security automation reference data is currently housed within the National Vulnerability Database (NVD). The NVD is the U.S. Government repository of security automation data based on security automation specifications. This data provides a standards-based foundation for the automation of software asset, vulnerability, and security configuration management; security measurement; and compliance activities. This data supports security automation efforts based on the Security Content Automation Protocols (SCAP). The NVD includes databases of security configuration checklists for the NCP, listings of publicly known software flaws, product names, and impact metrics. A formal validation program tests the ability of vendor products to use some forms of security automation data based on a product's conformance in support of specific enterprise capabilities.","language":["en"],"title":"National Vulnerability Database","distribution":[{"accessURL":"https://nvd.nist.gov/","format":"html web page","description":"The NVD is the U.S. government repository of standards based vulnerability management data represented using the Security Content Automation Protocol (SCAP). This data enables automation of vulnerability management, security measurement, and compliance. The NVD includes databases of security checklist references, security-related software flaws, misconfigurations, product names, and impact metrics.","title":"National Vulnerability Database"},{"accessURL":"https://doi.org/10.18434/M3436","title":"DOI Access for National Vulnerability Database"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2025-07-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1D","theme":["Information Technology:Cybersecurity","Information Technology"],"issued":"2022-01-11","keyword":["CVE","CVSS","SCAP","800-53","Vulnerability","NVD","Checklists"]},{"identifier":"1E651A532AFD8816E0531A570681A662439","accessLevel":"public","references":["https://www.nist.gov/document/readmesd18pdf"],"contactPoint":{"hasEmail":"mailto:karen.marshall@nist.gov","fn":"Karen Marshall"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/1E651A532AFD8816E0531A570681A662439","description":"This database was distributed for use in development and testing of automated mugshot identification systems. The database consists of one zipped file, containing a total of 3,248 images of variable size using PNG format for the images and TXT format for corresponding metadata files. There are images of 1,573 individuals (cases) 1,495 male and 78 female. The database contains both front and side (profile) views when available. Separating front views and profiles, there are 131 cases with two or more front views and 1,418 with only one front view. Profiles have 89 cases with two or more profiles and 1,268 with only one profile. Cases with both fronts and profiles have 89 cases with two or more of both fronts and profiles, 27 with two or more fronts and one profile, and 1,217 with only one front and one profile.","language":["en"],"title":"NIST Mugshot Identification Database (MID) - NIST Special Database 18","distribution":[{"accessURL":"https://doi.org/10.18434/t4159s","format":"text/html","description":"DOI Access to NIST Mugshot Identification Database (MID) - NIST Special Database 18","title":"DOI Access to NIST Mugshot Identification Database (MID) - NIST Special Database 18"},{"accessURL":"https://www.nist.gov/srd/nist-special-database-18","format":"the downloaded file is in zipped format with the images in PNG format","description":"This database is being distributed for use in developing and testing of mugshot identification systems. The database contains images of 1,573 individuals (cases) for a total of 3,248 images stored in PNG format. The mugshots are mainly of male cases, with the database containing 1495 male cases and 78 female cases. The gender and age of each individual are stored in a text file that accompanies each image.","mediaType":"application/zip","title":"Special  Database 18 -NIST Mugshot Identification Database (MID)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Use of this data requires users agree to be bound by the following terms and conditions. The database will only be used for biometrics related research. The database will not be further distributed, published, copied, or disseminated in any way or form.","modified":"2005-01-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Biometrics"],"keyword":["8 bit gray scales","facial recognition","front views","law enforcement","matching","mugshots","profile views","profiles"]},{"identifier":"25C39B1D2C1549AFE0531A5706812CBC1445","accessLevel":"public","references":["https://www.nist.gov/pml/time-and-frequency-division/services/internet-time-service-its"],"contactPoint":{"hasEmail":"mailto:judah.levine@nist.gov","fn":"Judah Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/25C39B1D2C1549AFE0531A5706812CBC1445","description":"Distributes NIST estimate of official U.S. time over the Internet in real time, using Network Time Protocol (NTP) and other time data formats to automatically synchronize clocks in computers and network devices to official U.S. time as realized by NIST several billions of times per day. This official U.S. time is the NIST estimate of Coordinated Universal Time (UTC), and called UTC(NIST). The accuracy of UTC(NIST) as distributed through the Internet Time Service (ITS) is on the order of 0.001 seconds (one millisecond), although accuracy can vary depending on network conditions and other parameters. Note that unlike most traditional datasets, time is intrinsically a transient, ever-changing quantity. As soon as UTC(NIST) is transmitted to a client, that particular value of UTC(NIST) no longer reflects the current time, which is constantly changing. There is thus no static storage of any time data, apart from internal diagnostic information not released to the public which ensures that UTC(NIST) as disseminated through the Internet Time Service (ITS) is commensurate with the official UTC(NIST) realization within the uncertainties of the system. The vast majority of UTC(NIST) information distributed through ITS is provided freely, anonymously and automatically to the public. Any IP address can request UTC(NIST) through the ITS and the information is automatically and anonymously provided at no cost to the user. Full documentation of the ITS including all the source code is available to the public through the web site http://www.nist.gov/pml/div688/.NIST provides an authenticated version of ITS to a limited number of users (approximately 500 users near the end of calendar year 2015) who for various reasons want to ensure they are receiving UTC(NIST) without spoofing or interference with the information. This service uses public key encryption for the set of registered users to provide authenticated UTC(NIST).","language":["en"],"title":"NIST Internet Time Service","distribution":[{"downloadURL":"ftp://ftp.nist.gov/pub/time/software/daytime/nistime-32bit.exe","format":"Windows .exe","description":"A program to request the time from one of the NIST time servers and use the reply to set the clock on the local system. The network address of the time server and the format of the time query can be changed as described in the various help messages. The program can be used in several different modes as described in help messages.","mediaType":"application/http","title":"Time client for Microsoft Windows"}],"describedByType":"text/plain","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-11-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://www.nist.gov/pml/time-and-frequency-division/services/internet-time-service-its","accrualPeriodicity":"R/PT1S","theme":["Physics:Time and frequency"],"spatial":"Applicable across the world.","keyword":["time","Internet","Internet Time Service","Coordinated Universal Time","UTC"]},{"identifier":"26019888444DC5A7E0531A570681A2B41446","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.donahue@nist.gov","fn":"Michael J. Donahue"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"http://math.nist.gov/phaml","description":"Software for the solution of elliptic partial differential equations using finite elements with adaptive mesh refinement and multigrid techniques.","language":["en"],"title":"Parallel Hierarchical Adaptive Multilevel Project (PHAML)","distribution":[{"downloadURL":"http://math.nist.gov/phaml","format":"html web page","mediaType":"application/http","title":"PHAML - The Parallel Hierarchical Adaptive Multilevel Project"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-09-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Numerical methods and software"],"keyword":["partial differential equations","finite elements","adaptive mesh refinement","multigrid","parallel algorithms"]},{"identifier":"266A9A71D2153580E0531A570681D80E1447","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8347"],"contactPoint":{"hasEmail":"mailto:david.cooper@nist.gov","fn":"David Cooper"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/266A9A71D2153580E0531A570681D80E1447","description":"he set of NIST Test PIV Cards contains sixteen smart cards that are loaded with a PIV Card Application, as specified in NIST Special Publication 800-73-4. The PIV Card Applications on the smart cards are loaded with test data and keys that are similar to what might appear on actual PIV Cards, with the exception that the certificates on the test PIV Cards were issued from a test public key infrastructure. The currently available set of test PIV cards, version 2, includes examples of new, optional features that were introduced in SP 800-73-4, such as on-card biometric comparison, secure messaging, and the virtual contact interface. The set of test cards includes not only examples that are similar to cards issued today, but also examples of cards with features that are expected to appear in cards that will be issued in the future. For example, while the certificates and data objects on most, if not all, cards issued today are signed using RSA PKCS #1 v1.5, the set of test cards include examples of certificates and data objects that are signed using each of the algorithms and key sizes listed in Table 3-2 of Special Publication 800-78-4, including RSASSA-PSS and ECDSA. Similarly, the infrastructure supporting the test cards provides examples of CRLs and OCSP responses that are signed using each of these signature algorithms. The set of test cards also includes certificates with elliptic curve subject public keys in addition to RSA subject public keys, as is permitted by Table 3-1 of Special Publication 800-78-4. The set of test cards, collectively, also include all of the mandatory and optional data objects listed in Section 3 of SP 800-73-4 Part 1, except for Cardholder Iris Images. Several of the cards include a Key History object along with retired key management keys.","language":["en"],"title":"NIST Test Personal Identity Verification (PIV) Cards - Special Database 33","distribution":[{"accessURL":"https://www.nist.gov/srd/nist-special-database-33","format":"text/html","title":"NIST Test Personal Identity Verification (PIV) Cards - Special Database 33"},{"accessURL":"https://doi.org/10.18434/t4b887","mediaType":"text/html","title":"DOI access to NIST Test Personal Identity Verification (PIV) Cards - Special Database 33"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Access to NIST Special Database 33 is available for a fee. For more information please visit https://www.nist.gov/srd/nist-special-database-33.","modified":"2021-03-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data","Information Technology:Federal information standards (FIPS)","Cryptography","Access control"],"issued":"2012-07-16","keyword":["Personal Identity Verification","PIV","smart card","Public Key Infrastructure","PKI"]},{"identifier":"26DEA39AD677678AE0531A570681F32C1449","accessLevel":"public","references":["https://www.nist.gov/publications/multiple-encounter-dataset-i-meds-i"],"contactPoint":{"hasEmail":"mailto:karen.marshall@nist.gov","fn":"Karen Marshall"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/itl/iad/image-group/special-database-32-multiple-encounter-dataset-meds","description":"Multiple Encounter Dataset (MEDS-I) is a test corpus organized from an extract of submissions of deceased persons with prior multiple encounters. MEDS is provided to assist the FBI and partner organizations refine tools, techniques, and procedures for face recognition as it supports Next Generation Identification (NGI), forensic comparison, training, and analysis, and face image conformance and inter-agency exchange standards. The MITRE Corporation (MITRE) prepared MEDS in the FBI Data Analysis Support Laboratory (DASL) with support from the FBI Biometric Center of Excellence.","language":["en"],"title":"Multiple Encounter Dataset (MEDS-I) - NIST Special Database 32","distribution":[{"accessURL":"https://www.nist.gov/itl/iad/image-group/special-database-32-multiple-encounter-dataset-meds","downloadURL":"http://nigos.nist.gov:8080/nist/sd/32/NIST_SD32_MEDS-I_face.zip","format":"JPEG formatted images","description":"Zip file with JPEG formatted face image files.","mediaType":"application/zip","title":"Multiple Encounter Dataset (MEDS)"},{"accessURL":"https://www.nist.gov/itl/iad/image-group/special-database-32-multiple-encounter-dataset-meds","downloadURL":"http://nigos.nist.gov:8080/nist/sd/32/NIST_SD32_MEDS-I_html.zip","format":"zip file with html and jpeg formatted images","description":"zip file with html page with jpeg images of faces","mediaType":"application/zip","title":"Multiple Encounter Dataset(MEDS-I)"}],"describedByType":"text/csv","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2011-07-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"http://nigos.nist.gov:8080/nist/sd/32/NIST_SD32_MEDS-I_metadata.csv","accrualPeriodicity":"irregular","theme":["Information Technology:Biometrics"],"keyword":["face","biometrics","forensic"]},{"identifier":"26DEBF5B517C6750E0531A570681AA751450","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.7807"],"contactPoint":{"hasEmail":"mailto:karen.marshall@nist.gov","fn":"Karen Marshall"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/itl/iad/image-group/special-database-32-multiple-encounter-dataset-meds","description":"Multiple Encounter Dataset (MEDS-II) is a test corpus organized from an extract of submissions of deceased persons with prior multiple encounters. MEDS is provided to assist the FBI and partner organizations refine tools, techniques, and procedures for face recognition as it supports Next Generation Identification (NGI), forensic comparison, training, analysis, face image conformance, and inter-agency exchange standards. The MITRE Corporation (MITRE) prepared MEDS in the FBI Data Analysis Support Laboratory (DASL) with support from the FBI Biometric Center of Excellence.","language":["en"],"title":"Multiple Encounter Dataset (MEDS-II) - NIST Special Database 32","distribution":[{"accessURL":"https://www.nist.gov/itl/iad/image-group/special-database-32-multiple-encounter-dataset-meds","downloadURL":"http://nigos.nist.gov:8080/nist/sd/32/NIST_SD32_MEDS-II_face.zip","format":"zip file with jpeg images","describedBy":"http://nigos.nist.gov:8080/nist/sd/32/NIST_SD32_MEDS-II_metadata.zip","description":"zip file with JPEG images of faces","mediaType":"application/zip","title":"Multiple Encounter Dataset (MEDS-II)","describedByType":"text/csv"}],"describedByType":"text/csv","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2011-07-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"http://nigos.nist.gov:8080/nist/sd/32/NIST_SD32_MEDS-II_metadata.zip","accrualPeriodicity":"irregular","theme":["Information Technology:Biometrics"],"keyword":["face","forensic","biometrics"]},{"identifier":"276B3C17848DEC5BE0531A570681E6081452","accessLevel":"public","contactPoint":{"hasEmail":"mailto:rosemary.astheimer@nist.gov","fn":"Rosemary Astheimer"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/276B3C17848DEC5BE0531A570681E6081452","description":"NIST has created a Test System to measure conformance of Computer-Aided Design (CAD) software to American Society of Mechanical Engineers (ASME) standards for Product and Manufacturing Information (PMI), specifically geometric dimensioning and tolerancing (GD&T) information.  The test system includes: test case definitions (2D CAD drawings with PMI), CAD models generated from the test cases, STEP files generated from the CAD models, and verification and validation reports.","language":["en-US"],"title":"NIST CAD Models and STEP Files with PMI","distribution":[{"accessURL":"https://www.nist.gov/el/systems-integration-division-73400/mbe-pmi-validation-and-conformance-testing","description":"Two Datasets of test cases, CAD models, and reports are available.","mediaType":"application/zip","title":"NIST CAD Models and STEP Files with PMI"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-11-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Interoperability in manufacturing"],"keyword":["manufacturing","CAD","PMI","interoperability","data exchange"]},{"identifier":"2F4AD9932475971EE0531A57068107B31453","accessLevel":"public","references":["https://dx.doi.org/10.6028/jres.121.005"],"contactPoint":{"hasEmail":"mailto:donald.burgess@nist.gov","fn":"Donald R. Burgess"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/2F4AD9932475971EE0531A57068107B31453","description":"Datasets in digital electronic formats are provided for data contained in the publication \"Recommended Values for the Gas Phase Enthalpies of Formation of Hydrogen-Oxygen Species;\" J. Res. Natl. Inst. Stand. Technol. 121, 108-138 (2016); DOI: 10.6028/jres.121.005. In this work, we compiled gas phase enthalpies of formation for nine hydrogen-oxygen species (HxOy) and selected values for use. The compilation consists of values derived from experimental measurements, quantum chemical calculations, and evaluations. This work updates the recommended values in the NIST-JANAF (1985) and Gurvich et al (1989) thermochemical tables for seven species. For two species, HO3 and H2O3 (important in atmospheric chemistry) and not found in prior thermochemical evaluations, we also provide tables of thermochemical functions (Cp, S°, H°, and ?fH°) as a function of temperature. In this work, we also provide supplementary data for the species consisting of zero point energies, vibrational frequencies, and ion reaction energetics.","language":["en"],"title":"Recommended Gas Phase Enthalpies of Formation for Hydrogen-Oxygen (HxOy) Species","distribution":[{"accessURL":"https://doi.org/doi:10.18434/M34K5M"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-03-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data","Chemistry:Chemical thermodynamics and chemical properties"],"keyword":["Standard reference data","Critical evaluation","Data compiliation","Thermochemical database","ab initio thermochemistry; atomization energies; bond dissociation energies; enthalpy of formation; hydrogen-oxygen species; thermochemical network; thermodynamic properties."]},{"identifier":"2FA9DBBE493E7AE0E0531A5706819F5A1454","accessLevel":"public","references":["https://doi.org/10.5194/amt-9-1627-2016"],"contactPoint":{"hasEmail":"mailto:zachary.levine@nist.gov","fn":"Zachary H. Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/2FA9DBBE493E7AE0E0531A5706819F5A1454","description":"The data is for the companion paper:  ZH Levine et al., The detection of carbon dioxide leaks using quasi-tomographic laser absorption spectroscopy, Atmospheric Measurement Techniques, in press (to appear 2016). The data include measurements carbon dioxide in the atmosphere in a field in Ft. Wayne, Indiana as well as simulations of such measurements.","language":["en"],"title":"Supplementary material for: The detection of carbon dioxide leaks using quasi-tomographic laser absorption spectroscopy measurements in variable wind","distribution":[{"downloadURL":"https://opendata.nist.gov/1454_amt-9-1627-2016-supplement.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash for Supplemental Data"},{"downloadURL":"http://www.atmos-meas-tech.net/9/1627/2016/amt-9-1627-2016-supplement.zip","format":"zipped supplemental data file","description":"Supplemental material for: Levine, Z. H., Pintar, A. L., Dobler, J. T., Blume, N., Braun, M., Zaccheo, T. S., and Pernini, T. G.: The detection of carbon dioxide leaks using quasi-tomographic laser absorption spectroscopy measurements in variable wind, Atmos. Meas. Tech., 9, 1627-1636, doi:10.5194/amt-9-1627-2016, 2016.","mediaType":"application/zip","title":"Externally Hosted Supplement to the final revised paper"},{"downloadURL":"https://opendata.nist.gov/1454_amt-9-1627-2016-supplement.zip","description":"Supplemental material for: Levine, Z. H., Pintar, A. L., Dobler, J. T., Blume, N., Braun, M., Zaccheo, T. S., and Pernini, T. G.: The detection of carbon dioxide leaks using quasi-tomographic laser absorption spectroscopy measurements in variable wind, Atmos. Meas. Tech., 9, 1627-1636, doi:10.5194/amt-9-1627-2016, 2016.","mediaType":"application/zip","title":"NIST-hosted Supplement to the final revised paper"},{"accessURL":"https://doi.org/10.18434/T4JC7R","format":"ZIP","description":"DOI Access to Supplementary material for: The detection of carbon dioxide leaks using quasi-tomographic laser absorption spectroscopy measurements in variable wind","mediaType":"application/zip","title":"DOI Access to Supplementary material for: The detection of carbon dioxide leaks using quasi-tomographic laser absorption spectroscopy measurements in variable wind"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"No restrictions","modified":"2015-11-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Metrology:Environmental metrology"],"spatial":"Ft. Wayne, IN","keyword":["carbon sequestration","laser absorption spectroscopy"],"temporal":"2015-02-05/2015-02-05"},{"identifier":"333921C30B07D332E0531A5706812AD51458","accessLevel":"public","references":["https://dx.doi.org/10.1002/rcm.7475"],"contactPoint":{"hasEmail":"mailto:william.wallace@nist.gov","fn":"William E. Wallace III"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"http://chmdatafnx.chemref-838.nist.gov/dokuwiki/doku.php?id=chemdata:srm1950raw","description":"Liquid-chromatography-mass spectrometry (LC-MS) raw data sets from various instruments delivered in their native instrument format.  31 files in all.  7.5 GB data.","language":["en"],"title":"Raw data files that were used in writing \"Analysis of human plasma metabolites across different liquid chromatography/mass spectrometry platforms: Cross-platform transferable chemical signatures\" by Kelly H. Telu, Xinjian Yan, William E. Wallace, Stephen E. Stein and Yamil Simón-Manso, Published paper: DOI: 10.1002/rcm.7475","distribution":[{"accessURL":"https://dx.doi.org/10.18434/T4K88D","format":"Agilent Technologies and Thermo Scientific raw data files","description":"Analysis of  NIST Standard Reference Material 1950 across several LC/MS platforms following the same sample preparation procedures. These included different liquid chromatography separations (conventional HPLC, UHPLC and nanoLC) and different mass spectrometers (Q-TOF and Orbitrap).","title":"Raw LC-MS/MS Data of Standard Reference Material 1950, Metabolites in Human Plasma"},{"accessURL":"http://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:srm1950raw","format":"Agilent Technologies and Thermo Scientific raw data files","description":"Analysis of  SRM 1950 across several LC/MS platforms ? following the same sample preparation procedures. These included different liquid chromatography separations (conventional HPLC, UHPLC and nanoLC) and different mass spectrometers (Q-TOF and Orbitrap).","mediaType":"text/html","title":"Raw LC-MS/MS Data of Standard Reference Material 1950, Metabolites in Human Plasma"},{"downloadURL":"https://opendata.nist.gov/1458_Telu_human_plasma_metabolites.zip","format":"Agilent Technologies and Thermo Scientific raw data files","description":"Analysis of  NIST Standard Reference Material 1950 across several LC/MS platforms following the same sample preparation procedures. These included different liquid chromatography separations (conventional HPLC, UHPLC and nanoLC) and different mass spectrometers (Q-TOF and Orbitrap).","mediaType":"application/zip","title":"Zipped file of Telu human plasma metabolites data"},{"downloadURL":"https://opendata.nist.gov/1458_Telu_human_plasma_metabolites.zip.sha256","format":"SHA-256 hash of zipped data file","description":"SHA-256 hash for verification of zipped data","mediaType":"text/plain","title":"SHA-256 hash of zipped data for Telu human plasma metabolites"},{"accessURL":"https://doi.org/10.18434/t4k88d","description":"DOI access to Raw data files that were used in writing \"Analysis of human plasma metabolites across different liquid chromatography/mass spectrometry platforms: Cross-platform transferable chemical signatures\"","title":"DOI Access to Raw data files that were used in writing 'Analysis of human plasma meta...'"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-05-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Health:Clinical diagnostics","Chemistry:Analytical chemistry","Standards:Reference materials","Chemistry:Molecular characterization"],"keyword":["human blood plasma","metabolomics","mass spectrometry","liquid chromatography","SRM 1950"]},{"identifier":"349FC6C113FA5700E0531A5706810A431464","accessLevel":"public","contactPoint":{"hasEmail":"mailto:simon.frechette@nist.gov","fn":"Simon P. 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Three channels of data dissemination are available or becoming available from the SMS Test Bed: (1) a volatile data stream using an MTConnect agent, (2) a query-able data repository using the NIST Material Data Curation System (MDCS), and (3) pre-compiled data packages that include a collection of CAx Lab data and associated Manufacturing Lab data.","language":["en"],"title":"Volatile Data Stream (VDS) for the Smart Manufacturing Systems (SMS) Test Bed using MTConnect","distribution":[{"accessURL":"https://doi.org/10.18434/T4FK54","title":"DOI Access for Volatile Data Streat"},{"downloadURL":"https://smstestbed.nist.gov/vds/current","description":"MTConnect agent of data collected in a real manufacturing facility","mediaType":"text/html","title":"Volatile Data Stream"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-06-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://smstestbed.nist.gov/vds/probe","accrualPeriodicity":"irregular","theme":["Information retrieval","Manufacturing:Factory operations planning and control","Information Technology:Cyber-physical systems","Manufacturing:Manufacturing systems design and analysis","Manufacturing:Machining"],"keyword":["MTConnect","Test Bed"]},{"identifier":"34A313AF57372708E0531A570681D9CF1465","accessLevel":"public","contactPoint":{"hasEmail":"mailto:simon.frechette@nist.gov","fn":"Simon P. 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Three channels of data dissemination are available or becoming available from the SMS Test Bed: (1) a volatile data stream using an MTConnect agent, (2) a query-able data repository using the NIST Material Data Curation System (MDCS), and (3) pre-compiled data packages that include a collection of CAx Lab data and associated Manufacturing Lab data.","language":["en"],"title":"Technical Data Packages (TDPs) from the Smart Manufacturing Systems (SMS) Test Bed","distribution":[{"accessURL":"https://smstestbed.nist.gov/tdp","format":"CAD file , CAM file, TXT, XML","description":"Packages of related data collected in a real manufacturing facility","title":"Technical Data Packages"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-06-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information retrieval","Manufacturing:Machining","Manufacturing:Factory operations planning and control","Information Technology:Cyber-physical systems","Manufacturing:Manufacturing systems design and analysis"],"keyword":["Test Bed"]},{"identifier":"3541C38AF67059C5E0531A57068159D51467","accessLevel":"public","references":["https://doi.org/10.1088/1681-7575/aa4e55"],"contactPoint":{"hasEmail":"mailto:john.pagliaro@nist.gov","fn":"John L. 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The National Institute of Standards and Technology (NIST) is serving as Technical Agent for distribution of the FERET database. The goal of the FERET program is to develop new techniques, technology, and algorithms for the automatic recognition of human faces. As part of the FERET program, a database of facial imagery was collected between December 1993 and August 1996. The database is used to develop, test, and evaluate face recognition algorithms.","language":["en"],"title":"Color FERET Database","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Users must abide by the release agreement and request an account to obtain the data. 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In its first release, the DMC toolkit supports signature and validation of data in the following formats: ISO 10303-21 (STEP), ISO 6983 (G-code), ISO 32000 (PDF) and 14739 (PRC, aka 3D PDF), and Quality Information Framework (QIF). The software offers both the Toolkit as a C# API and a fully-functional demonstration application. A use case and detailed description of the technology is available in an article titled, \"Embedding X.509 Digital Certificates in Three-Dimensional Models for Authentication, Authorization, and Traceability of Product Data,\" published in the Journal of Computing and Information Science in Engineering (doi:10.1115/1.4034131)","language":["en"],"title":"Digital Manufacturing Certificate Toolkit","distribution":[{"accessURL":"https://doi.org/10.18434/T4P30K","format":"Digital Object Identifier, a persistent identifier","mediaType":"text/html","title":"DOI access for Digital Manufacturing Certificate Toolkit"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-06-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Cryptography","Manufacturing:Interoperability in manufacturing"],"keyword":["Trustworthiness","Authentication","Authorization","Product Data Quality (PDQ)","Model-Based Enterprise (MBE)"]},{"identifier":"3C53B142D0C3268EE0531A570681EA991497","accessLevel":"public","references":["https://www.nist.gov/el/net-zero-energy-residential-test-facility/nzertf-publications","https://doi.org/10.6028/jres.122.014"],"contactPoint":{"hasEmail":"mailto:william.healy@nist.gov","fn":"William M. 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Those parameters include indoor temperatures and humidities, outdoor weather conditions, electrical consumption by all devices, hot water consumption, photovoltaic energy generation, and operational schedules of the virtual occupants of the facility.  The data presented here are for the second year of operation which occurred from February 1, 2015 through January 31, 2016","language":["en"],"title":"Net Zero Energy Residential Test Facility Instrumented Data;  Year 2","distribution":[{"accessURL":"https://pages.nist.gov/netzero/data.html","format":"Comma-Separated Values (CSV) text","description":"Minutely data for the entire year is downloaded by subsystem or for the entire facility.","mediaType":"text/csv","title":"NIST Net-Zero - Data Page"},{"accessURL":"https://doi.org/10.18434/T46W2X","format":"Web page","mediaType":"text/html","title":"DOI Access for Net Zero Energy Residential Test Facility Instrumented Data; Year 2"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-12-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://pages.nist.gov/netzero/data.html#data_dictionary","accrualPeriodicity":"irregular","theme":["Energy:Energy efficiency","Buildings and Construction:Indoor air quality","Energy:Alternative energy","Heating and cooling equipment"],"spatial":"Latitude:  39.138 N, Longitude:  77.219 W","keyword":["residential energy use; energy conservation; photovoltaics; net-zero energy; heating","ventilation","and air conditioning; water heating;"],"temporal":"2015-02-01/2016-01-31"},{"identifier":"3CF6F926D7309656E0531A5706810E0D1498","accessLevel":"public","contactPoint":{"hasEmail":"mailto:zachary.levine@nist.gov","fn":"Zachary Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/programs-projects/x-ray-computed-tomography-ct","description":"DICOM files are given for 11 CT scans which were used in a research article.  Each scan contains about 1250 slices with 512x512 gray scale images, each in its own directory.  The low number slices contain the diapers in the order D5 ... D1, then the vials of powder are contained in the order V11 ... V2.   The symbols correspond to the injected masses which are given in the paper and which are repeated here in a file called \"mass.txt\".   There is also a file \"README.txt\" which describes the directory structure.","language":["en"],"title":"DICOM files for \"RECIST and Volumetric CT Measurements of Injected-Water Phantoms\" by ZH Levine, HH Chen-Mayer, AP Peskin, and AL Pintar.","distribution":[{"downloadURL":"https://opendata.nist.gov/1498_IWP_4.0mLb.zip","mediaType":"application/zip","title":"nominal 4.0 mL injection scan 2"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_4.0mLa.zip","mediaType":"application/zip","title":"nominal 4.0 mL injection scan 1"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_2.0mLb.zip","mediaType":"application/zip","title":"nominal 2.0 mL injection scan 2"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_2.5mLa.zip","mediaType":"application/zip","title":"nominal 2.5 mL injection scan 1"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_2.5mLb.zip","mediaType":"application/zip","title":"nominal 2.5 mL injection scan 2"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_3.0mLb.zip","mediaType":"application/zip","title":"nominal 3.0 mL injection scan 2"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_2.0mLa.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for nominal 2.0 mL injection scan 1"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_2.5mLa.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for nominal 2.5 mL injection scan 1"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_3.0mLa.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for nominal 3.5 mL injection scan 1"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_3.5mLa.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for nominal 3.5 mL injection scan 1"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_4.0mLa.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for nominal 4.0 mL injection scan 1"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_dry.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for dry diapers and vials"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_2.0mLb.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for nominal 2.0 mL injection scan 2"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_2.5mLb.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for nominal 2.5 mL injection scan 2"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_3.0mLb.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for nominal 3.0 mL injection scan 2"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_3.5mLb.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for nominal 3.5 mL injection scan 2"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_4.0mLb.zip.sha256","mediaType":"text/plain","title":"SHA-256 hash file for nominal 4.0 mL injection scan 2"},{"downloadURL":"https://opendata.nist.gov/1498_README.txt.sha256","mediaType":"text/plain","title":"SHA-256 hash file for README file (overview)"},{"accessURL":"https://doi.org/10.18434/M31592","mediaType":"text/html","title":"DOI Access to DICOM files"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_3.5mLb.zip","mediaType":"application/zip","title":"nominal 3.5 mL injection scan 2"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_2.0mLa.zip","mediaType":"application/zip","title":"nominal 2.0 mL injection scan 1"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_dry.zip","mediaType":"application/zip","title":"dry diapers and vials"},{"downloadURL":"https://opendata.nist.gov/1498_README.txt","mediaType":"text/plain","title":"README file (overview)"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_3.0mLa.zip","mediaType":"application/zip","title":"nominal 3.0 mL injection scan 1"},{"downloadURL":"https://opendata.nist.gov/1498_IWP_3.5mLa.zip","mediaType":"application/zip","title":"nominal 3.5 mL injection scan 1"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2012-03-14","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Health: Medical imaging"],"keyword":["x-ray computed tomography ; medical phantom ; Hounsfield unit ; volume ; shape"]},{"identifier":"3DA7897F530C7988E0531A570681AAF01502","accessLevel":"public","contactPoint":{"hasEmail":"mailto:mark.henn@nist.gov","fn":"Mark-Alexander Henn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/3DA7897F530C7988E0531A570681AAF01502","description":"The dataset contains several MATLAB files and input files for the software package JCMsuite that enable the modeling of optical imaging of finite multi-line arrays.","language":["en"],"title":"A Library to Enable the Modeling of Optical Imaging of Finite Multi-Line Arrays.","distribution":[{"downloadURL":"https://opendata.nist.gov/1474_EMHenn.zip","format":"ZIP","mediaType":"application/zip","title":"Data package for: A Library to Enable the Modeling of Optical Imaging of Finite Multi-Line Arrays"},{"accessURL":"https://doi.org/10.18434/T42C7D","title":"A Library to Enable the Modeling of Optical Imaging of Finite Multi-Line Arrays"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-07-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Modeling and simulation research"],"keyword":["optical imaging","computational electromagnetics","modeling"]},{"identifier":"3DF9EB5C7EEFBBE3E0531A570681B5811503","accessLevel":"public","references":["https://doi.org/10.6028/jres.122.021"],"contactPoint":{"hasEmail":"mailto:benjamin.place@nist.gov","fn":"Benjamin Place"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/3DF9EB5C7EEFBBE3E0531A570681B5811503","description":"Sulfonephthalein pH indicator dyes have historically been noted for impurities originating from the synthesis process which lead to batch to batch differences in pH measurements. Uncertainties up to 0.1 pH units have been attributed to impurities in these reagents. In an effort to understand the extent of impurities in these dyes and the potential impact on the expanded uncertainty of pH measured by this technique, we have examined seven commercially available sulfonephthalein pH indicators using liquid chromatography-mass spectrometry (LC-MS): bromocresol green (BCG), bromocresol purple (BCP), bromothymol blue (BTB), cresol red (CR), meta-cresol purple (MCP), phenol red (PR), and thymol blue (TB). Peaks eluting from the LC (Figure 1) were subjected to fragmentation by mass spectrometry. The resulting dataset will aid in efforts to improve the synthesis or purification of these dyes.","language":["en"],"title":"Mass spectra of sulfonephthalein pH indicator dyes and their impurities","distribution":[{"downloadURL":"https://opendata.nist.gov/1503_BCP_AA1_1.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_PR_AA1_2.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BTB_LG1_2.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BTB_LG1_3.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_TB_AC1_1.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_TB_AC1_3.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BCG_AA1_1.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_MCP_PB1_3.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BCP_AA1_3.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BTB_LG1_1.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_MCP_PB1_2.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_PR_AA1_1.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_CR_AA1_1.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_CR_AA1_2.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_PR_AA1_3.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_TB_AC1_2.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BCG_AA1_2.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BCG_AA1_3.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BCP_AA1_2.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_CR_AA1_3.cdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_MCP_PB1_1.cdf.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/doi:10.18434/M3JP40"},{"downloadURL":"https://opendata.nist.gov/1503_BCP_AA1_1.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BTB_LG1_2.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_PR_AA1_2.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BTB_LG1_3.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_MCP_PB1_3.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_TB_AC1_1.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BCG_AA1_1.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BCP_AA1_3.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BTB_LG1_1.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_TB_AC1_3.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_MCP_PB1_2.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_PR_AA1_1.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_CR_AA1_1.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_CR_AA1_2.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_PR_AA1_3.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_TB_AC1_2.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BCG_AA1_3.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BCP_AA1_2.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_BCG_AA1_2.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_CR_AA1_3.cdf","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1503_MCP_PB1_1.cdf","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-11-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Environment:Air / water / soil quality","Chemistry:Analytical chemistry","Environment:Environmental health"],"keyword":["pH indicators","mass spectrometry","seawater pH"]},{"identifier":"407FBA8015D338A3E0531A57068138FE1508","accessLevel":"public","contactPoint":{"hasEmail":"mailto:richard.candell@nist.gov","fn":"Rick Candell Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/407FBA8015D338A3E0531A57068138FE1508","description":"This computer code contains the Tennessee Eastman (TESIM) chemical process model to be simulated using hardware-based simulation approaches. The code is optimized for wireless system integration as a part of the NIST Industrial Wireless project.  This code allows for integration with external sensor and actuator equipment and the evaluation of operational performance under different wireless scenarios.  Disclaimer: Certain commercial equipment, instruments, or materials are identified in this paper in order to specify the experimental procedure adequately.  Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.","language":["en"],"title":"Computer Code for Tennesse Eastman Industrial Wireless Systems Performance Evaluation","distribution":[{"accessURL":"https://github.com/usnistgov/tesim","format":"A GITHUB Repository","description":"This computer code that allows the Tennessee Eastman (TESIM) chemical process model to be simulated using hardware-based simulator architectures. The code within this repository allows for integration with external sensor and actuator equipment.  The TESIM simulator is intended for wireless and cyber-security research purposes. The code is optimized for wireless system integration as a part of the NIST Industrial Wireless project; however, the original purpose was for use with the NIST industrial cybersecurity project.","mediaType":"model/example","title":"Tennessee Eastman HIL Simulator GitHub Repository"},{"accessURL":"https://doi.org/10.18434/T4ZC7G","mediaType":"text/html","title":"DOI Access to Computer Code for Tennessee Eastman Industrial Wireless Systems Performance Evaluation"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-11-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Risk management","Information Technology:Cyber-physical systems","Advanced Communications:Wireless (RF)","Manufacturing:Factory operations planning and control"],"keyword":["Factory Simulation","chemical process simulation","factory operations","wireless performance evaluation","cyber-security performance evaluation","industrial control systems"]},{"identifier":"408189683B6DD845E0531A570681B1141509","accessLevel":"public","references":["https://doi.org/10.6028/nist.tn.1951"],"contactPoint":{"hasEmail":"mailto:richard.candell@nist.gov","fn":"Rick Candell Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/408189683B6DD845E0531A570681B1141509","description":"This repository contains code for analyzing industrial wireless sounder measurements and the generation of wireless scenarios. DISCLAIMER: Certain commercial equipment, instruments, or materials are identified in the associated paper (https://doi.org/10.6028/nist.tn.1951) in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.","language":["en"],"title":"Computer Code for Industrial Wireless Measurement Analysis and Scenario Generation","distribution":[{"accessURL":"https://github.com/usnistgov/IndustrialWirelessAnalysis","format":"MATLAB and Python Code","description":"GitHub computer code repository","mediaType":"application/example","title":"Computer Code for Industrial Wireless Analysis and Scenario Generation"},{"accessURL":"https://doi.org/10.18434/T4359D","description":"DOI access","mediaType":"text/html","title":"DOI Access to Computer Code for Industrial wireless Measurement Analysis and Scenario Generation"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-11-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)","Manufacturing:Factory operations planning and control","Information Technology:Cyber-physical systems"],"keyword":["radio frequency propagation","industrial wireless","wireless sensor networks","measurement analysis"]},{"identifier":"41709F592F4059B4E0531A570681F8C01510","accessLevel":"public","references":["https://dx.doi.org/10.6028/NIST.IR.8160"],"contactPoint":{"hasEmail":"mailto:matthew.hoehler@nist.gov","fn":"Matthew Hoehler"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/41709F592F4059B4E0531A570681F8C01510","description":"Six 2.7 m by 3.7 m shear wall specimens were fabricated consisting of 150 mm wide CFS framing sheathed on one side with sheet steel adhered to Type X gypsum board and on the opposite side with Type X gypsum board. The specimens were subjected sequentially to varied combinations of mechanical (shear) deformation and thermal (fire) loading. Specimen 1 (CFS01) was used to establish the monotonic 'pushover' load-displacement capacity of the wall system and subsequently to shake down the fire test setup. Specimen 2 (CFS02) was loaded by symmetric-amplitude reverse-cyclic shear deformation to destruction (defined here as 2.8 % drift ratio) to establish the cyclic load-displacement response. Specimen 3 and 4 (CFS03 and CFS04) were cycled to deformations just before and after the peak load was achieved, respectively, burned for 13 min and 20 s and then cycling was continued until destruction of the wall. For Specimen 5 (CFS05), an undamaged wall was exposed to fire for 13 min and 20 s and then cycled to destruction. Specimen 6 (CFS06) was tested similarly to Specimen 3, however, the burn duration was doubled. The datasets include measured force, displacement and temperature data, as well as select videos.","language":["en"],"title":"Data from Influence of Fire on the Lateral Resistance of Cold-Formed Steel Shear Walls - Phase I","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1422515","format":"text/html","description":"DOI Access for Influence of FIre on the Lateral Resistance of Cold-Formed Steel Shear Walls - Phase 1","title":"DOI Access for Influence of FIre on the Lateral Resistance of Cold-Formed Steel Shear Walls - Phase 1"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-02-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Buildings and Construction:Structural engineering","Fire:Structural fire resistance","Buildings and Construction:Building materials"],"keyword":["cold-formed steel; shear walls; earthquake; fire; fire following earthquake"]},{"identifier":"41E6FA49E7B9A993E0531A570681E2311511","accessLevel":"public","contactPoint":{"hasEmail":"mailto:lane.sander@nist.gov","fn":"Lane Sander"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/programs-projects/tutorials-analytical-chemistry","description":"The data consists of video files and associated abstracts for training and education in chemical metrology.  The videos are focused on laboratory operations for quantitative analysis of complex matrix samples.  Topics include theory and practice of liquid chromatography, sample extraction and processing, and aspects of quantitative analysis.","language":["en"],"title":"Tutorials in Analytical Chemistry","distribution":[{"accessURL":"https://www.nist.gov/programs-projects/tutorials-analytical-chemistry","format":"video files on NIST Tube","description":"Tutorials in Analytical Chemistry","mediaType":"video/mp4","title":"Tutorials in Analytical Chemistry"},{"accessURL":"https://doi.org/10.18434/T4DK5T","mediaType":"text/html","title":"DOI Access for Tutorials in Analytical Chemistry"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-11-01","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Analytical chemistry"],"keyword":["quantitation","liquid chromatography","Soxhlet extraction","balances","calibration solutions","liquid density","pressurized fluid extraction","practice of liquid chromatography","troubleshooting liquid chromatography","volumetric liquid transfer","liquid concentration"]},{"identifier":"41FBDE2041691326E0531A57068177B71512","accessLevel":"public","contactPoint":{"hasEmail":"mailto:derek.juba@nist.gov","fn":"Derek Juba"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/ZENO","description":"ZENO is a software tool, which computes material, solution and suspension properties for a specified particle shape or molecular structure using path-integral and Monte Carlo methods. These properties include: capacitance, electric polarizability tensor, intrinsic conductivity, volume, gyration tensor, hydrodynamic radius, intrinsic viscosity, friction coefficient, diffusion coefficient, sedimentation coefficient, and related quantities.","language":["en"],"title":"ZENO: Software for calculating hydrodynamic, electrical, and shape properties of polymer and particle suspensions","distribution":[{"accessURL":"https://doi.org/10.18434/T48W2J","format":"Github repository site for software","description":"Github repository site for ZENO software","mediaType":"text/html","title":"DOI Access for ZENO: Software for calculating hydrodynamic, electrical, and shape properties of polymer and particle suspensions"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-10-25","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology: Computational science","Materials : Modeling and computational material science"],"keyword":["Monte Carlo Methods","nanomaterial","parallel","Walk on Spheres"]},{"identifier":"43A15F2DF1A65F6FE0531A5706810D411515","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.1947"],"contactPoint":{"hasEmail":"mailto:thomas.cleary@nist.gov","fn":"Thomas Cleary"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/43A15F2DF1A65F6FE0531A5706810D411515","description":"The data files are a compilation of experimental results referenced in NIST Technical Note 1947 and include tabulated summary smoke alarm response results for smoke box and room experiments and individual experiment data files containing smoke beam obscuration, MIC current, gas species concentration, temperature and relative humidity measurements as a function of time during each experiment. Descriptions of the file name structures, data file contents and uncertainty estimates for each reported measurand are provided on the data landing page.","language":["en"],"title":"Data on Smoke Alarm Performance: A Compilation to NIST TN 1947","distribution":[{"accessURL":"https://doi.org/10.18434/T4530X","mediaType":"text/html","title":"DOI access to Data on Smoke Alarm Performance"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-12-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Fire:Fire detection"],"keyword":["smoke alarms","detection","fire safety"]},{"identifier":"4765EE7CC529A396E0531A57068160031518","accessLevel":"public","references":["https://doi.org/10.1007/s00216-015-8567-8"],"contactPoint":{"hasEmail":"mailto:lee.yu@nist.gov","fn":"Lee L. Yu"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4765EE7CC529A396E0531A57068160031518","description":"A method for the identification and determination of arsenosugars in the extract of kelp","language":["en"],"title":"An approach for identification and determination of arsenic","distribution":[{"accessURL":"https://doi.org/10.18434/M31518","title":"DOI Access for An approach for identification and determination of arsenic"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC529A396E0531A57068160031518/Kelp%20Archival%20Data.docx","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"Kelp Archival Data"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC529A396E0531A57068160031518/Kelp%20Archival%20Data.docx.sha256","mediaType":"text/plain","title":"SHA256 File for Kelp Archival Data"}],"bureauCode":["006:55"],"modified":"2017-01-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Health:Food and nutrition"],"keyword":["kelp","arsenosugar","arsenic species","INAA","LC-ICP-MS","identification","IT-TOF","microwave digestion","Food and Nutrition"]},{"identifier":"4765EE7CC53FA396E0531A57068160031540","accessLevel":"public","references":["https://doi.org/10.1007/s00216-021-03470-w"],"contactPoint":{"hasEmail":"mailto:johanna.camara@nist.gov","fn":"Johanna Camara"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4765EE7CC53FA396E0531A57068160031540","description":"Interlaboratory data from assessments of SRMs and other method comparison samples","language":["en"],"title":"Commutability assessment of selected SRMs","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC53FA396E0531A57068160031540/Camara2021_Article_AssessmentOfSerumTotal25-hydro.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC53FA396E0531A57068160031540/1540_README.txt","mediaType":"text/plain","title":"README file for Commutability assessment of selected SRMs"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC53FA396E0531A57068160031540/216_2021_3470_MOESM1_ESM%20%281%29.docx","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"Supplemental material to publication https://doi.org/10.1007/s00216-021-03470-w"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC53FA396E0531A57068160031540/216_2021_3470_MOESM2_ESM.xlsx","description":"Data collected from individual measurements results from study participants","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Supplemental to publication  https://doi.org/10.1007/s00216-021-03470-w"}],"bureauCode":["006:55"],"modified":"2015-04-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials","Health:Clinical diagnostics","Chemistry:Analytical chemistry","Health"],"issued":"2022-10-07","keyword":["Biosciences and Health"]},{"identifier":"4765EE7CC554A396E0531A57068160031561","accessLevel":"public","contactPoint":{"hasEmail":"mailto:stephanie.outcalt@nist.gov","fn":"Stephanie L. 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Temperature range 270 K to 470 K up to pressures of 50MPa.","language":["en"],"title":"High Temperature High Pressure U-tube Density Measurements","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC554A396E0531A57068160031561/MIDAS%20DMCHeptane%20README.docx","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC554A396E0531A57068160031561/MIDAS%20DMCHeptane%20README.docx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC554A396E0531A57068160031561/DMCHeptane1090.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC554A396E0531A57068160031561/DMCHeptane1090.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC554A396E0531A57068160031561/DMCHeptane3070.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC554A396E0531A57068160031561/DMCHeptane3070.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC554A396E0531A57068160031561/DMCHeptane6040.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/4765EE7CC554A396E0531A57068160031561/DMCHeptane6040.csv.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-12-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials"],"keyword":["density","vibrating U-tube","compressed-liquids","Energy"]},{"identifier":"4765EE7CC58FA396E0531A57068160031620","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. 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This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1297 Stainless Steel (SAE 201)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["stainless steel","steel","elemental analysis","Manufacturing"]},{"identifier":"4765EE7CC590A396E0531A57068160031621","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=131h","description":"Standard Reference Material 131h Refined Cast Iron - This Standard Reference Material (SRM) is intended primarily for use in validation of chemical and instrumental methods of analysis. It can be used to validate value assignment of in-house reference materials and, if necessary, to calibrate carbon/sulfur analyzers. A unit of SRM 131h consists of a bottle containing 100 g of chips. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 131h Refined Cast Iron","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["low alloy steel","cast iron","carbon","sulfur","Standard Reference Material","SRM","SRM 131h","combustion","infrared detection","manufacturing specification","Manufacturing"]},{"identifier":"4765EE7CC593A396E0531A57068160031624","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1546a","description":"SRM 1546a Meat Homogenate - This Standard Reference Material (SRM) is intended primarily for validation of methods for determining fatty acids, cholesterol, proximates, calories, elements, vitamins, and amino acids in canned meat products and similar materials.  This SRM can also be used for quality assurance when assigning values to in-house reference materials.  The meat homogenate is a mixture of pork and chicken products blended together in a commercial process.  A unit of SRM 1546a consists of four cans, each containing approximately 85 g of material. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1546a Meat Homogenate","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference materials","SRM","SRM 1546a","Meat Homogenate","food","nutrition","certified value","reference value","Food and Nutrition"]},{"identifier":"4765EE7CC595A396E0531A57068160031626","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1632d","description":"Standard Reference Material 1632d Trace Elements in Coal (Bituminous) - This Standard Reference Material (SRM) is intended primarily for use in the evaluation of techniques used in the analysis of coals and materials of a similar matrix. A unit of SRM 1632d consists of 50 g of bituminous coal that was ground to pass a 250 µm (60 mesh) sieve, homogenized, bottled under an argon atmosphere, and sealed in an aluminized bag. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1632d Trace Elements in Coal (Bituminous)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","sulfur","mercury","chlorine","trace elements","Energy"]},{"identifier":"4765EE7CC596A396E0531A57068160031627","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Thomas W. Vetter"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4765EE7CC596A396E0531A57068160031627","description":"SRM 1632e Trace Elements in Coal (Bituminous) - This Standard Reference Material (SRM) is intended primarily for use in the evaluation of techniques used in the analysis of coals and materials of a similar matrix.","language":["en"],"title":"SRM 1632e Trace Elements in Coal (Bituminous)","distribution":[{"accessURL":"https://doi.org/10.18434/M31627","title":"DOI Access for SRM 1632e Trace Elements in Coal (Bituminous)"}],"bureauCode":["006:55"],"modified":"2015-09-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials","Energy"],"keyword":["Standard Reference Material","SRM","trace elements","sulfur","mercury","chlorine","Energy"]},{"identifier":"4765EE7CC597A396E0531A57068160031628","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1633c","description":"SRM 1633c Trace Elements in Coal Fly Ash - This Standard Reference Material (SRM) is intended for use in the evaluation of analytical methods for the determination of constituent elements in coal fly ash or materials of a similar matrix. SRM 1633c is a bituminous coal fly ash that was sieved through a nominal sieve opening of 74 µm (200 mesh) and then blended to assure homogeneity. A unit of SRM 1633c consists of 75 g of powdered material. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1633c Trace Elements in Coal Fly Ash","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal fly ash","sulfur","mercury","chlorine","trace elements","Energy"]},{"identifier":"4765EE7CC598A396E0531A57068160031629","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1635a","description":"SRM 1635a Trace Elements in Coal (Subbituminous) - This Standard Reference Material (SRM) is intended primarily for the evaluation of techniques used in the analysis of coals and materials of a similar matrix. A unit of SRM 1635a consists of 50 g of subbituminous coal that was ground to pass a 250 µm (60 mesh) sieve, homogenized, bottled under an argon atmosphere, and sealed in an aluminized bag. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1635a Trace Elements in Coal (Subbituminous)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","sulfur","mercury","chlorine","trace elements","Energy"]},{"identifier":"4765EE7CC59AA396E0531A57068160031631","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1849a","description":"SRM 1849a Infant/Adult Nutritional Formula - This Standard Reference Material (SRM) is intended primarily for validation of methods for determining proximates, fatty acids, cholesterol, vitamins, elements, amino acids, and nucleotides in infant and adult nutritional formulas and similar materials. This SRM can also be used for quality assurance when assigning values to in-house reference materials. The SRM is a milk-based, hybrid infant/adult nutritional powder prepared by a manufacturer of infant formula and adult nutritional products. A unit of SRM 1849a consists of 10 packets, each containing approximately 10 g of material. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1849a Infant/Adult Nutritional Formula","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference materials","SRM","SRM 1849b","infant formula","adult nutritional formula","food","nutrition","certified value","reference value","Food and Nutrition"]},{"identifier":"4765EE7CC59BA396E0531A57068160031632","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1877","description":"SRM 1877 Beryllium Oxide Powder - This Standard Reference Material (SRM) is intended for use in laboratory analysis and health research for the development and validation of analytical methods and instruments used to determine beryllium, as well as for proficiency testing of laboratories involved in beryllium determinations. A unit of SRM 1877 consists of one bottle containing 20 g of beryllium oxide powder. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1877 Beryllium Oxide Powder","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Advanced Materials"]},{"identifier":"4765EE7CC59DA396E0531A57068160031634","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1941B","description":"SRM 1941b Organics in Marine Sediment - This Standard Reference Material (SRM) is marine sediment collected at the mouth of the Baltimore (MD) Harbor.  SRM 1941b is intended for use in evaluating analytical methods for the determination of selected polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyl (PCB) congeners, and chlorinated pesticides in marine sediment and similar matrices. Information values are also provided for total organic carbon (TOC), total carbon, hydrogen, and nitrogen. All of the constituents for which certified, reference, and information values are provided in SRM 1941b were naturally present in the sediment before processing. A unit of SRM 1941b consists of a bottle containing 50 g of radiation-sterilized, freeze-dried sediment. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1941b Organics in Marine Sediment","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["PCBs","PAHs","Pesticides","Environment and Climate"]},{"identifier":"4765EE7CC59EA396E0531A57068160031635","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1944","description":"SRM 1944  New York/New Jersey Waterway Sediment - Standard Reference Material (SRM) 1944 is a mixture of marine sediment collected near urban areas in New York and New Jersey. SRM 1944 is intended for use in evaluating analytical methods for the determination of selected polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, and trace elements in marine sediment and similar matrices. Reference values are also provided for selected polybrominated diphenyl ether (PBDE) congeners, selected dibenzo-p-dioxin and dibenzofuran congeners, total organic carbon, total extractable material, and particle size characteristics. Information values are provided for selected polychlorinated naphthalenes (PCNs) and hexabromocyclododecanes (HBCDs). All of the constituents for which certified, reference, and information values are provided in SRM 1944 were naturally present in the sediment before processing. A unit of SRM 1944 consists of a bottle containing 50 g of radiation-sterilized, freeze-dried sediment. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1944  New York/New Jersey Waterway Sediment","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["PCBs","PAHs","trace elements","flame retardants","polychlorinated naphthalenes","Environment and Climate"]},{"identifier":"4765EE7CC59FA396E0531A57068160031636","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1945","description":"SRM 1945 Organics in Whale Blubber - This Standard Reference Material (SRM) is a frozen whale blubber homogenate intended for use in evaluating analytical methods for the determination of selected polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, polybrominated diphenyl ether (PBDE) congeners, and toxaphene congeners. SRM 1945 has been reanalyzed, and the current Certificate of Analysis has values assigned for additional PCB congeners and pesticides, as well as values assigned for selected PBDE and toxaphene congeners. A unit of SRM 1945 consists of two screw-capped glass bottles, each containing approximately 15 g of frozen whale blubber homogenate. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1945 Organics in Whale Blubber","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["PCBs","pesticides","fatty acids","flame retardants","Environment and Climate"]},{"identifier":"4765EE7CC5A0A396E0531A57068160031637","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4765EE7CC5A0A396E0531A57068160031637","description":"SRM 1946 Lake Superior Fish Tissue - This Standard Reference Material (SRM) is a frozen fish tissue homogenate that was prepared from lake trout (Salvelinus namaycush namaycush) collected near the Apostle Islands in Lake Superior (U.S./Canada), and is intended primarily for use in evaluating analytical methods for the determination of polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, polybrominated diphenyl ether (PBDE) congeners, perfluorooctanesulfonic acid (PFOS), fatty acids (including omega-3 fatty acids), extractable fat, methylmercury, total mercury, proximates , ?-hexabromocyclododecane (?-HBCD), and selected trace elements in fish tissue and similar matrices. All of the constituents for which certified, reference, and information mass fraction values are provided are naturally present in the fish tissue homogenate. A unit of SRM 1946 consists of five bottles, each containing approximately 7 g to 9 g (wet basis) of frozen tissue homogenate. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1946 Lake Superior Fish Tissue","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["PCBs","pesticides","fatty acids","mercury","trace elements","Environment and Climate"]},{"identifier":"4765EE7CC5A1A396E0531A57068160031638","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1947","description":"SRM 1947 Lake Michigan Fish Tissue - This Standard Reference Material (SRM) is a frozen fish tissue homogenate that was prepared from fish collected from Lake Michigan, and is intended primarily for use in evaluating analytical methods for the determination of selected trace elements, methylmercury, total mercury, polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, polybrominated diphenyl ether (PBDE) congeners, perfluoroalkyl acids (PFAAs), proximates, ?-hexabromocyclododecane (?-HBCD), caloric content, and fatty acids in fish tissue and similar matrices. All of the constituents for which certified, reference, and information mass fraction values are provided are naturally present in the fish tissue homogenate. A unit of SRM 1947 consists of five bottles, each containing approximately 8 g (wet basis) of frozen tissue homogenate. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1947 Lake Michigan Fish Tissue","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["PCBs","Pesticides","perfluorinated substances","fatty acids","Environment and Climate"]},{"identifier":"4765EE7CC5A2A396E0531A57068160031639","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=194A","description":"SRM 194a Ammonium Dihydrogen Phosphate - This Standard Reference Material (SRM) is a highly purified and homogeneous lot of crystalline ammonium dihydrogen phosphate (NH4H2PO4). It is intended primarily for use as a working standard in the calibration and standardization of procedures employed in the fertilizer industry for the determination of ammoniacal nitrogen and phosphorus. A unit of SRM 194a consists of one bottle containing 90 g of crystalline ammonium dihydrogen phosphate. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 194a Ammonium Dihydrogen Phosphate","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","fertilizer","ammonium","phosphate","Environment and Climate"]},{"identifier":"4765EE7CC5A3A396E0531A57068160031640","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1950","description":"SRM 1950 Metabolites in Frozen Human Plasma - This Standard Reference Material (SRM) is intended primarily for validation of methods for determining metabolites such as fatty acids, electrolytes, vitamins, hormones, and amino acids in human plasma and similar materials. This SRM can also be used for comparison of measurement technologies used in metabolomic studies and for quality assurance when assigning values to in-house reference materials. This SRM is intended to represent \"normal\" human plasma. A unit of SRM 1950 consists of five vials, each containing approximately 1.0 mL of plasma. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1950 Metabolites in Frozen Human Plasma","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-09-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","SRM 1950","human plasma","certified values","reference values","metabolites","Biosciences and Health"]},{"identifier":"4765EE7CC5A4A396E0531A57068160031641","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1953","description":"SRM 1953 Organic Contaminants in Non-Fortified Human Milk - This Standard Reference Material (SRM) is intended for use in evaluating analytical methods for the determination of selected polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, polybrominated diphenyl ether (PBDE) congeners, and elements in human milk and similar matrices. Reference concentration values are provided for polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs), as well as some inorganic constituents. An information concentration value is provided for the PCB mixture Aroclor 1260. All of the constituents for which certified, reference, and information concentration values are provided in SRM 1953 are naturally present in the milk. A unit of SRM 1953 consists of five bottles of approximately 5 mL of frozen non-fortified human milk. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1953 Organic Contaminants in Non-Fortified Human Milk","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["environment","standard reference materials","reference materials","SRM","RM","Environment and Climate"]},{"identifier":"4765EE7CC5A5A396E0531A57068160031642","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1954","description":"SRM 1954 Organic Contaminants in Fortified Human Milk - This Standard Reference Material (SRM) is intended for use in evaluating analytical methods for the determination of selected polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, and polybrominated diphenyl ether (PBDE) congeners in human milk and similar matrices. Reference concentration values are provided for polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs), as well as some inorganic constituents. An information concentration value is provided for the PCB mixture Aroclor 1260. The compounds in Appendix A were spiked into the milk prior to bottling. A unit of SRM 1954 consists of five bottles of approximately 5 mL fortified human milk. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1954 Organic Contaminants in Fortified Human Milk","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Environment and Climate"]},{"identifier":"4765EE7CC5A6A396E0531A57068160031643","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4765EE7CC5A6A396E0531A57068160031643","description":"SRM 1957 Organic Contaminants in Non-Fortified Human Serum (Freeze-Dried) - This Standard Reference Material (SRM) is intended for use in evaluating analytical methods for the determination of selected polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, and polybrominated diphenyl ether (PBDE) congeners in human serum and similar matrices. Reference values are provided for selected polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), perflourinated compounds (PFCs), and serum lipid. Information values are provided for selected hydroxylated compounds. All of the constituents for which values are provided in SRM 1957 are naturally present in the freeze-dried human serum. A unit of SRM 1957 consists of five 30 mL vials each containing freeze-dried non-fortified human serum. Before use, the serum in each vial must be reconstituted with 10.7 mL of distilled or HPLC-grade water. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1957 Organic Contaminants in Non-Fortified Human Serum (Freeze-Dried)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Environment and Climate"]},{"identifier":"4765EE7CC5A7A396E0531A57068160031644","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1958","description":"SRM 1958 Organic Contaminants in Fortified Human Serum (Freeze-Dried) - This Standard Reference Material (SRM) is intended for use in evaluating analytical methods for the determination of selected polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, and polybrominated diphenyl ether (PBDE) congeners in human serum and similar matrices. Reference values are provided for selected polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), non-ortho PCB congeners, perfluorinated compounds (PFCs), and serum lipid. Information values are provided for selected toxaphene congeners and hydroxylated compounds. A unit of SRM 1958 consists of five vials, each containing freeze-dried fortified human serum. Before use, the serum in each bottle must be reconstituted with 10.7 mL of distilled or HPLC-grade water. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1958 Organic Contaminants in Fortified Human Serum (Freeze-Dried)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Environment and Climate"]},{"identifier":"4765EE7CC5A8A396E0531A57068160031645","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=1974c","description":"Organics in Mussel Tissue (Mytilus edulis) - This Standard Reference Material (SRM) is a frozen mussel tissue homogenate intended for use in evaluating analytical methods for the determination of selected polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, and polybrominated diphenyl ether (PBDE) congeners in marine bivalve mollusk tissue and similar matrices. All of the constituents for which certified and reference values are provided in SRM 1974c were naturally present in the tissue material before processing. A unit of SRM 1974c consists of five jars each containing approximately 10 g (wet basis) of frozen tissue homogenate. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 1974c Organics in Mussel Tissue (Mytilus edulis)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["reference materials","PAHs","PBDEs","PCBs","pesticides","Environment and Climate"]},{"identifier":"4765EE7CC5A9A396E0531A57068160031646","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=200B","description":"Standard Reference Material 200b Potassium Dihydrogen Phosphate (KH2PO4) - This Standard Reference Material (SRM) is a highly purified and homogeneous lot of crystalline potassium dihydrogen phosphate (KH2PO4). It is intended primarily for use as a working standard in the calibration and standardization of procedures employed in the fertilizer industry for the determination of potassium and phosphorus. A unit of SRM 200b consists of one bottle containing 90 g of crystalline potassium dihydrogen phosphate. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 200b Potassium Dihydrogen Phosphate (Fertilizer Standard)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","fertilizer","potassium","phosphate","Environment and Climate"]},{"identifier":"4765EE7CC5ADA396E0531A57068160031650","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2193A","description":"SRM 2193a Calcium Carbonate pH Standard [used as saturated Ca(OH)2 solution] - This Standard Reference Material (SRM) is intended for use in preparing solutions for calibrating electrodes for pH measuring systems at pH values above 11.0. This lot of calcium carbonate (CaCO3) was selected for its low level of alkali metal impurities. However, this SRM is certified ONLY as a pH standard, NOT as a pure substance. A unit of SRM 2193a consists of 30 g of CaCO3. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2193a Calcium Carbonate pH Standard [used as saturated Ca(OH)2 solution]","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-29 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Biosciences and Health","Environment and Climate","Food and Nutrition"]},{"identifier":"4765EE7CC5AEA396E0531A57068160031651","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2378","description":"Donor serum for SRM 2378 was collected from three groups of individuals: (level 1) three healthy donors who took 1000 mg/day of fish oil supplements for a minimum of one month prior to collection; (level 2) three healthy donors who took 1000 mg/day of flaxseed oil supplements for a minimum of one month prior to collection; and (level 3) three healthy donors who did not take either fish or flaxseed oil supplements for one month prior to collection.  Value assignment of the mass fractions of the fatty acids in SRM 2378 was based on the results of one or two analytical methods at NIST and one analytical method at the Centers for Disease Control (CDC) with confirmation by data provided by an interlaboratory comparison. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2378 Fatty Acids in Human Serum","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["fatty acids","serum","Biosciences and Health"],"temporal":"2015-01-01/2025-01-01"},{"identifier":"4765EE7CC5AFA396E0531A57068160031652","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2384","description":"SRM 2384 Baking Chocolate - This Standard Reference Material (SRM) is intended primarily for use in validating methods for determining proximates, fatty acids, calories, vitamins, elements, catechins, caffeine, and theobromine in baking chocolate and similar matrices. This SRM can also be used for quality assurance when assigning values to in-house reference materials. The SRM is baking chocolate prepared from 100 % cocoa beans by a manufacturer of baking chocolate and consists of a single production lot. A unit of SRM 2384 consists of five 91 g (3.2 oz) individually wrapped bars of baking chocolate. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2384 Baking Chocolate","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","SRM 2384","baking chocolate","certified values","reference values","food","nutrition","Food and Nutrition"]},{"identifier":"4765EE7CC5B1A396E0531A57068160031654","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2387","description":"SRM 2387 Peanut Butter - This Standard Reference Material (SRM) is intended primarily for use in validating methods for determining proximates, fatty acids, calories, vitamins, elements, amino acids, aflatoxins, and acrylamide in peanut butter and similar matrices. This SRM can also be used for quality assurance when assigning values to in-house reference materials. The SRM is a creamy peanut butter prepared by a manufacturer of peanut butter products. A unit of SRM 2387 consists of three jars of peanut butter containing 170 g each. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2387 Peanut Butter","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","SRM 2387","peanut butter","certified values","reference values","food","nutrition","Food and Nutrition"]},{"identifier":"4765EE7CC5B2A396E0531A57068160031655","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2452","description":"SRM 2452 Hydrogen in Titanium (Nominal Mass Fraction 60 mg/kg) - This Standard Reference Material (SRM) is intended for use in the evaluation of methods and the calibration of equipment used in the determination of hydrogen in titanium alloy. SRM 2452 consists of one bottle containing 10 g of titanium alloy chips. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2452 Hydrogen in Titanium Alloy","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["elemental analysis","titanium","hydrogen in titanium","ASTM E1447","Manufacturing"]},{"identifier":"4765EE7CC5B4A396E0531A57068160031657","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2585","description":"SRM 2585 Organic Contaminants in House Dust - This Standard Reference Material (SRM) is intended for use in evaluating analytical methods for the determination of selected polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyl (PCB) congeners, chlorinated pesticides, and polybrominated diphenyl ether (PBDE) congeners in house dust and similar matrices. All of the constituents for which certified, reference, and information values are provided in SRM 2585 were naturally present in the dust material before processing. A unit of SRM 2585 consists of one bottle containing approximately 10 g of house dust. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2585 Organic Contaminants in House Dust","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","SRM 2585","House Dust","Certified values","Environment and Climate"]},{"identifier":"4765EE7CC5B5A396E0531A57068160031658","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2631A","description":"This Standard Reference Material (SRM) is a primary gas mixture for which the amount-of-substance fraction, expressed as concentration, may be related to secondary working standards. This SRM is intended for the calibration of instruments used for nitric oxide determinations and for other applications. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2631a Nitric Oxide in Nitrogen (Nominal Amount-of-Substance Fraction 3000 µmol/mol)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-07-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","Nitric Oxide","Gas","Cylinder","Environment","Environment and Climate"]},{"identifier":"4765EE7CC5B6A396E0531A57068160031659","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2682c","description":"Standard Reference Material 2682c Subbituminous Coal (Nominal Mass Fraction 0.5 % Sulfur) - This Standard Reference Material (SRM) is intended primarily for use in the evaluation of techniques used in the analysis of coals and materials of a similar matrix. A unit of SRM 2682c consists of 50 g of subbituminous coal that was ground to pass a 250 µm (60 mesh) sieve, homogenized, packaged in an amber glass bottle under an argon atmosphere, and then sealed in an aluminized bag. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2682c Subbituminous Coal (Nominal Mass Fraction 0.5 % Sulfur)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","sulfur","mercury","chlorine","Energy"]},{"identifier":"4765EE7CC5B7A396E0531A57068160031660","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2683c","description":"SRM 2683c Bituminous Coal (Nominal Mass Fraction 2 % Sulfur) - This Standard Reference Material (SRM) is intended primarily for use in the evaluation of techniques employed in the determination of sulfur, mercury, and chlorine in coal and materials of a similar matrix. A unit of SRM 2683c consists of 50 g of bituminous coal ground to pass a 250 µm (60 mesh) sieve, homogenized, packaged in an amber glass bottle under an argon atmosphere, and sealed in an aluminized bag. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2683c Bituminous Coal (Nominal Mass Fraction 2 % Sulfur)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","sulfur","mercury","chlorine","Energy"]},{"identifier":"4765EE7CC5B8A396E0531A57068160031661","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2684c","description":"Standard Reference Material 2684c Bituminous Coal (Nominal Mass Fraction 3 % Sulfur) - This Standard Reference Material (SRM) is intended primarily for use in the evaluation of techniques used in the analysis of coals and materials of a similar matrix. A unit of SRM 2684c consists of 50 g of bituminous coal that was ground to pass a 212 µm (70 mesh) sieve, homogenized, packaged in an amber glass bottle under an argon atmosphere, and then sealed in an aluminized bag. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2684c Bituminous Coal (Nominal Mass Fraction 3 % Sulfur)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","sulfur","mercury","chlorine","Energy"]},{"identifier":"4765EE7CC5B9A396E0531A57068160031662","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4765EE7CC5B9A396E0531A57068160031662","description":"This  Standard  Reference  Material (SRM)  is  intended  primarily  for  use  in  the  evaluation  of  techniques used  in  the analysis of coals and materials of a similar matrix.  A unit of SRM 2685c consists of 50 g of bituminous coal ground to pass a 250 ?m (60 mesh) sieve, homogenized, and packaged in an amber glass bottle under an argon atmosphere, and sealed in an aluminized bag.","language":["en"],"title":"SRM 2685c Bituminous Coal (Nominal Mass Fraction 5 % Sulfur)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","sulfur","mercury","chlorine","Energy"]},{"identifier":"4765EE7CC5BAA396E0531A57068160031663","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2689","description":"Standard Reference Material 2689 Coal Fly Ash - This Standard Reference Material (SRM) is intended for use in the evaluation of analytical methods used for the classification of coal fly ash and for the determination of constituent elements in coal fly ash or materials of a similar matrix. A unit of SRM 2689 consists of three 10 g hermetically sealed glass vials of fly ash pulverized to less than 150 µm particle size and blended to a high degree of homogeneity. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2689 Coal Fly Ash","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","coal fly ash","Energy"]},{"identifier":"4765EE7CC5BBA396E0531A57068160031664","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2690","description":"Standard Reference Material 2690 Coal Fly Ash - This Standard Reference Material (SRM) is intended for use in the evaluation of analytical methods used for the classification of coal fly ash and for the determination of constituent elements in coal fly ash or materials of a similar matrix. A unit of SRM 2690 consists of three 10 g hermetically sealed glass vials of fly ash pulverized to less than 150 ?m particle size and blended to a high degree of homogeneity. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2690 Coal Fly Ash","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","coal fly ash","sulfur","Energy"]},{"identifier":"4765EE7CC5BCA396E0531A57068160031665","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2691","description":"Standard Reference Material 2691 Coal Fly Ash - This Standard Reference Material (SRM) is intended for use in the evaluation of analytical methods used for the classification of coal fly ash and for the determination of constituent elements in coal fly ash or materials of a similar matrix. A unit of SRM 2691 consists of three 10 g hermetically sealed glass vials of fly ash pulverized to less than 150 ?m particle size and blended to a high degree of homogeneity. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2691 Coal Fly Ash","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","coal fly ash","sulfur","Energy"]},{"identifier":"4765EE7CC5BDA396E0531A57068160031666","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2692c","description":"SRM 2692c Bituminous Coal (Nominal Mass Fraction 1 % Sulfur) - This Standard Reference Material (SRM) is intended primarily for use in the evaluation of techniques employed in the determination of sulfur, mercury, chlorine, and ash content in coal and materials of a similar matrix. A unit of SRM 2692c consists of 50 g of bituminous coal ground to pass a 250 µm (60 mesh) sieve, homogenized, and packaged in an amber glass bottle. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2692c Bituminous Coal (Nominal Mass Fraction 1 % Sulfur)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","sulfur","mercury","chlorine","Energy"]},{"identifier":"4765EE7CC5BEA396E0531A57068160031667","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2693","description":"Standard Reference Material 2693 Bituminous Coal (Nominal Mass Fraction 0.5 % Sulfur)  - This Standard Reference Material (SRM) is intended primarily for use in the evaluation of techniques employed in the determination of sulfur, mercury, chlorine, and ash content in coal and materials of a similar matrix. A unit of SRM 2693 consists of 50 g of bituminous coal ground to pass a 250 µm (60 mesh) sieve, homogenized, and packaged in an amber glass bottle and then sealed in an aluminized bag. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2693 Bituminous Coal (Nominal Mass Fraction 0.5 % Sulfur)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coal","sulfur","mercury","chlorine","Energy"]},{"identifier":"4765EE7CC5BFA396E0531A57068160031668","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2718a","description":"Standard Reference Material 2718a Green Petroleum Coke - This Standard Reference Material (SRM) is intended primarily for use in the calibration of apparatus and the evaluation of techniques used in the analysis of green (raw) petroleum coke and other materials of a similar matrix. It can be used to validate value assignment of in-house reference materials. A unit of SRM 2718a consists of 50 g of green petroleum coke ground to pass a 250 µm (60 mesh) sieve, homogenized, packaged in an amber glass bottle under an argon atmosphere, and then sealed in an aluminized bag. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2718a Green Petroleum Coke","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","petroleum coke","sulfur","Energy"]},{"identifier":"4765EE7CC5C0A396E0531A57068160031669","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2719","description":"Standard Reference Material 2719 Calcined Petroleum Coke - This Standard Reference Material (SRM) is intended primarily for use in the calibration of apparatus and the evaluation of techniques employed in the analysis of calcined petroleum coke and other materials with a similar matrix. SRM 2719 consists of 50 g of calcined petroleum coke ground to pass a 250 µm (60 mesh) sieve, homogenized, and bottled under an argon atmosphere. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2719 Calcined Petroleum Coke","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coke","sulfur","Energy"]},{"identifier":"4765EE7CC5C1A396E0531A57068160031670","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2775","description":"Standard Reference Material 2775 Sulfur in Foundry Coke - This Standard Reference Material (SRM) is intended primarily for use in the evaluation of test methods and for the calibration of instruments used to determine sulfur in foundry (metallurgical) coke. Each unit of SRM 2775 consists of 50 g of foundry coke that was ground to pass a 60 mesh (250 ?m) sieve, homogenized, and bottled under an argon atmosphere. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2775 Sulfur in Foundry Coke","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coke","sulfur","Energy"]},{"identifier":"4765EE7CC5C2A396E0531A57068160031671","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2776","description":"Standard Reference Material 2776 Sulfur in Furnace Coke - This Standard Reference Material (SRM) is intended primarily for use in the evaluation of test methods and for the calibration of instruments used to determine sulfur in furnace (metallurgical) coke. Each unit of SRM 2776 consists of 50 g of furnace coke that was ground to pass a 250 ?m (60 mesh) sieve, homogenized, and bottled under an argon atmosphere. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2776 Sulfur in Furnace Coke","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","Standard Reference Material","coke","sulfur","Energy"]},{"identifier":"4765EE7CC5C7A396E0531A57068160031676","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2950a","description":"SRM 2950a Respirable Alpha Quartz on Filter Media (Nominal Mass of Alpha Quartz: 5 µg to 500 µg) - This Standard Reference Material (SRM) is intended primarily for the calibration of X-ray diffraction (XRD) spectrometers for the determination of respirable alpha quartz using National Institute for Occupational Safety and Health (NIOSH) Method 7500, or the equivalent. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2950a Respirable Alpha Quartz on Filter Media (Nominal Mass of Alpha Quartz: 5 µg to 500 µg)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM 2950a","respirable","alpha quartz","filter","SRM","standard reference material","Biosciences and Health"]},{"identifier":"4765EE7CC5C8A396E0531A57068160031677","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=2973","description":"SRM 2973 Vitamin D Metabolites in Frozen Human Serum (High Level) - This Standard Reference Material (SRM) is intended for use as an accuracy control in the critical evaluation of methods for determining the amount-of-substance concentration of vitamin D metabolites in human serum. This SRM can also be used as a quality assurance tool for assigning values to in-house control materials for these constituents. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 2973 Vitamin D Metabolites in Frozen Human Serum (High Level)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","SRM 2973","Frozen Human Serum","Certified Values","Reference Values","Vitamin D Metabolites","25-hydroxyvitamin D3","25-hydroxyvitamin D2","3-epi-25-hydroxyvitamin D3","24R","25-dihydroxyvitamin D3","Biosciences and Health"]},{"identifier":"4765EE7CC5D0A396E0531A57068160031685","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3101a","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of aluminum.  A unit of SRM 3101a consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of aluminum.  The solution contains nitric acid at a volume fraction of approximately 10 %, equivalent to an amount of substance concentration (molarity) of approximately 1.6 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3101a Aluminum (Al) Standard Solution, Lot No. 140903","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["aluminum","pure materials","cations","single-element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5D1A396E0531A57068160031686","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3102a","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of antimony.  A unit of SRM 3102a consists of 50 mL of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of antimony in a high-density polyethylene bottle sealed in an aluminized bag.  The solution contains nitric acid at a volume fraction of approximately 10 % and hydrofluoric acid at a volume fraction of approximately 2 %, equivalent to amount of substance concentrations (molarities) of approximately 1.6 mol/L and 0.6 mol/L respectively. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3102a Antimony (Sb) Standard Solution Lot No. 140911","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["antimony","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5D2A396E0531A57068160031687","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3104a","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of barium.  A unit of SRM 3104a consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of barium.  The solution contains nitric acid at a volume fraction of approximately 1 %, which is equivalent to a concentration (molarity) of approximately 0.16 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3104a Barium (Ba) Standard Solution Lot No. 140909","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["barium","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5D3A396E0531A57068160031688","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3110","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of cerium.  A unit of SRM 3110 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of cerium.  The solution contains nitric acid at a mass fraction of approximately 16 %. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3110 Cerium (Ce) Standard Solution Lot No. 160830","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["cerium","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5D4A396E0531A57068160031689","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3120a","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of germanium.  A unit of SRM 3120a consists of 50 mL of a single element solution in a high density polyethylene bottle sealed in an aluminized bag.  The solution was prepared gravimetrically to contain a known mass fraction of germanium.  The solution contains nitric acid at a concentration of approximately 1.6 mol/L and hydrofluoric acid at a concentration of approximately 0.6 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3120a Germanium (Ge) Standard Solution Lot No. 080429","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["germanium","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5D5A396E0531A57068160031690","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3122","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of hafnium.  A unit of SRM 3122 consists of 50 mL of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of hafnium in a high density polyethylene bottle sealed in an aluminized bag.  The solution contains approximately 8 % nitric acid (volume fraction) and approximately 1 % hydrofluoric acid (volume fraction), equivalent to amount of substance concentrations (molarities) of 1.3 mol/L and 0.3 mol/L, respectively. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3122 Hafnium (Hf) Standard Solution Lot No. 151120","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["hafnium","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5D6A396E0531A57068160031691","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3127a","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of lanthanum.  A unit of SRM 3127a consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of lanthanum.  The solution contains nitric acid at a volume fraction of approximately 10 %, equivalent to an amount of substance concentration (molarity) of approximately 1.6 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3127a Lanthanum (La) Standard Solution Lot No. 151030","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["lanthanum","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5D7A396E0531A57068160031692","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3131a","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of magnesium.  A unit of SRM 3131a consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of magnesium.  The solution contains nitric acid at a volume fraction of approximately 10 %, equivalent to an amount of substance concentration (molarity) of approximately 1.6 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3131a Magnesium (Mg) Standard Solution Lot 140110","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Magnesium","pure materials","cations","single-element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5D8A396E0531A57068160031693","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3133","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of mercury.  A unit of SRM 3133 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of mercury.  The solution contains nitric acid at an approximate mass fraction of 10 %. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3133 Mercury (Hg) Standard Solution Lot No. 160921","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["mercury","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5D9A396E0531A57068160031694","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3143","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of rhenium.  A unit of SRM 3143 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of rhenium.  The solution contains nitric acid at a volume fraction of approximately 10 %, equivalent to an amount of substance concentration (molarity) of approximately 1.6 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3143 Rhenium (Re) Standard Solution Lot No. 140825","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["rhenium","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5DAA396E0531A57068160031695","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3147a","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of samarium.  A unit of SRM 3147a consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of samarium.  The solution contains nitric acid at a volume fraction of approximately 10 %, which is equivalent to a concentration (molarity) of approximately 1.6 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3147a Samarium (Sm) Standard Solution Lot No. 140115","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["samarium","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5DBA396E0531A57068160031696","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3150","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of silicon.  A unit of SRM 3150 consists of 50 mL of solution prepared gravimetrically to contain a known mass fraction of silicon in a high-density polyethylene bottle sealed in an aluminized bag.  The solution is prepared gravimetrically from sodium metasilicate nonahydrate to contain a known mass fraction of silicon in water.  This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3150 Silicon (Si) Standard Solution, Lot No. 130912","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Silicon","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5DCA396E0531A57068160031697","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3151","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of silver.  A unit of SRM 3151 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of silver.  The solution contains nitric acid at a mass fraction of approximately 14 %. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3151 Silver (Ag) Standard Solution Lot No. 160729","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["silver","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5DDA396E0531A57068160031698","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3156","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of tellurium.  A unit of SRM 3156 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of tellurium.  The solution contains hydrochloric acid at a volume fraction of approximately 10 %, which is equivalent to an amount of substance concentration (molarity) of approximately 1.2 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3156 Tellurium (Te) Standard Solution Lot No. 140830","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["tellurium","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5DEA396E0531A57068160031699","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3158","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of thallium.  A unit of SRM 3158 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of thallium.  The solution contains nitric acid at a volume fraction of approximately 10 %. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3158 Thallium (Tl) Standard Solution Lot No. 993012","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["thallium","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5DFA396E0531A57068160031700","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3163","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of tungsten.  A unit of SRM 3163 consists of 50 mL of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of tungsten in a high-density polyethylene bottle sealed in an aluminized bag.  The solution contains nitric acid at a volume fraction of approximately 7 % and hydrofluoric acid at a volume fraction of approximately 4 %, equivalent to amount of substance concentrations (molarities) of approximately 1.2 mol/L and 1.1 mol/L respectively. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3163 Tungsten (W) Standard Solution, Lot No. 140606","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["tungsten","pure materials","cations","single-element solutions","spectrometry","Biosciences and Health"]},{"identifier":"4765EE7CC5E0A396E0531A57068160031701","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3165","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of vanadium.  A unit of SRM 3165 consists of five 10  mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of vanadium.  The solution contains nitric acid at an amount of substance concentration (molarity) of approximately 1.6 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3165 Vanadium (V) Standard Solution Lot No. 992706","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["vanadium","pure materials","cations","metals","single element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5E1A396E0531A57068160031702","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3166a","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of ytterbium.  A unit of SRM 3166a consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of ytterbium.  The solution contains nitric acid at a volume fraction of approximately 10 %, equivalent to an amount of substance concentration (molarity) of approximately 1.6 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3166a Ytterbium (Yb) Standard Solution Lot No 140114","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["ytterbium","pure materials","cations","single-element solutions","spectrometry","Advanced Materials"]},{"identifier":"4765EE7CC5E2A396E0531A57068160031703","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3180","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of iodide.  A unit of SRM 3180 consists of five 5 mL, sealed, amber, borosilicate glass ampoules of solution prepared gravimetrically to contain a known mass fraction of iodide.  The matrix of the solution is water containing sodium sulfite at a concentration of approximately 0.01 mol/L and sodium hydroxide at a concentration of approximately 0.005 mol/L. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3180 Iodide Anion (I-) Standard Solution","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","iodide","anion","Advanced Materials"]},{"identifier":"4765EE7CC5E3A396E0531A57068160031704","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3181","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of sulfate using anion ion chromatography (IC) or other methods.  A unit of SRM 3181 consists of five 10 mL sealed borosilicate glass ampoules of solution prepared gravimetrically to contain a known mass fraction of sulfate dissolved in filtered (0.22 ?m) water having a minimum resistivity of 18 megaohm centimeters. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3181 Sulfate Anion (SO42-) Standard Solution","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","sulfate","anion","Advanced Materials"]},{"identifier":"4765EE7CC5E4A396E0531A57068160031705","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3182","description":"This Standard Reference Material (SRM) is intended as a primary standard for the quantitative determination of chloride using anion ion chromatography (IC) or other methods.  A unit of SRM 3182 consists of five 10 mL sealed borosilicate glass ampoules of solution.  The solution is prepared gravimetrically to contain a known mass fraction of chloride dissolved in filtered (0.22 µm) water having a minimum resistivity of 18 megaohm centimeters. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3182 Chloride Anion (Cl-) Standard Solution","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","chloride","anion","Advanced Materials"]},{"identifier":"4765EE7CC5E5A396E0531A57068160031706","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3183","description":"This Standard Reference Material (SRM) is intended as a primary calibration standard for the quantitative determination of fluoride using anion ion chromatography (IC) or other methods.  A unit of SRM 3183 consists of 50 mL of solution in a high density polyethylene bottle sealed in an aluminized bag.  The solution is prepared gravimetrically to contain a known mass fraction of fluoride dissolved in filtered (0.22 µm) water having a minimum resistivity of 18 megaohm centimeters. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3183 Fluoride Anion (F-) Standard Solution","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","fluoride","anion","Advanced Materials"]},{"identifier":"4765EE7CC5E6A396E0531A57068160031707","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3184","description":"This Standard Reference Material (SRM) is intended as a primary calibration standard for the quantitative determination of bromide using anion ion chromatography (IC) or other methods.  A unit of SRM 3184 consists of five 10 mL sealed borosilicate glass ampoules of solution prepared gravimetrically to contain a known mass fraction of bromide dissolved in filtered (0.22 µm) water having a minimum resistivity of 18 megaohm centimeters. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3184 Bromide Anion (Br-) Standard Solution","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","bromide","anion","Advanced Materials"]},{"identifier":"4765EE7CC5E7A396E0531A57068160031708","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3185","description":"This Standard Reference Material (SRM) is intended as a primary standard for the quantitative determination of nitrate using anion ion chromatography (IC) or other methods.  A unit of SRM 3185 consists of five 10 mL sealed borosilicate glass ampoules of solution.  The solution is prepared gravimetrically to contain a known mass fraction of nitrate dissolved in filtered (0.22 µm) water having a minimum resistivity of 18 megaohm centimeters. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3185 Nitrate Anion (NO3-) Standard Solution","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","nitrate","anion","Advanced Materials"]},{"identifier":"4765EE7CC5E8A396E0531A57068160031709","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3186","description":"This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of phosphate using anion ion chromatography (IC) or other methods.  A unit of SRM 3186 consists of five 10 mL sealed borosilicate glass ampoules of solution prepared gravimetrically to contain a known mass fraction of phosphate dissolved in filtered (0.22 ?m) water having a minimum resistivity of 18 megaohm centimeters. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3186 Phosphate Anion (PO43-) Standard Solution","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","phosphate","anion","Advanced Materials"]},{"identifier":"4765EE7CC5EAA396E0531A57068160031711","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3235","description":"SRM 3235 Soy Milk - This Standard Reference Material (SRM) is intended primarily for validation of methods for determining vitamins, elements, proximates, fatty acids, and amino acids in soy milk and similar materials. This SRM can also be used for quality assurance when assigning values to in-house reference materials. The SRM is a soy milk prepared by a commercial manufacturer. A unit of SRM 3235 consists of 10 ampoules, each containing approximately 10 mL of material. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3235 Soy Milk","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","SRM 3235","soy milk","soy","certified values","reference values","food","nutrition","dietary supplements","Food and Nutrition"]},{"identifier":"4765EE7CC5EBA396E0531A57068160031712","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3239","description":"SRM 3239 Isoflavones Calibration Solution - This Standard Reference Material (SRM) is intended primarily for use in calibration of instruments and techniques used for the determination of isoflavones. SRM 3239 consists of two solutions containing isoflavones at levels that reflect the mass fraction ratios found in soy products. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3239 Isoflavones Calibration Solution","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["Standard Reference Material","SRM","SRM 3239","certified values","nutrition","dietary supplements","isoflavones","Food and Nutrition"]},{"identifier":"4765EE7CC5ECA396E0531A57068160031713","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3252","description":"SRM 3252 Protein Drink Mix - This Standard Reference Material (SRM) is intended primarily for validation of methods for determining proximates, fatty acids, cholesterol, vitamins, elements, and amino acids in protein drink mixes and similar materials. This SRM can also be used for quality assurance when assigning values to in-house reference materials. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3252 Protein Drink Mix","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","SRM 3252","protein drink mix","certified values","reference values","food","nutrition","dietary supplements","Food and Nutrition"]},{"identifier":"4765EE7CC5EEA396E0531A57068160031715","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3262","description":"SRM 3262 St. John's Wort (Hypericum perforatum L.) Aerial Parts - This Standard Reference Material (SRM) is intended primarily for use in validating analytical methods for the determination of chlorogenic acid, flavonoids, naphthodianthrones, and toxic elements in Hypericum perforatum L. and similar materials. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3262 St. John's Wort (Hypericum perforatum L.) Aerial Parts","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference materials","SRM","SRM 3262","St. John's wort","nutrition","dietary supplements","certified value","reference value","Food and Nutrition"]},{"identifier":"4765EE7CC5EFA396E0531A57068160031716","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3264","description":"SRM 3264 St. John's Wort (Hypericum perforatum L.) Methanol Extract - This Standard Reference Material (SRM) is intended primarily for use in validating analytical methods for the determination of chlorogenic acid, flavonoids, naphthodianthrones, and toxic elements in methanol extracts of Hypericum perforatum L. and similar materials. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3264 St. John's Wort (Hypericum perforatum L.) Methanol Extract","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference materials","SRM","SRM 3264","St. John's wort","nutrition","dietary supplements","certified value","reference value","Food and Nutrition"]},{"identifier":"4765EE7CC5F1A396E0531A57068160031718","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3281","description":"SRM 3281 Cranberry (Fruit) - This Standard Reference Material (SRM) is intended primarily for use in validating analytical methods for the determination of organic acids in the fruit of cranberries and similar matrices. This SRM can also be used for quality assurance when assigning values to in-house control materials. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3281 Cranberry (Fruit)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference materials","SRM","SRM 3281","Cranberry","food","nutrition","dietary supplement","anthocyanin","certified value","reference value","Food and Nutrition"]},{"identifier":"4765EE7CC5F2A396E0531A57068160031719","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3282","description":"SRM 3282 Low-Calorie Cranberry Juice Cocktail. This Standard  Reference  Material (SRM) is  intended  primarily  for  use  in  validating  analytical  methods for the determination of organic acids and nutrient elements in cranberry juice cocktails and similar matrices. This SRM can also be used for quality assurance when assigning values to in-house control materials.  A unit of SRM 3282 consists of five ampoules, each containing approximately 1.2 mL of material. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3282 Low-Calorie Cranberry Juice Cocktail","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference materials","SRM","SRM 3282","cranberry juice","juice","food","nutrition","anthocyanin","certified value","reference value","Food and Nutrition"]},{"identifier":"4765EE7CC5F3A396E0531A57068160031720","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4765EE7CC5F3A396E0531A57068160031720","description":"SRM 3287 Blueberry (Fruit). This Standard  Reference  Material (SRM) is intended  primarily  for  use  in  validating analytical  methods  for the determination of  organic  acids  and  nutrients in blueberries and  similar  materials.  This  SRM can also be used for quality assurance when assigning values to in-house control materials.  A unit of SRM 3287 consists of five packets, each containing approximately 5 g of freeze-dried, powdered fruit. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3287 Blueberry (Fruit)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference materials","SRM","SRM 3287","blueberry","food","nutrition","dietary supplement","anthocyanin","certified value","reference value","Food and Nutrition"]},{"identifier":"4765EE7CC5F4A396E0531A57068160031721","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4765EE7CC5F4A396E0531A57068160031721","description":"SRM 3290 Dry Cat Food - This Standard Reference Material (SRM) is intended primarily for use in validating methods for determining proximates, fatty acids, vitamins, elements, and amino acids in dry cat food and similar matrices. This SRM can also be used for quality assurance when assigning values to in-house control materials. This SRM is a blend of commercially available dry cat foods. A unit of SRM 3290 consists of five heat-sealed, aluminized pouches, each containing approximately 10 g of material. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3290 Dry Cat Food","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference Material","SRM","SRM 3290","dry cat food","certified values","reference values","food","nutrition","Food and Nutrition"]},{"identifier":"4765EE7CC5F7A396E0531A57068160031724","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3532","description":"SRM 3532 Calcium-Containing Solid Oral Dosage Form - This Standard Reference Material (SRM) is intended primarily for validation of methods for determining cholecalciferol (vitamin D3) and elements in a calcium dietary supplement and similar materials. This SRM can also be used for quality assurance when assigning values to in-house reference materials. A unit of SRM 3532 consists of five packets, each containing approximately 10 g of material. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3532 Calcium-Containing Solid Oral Dosage Form","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Standard Reference materials","SRM","SRM 3532","calcium supplement","food","nutrition","dietary supplements","certified value","reference value","Food and Nutrition"]},{"identifier":"4765EE7CC5F8A396E0531A57068160031725","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3669","description":"SRM 3669 Arsenic Species in Frozen Human Urine (Elevated Levels) - This Standard Reference Material (SRM) is intended primarily for validating analytical methods and measurements for the determination of arsenic species in human urine. A unit of SRM 3669 consists of five pouches each containing a vial of arsenic species in frozen human urine at elevated levels. Each vial contains nominally 1.5 mL of urine. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3669 Arsenic Species in Frozen Human Urine (Elevated Levels)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM 3669","arsenic","arsenic species","urine","Biosciences and Health"]},{"identifier":"4765EE7CC5F9A396E0531A57068160031726","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3672","description":"SRM 3672 Organic Contaminants in Smokers' Urine (Frozen) - This Standard Reference Material (SRM) is intended for use in evaluating analytical methods for the determination of selected hydroxylated polycyclic aromatic hydrocarbons (hydroxylated PAHs) and phthalate, phenol, and volatile organic compound (VOC) metabolites in urine. All of the constituents for which certified and reference values are provided are naturally present in the urine. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3672 Organic Contaminants in Smokers' Urine (Frozen)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["reference materials","hydroxylated PAHs","phthalate metabolites","phenol metabolites","VOC metabolites","drugs","health markers","Environment and Climate"]},{"identifier":"4765EE7CC5FAA396E0531A57068160031727","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=3673","description":"SRM 3673 Organic Contaminants in Non-Smokers' Urine (Frozen) - This Standard Reference Material (SRM) is intended for use in evaluating analytical methods for the determination of selected hydroxylated polycyclic aromatic hydrocarbons (hydroxylated PAHs) and phthalate, phenol, and volatile organic compound (VOC) metabolites in urine. All of the constituents for which certified and reference values are provided are naturally present in the urine. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 3673 Organic Contaminants in Non-Smokers' Urine (Frozen)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["reference materials","hydroxylated PAHs","phthalate metabolites","phenol metabolites","VOC metabolites","drugs","health markers","Environment and Climate"]},{"identifier":"4765EE7CC5FBA396E0531A57068160031728","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Johanna Camara"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4765EE7CC5FBA396E0531A57068160031728","description":"Standard Reference Material 3949 Folate Vitamers in Frozen Human Serum","language":["en"],"title":"SRM 3949  Folate Vitamers in Frozen Human Serum","distribution":[{"accessURL":"https://www-s.nist.gov/srmors/certificates/3949.pdf","format":"pdf","title":"SRM 3949 Folate Vitamers in Frozen Human Serum Certificate of Analysis"},{"accessURL":"https://doi.org/10.18434/M31728","title":"DOI Access for SRM 3949 Folate Vitamers in Frozen Human Serum"}],"bureauCode":["006:55"],"modified":"2015-08-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"issued":"2020-03-19","keyword":["Standard Reference Material","SRM","SRM 3949","Frozen Human Serum","certified values","reference values","folate vitamers","folates","folic acid","Biosciences and Health"]},{"identifier":"4765EE7CC5FCA396E0531A57068160031729","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_cert.cfm?srm=635a","description":"SRM 635a Portland Cement (Blended with Slag) - This Standard Reference Material (SRM) is intended primarily for use in validation of chemical and instrumental methods of analysis of cements and materials of similar matrix for elemental contents. It can be used to validate value assignment of in-house reference materials. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 635a Portland Cement (Blended with Slag)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-09-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Manufacturing"]},{"identifier":"4765EE7CC5FDA396E0531A57068160031730","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=671","description":"SRM 671 Nickel Oxide, No. 1 - Standard Reference Material 671, Nickel Oxide No. 1 has been discontinued and is no longer being produced. The data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 671 Nickel Oxide No. 1","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Advanced Materials"]},{"identifier":"4765EE7CC5FEA396E0531A57068160031731","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=672","description":"Standard Reference Material 672 Nickel Oxide, No. 2 has been discontinued and is no longer being produced. The data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 672 Nickel Oxide No. 2","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Advanced Materials"]},{"identifier":"4765EE7CC5FFA396E0531A57068160031732","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=673","description":"Standard Reference Material 673 Nickel Oxide, No. 3 has been discontinued and is no longer being produced. The data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 673 Nickel Oxide No. 3","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["Advanced Materials"]},{"identifier":"4765EE7CC600A396E0531A57068160031733","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=690","description":"SRM 690 Iron Ore (Canada) is for use in checking chemical methods of analysis and in calibration with instrumental methods of analysis. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 690 Iron Ore (Canada)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["iron ore","iron","elemental analysis","Manufacturing"]},{"identifier":"4765EE7CC601A396E0531A57068160031734","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=691","description":"SRM 691 Iron Oxide, Reduced is intended primarily for use in evaluating chemical methods and in calibrations associated with optical emission and x-ray spectrometric methods of analysis. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 691 Iron Oxide Reduced","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["iron oxide","iron ore","elemental analysis","Manufacturing"]},{"identifier":"4765EE7CC602A396E0531A57068160031735","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=692","description":"SRM 692 iron Ore (Labrador) is intended primarily for use in checking chemical methods of analysis and in calibration with instrumental methods of analysis. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 692 Iron Ore Labrador","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["iron ore","elemental analysis","Manufacturing"]},{"identifier":"4765EE7CC603A396E0531A57068160031736","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www-s.nist.gov/srmors/view_detail.cfm?srm=693","description":"SRM 693 Iron Ore (Nimba) - Standard Reference Material 693 Iron Ore (Nimba) is material in the form of powder (<0.1 mm) for use in checking chemical methods of analysis and in calibration with instrumental methods of analysis.  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The database was developed for collecting the seismic response and performance of acceleration-sensitive NCSsduring the simulated seismic tests and field earthquakes. The data of an individual NCS associated with a specific test are organized row-by-row, while different data entries are contained in separate columns. The data entries of the spreadsheet table, in a left-to-right order, include metadata and test results in the following categories: -Test program or earthquake: contains information about the test or earthquake short name, test type (e.g., system-level shake table test, component-level pseudo-static tests), test facility, and references.- NCS metadata: contains general descriptions of NCS type/subtype (e.g., mechanical, electrical, contents), geometry, weight, attachment details, mountingcondition, frequency/period, and etc.- NCS design basis: design standard, short-period design spectral acceleration, design seismic force demand.- Target and measured response: measured acceleration of the input, building, NCSs, and floor response spectral values.- Damage state: damage states of the component, support, and anchors (e.g., none, minor, moderate, severe).Currently the database contains test results of NCSs from one system-level experimental project and three component-level projects. In addition, field observations of the response of NCSs during the 1994 Northridge earthquake are incorporated into the database.","language":["en"],"title":"Nonstructural Component and System Testing Database (NIST/ATC-120)","distribution":[{"accessURL":"https://opendata.nist.gov/1823_ATC-120_NCSC_Testing_Database(03012017).xlsx","format":"Excel spreadsheet","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Nonstructural Component and System Testing Database"},{"accessURL":"https://doi.org/doi:10.18434/M3KW2M"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-02-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Resilience:Earthquake risk reduction"],"keyword":["Nonstructural Components and Systems; Earthquake; Seismic; Performance"]},{"identifier":"49B04D04A102CDBEE0531A570681D69D1824","accessLevel":"public","contactPoint":{"hasEmail":"mailto:peter.bajcsy@nist.gov","fn":"Peter Bajcsy"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/49B04D04A102CDBEE0531A570681D69D1824","description":"Cell-scaffold contact measurements are derived from pairs of co-registered volumetric fluorescent confocal laser scanning microscopy (CLSM) images (z-stacks) of stained cells and spun coat, large microfiber, and medium microfiber scaffolds. Our analysis of the acquired terabyte-sized collection is motivated by the need to understand dimensionality of cell-scaffold interactions relevant to tissue engineers that grow cells on biomaterial scaffolds. The raw and processed data are publicly available from https://isg.nist.gov/deepzoomweb/data together with the web-based verification system.","language":["en"],"title":"Cell-scaffold contact","distribution":[{"accessURL":"https://isg.nist.gov/deepzoomweb/zstackContactDownload","format":"zip files","description":"Cell-scaffold contact raw and processed data","mediaType":"image/tiff","title":"Cell-scaffold contact"},{"accessURL":"https://isg.nist.gov/deepzoomweb/stemcells3dcontact/index.html","description":"Cell-scaffold contact images and movies for visual inspection","mediaType":"video/vnd.dece-mp4","title":"Cell-scaffold contact movies"},{"accessURL":"https://doi.org/doi:10.18434/M3V01R"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-02-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Computational science","Metrology"],"keyword":["CS-MET computational metrology"]},{"identifier":"49B0ACCC1A448241E0531A5706813A921825","accessLevel":"public","references":["http://onlinelibrary.wiley.com/doi/10.1111/jmi.12303/abstract"],"contactPoint":{"hasEmail":"mailto:peter.bajcsy@nist.gov","fn":"Peter Bajcsy"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/49B0ACCC1A448241E0531A5706813A921825","description":"This data set consists of 1000+ z-stack of actin and nucleus stained cells. The cells reside on 10 different scaffold types and are segmented from the z-stacks to investigate their shape changes.","language":["en"],"title":"1000+ z-stack experiment","distribution":[{"accessURL":"https://isg.nist.gov/deepzoomweb/zstackDownload","description":"1000+ z-stack images corresponding to cells residing on 10 different scaffold types","mediaType":"image/fits","title":"1000+ z-stack of cell actin and nucleus"},{"accessURL":"https://isg.nist.gov/deepzoomweb/stemcells3d/index.html","description":"3D viewer of 1000+ z-stacks of cell actin and nucleus","mediaType":"image/fits","title":"1000+ z-stacks of cell actin and nucleus (3D viewer)"},{"accessURL":"https://doi.org/doi:10.18434/M3ZP4Q"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-02-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology","Information Technology:Computational science"],"keyword":["CS-MET computational metrology"]},{"identifier":"4A3BDFC9A11E4ECEE0531A570681E7481826","accessLevel":"public","references":["https://dx.doi.org/10.6028/NIST.IR.8111","https://dx.doi.org/10.1016/j.precisioneng.2016.09.010"],"contactPoint":{"hasEmail":"mailto:cheok@nist.gov","fn":"Geraldine Cheok"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/el/intelligent-systems-division-73500/3d-data-evaluation-point-based-rigid-body-registration-error","description":"Datasets to evaluate the performance of point-based, rigid-body registration may be downloaded from this site.  Registration is the process of transforming one coordinate frame to another coordinate frame.\n\nThe datasets contain 3D position measurements obtained from three instruments:  a laser tracker (LT), a motion capture system (System A), and a large-scale metrology system (System B).  The positions are for points that are in a semi-regular, 5 x 5 x 5 grid.  The grid covers a volume that is approximately (3 x 3 x 1.8) m [L x W x H].  The measurement uncertainties are ± 25e-03 mm for the laser tracker, ± 250e-03 mm for System B, while the accuracy of System A is only specified as sub-millimeter.  The datasets for each instrument were collected in the instrument's local coordinate frame.\n\nThe datasets contain measurements of 125 fiducials (points used for registration) and 16 test points.  Test points are points that are not used for registration but to which a transformation is applied; these points are used to evaluate the performance of the registration.","language":["en"],"title":"3D Data for the Evaluation of Point-Based, Rigid Body Registration Error","distribution":[{"downloadURL":"https://www.nist.gov/file/354986","format":"CSV files","description":"The datasets contain 3D position measurements obtained from three instruments:  a laser tracker (LT), a motion capture system (System A), and a large-scale metrology system (System B).  The positions are for points that are in a semi-regular, 5 x 5 x 5 grid.  The grid covers a volume that is approximately (3 x 3 x 1.8) m [L x W x H] .  The measurement uncertainties are ± 25e-03 mm for the laser tracker, ± 250e-03 mm for System B, while the accuracy of System A is only specified as sub-millimeter.  The datasets for each instrument were collected in the instrument's local coordinate frame.","mediaType":"text/csv","title":"3D Data for the Evaluation of Point-Based, Rigid Body Registration Error"},{"accessURL":"https://doi.org/doi:10.18434/M39G6H"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-03-02","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing: Robotics in manufacturing","Health: Medical imaging"],"keyword":["Coordinate transformation","registration fiducials","point-based registration","registration error."]},{"identifier":"4AF4333E4DEB5257E0531A570681AB8D1827","accessLevel":"public","references":["https://doi.org/10.6028/NIST.AMS.200-12"],"contactPoint":{"hasEmail":"mailto:rosemary.astheimer@nist.gov","fn":"Rosemary Astheimer"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4AF4333E4DEB5257E0531A570681AB8D1827","description":"The STEP File Analyzer and Viewer is a software tool that generates a spreadsheet or a set of  CSV (comma-separated value) files from a STEP (ISO 10303 Standard for Exchange of Product model data) Part 21 file. STEP files are used to represent product and manufacturing information (PMI) and for data exchange and interoperability between Computer-Aided Design (CAD), Manufacturing (CAM), Analysis (CAE), and Inspection (CMM) software related to the smart manufacturing digital thread. STEP is also used for the long-term archiving and retrieval of product data.  The software can also open a STEP file to view boundary representation part geometry,  tessellated part geometry, PMI for geometric dimensioning and tolerancing (GD&T), supplemental geometry, and finite element models.","language":["en"],"title":"STEP File Analyzer and Viewer Software","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-09-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Interoperability in manufacturing"],"keyword":["manufacturing","interoperability","conformance","CAD","CAE"]},{"identifier":"4AF442F712E06F37E0531A57068106B51828","accessLevel":"public","contactPoint":{"hasEmail":"mailto:rosemary.astheimer@nist.gov","fn":"Rosemary Astheimer"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-1828","description":"The IFC File Analyzer software generates a spreadsheet or a set of CSV (comma-separated value) files from an IFC file. IFC (Industry Foundation Classes) is the data exchange standard used to facilitate interoperability in the building and construction industry. IFC is developed by buildingSMART and is an ISO standard - ISO 16739.","language":["en"],"title":"IFC File Analyzer Software","distribution":[{"accessURL":"https://doi.org/doi:10.18434/M3F30P"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Buildings and Construction:Building codes and standards"],"keyword":["buildings","interoperability","conformance","IFC","BIM"]},{"identifier":"4C0CE03134A120F3E0531A57068159621832","accessLevel":"public","contactPoint":{"hasEmail":"mailto:catherine.cooksey@nist.gov","fn":"Catherine Cooksey"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This data set contains 100 reference reflectance spectra of human skin, spanning the wavelength region from 250 nm to 2500 nm.  The spectra were acquired with a commercially available spectrophotometer and are directly traceable to the national scale for directional-hemispherical reflectance factor.","language":["en"],"title":"Reference Data Set of Human Skin Reflectance","distribution":[{"accessURL":"https://doi.org/doi:10.18434/M38597"},{"downloadURL":"https://opendata.nist.gov/1832_Data_JResNIST_skinrefl%20v3.txt","mediaType":"text/csv"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-03-31","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Health: Medical imaging","Physics: Spectroscopy"],"keyword":["medical","tissue","skin","reflectance","spectral","hyperspectral","remote sensing"]},{"identifier":"4E3AF57724AEF2DAE0531A57068162B11834","accessLevel":"public","references":["http://pubs.acs.org/doi/abs/10.1021/acsnano.6b06582"],"contactPoint":{"hasEmail":"mailto:keana.scott@nist.gov","fn":"Keana C. K. Scott"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/4E3AF57724AEF2DAE0531A57068162B11834","description":"This folder contains image data sets from 14 separate serial sectioning sessions.  The entire data folder consists of 1379 8 bit tif images and is 47.3 GB in size.  Serial sectioning was performed using FEI  Helios 660 NanoLab focused ion beam scanning electron microscope (FIB SEM) and Auto Slice and View G3 software.  The sample was a heavy metal stained and resin embedded Caenorhabditis elegans (C. elegans) that were exposed to 60 nm Au nanoparticles.  Detailed descriptions of the worm preparation and resin block processing are described in Johnson, M.E. et al. (ACS Nano, 2016). Although the images were collected over 14 different sessions, they represent a contiguous section of a worm.","language":["en"],"title":"FIB SEM image data set of Caenorhabditis elegans exposed to 60 nm Au nanoparticles","distribution":[{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set2-120.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set1-106.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set10-100.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set12-79.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M3C09F"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set4-120.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set5-120.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set8-120.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set9-108.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set13-32.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set6-98.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set11-50.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set1-106.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set10-100.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set12-79.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_C.elegansFIBSEMDataManifest.docx.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set7-86.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set14-120.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set3-120.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1834_C.elegansFIBSEMDataManifest.docx","mediaType":"application/msword"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set7-86.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set14-120.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set3-120.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set4-120.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set5-120.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set8-120.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set9-108.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set13-32.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set6-98.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set11-50.zip","mediaType":"application/zip"},{"downloadURL":"https://opendata.nist.gov/1834_FIBSEM-Celegans-tiff-set2-120.zip","mediaType":"application/zip"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-09-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Nanotechnology","Manufacturing","Environment","Materials","Health","Bioscience"],"keyword":["Advanced Materials","Biosciences and Health","Environment and Climate","Manufacturing","Visualization Research","Nanotechnology"]},{"identifier":"4FA52896F612CAE3E0531A570681BC801836","accessLevel":"public","references":["https://doi.org/10.6028/jres.122.025"],"contactPoint":{"hasEmail":"mailto:wesley.griffin@nist.gov","fn":"Wesley Griffin"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/cubemap-stitch","description":"We have developed a utility to both stitch cube maps into other types of texture maps (equirectangular, dual paraboloid, and octahedral), and stitch those other types back into cube maps. The utility allows for flexibility in the image size of the conversion - the user can specify the desired image width, and the height is computed (cube, paraboloid, and octahedral mappings are square, and spherical maps are generated to have 16:9 aspect ratio). Moreover, the utility is sampling-agnostic, so the user can select whether to use uniform or jittered sampling over the pixels, as well as the number of samples to use per pixel. The rest of this paper discusses the mathematical framework for projecting from cube maps to equirectangular, dual paraboloid, and octahedral environment maps, as well as the mathematical framework for the inverse projections. We also describe two sampling techniques: uniform sampling and correlated multi-jittered sampling. We perform an evaluation of the sampling techniques and a comparative analysis of the different projections using objective image quality assessment metrics.","language":["en"],"title":"Sampling-Agnostic Software Framework for Converting Between Texture Map Representations of Virtual Environments","distribution":[{"accessURL":"https://github.com/usnistgov/cubemap-stitch","format":"Github Source Repository","description":"Github Source Repository","mediaType":"text/html","title":"Github Source Repository"},{"accessURL":"https://doi.org/doi:10.18434/M3P88M"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-05-16","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology: Visualization research"],"keyword":["equirectangular; octahedral; paraboloid; projection; sampling; texture map; transformation"]},{"identifier":"5030FC3611064703E0531A570681EF031838","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"Steven J. Choquette"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/5030FC3611064703E0531A570681EF031838","description":"This Standard Reference Material (SRM) is intended for use as an analytical standard of known purity.  It is intended primarily for use in the calibration and standardization of procedures for potassium (K) and chloride (Cl-) determinations employed in clinical analysis, including those using ion-selective electrodes, and for routine critical evaluation of the daily working standards used in these procedures.  This lot of potassium chloride (KCl) was prepared to ensure a material of high purity and homogeneity and has been assayed after heating at 110 degrees C to 120 degrees C.  A unit of SRM 918c consists of a single glass bottle containing 30 g of the material. This data is public in the Certificate of Analysis for this material.","language":["en"],"title":"SRM 918c Potassium Chloride General and Ion Activity Standard","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-02-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials"],"keyword":["SRM","standard reference material","potassium chloride"]},{"identifier":"505F5B02CE53D819E0531A570681F84B1840","accessLevel":"public","references":["https://journals.aps.org/pra/abstract/10.1103/PhysRevA.95.053806","https://doi.org/10.1103/PhysRevA.95.053806","https://arxiv.org/abs/1704.05503"],"contactPoint":{"hasEmail":"mailto:sergey.polyakov@nist.gov","fn":"Sergey Polyakov"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/FSMR","description":"The mode structure fully describes a light field and contains the information about the source of light without a direct access to the source. Here we offer the tool to extract this information from the measured photon number resolved (PNR) distribution. We present a software package aimed at simulating photon-number probability distributions of a range of naturally occurring classical and non-classical states of light. This software can generate arbitrary probability distributions based on the known mode structure of a light field. It also can solve the reverse problem, i.e. reconstructing the mode structure of a light field based on a given probability distribution.","language":["en"],"title":"Full Statistical Mode Reconstruction of a light field","distribution":[{"accessURL":"https://github.com/usnistgov/FSMR","format":"Source code in C, precompiled examples","description":"We present a software package aimed at simulating photon-number probability distributions of a range of naturally occurring classical and non-classical states of light. This software can generate arbitrary probability distributions based on the known mode structure of a light field. It also can solve the reverse problem, i.e. reconstructing the mode structure of a light field based on a given probability distribution.","mediaType":"text/plain","title":"Full Statistical Mode Reconstruction of a light field"},{"accessURL":"https://doi.org/10.18434/M3388B"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-05-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Metrology:Optical, photometry, and laser metrology","Mathematics and Statistics:Statistical analysis","Physics:Optical physics","Physics:Quantum information science","Mathematics and Statistics:Numerical methods and software"],"keyword":["photon-number resolved detection","quantum optics","parametric down-conversion","four-wave mixing","optical modes","nonclassicality","mesoscopic states","thermal statistics","poisson statistics","binomial statistics"]},{"identifier":"50FF29D41202478EE0531A57068105821843","accessLevel":"public","references":["http://dx.doi.org/10.1021/acs.langmuir.7b02400"],"contactPoint":{"hasEmail":"mailto:aron.newman@nist.gov","fn":"Aron Newman"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-1843","description":"The probabilistic distribution of dissolution rate constants for cubic tricalcium aluminate (C3A) in water was quantified based on in-situ measurements of the changes in the nanoscale surface topography using reflection digital holographic microscopy. Measurements were performed on polished sintered pellets of C3A from two sources. C3A#1 consists of 94.2 % crystalline C3A-c, 2.0 % free CaO, and 3.8 % amorphous content. C3A#2 consists of 84.3 % crystalline C3A-c, 1.0 % free CaO, 4.6 % mayenite, and 10.1 % amorphous content. The data presented in the two text files, labeled C3A#1 and C3A#2, have two columns of values corresponding to the probabilistic distribution (histogram) data; the first column is the frequency and the second column is the rate constant value in units of umol m^-2 s^-1. All uncertainties associated with this dataset are discussed in an accompanying paper.","language":["en"],"title":"Probabilistic distribution of dissolution rate constants for cubic tricalcium aluminate in water","distribution":[{"downloadURL":"https://s3.amazonaws.com/nist-midas/1843/C3A%232.txt","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1843/C3A%232.txt.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/doi:10.18434/M3SW2G"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1843/C3A%231.txt","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1843/C3A%231.txt.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-06-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Image and signal processing","Materials:Concrete/cement","Materials:Ceramics","Materials:Materials characterization"],"keyword":["tricalcium aluminate","dissolution","kinetics","rate constant","portland cement"]},{"identifier":"52DE8EB3CB428107E0531A57068170581847","accessLevel":"public","contactPoint":{"hasEmail":"mailto:joseph.conny@nist.gov","fn":"Joseph Conny"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This project involves the analysis and modeling of individual urban atmospheric dust particles using scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDX), focused ion-beam (FIB) tomography, 3-D reconstructions of the particles, and the discrete dipole approximation method for calculating particle optical properties. The particles are heterogeneous in that they have multiple chemical phases. The collected data sets are: 1) element concentrations for selected particles from filter samples using SEM-EDX, 2) element spatial maps of particles using SEM-EDX, 3) secondary electron images from SEM, 4) text files containing information on the locations of chemical phases for each particle based on 3-D reconstructions from FIB tomography, and 5) optical property calculations of particles using the DDA program DDSCAT.","language":["en"],"title":"Effect of Heterogeneity and Shape on Optical Properties of Urban Dust Based on 3-Dimensional Modeling of Individual Particles","distribution":[{"downloadURL":"https://opendata.nist.gov/1847/Dataset_UrbanDustOpticalProperties.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1847/Dataset_UrbanDustOpticalProperties.zip","mediaType":"application/zip"},{"accessURL":"https://doi.org/10.18434/M33X0V"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-06-23","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Environment: Air / water / soil quality"],"keyword":["atmospheric aerosol","urban dust","light scattering","discrete dipole approximation","focused ion-beam scanning electron microscopy","FIB-SEM","FIB tomography","energy dispersive x-ray spectroscopy"]},{"identifier":"54895E1BEA5A699DE0531A57068160C01850","accessLevel":"public","contactPoint":{"hasEmail":"mailto:mwinchester@nist.gov","fn":"Michael R. Winchester"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/gfsarmanho/NanoUV-VIS","description":"Shiny app for monitoring the evolution of optical properties of nanoparticles throughout synthesis reactions.","language":["en"],"title":"NanoUV-VIS- An interactive visualization tool for monitoring the evolution of optical properties of nanoparticles throughout synthesis reactions.","distribution":[{"accessURL":"https://doi.org/10.18434/M3T952"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-07-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Nanotechnology:Nanomaterials","Nanotechnology:Nanofabrication/manufacturing","Nanotechnology:Nanochemistry"],"keyword":["Advanced Functional Materials","Advanced Materials"]},{"identifier":"54AE54FB37AC022DE0531A570681D4291851","accessLevel":"public","contactPoint":{"hasEmail":"mailto:richard.ricker@nist.gov","fn":"Richard Ricker"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/54AE54FB37AC022DE0531A570681D4291851","description":"Corrosion is defined as the deterioration of a material due to chemical reactions with its environment. Understanding corrosion, and corrosion rates, is necessary to ensure that the design of objects we interact with every day, from the bridges we drive over to the containers used to transport chemicals, from the pipes that carry our water to the utensils we cook with, are safe and perform to specifications.\n\nThe Corr-Data dataset was created by the staff of the NACE-NIST Corrosion Data Program. This program was established by a joint agreement between NACE (National Association of Corrosion Engineers) International and NIST (National Institute of Standards and Technology) in Dec. of 1982 and ran until 1997. At its founding, the objective of this program was to provide engineers with software for the then new personal computers that would help them solve corrosion problems. Over the 15 years this program ran, over 20 databases and expert advisory systems for alloys selection were produced and released. While software can go out of date, the data remains relevant. The data describe observations of various samples in potentially corrosive environments under particular conditions (concentrations and temperatures) to see if, how, and at what rate they corrode. The data include over 24,000 records which have been extracted from over 250 different source documents.\n\nThe data provided here is from published results, but it should only be considered indicative of how the specified material was observed to perform in the given environment one time. The user needs to keep in mind that corrosion is a highly stochastic process and that behavior can vary greatly with small variations in the environment or material. This data is provided to help users select materials for further evaluation and should not be considered, or construed, as advice on the usability of any material in any environment. Only thoroughly evaluated test results for the whole range of material, loading, and environmental conditions should be used for critical decision making.","language":["en"],"title":"CORR-DATA","distribution":[{"accessURL":"https://doi.org/10.18434/M3TH4R"},{"downloadURL":"https://opendata.nist.gov/1851/CORR-DATA_Database.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/od/ds/54AE54FB37AC022DE0531A570681D4291851/CORR-DATA-fields.txt","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/1851/CORR-DATA_Database.zip","mediaType":"text/plain"},{"downloadURL":"https://opendata.nist.gov/od/ds/54AE54FB37AC022DE0531A570681D4291851/CORR-DATA-fields.txt.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"1997-01-01","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Materials characterization","Materials:Metals"],"keyword":["corrosion","NACE"]},{"identifier":"55CC9271C9B75BE9E0531A57068105501852","accessLevel":"public","contactPoint":{"hasEmail":"mailto:mina.seif@nist.gov","fn":"Mina Seif"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/55CC9271C9B75BE9E0531A57068105501852","description":"Abstract: The behavior of high-strength structural steel at elevated temperatures, especially under shear loading, is not well established in the literature. Set of data presents results from recently conducted tests on high-strength structural bolts subject to double shear loading at elevated temperatures. The parameters varied between tests included the bolt grade, bolt diameter, and temperature. Bolt grades A325 and A490 were tested. For each bolt grade, three different diameters were tested (19 mm (3/4 in), 22 mm (7/8 in), and 25.4 mm (1 in)) at five different temperatures (20 ºC, 200 ºC, 400 ºC, 500 ºC, and 600 ºC). At least three tests were conducted for each combination of parameters. The load versus displacement curves are presented herein. The results from these experiments fill a critical knowledge gap currently present in the literature regarding the behavior of high-strength structural bolts under shear loading at elevated temperatures. These data will ultimately provide a thorough understanding of the overall behavior of structural steel systems under realistic fire loading by clarifying the (i) shear behavior of high-strength structural steel bolts at elevated temperatures, and (ii) degradation in the mechanical and material properties of high-strength steel bolts with increasing temperatures. It is noted that additional details, experimental procedures, and uncertainty analysis for this experimental series are provided in in the publications listed below. Description:  This data set is presented in six files. One file for each diameter size (19 mm (3/4 in), 22 mm (7/8 in) and 25.4 mm (1 in)) for both bolt types (A325 and A490). Within the files, there are separate tables representing the load-displacement curves for each test. Each test was assigned a unique name. The nomenclature includes the bolt diameter, type, test temperature, and specimen number. For example, test \"19A325T20-1\". The first two digits represent the bolt diameter (19 mm (3/4 in), 22 mm (7/8 in), or 25.4 mm (1 in)), the next four characters represent the bolt type (A325 or A490), followed by the letter T and the temperature at which that bolt was tested (T20= 20 ºC (Ambient Temperature); T200= 200 ºC; T400= 400 ºC; T500= 500 ºC; T600= 600 ºC), and lastly, the number of the specimen.","language":["en"],"title":"Results from double-shear tests of high-strength structural bolts at elevated temperatures","distribution":[{"accessURL":"https://doi.org/10.18434/M33T0T"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-08-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Modeling and computational material science","Fire:Structural fire resistance","Buildings and Construction:Structural engineering","Buildings and Construction:Building materials","Fire:Fire risk reduction","Materials:Materials characterization"],"keyword":["Elevated-temperature; High-strength structural bolts; Shear loading","Structural fire effects"]},{"identifier":"5707C8235EC46BBEE0531A570681990A1855","accessLevel":"public","contactPoint":{"hasEmail":"mailto:lee.yu@nist.gov","fn":"Lee L. Yu"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/5707C8235EC46BBEE0531A570681990A1855","description":"A comparison of the expanded uncertainty for certified values of iodine in food matrix CRMs.  The CRMS were found by searching The European Virtual Institute for Speciation Analysis (EVISA) database using the terms \"Iodine\" and \"Certified\" in the Material category.  The uncertainties are expanded uncertainties at approximately 95% confidence.","language":["en"],"title":"Certified values of iodine in CRMs of food matrices in mg/kg units.  The uncertainties are expanded uncertainties at approximately 95% confidence.","distribution":[{"downloadURL":"https://s3.amazonaws.com/nist-midas/1855/Table%20S1.docx","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1855/Table%20S1.docx.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M3PM35"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-08-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials","Health:Food and nutrition","Chemistry:Analytical chemistry"],"keyword":["food","nutrition","dietary supplements","standard reference materials","reference materials","SRM","RM","Food and Nutrition"]},{"identifier":"59145AB0040F183BE0531A5706818E571856","accessLevel":"public","references":["https://doi.org/10.1007/s10694-016-0646-7"],"contactPoint":{"hasEmail":"mailto:lisa.choe@nist.gov","fn":"Lisa Choe"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/59145AB0040F183BE0531A5706818E571856","description":"A total of nine localized fire tests on steel beams were designed and conducted in the National Fire Research Laboratory as part of its commissioning project, including thermal tests (Tests 1 through 5) and four-point bending test at ambient (Test 6) and elevated temperatures (Tests 7 through 9). All the tested specimens were nominally 6.2-m long W16×26 beams made of ASTM A992 steel. Each specimen was supported by one of the two following connections: (i) simple support (Tests 1 through 8), and (ii) double-angles bolted to laterally braced support columns (Test 9). The midspan of each specimen was exposed to an open-flame fire using the 1-m2 natural gas burners. The burner was located 1.1 m below the bottom flange of the beam at midspan. A four-point flexural loading scheme was used to apply concentrated forces at two locations 2.44 m apart around midspan. The data included temperatures, the heat release rates from the burner, and structural measurements including forces, displacements and strains. The Type B standard uncertainties in various measurements were also included. Overall, the test results showed that the heating rate of the specimen was sensitive to the prescribed heat release rate-time relationship. However, the thermal gradient developed in the fire-exposed cross sections of the beam never achieved linearity under the localized fire exposure. Regardless of the connection types and fire conditions (i.e., steady-state or transient-state fire), the beams exhibited a similar behavior and failure mode. When subjected to combined fire and flexural loads, the beam specimens exhibited the lateral-torsional buckling followed by runaway vertical displacements at midspan. Additional details of experimental procedures and uncertainty analysis for this experimental series are provided in the following publications: 1) Zhang, C., Choe, L., Gross, J., Ramesh, S., Bundy, M. (2017). Engineering Approach for Designing a Thermal Test of Real-scale Steel Beam Exposed to Localized Fire. Fire Technology, Vol 53, Issue 4, pp 1535-1554. DOI: 10.1007/s10694-016-0646-7 2) Choe, L., Ramesh, S., Zhang, C., Gross, J. (2016). The Performance of Structural Steel Beams Subjected to a Localized Fire. The 9th International Conference on Structures in Fire (SiF 16), June 8-10, 2016, Princeton, NJ.","language":["en"],"title":"Measurement of the Behavior of Steel Beams under Localized Fire Exposure","distribution":[{"downloadURL":"https://s3.amazonaws.com/nist-midas/1856/NFRL_LocalizedFireTest_SteelBeam_09012017.xlsx","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1856/NFRL_LocalizedFireTest_SteelBeam_09012017.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1856/NFRL_LocalizedFireTest_SteelBeam_08112017.xlsx","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1856/NFRL_LocalizedFireTest_SteelBeam_08112017.xlsx.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M37H4G"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-08-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Fire:Structural fire resistance"],"keyword":["steel beam; localized fire; thermal and mechanical behavior; experimental test"]},{"identifier":"576CE869311197F3E0531A570681C5C71857","accessLevel":"public","references":["http://dx.doi.org/10.6028/jres.115.026","https://dx.doi.org/10.6028/NIST.IR.7415"],"contactPoint":{"hasEmail":"mailto:richard.ricker@nist.gov","fn":"Richard Ricker"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/576CE869311197F3E0531A570681C5C71857","description":"Between 1922 and 1940, the National Bureau of Standards (NBS) conducted a long-term investigation into the corrosion of bare steel and iron, pipes buried underground at 47 sites representing different soil types in the United States. Following the passage of the 2004 Pipeline Safety Improvement Act, the Department of Transportation's Office of Pipeline Safety requested that NIST review and reanalyze the data from the NBS study using modern statistical analysis tools. For this analysis, NIST compiled an updated database from the data in the publications by K. H. Logan (i.e. NBS C450, 1945), M. Romanoff (i.e. NBS C579, 1957), and others, including examination of original logbooks. NIST published a report analyzing this data with modern computer assisted statistical analysis tools (doi.org/10.6028/NIST.IR.7415) and an archival paper summarizing the findings (doi:10.6028/jres.115.026). This data is provided in this format so that others will have the best available data from this research for future analyses.","language":["en"],"title":"Tables from \"Analysis of Pipeline Steel Corrosion Data From NBS (NIST) Studies Conducted Between 1922-1940 and Relevance to Pipeline Management\" (NISTIR 7415)","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1423308","format":"text/html","description":"DOI Access to Tables from Analysis of Pipeline Steel Corrosion Data From NBS (NIST) Studies Conducted Between 1922-1940 and Relevance to Pipeline Management (NISTIR 7415)","title":"DOI Access to Tables from Analysis of Pipeline Steel Corrosion Data From NBS (NIST) Studies Conducted Between 1922-1940 and Relevance to Pipeline Management (NISTIR 7415)"},{"downloadURL":"https://data.nist.gov/od/ds/576CE869311197F3E0531A570681C5C71857/NISTIR%207415_Appendix%20C.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/576CE869311197F3E0531A570681C5C71857/NISTIR%207415_Appendix%20C.xlsx.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2007-01-01","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Materials characterization","Materials:Metals"],"keyword":["corrosion","pipeline","steel","underground"]},{"identifier":"5887178FE62C46F8E0531A57068103631858","accessLevel":"public","references":["https://www.nist.gov/publications/effect-powder-cooling-rate-and-melt-pool-length-measurements-using-situ-thermographic"],"contactPoint":{"hasEmail":"mailto:brandon.lane@nist.gov","fn":"Brandon Lane"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This dataset contains thermographic measurements acquired during single and multiple track scans on bare substrates and on single layers of powder. The substrates and powder are nickel alloy 625 and the experiments are performed inside a commercial laser powder bed fusion machine. There are four experiment cases: 1) a single scan track on a bare substrate, 2) a single scan track on a single hand-spread layer of powder, 3) multiple (39) scan tracks covering an area on a bare substrate, and 4) multiple (39) scan tracks solidifying a single hand-spread layer of powder. Thermographic measurements are performed using a camera system sensitive to wavelengths between 1350 nm and 1600 nm. The camera acquires frames with an integration time of 0.04 ms and a frame rate of 1800 frames per s. The camera signal and radiant temperature values based on a black body calibration are provided. True temperature is not provided because emissivity of the surfaces is unknown. This data was used to measure melt pool length and cooling rate based on radiant temperature as part of the work in: Heigel, J. C. & Lane, B. (2017). \"The effect of powder on cooling rate and melt pool length measurements using in situ thermographic techniques.\" In Proceedings of the 2017 Annual International SFF Symposium (https://www.nist.gov/publications/effect-powder-cooling-rate-and-melt-pool-length-measurements-using-situ-thermographic) ","language":["en"],"title":"Thermographic measurements of single and multiple scan tracks on nickel alloy 625 substrates with and without a powder layer in a commercial laser powder bed fusion process (an additive manufacturing technology)","distribution":[{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/RawCameraData.zip","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/RawCameraData.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/20170213_PowderPlate2_Pad.zip","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/20170213_PowderPlate2_Pad.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/20170215_PowderPlate6_Bare_SingleLine_195W_800mmPs.zip","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/20170215_PowderPlate6_Bare_SingleLine_195W_800mmPs.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/20170213_PowderPlate1_SingleLine.zip","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/20170213_PowderPlate1_SingleLine.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/20170215_PowderPlate7_Bare_Pad_195W_800mmPs.zip","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/20170215_PowderPlate7_Bare_Pad_195W_800mmPs.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/DataSetOverviewAndTempProfiles.xlsx","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1858/DataSetOverviewAndTempProfiles.xlsx.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M3C37Q"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Additive manufacturing","Materials:Metals","Materials:Modeling and computational material science"],"keyword":["additive manufacturing","powder bed fusion","laser","thermography","temperature measurement","melt pool","melt pool length","cooling rate","Inconel 625","IN 625","nickel alloy 625","model validation"]},{"identifier":"59275E35E82149D8E0531A57068146E31859","accessLevel":"public","references":["https://dx.doi.org/10.6028/NIST.TN.1714","https://doi.org/10.6028/nist.ncstar.1-3d","https://dx.doi.org/10.6028/NIST.TN.1907"],"contactPoint":{"hasEmail":"mailto:william.luecke@nist.gov","fn":"William E. Luecke"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/59275E35E82149D8E0531A57068146E31859","description":"The National Institute of Standards and Technology, as part of its report on the collapse of the World Trade Center, characterized many important steels recovered from the buildings to provide stress-strain models to analyze the impact, fires, and resulting collapse. Those tests represent a large additional data set that can be used for modeling the response of steel structures to fire. The nine steels described in this data represent a selection of the steel most likely to have been involved in the fires in the World Trade Center.This data was originally published in \"High-temperature tensile constitutive data and models for structural steels in fire (NIST Technical Note 1714)\" authored by William Luecke, Stephen W. Banovic, J. David McColskey. (DOI: https://dx.doi.org/10.6028/NIST.TN.1714)","language":["en"],"title":"Data from \"High-temperature tensile constitutive data and models for structural steels in fire (NIST Technical Note 1714)\"","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1422744","format":"text/html","description":"DOI Access to Data from High-temperature tensile constitutive data and models for structural steels in fire (NIST Technical Note 1714)","title":"DOI Access to Data from High-temperature tensile constitutive data and models for structural steels in fire (NIST Technical Note 1714)"},{"downloadURL":"https://data.nist.gov/od/ds/59275E35E82149D8E0531A57068146E31859/TN1714data.csv","mediaType":"text/csv","title":"TN1714data"},{"downloadURL":"https://data.nist.gov/od/ds/59275E35E82149D8E0531A57068146E31859/TN1714-data-bibliography.pdf","mediaType":"application/pdf","title":"TN1714-data-bibliography"},{"downloadURL":"https://data.nist.gov/od/ds/59275E35E82149D8E0531A57068146E31859/TN1714data-attributes.csv","mediaType":"text/csv","title":"TN1714data-attributes"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Purchase is not required for data downloading. Users must complete registration form to download data.","modified":"2011-10-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Metals","Buildings and Construction:Building materials","Materials:Materials characterization"],"keyword":["steel","constitutive law","fire","World Trade Center Investigation","elevated temperature"]},{"identifier":"592933731171AB19E0531A570681F8251860","accessLevel":"public","contactPoint":{"hasEmail":"mailto:david.sheen@nist.gov","fn":"David Sheen"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pages.nist.gov/mumpce_py/","description":"The Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) was developed as a software tool to constrain physical models against experimental measurements. These models contain parameters that cannot be easily determined from first principles and so must be measured, and some which cannot even be easily measured. In such cases, the models are validated and tuned against a set of global experiments which may depend on the underlying physical parameters in a complex way. The measurement uncertainty will affect the uncertainty in the parameter values.","language":["en"],"title":"mumpcepy: A Python implementation of the Method of Uncertainty Minimization using Polynomial Chaos Expansions","distribution":[{"accessURL":"https://github.com/usnistgov/mumpce_py","format":"A GitHub repository","description":"A repository containing the Python source code for the mumpce_py software. This software can be downloaded onto any machine with a Python interpreter or it can be cloned from GitHub.","title":"mumpce_py"},{"accessURL":"https://doi.org/10.18434/M3WT1B"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-09-14","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology: Computational science","Mathematics and Statistics: Uncertainty quantification","Chemistry: Chemical thermodynamics and chemical properties"],"keyword":["experimental database; experimental design; optimization; outlier detection; uncertainty analysis."]},{"identifier":"59C7D390830CAAB9E0531A57068196781863","accessLevel":"public","references":["https://doi.org/10.6028/jres.122.038"],"contactPoint":{"hasEmail":"mailto:zachary.levine@nist.gov","fn":"Zachary Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"C++ code for Monte Carlo calculation of optical scattering in multi-layer material.   Described in RH Streater, A-MR Lieberson, AL Pintar. and ZH Levine, \"A parallel version of MCML and an Inverse Monte Carlo Algorithm to Calculate Optical Scattering Parameters,\" J. Res. NIST, https://doi.org/10.6028/jresnist.122.038.   See also the main article RH Streater, A-MR Lieberson, AL Pintar, CC Cooksey, and P Lemaillet, unpublished.","language":["en"],"title":"MCMLpar:  A parallel version of the MCML code in C++","distribution":[{"accessURL":"https://github.com/usnistgov/MCMLpar","format":"ASCII","description":"MCML calculates optical scattering distributions give a turbid multiayer characterized by a few parameters such as the absorption lengths, scattering lengths, anisotropy parameter, index of refraction, and thicknesses.  This is a re-implementation which does parallel Monte Carlo.","title":"MCMLpar: A parallel version of the MCML code"},{"accessURL":"https://doi.org/10.18434/M3NM2G"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-09-20","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics: Optical physics"],"keyword":["MCML ; light scattering ; optical scattering ; C++ ; parallel ; Monte Carlo"]},{"identifier":"59C815549D561E18E0531A57068175F41864","accessLevel":"public","references":["https://doi.org/10.6028/jres.122.038"],"contactPoint":{"hasEmail":"mailto:zachary.levine@nist.gov","fn":"Zachary H. Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/MCSLinv","description":"C++ code for inverse solution of Monte Carlo calculation of optical scattering in single-layer material, i.e., determination of optical scattering parameters from the Angle-Resolved Scattering   Described in RH Streater, A-MR Lieberson, AL Pintar. and ZH Levine, \"A parallel version of MCML and an Inverse Monte Carlo Algorithm to Calculate Optical Scattering Parameters,\" J. Res. NIST, https://doi.org/10.6028/jresnist.122.038.   See also the main article RH Streater, A-MR Lieberson, AL Pintar, CC Cooksey, and P Lemaillet, unpublished.","language":["en"],"title":"MCSLinv: An inverse Monte Carlo code  to calculate optical scattering parameters in C++","distribution":[{"accessURL":"https://github.com/usnistgov/MCSLinv","title":"MCSLinv:  An Inverse Monte Carlo Algorithm to Calculate Optical Scattering Parameters"},{"accessURL":"https://doi.org/10.18434/M3HW9N"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-09-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Optical physics"],"keyword":["MCML ; light scattering ; optical scattering ; C++ ; parallel ; Monte Carlo ; inverse problem"]},{"identifier":"5AA696C0E091D243E0531A570681AF2C1865","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8206"],"contactPoint":{"hasEmail":"mailto:alison.kahn@nist.gov","fn":"Alison Kahn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/5AA696C0E091D243E0531A570681AF2C1865","description":"Mouth-to-ear (M2E) latency describes the time it takes speech input in a voice communication transmit device to be output from a receiving device, and has been identified as a key component of quality of experience (QoE) in communications. NIST's PSCR division developed a method to measure and quantify the M2E latency of any communications system transmitting audio, with specific emphasis on push to talk (PTT) devices. This measurement method is the first step in establishing QoE key performance indicators (KPI) for mission critical voice (MCV) and a measurement system to quantify these QoE KPIs. Additional measurement methods will be established and published in the near future. The measurement system provides a fair platform for the comparisons of M2E latency across radio communications technologies. Both single and two location measurement systems were developed. The single location measurement system is a simpler setup ideal for measurements performed in a single, controlled setting. The two location system allows for the measurement of M2E latency between devices in two distinct locations and adds the capability to see potential effects of distance and signal propagation on the latency a user experiences. Example measurements of the M2E latency of VHF and UHF land mobile radios (LMR) operating in both direct mode and in trunked mode were performed. These tests demonstrated that both the single and two location tests return consistent measurement results.","language":["en"],"title":"MCV QoE Mouth To Ear Latency Measurement Data","distribution":[{"downloadURL":"https://s3.amazonaws.com/nist-midas/1865/Delay_Values.zip","mediaType":"application/zip"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1865/Delay_Values.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1865/Processed-Audio.zip","mediaType":"application/zip"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1865/Processed-Audio.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1865/README.txt","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1865/README.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1865/Raw%20Data.zip","mediaType":"application/zip"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1865/Raw%20Data.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://github.com/usnistgov/mouth2ear","format":"website","description":"The purpose of this software is to measure the mouth-to-ear (M2E) latency of a push-to-talk network. M2E latency characterizes the time between speech input into one communications device and its output through another. M2E latency has been identified as a key metric of quality of experience (QoE) in communications. NIST?s PSCR group developed this software to measure and quantify the M2E latency of Push To Talk (PTT) devices.","mediaType":"text/html","title":"mouth2ear repository"},{"downloadURL":"https://doi.org/10.18434/M32086","format":"website","description":"This Repository is for the microcontroller utilized in the PSCR MCV QoE measurement system. This code is utilized by the measurement systems described in NIST IR 8206 and NIST IR 8275. The code is written for the TI MSP430F5529 processor and was developed using a MSP EXP430F5529LP LaunchPad board.  The code was compiled and loaded using TI Code Composer Studio (CCS) IDE, available here: http://www.ti.com/tool/CCSTUDIO.","mediaType":"text/html","title":"Microcontroller Firmware"},{"downloadURL":"https://doi.org/10.6028/NIST.IR.8206","format":"PDF","description":"Mouth-to-ear (M2E) latency describes the time it takes speech input in a voice communication transmit device to be output from a receiving device, and has been identifed as a key component of quality of experience (QoE) in communications. NIST?s PSCR division developed a method to measure and quantify the M2E latency of any communications system transmitting audio, with specifc emphasis on push to talk (PTT) devices. This measurement method is the frst step in establishing QoE key performance indicators (KPI) for mission critical voice (MCV) and a measurement system to quantify these QoE KPIs. Additional measurement methods will be established and published in the near future. The measurement system provides a fair platform for the comparisons of M2E latency across radio communications technologies. Both single and two location measurement systems were developed. The single location measurement system is a simpler setup ideal for measurements performed in a single, controlled setting. The two location system allows for the measurement of M2E latency between devices in two distinct locations and adds the capability to see potential effects of distance and signal propagation on the latency a user experiences. Example measurements of the M2E latency of VHF and UHF land mobile radios (LMR) operating in both direct mode and in trunked mode were performed. These tests demonstrated that both the single and two location tests return consistent measurement results.","mediaType":"application/pdf","title":"Mission Critical Voice QoE Mouth-to-Ear Latency Measurement Methods"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)","Public Safety:Public safety communications research","Public Safety:First responder preparedness"],"keyword":["Audio; Communications; Delay; Direct Mode; Handset; Key Performance Indicator (KPI); Land Mobile Radio (LMR); Latency; Mission Critical Push To Talk (MCPTT); Mission Critical Voice (MCV); Mouth-to-ear (M2E); Project 25 (P25); Public Safety; Push To Talk (PTT); Quality of Experience (QoE); Quality of Service (QoS); Repeater; Trunked Mode; Ultra High Frequency (UHF); Very High Frequency (VHF)."]},{"identifier":"5ABDF1A9A29D4CC4E0531A57068118751866","accessLevel":"public","contactPoint":{"hasEmail":"mailto:kenneth.kimble@nist.gov","fn":"Kenny Kimble"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/5ABDF1A9A29D4CC4E0531A57068118751866","description":"NIST is developing metrics and test methods to benchmark the performance of robotic systems when performing manufacturing tasks. The ability to perform simple insertions is critical for robotic systems in manufacturing. A simple peg-in-hole test was designed to measure a robotic system's capability for performing these simple insertions. The dataset captures the performance metrics of a robotic system outfitted with a robotic hand and a robotic gripper to study the effect of next-generation robotic hand technology versus conventional parallel gripper technologies.","language":["en"],"title":"Performance data of a robotic system with a robotic hand and a robotic gripper completing a peg-in-hole assembly task","distribution":[{"downloadURL":"https://www.nist.gov/file/374826","format":"Compressed ZIP file containing CSV data files.","description":"Peg-in-hole performance data of a robotic system with two different end-effectors.","mediaType":"text/csv","title":"Peg hole performance data"},{"accessURL":"https://doi.org/10.18434/M3GQ1K","title":"DOI Access for 'Performance data of a robotic system with a robotic hand and a robotic gripper completing a peg-in-hole assembly task'"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-10-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Robotics in manufacturing"],"keyword":["manufacturing","robotics","robotic hands","assembly"]},{"identifier":"5BC2E596EDC621A9E0531A57068194921868","accessLevel":"public","contactPoint":{"hasEmail":"mailto:richard.candell@nist.gov","fn":"Rick Candell Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/5BC2E596EDC621A9E0531A57068194921868","description":"This software provides a framework to generate events with both application payload identification and timestamps.  Events information is logged at each producer and consumer.  The logs can be used to derive latency and reliability metrics for cyber-physical systems experiments in which wireless communication is used for messaging.","language":["en"],"title":"Intel Edison wireless latency and reliability computing code","distribution":[{"downloadURL":"https://github.com/usnistgov/Intel-Edison-PS.git","format":"This is a github repository primarily written in javascript.","description":"A software repository where operational computer code, utility scripts, and analysis code are stored.","mediaType":"text/javascript","title":"Embedded Subscribe/Publish Networking Software for the Intel Edison Platform"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1868/Intel-Edison-PS-master.zip","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1868/Intel-Edison-PS-master.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1868/Intel-Edison-PS-master.zip","mediaType":"application/zip"},{"accessURL":"https://doi.org/10.18434/1422517","format":"text/html","description":"DOI Access to Intel Edison wireless latency and reliability computing code","title":"DOI Access to Intel Edison wireless latency and reliability computing code"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-10-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Factory communications","Advanced Communications:Wireless (RF)","Information Technology:Software research"],"keyword":["IoT","Wireless","RF","Manufacturing","Node.js"]},{"identifier":"5BD6911D381AB2E3E0531A57068151FA1869","accessLevel":"public","contactPoint":{"hasEmail":"mailto:kenneth.cole@nist.gov","fn":"Kenneth Cole"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Droplet digital PCR (ddPCR) is being advocated as a reference method to measure rare genomic targets.  It has consistently been proven to be more sensitive and direct at discerning copy numbers of DNA than other quantitative methods.  However, one of the largest obstacles to measuring microRNA (miRNA) using ddPCR is that reverse transcription efficiency depends upon the target, meaning small RNA nucleotide composition directly effects primer specificity in a manner that prevents traditional quantitation optimization strategies.  Additionally, the use of reagents that are optimized for miRNA measurements using quantitative real time PCR (qRT PCR) appear to either cause false positive or false negative detection of certain targets when used with traditional ddPCR quantification methods.  False readings are often related to using inadequate enzymes, primers and probes.  Given that two step miRNA quantification using ddPCR relies solely on reverse transcription and uses proprietary reagents previously optimized only for qRT PCR, these barriers are substantial.  Therefore, here we outline essential controls, optimization techniques, and an efficacy model to improve the quality of ddPCR miRNA measurements.  We have applied two step principles used for miRNA qRT PCR measurements and leveraged the use of synthetic miRNA targets to evaluate ddPCR following cDNA synthesis with four different commercial kits.  We have identified inefficiencies and limitations as well as proposed ways to circumvent identified obstacles.  Lastly, we show that we can apply these criteria to a model system to confidently quantify miRNA copy number.  Our measurement technique is a novel way to quantify specific miRNA copy number in a single sample, without using standard curves for individual experiments.  Our methodology can be used for validation and control measurements, as well as a diagnostic technique that allows scientists, technicians, clinicians, and regulators to base miRNA measures on a single unit of measurement rather than a ratio of values.","language":["en"],"title":"Steps to achieve quantitative measurements of microRNA using two-step droplet digital PCR","distribution":[{"downloadURL":"https://s3.amazonaws.com/nist-midas/1869/ddPCR%20Raw%20Data_Stein%20et%20al%20PLOSOne%202017.zip","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32Q1V"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-10-19","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience: Genomic measurements"],"keyword":["Biotechnology","microRNAs","Biological Measurements","Genomics","Research and Analysis Methods","quantitative analysis"]},{"identifier":"5BD6C3C9109D1355E0531A570681CCEF1870","accessLevel":"public","references":["https://doi.org/10.1007/978-3-540-24750-0_3","https://doi.org/10.1145/507533.507536","https://json-schema.org/","https://www.w3.org/TR/vocab-dcat-2/","https://resources.data.gov/resources/dcat-us/"],"contactPoint":{"hasEmail":"mailto:raymond.plante@nist.gov","fn":"Raymond Plante"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/5BD6C3C9109D1355E0531A570681CCEF1870","description":"The NIST Extensible Resource Data Model (NERDm) is a set of schemas for encoding in JSON format metadatathat describe digital resources. The variety of digital resources it can describe includes not onlydigital data sets and collections, but also software, digital services, web sites and portals, anddigital twins. It was created to serve as the internal metadata format used by the NIST Public DataRepository and Science Portal to drive rich presentations on the web and to enable discovery; however, itwas also designed to enable programmatic access to resources and their metadata by external users.Interoperability was also a key design aim: the schemas are defined using the JSON Schema standard,metadata are encoded as JSON-LD, and their semantics are tied to community ontologies, with an emphasison DCAT and the US federal Project Open Data (POD) models. Finally, extensibility is also central to itsdesign: the schemas are composed of a central core schema and various extension schemas. New extensionsto support richer metadata concepts can be added over time without breaking existing applications.Validation is central to NERDm's extensibility model. Consuming applications should be able to choosewhich metadata extensions they care to support and ignore terms and extensions they don't support.Furthermore, they should not fail when a NERDm document leverages extensions they don't recognize, evenwhen on-the-fly validation is required. To support this flexibility, the NERDm framework allowsdocuments to declare what extensions are being used and where. We have developed an optional extensionto the standard JSON Schema validation (see ejsonschema below) to support flexible validation: while astandard JSON Schema validater can validate a NERDm document against the NERDm core schema, our extensionwill validate a NERDm document against any recognized extensions and ignore those that are notrecognized.The NERDm data model is based around the concept of resource, semantically equivalent to a schema.orgResource, and as in schema.org, there can be different types of resources, such as data sets andsoftware. A NERDm document indicates what types the resource qualifies as via the JSON-LD \"@type\"property. All NERDm Resources are described by metadata terms from the core NERDm schema; however,different resource types can be described by additional metadata properties (often drawing on particularNERDm extension schemas). A Resource contains Components of various types (includingDCAT-defined Distributions) that are considered part of the Resource; specifically, these can include downloadable data files, hierachical datacollecitons, links to web sites (like software repositories), software tools, or other NERDm Resources.Through the NERDm extension system, domain-specific metadata can be included at either the resource orcomponent level. The direct semantic and syntactic connections to the DCAT, POD, and schema.org schemasis intended to ensure unambiguous conversion of NERDm documents into those schemas.As of this writing, the Core NERDm schema and its framework stands at version 0.7 and is compatible withthe \"draft-04\" version of JSON Schema. Version 1.0 is projected to be released in 2025. In thatrelease, the NERDm schemas will be updated to the \"draft2020\" version of JSON Schema. Other improvementswill include stronger support for RDF and the Linked Data Platform through its support of JSON-LD.","language":["en"],"title":"The NIST Extensible Resource Data Model (NERDm):  JSON schemas for rich description of data resources","distribution":[{"accessURL":"https://github.com/usnistgov/oar-metadata/tree/integration/model","format":"GitHub folder","description":"This directory contains the latest (and previous) versions of the core NERDm Schema and various extensions.  All files with names of the form, \"*-schema*.json\" are JSON Schema definition files; those that do not include a version in the file name represent the latest versions.  The latest version of the core schema is called \"nerdm-schema.json\", and schemas with names of the form, \"nerdm-[ext]-schema.json\", contain extension schemas.  All NERDm schemas here are documented internally, including semantic definitions of all terms.","title":"The NERDm JSON Schema Files"},{"accessURL":"https://github.com/usnistgov/ejsonschema","format":"GitHub repository","description":"This software repository provides Python software that extends the community software library, python-jsonschema (https://github.com/python-jsonschema/jsonschema) to support NERDm's extension framework.  Use the scripts/validate script to validate NERDm documents on the command line.  (Type \"validate -h\" for more information.)","title":"ejsonschema: Software for Validating JSON supporting extension schemas"},{"accessURL":"https://github.com/usnistgov/oar-metadata/tree/integration/model/examples","format":"GitHub folder","description":"This folder contains example NERDm documents that illustrate the NERDm data model and use of extension schemas.  These all can be validated using the ejsonschema validate script.","title":"Example NERDm Documents"},{"accessURL":"https://github.com/usnistgov/oar-metadata","format":"GitHub repository","description":"This software repository includes a Python package, \"nistoar.nerdm\", that aids in creating and handling NERDm documents.  In particular, it includes converters that convert NERDm instances into other formats (like POD, schema.org, and DCAT).  It can also transform NERDm documents conforming to earlier versions of the schemas to that of the latest versions.","title":"NERDm Support Software"},{"accessURL":"https://github.com/usnistgov/oar-metadata/blob/integration/model/README-NERDm.md","format":"text file","description":"A general overview of the NERDm schema design and files","title":"README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-09-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Data and informatics"],"conformsTo":"http://json-schema.org/draft-04/schema","issued":"2025-02-13","keyword":["metadata","schema","data management","data repositories","JSON-LD","FAIR"]},{"identifier":"5BD81D0B67AA9AFAE0531A57068100201871","accessLevel":"public","references":["https://www.nature.com/articles/sdata2016125","https://www.nature.com/articles/s41598-017-05402-0"],"contactPoint":{"hasEmail":"mailto:kamal.choudhary@nist.gov","fn":"Kamal Choudhary"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.ctcms.nist.gov/~knc6/JARVIS.html","description":"JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments.\n\nThe Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials.\n\nThe Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.\n\nThe Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT data. Some of the ML-predictions focus on energetics, heat of formation, GGA/METAGGA bandgaps, bulk and shear modulus.","language":["en"],"title":"JARVIS: Joint Automated Repository for Various Integrated Simulations","distribution":[{"accessURL":"https://www.ctcms.nist.gov/~knc6/JVASP.html","format":"text/html","title":"JARVIS for DFT"},{"accessURL":"https://www.ctcms.nist.gov/~knc6/periodic.html","format":"text/html","title":"JARVIS for Force-fields"},{"accessURL":"https://doi.org/10.18434/M3HQ1W"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-10-18","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics: Condensed matter","Materials : Modeling and computational material science","Electronics: Thin-film electronics","Electronics: Optoelectronics","Chemistry: Molecular characterization","Chemistry: Theoretical chemistry and modeling","Chemistry: Chemical thermodynamics and chemical properties","Electronics: Semiconductors","Materials : Materials characterization","Physics: Atomic, molecular, and quantum","Physics: Optical physics"],"keyword":["Density functional theory","classical interatomic potential","force-field","python","JARVIS","MGI","MDCS","RESTAPI","automation"]},{"identifier":"5C37CA89A8E4E6C2E0531A5706818FF61872","accessLevel":"public","contactPoint":{"hasEmail":"mailto:francesca.tavazza@nist.gov","fn":"Francesca Tavazza"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/5C37CA89A8E4E6C2E0531A5706818FF61872","description":"All the data in this project are generated running Density Functional (DFT) simulation either on NIST computer clusters of through our collaborators at University of Florida. The final data consist in relaxed atomic structures and corresponding energetic, mechanical, electronic and phonon properties. The metadata consist in the initial and relaxed structures, the description of the code, of the k points and of all the parameters used in running the simulations (energy or real space cutoff, type of minimization, occupation, etc.).","language":["en"],"title":"DFT Benchmarking","distribution":[{"accessURL":"https://doi.org/10.18434/M3ND5J"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1872/Precs.csv","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1872/Precs.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1872/Pade.csv","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1872/Pade.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1872/MIDAS-README-Tavazza.docx","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1872/MIDAS-README-Tavazza.docx.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1872/DFT-Benchmarking-data.zip.sha256","mediaType":"text/plain","title":"Hash file for DFT-Benchmarking-data.zip"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1872/DFT-Benchmarking-data.zip","description":"Three files: Pade.csv, Precs.csv, and a README.txt file to describe the data and project.","mediaType":"text/plain","title":"Zipped DFT project files"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-10-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Materials characterization"],"keyword":["Density Functional Theory","DFT","Uncertainty","UQ","exchange-correlation comparison","k points","energy precision","energy","elastic constants","energy gap","bulk modulus"]},{"identifier":"5CF421B31BE80304E0531A57068197E41874","accessLevel":"public","references":["https://www.nist.gov/system/files/documents/2018/02/14/task_2_3_report_-_clt_compartment_fire_tests.pdf"],"contactPoint":{"hasEmail":"mailto:matthew.hoehler@nist.gov","fn":"Matthew Hoehler"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/5CF421B31BE80304E0531A57068197E41874","description":"This study quantifies the contribution of CLT building elements to compartment fires and characterizes the effect of gypsum board on delaying or preventing involvement of the CLT in the fire under varied ventilation conditions. Six compartments (9.1 m long x 4.6 m wide x 2.7 m high) were assembled out of prefabricated CLT panels.  Each panel was factory assembled using five plies of 35 mm thick lumber for a total thickness of 175 mm. A polyurethane adhesive was used to hold the plies together. The compartment had a rough opening in Wall W2 (front) of 1.8 m wide x 2.0 m high in four tests and 3.6 m wide x 2.0 m high in two tests. The inside of the compartments was fully or partially lined using multiple layers of 15.9-mm thick Type X gypsum board. Real residential contents and furnishings were used to provide a fire load density of 550 MJ/m2. The fire tests were conducted without sprinklers and without firefighting intervention.The dataset includes Excel files with the data from each test, as well as videos and 360 degree images.","language":["en"],"title":"Data from Fire Safety Challenges of Tall Wood Buildings - Phase 2: Task 3 - Cross Laminated Timber Compartment Fire Tests","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1422512","format":"text/html","description":"DOI Access for Fire Safety Challenges of Tall Wood Buildings - Phase 2: Task 3 - Cross Laminated Timber Compartment Fire Tests","title":"DOI Access for Fire Safety Challenges of Tall Wood Buildings - Phase 2: Task 3"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-02-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Buildings and Construction:Structural engineering","Fire:Structural fire resistance","Buildings and Construction:Building materials"],"keyword":["cross-laminated timber; CLT; fire; compartment; charring; ventilation"]},{"identifier":"5D00D811BC74B453E0531A570681BC861875","accessLevel":"public","contactPoint":{"hasEmail":"mailto:harold.hatch@nist.gov","fn":"Harold Hatch"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pages.nist.gov/feasst","description":"The Free Energy and Advanced Sampling Simulation Toolkit (FEASST) is a free, open-source, modular program to conduct molecular and particle-based simulations with flat-histogram Monte Carlo and molecular dynamics methods. It is a software written in C++ and python which is made publicly available to aid in reproducibility. It is also provided as a service to the scientific community in which there are few , if any, Monte Carlo programs that support flat histogram methods and advanced sampling algorithms. This software is expected to be updated frequently with new methods.","language":["en"],"title":"FEASST: Free Energy and Advanced Sampling Simulation Toolkit","distribution":[{"accessURL":"https://github.com/usnistgov/feasst","format":"A GitHub repository","description":"A git repository containing the C++ and python source code for the FEASST software. This software can be downloaded from GitHub.","title":"feasst"},{"accessURL":"https://doi.org/10.18434/M3S095"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-11-02","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics: Thermodynamics","Mathematics and Statistics: Numerical methods and software","Materials : Modeling and computational material science","Chemistry: Theoretical chemistry and modeling","Chemistry: Chemical thermodynamics and chemical properties","Manufacturing: Biomanufacturing","Mathematics and Statistics: Modeling and simulation research","Nanotechnology: Nanomaterials","Physics: Biological physics"],"keyword":["Monte Carlo","molecular simulation","free energy","flat histogram","C++","python"]},{"identifier":"5D7F9397CCFDD7ACE0531A570681917D1876","accessLevel":"public","references":["https://dx.doi.org/10.1016/j.commatsci.2016.09.022"],"contactPoint":{"hasEmail":"mailto:daniel.wheeler@nist.gov","fn":"Daniel Wheeler"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pages.nist.gov/chimad-phase-field/","description":"CHiMaD Phase Field - Integrating the phase field community\n\n\n\nIn January 2015 a group of phase field theorists and code developers met at Northwestern University to discuss ways for the community to improve code collaboration efforts. 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Typical CT data sets are up to 1 gigabyte in size.  Medical CT yields data in DICOM format.","language":["en"],"title":"CT data for \"Intrinsic dimensionality of Hounsfield unit measurements: Implications for a link to the SI\"","distribution":[{"downloadURL":"https://s3.amazonaws.com/nist-midas/1878/Compounds.zip","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1878/Compounds.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1878/README.txt","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1878/README.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1878/ctBaltimore20170914_02.jpg","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1878/ctBaltimore20170914_02.jpg.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1878/ctBaltimoreB20170914.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1878/ctBaltimoreB20170914.csv","mediaType":"text/plain","title":"Powder masses"},{"accessURL":"https://doi.org/10.18434/M3M956"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-11-09","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Health: Medical imaging"],"keyword":["x-ray computed tomography ; medical phantom ; Hounsfield unit ; volume ; shape"]},{"identifier":"5EF93144BF795DA6E0531A570681F0951881","accessLevel":"public","references":["https://doi.org/10.1038/sdata.2018.82","http://corr.readthedocs.io/"],"contactPoint":{"hasEmail":"mailto:faical.congo@nist.gov","fn":"Faical Congo"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/5EF93144BF795DA6E0531A570681F0951881","description":"Cloud of Reproducible Records (CoRR) is a web platform to support computation version control tools such as Sumatra, Reprozip, CDE, and NoWorkflow. 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The software is written in our source languages. The current trend in computer architecture is for increasingly parallel computation while the clock frequency stagnates. The increase in computing speed is achieved by dividing a process into several threads which are executed in parallel on multiple processors, processors with multiple cores, cores that are able to handle multiple threads (hyper-threading), graphical processing units (GPU), or co-processors. In order to take advantage of these new architectures, algorithms that have historically been implemented for serial evaluation need to be refactored for parallelization. In this work, a native multithreading framework in C++11 for scientific and engineering model development is presented. The motivation for NISTfit is to develop a modern C++11-based library for this problem that is: ?          Cross-platform: NISTfit has only very minimal header-only dependencies (Eigen and ThreadPool), and builds reliably on all major architectures; a CMake build file is provided. ?          Easy to use: There are a multitude of similar libraries for model fitting (e.g., MINPACK 1, levmar2, Eigen?s Levenberg-Marquardt module 3, to name but a few) that a) are based on archaic FORTRAN/C/C++ constructs, b) require significant boilerplate to solve simple problems, or c) have difficult-to-build dependencies. It is the opinion of the authors of NISTfit that NISTfit strikes a good balance of power and ease-of-use for simple fitting problems. The code utilizes modern C++11 constructs and will build on any C++11 compliant compiler. ?          Parallelizable: The future is parallel, and NISTfit is able to achieve near theoretical speedup as more cores are made available to the fitting for sufficiently expensive models.","language":["en"],"title":"NISTfit","distribution":[{"downloadURL":"https://s3.amazonaws.com/nist-midas/1883/NISTfit.zip","mediaType":"text/plain"},{"downloadURL":"https://s3.amazonaws.com/nist-midas/1883/NISTfit.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M39W9S"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-12-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Numerical methods and software"],"keyword":["Model fitting","thermodynamics","optimization"]},{"identifier":"62447774F318FAC0E0531A57068119F81884","accessLevel":"public","references":["https://www.nist.gov/publications/mobile-manipulator-performance-measurement-data","https://doi.org/10.6028/NIST.TN.1965"],"contactPoint":{"hasEmail":"mailto:ya-shian.li-baboud@nist.gov","fn":"Ya-Shian Li-Baboud"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/62447774F318FAC0E0531A57068119F81884","description":"An advanced approach to flexible manufacturing is to move robotic manipulators (also referred to as industrial arms), using an AGV or mobile robot, between workstations. This integrated system is referred to as a mobile manipulator.  Prior to industrial acceptance and standards development for mobile manipulators, users of these new systems will expect manufacturers to provide real performance data to guide their procurement and assure suitability for given application tasks.   A test method that uses an artifact, called the Reconfigurable Mobile Manipulator Artifact (RMMA), is described in [Bostelman RV, Li-Baboud Y, Legowik S, Hong TH, Foufou S., \"Mobile Manipulator Performance Measurement Data\". 2017 Jun 27] and compared to an optical tracking system that was used as ground truth for the RMMA and mobile manipulator.  Measurement data of an AGV, an onboard robot arm, and an optical tracking system were recorded and are described in the paper and are available for download using the link available in this record. The data needed to make these three measurements was collected during two tests; both tests have corresponding timestamps relative to global positioning system (GPS) time, where the computer clocks are synchronized using the Network Time Protocol. 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This dataset provides the files to allow the participants in the study (modelers) to understand the experiments and measurements and the files to facilitate the model development. 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The benefits include cost savings and damage loss avoidance because enhancing resilience on a community scale creates value, including co-benefits, even if a hazard event does not strike.EDGe$ uses the approach seen in The NIST \"Community Resilience Economic Decision Guide for Buildings and Infrastructure Systems\" https://doi.org/10.6028/NIST.SP.1197","language":["en"],"title":"EDGe$ (Economic Decision Guide Software) Tool  (Beta)","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1423120","format":"text/html","description":"DOI Access for EDGe$ (Economic Decision Guide Software) Tool","title":"DOI Access for EDGe$ (Economic Decision Guide Software) Tool"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-01-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Resilience:Community resilience"],"keyword":["Benefit-cost analysis","buildings","communities","constructed facilities","resilience","economic analysis","economic decision tool","life-cycle costing","resilience dividend","software"]},{"identifier":"6576C69624E31C71E053245706815C531891","accessLevel":"public","contactPoint":{"hasEmail":"mailto:scott.glancy@nist.gov","fn":"Scott Glancy"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/state_meas_tomo/","description":"This software package performs joint quantum state and measurement tomography. The software is provided as Python source code. A description of the algorithms used is in \"Joint Quantum State and Measurement Tomography with Incomplete Measurements\" https://arxiv.org/abs/1803.08245Included are three example scripts that simulate data for one or two trapped ion systems with either symmetric or asymmetric measurements:- analysis_scripts/paper_simulations.py: produces all data and histograms shown in related publication with seed = 0. Also provides an example of symmetric measurements.- analysis_scripts/asym_simulations.py: produces simulated data from asymmetric measurements by similar methods as in previous script.- analysis_scripts/load_tutorial/load_simulations.py: gives an example of loading data with asymmetric measurements. 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However, the suitability of this camera to a specific application depends on resolution requirements.  This data set was collected to assess the image resolution of light field cameras during acquisition configurations similar to the ones encountered by forensic photographers at crime scenes. Examples of 3D crime scene objects include tire tread and shoe imprints in substances like mud or snow. More information about the acquisition is provided at https://isg.nist.gov/deepzoomweb/resources/csmet/pages/lytro_camera/lytro_camera.html. 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The original dataset, QFlow lite, consists of 1 001 idealized simulated measurements with gate configurations sampling over different realizations of the same type of device. Each sample data is stored as a 100 x 100-pixel map from plunger gate voltages to (i) current through the device at infinitesimal bias, (ii) output of the charge sensor evaluated as the Coulomb potential at the sensor location - the experimentally relevant parameters that can be measured, (iii) information about the number of charges on each dot (with a default value 0 for short circuit and a barrier), and (iv) a label determining the state of the device, distinguishing between a single dot, a double dot, a short circuit, and a barrier state. The expanded dataset, QFlow 2.0, consists of 1599 idealized simulated measurements stored as roughly 250 x 250-pixel maps from plunger gate voltages to (i) output of the charge sensor, (ii) net charge on each dot, and (iii) a label determining the state of the device, distinguishing between a left, central, and right single QD, a double QD, and a barrier or short circuit (no QD) state. In addition, the QFlow 2.0 dataset includes two sets of noisy simulated measurements, one with the noise level varied around 1.5 times the optimized noise level and the other one with the noise level ranging from 0 to 7 times the optimized noise level. See the \"Project description\" and \"Data structure\" documents for additional information about these datasets.Acknowledgments: This research is sponsored in part by the Army Research Office (ARO), through Grant No. W911NF-17-1-0274. The development and maintenance of the growth facilities used for fabricating samples were supported by the Department of Energy, through Grant No. DE-FG02-03ER46028. We acknowledge the use of clean room facilities supported by The National Science Foundation (NSF) through the UW-Madison MRSEC (DMR-1720415) and electron beam lithography equipment acquired with the support of the NSF MRI program (DMR-1625348). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ARO or the U.S. Government.  The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright noted herein. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.","language":["en"],"title":"QFlow 2.0: Quantum dot data for machine learning","distribution":[{"downloadURL":"https://s3.amazonaws.com/nist-midas/1894/license.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/66492819760D3FF6E05324570681BA721894/data_qflow_v2.zip","format":"zip","description":"This is the expanded dataset consisting of both noiseless and noisy simulated measurements.","mediaType":"application/zip","title":"QFlow 2.0"},{"accessURL":"https://doi.org/10.18434/T4/1423788","format":"text/html","description":"DOI access for \"Quantum dot data for machine learning\"","title":"DOI access for \"Quantum dot data for machine learning\""},{"downloadURL":"https://data.nist.gov/od/ds/66492819760D3FF6E05324570681BA721894/project_description.pdf","mediaType":"application/pdf","title":"project_description"},{"downloadURL":"https://data.nist.gov/od/ds/66492819760D3FF6E05324570681BA721894/dataset_structure.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/66492819760D3FF6E05324570681BA721894/data_qflow_lite.zip","mediaType":"application/x-zip-compressed"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-02-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Atomic, molecular, and quantum"],"keyword":["machine learning","quantum dots","simulated data"]},{"identifier":"66AF4AFEA96764C4E0532457068100261895","accessLevel":"public","contactPoint":{"hasEmail":"mailto:aron.newman@nist.gov","fn":"Aron Newman"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/66AF4AFEA96764C4E0532457068100261895","description":"These data are supplemental data that include digital holographic microscopy measurements of nanoscale surface topography changes occurring during the dissolution of beta-dicalcium silicate in water and in water-ethanol mixtures.   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The first singular vector is dominant and images of the coefficients of the first singular vector at each voxel look are similar to any of the single-energy reconstructions. Images of the coefficients of the second singular vector by itself appear to be noise. However, by averaging the reconstructed voxels in each of several narrow bands of radii, we can obtain values of the second singular vector at each radius. In the core region, where we expect the germanium doping to go from a peak value at the fiber center to zero at the core-cladding boundary, we find that a plot of the two coefficients of the singular vectors forms a line in the two-dimensional space consistent with the dopant decreasing linearly with radial distance from the core center. The coating, made of a polymer rather than silica, is not on this line indicating that the two-dimensional results are sensitive not only to the density but also to the elemental composition. A stack of reconstructions are given here as tiff files of individual slices. Each zip file corresponds to a tilt series at a given tube voltage, given in the file name.  The power is also given in the file name. (For example, file \u201c30kV-2W.zip\u201d was tube voltage at 30kV, power 2W.) The power was varied so that the signal-to-noise was approximately equal for the various reconstructions. The experiment is described in:  ZH Levine, AP Peskin, EJ Garboczi, and AD Holmgren, Multi-Energy X-Ray Tomography of an Optical Fiber: The Role of Spatial Averaging, Microscopy and Microanalysis 25 (1) 70-76 (2019). https://doi.org/10.1017/S1431927618016136","language":["en"],"title":"GI625 optical fiber data imaged on a Zeiss Versa XRM-500 microCT  at 12 tube voltages","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/60kV-5W.zip","mediaType":"application/zip","title":"Tube voltage at 60kV, power 5W"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/70kV-6W.zip","mediaType":"application/zip","title":"Tube voltage at 70kV, power 6W"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/80kV-7W.zip","mediaType":"application/zip","title":"Tube voltage at 80kV, power 7W"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/90kV-8W.zip","mediaType":"application/zip","title":"Tube voltage at 90kV, power 8W"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/100kV-9W.zip","mediaType":"application/zip","title":"Tube voltage at 100kV, power 9W"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/140kV-10W.zip","mediaType":"application/zip","title":"Tube voltage at 140kV, power 10W"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/120kV-10W.zip","mediaType":"application/zip","title":"Tube voltage at 120kV, power 10W"},{"accessURL":"https://doi.org/10.18434/M31908","title":"DOI Access for GI625 optical fiber data imaged on a Zeiss Versa XRM-500 microCT  at 12 tube voltages"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/30kV-2W.zip","mediaType":"application/zip","title":"Tube voltage at 30kV, power 2W"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/35kV-2.5W.zip","mediaType":"application/zip","title":"Tube voltage at 35kV, power 2.5W"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/40kV-3W.zip","mediaType":"application/zip","title":"Tube voltage at 40kV, power 3W"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/45kV-3.5W.zip","mediaType":"application/zip","title":"Tube voltage at 45kV, power 3.5W"},{"downloadURL":"https://data.nist.gov/od/ds/6920762299183C14E053245706817F891908/50kV-4W.zip","mediaType":"application/zip","title":"Tube voltage at 50kV, power 4W"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-08-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Optical physics"],"issued":"2019-12-03","keyword":["x-ray computed tomography ; optical fiber ; panchromatic sharpening"]},{"identifier":"696FD547910012A0E0532457068160E41910","accessLevel":"public","contactPoint":{"hasEmail":"mailto:tamae.wong@nist.gov","fn":"Tamae Wong"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/696FD547910012A0E0532457068160E41910","description":"Hestia Project quantifies, simulates and visualizes greenhouse gases such as carbon dioxide emitted in urban regions.  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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Forensics:DNA and biological evidence"],"keyword":["STR","SNP","forensic","sequence","population","allele frequency","haplotype"]},{"identifier":"6A11BB04A45032D6E053245706817FC91915","accessLevel":"public","references":["http://dx.doi.org/10.1016/j.ijhydene.2012.03.074"],"contactPoint":{"hasEmail":"mailto:jiann.yang@nist.gov","fn":"Jiann Yang"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/el/fire-research-division-73300/dispersion-and-burning-behavior-hydrogen-released-full-scale","description":"The supplemental materials reported here provide standard high-definition (HD), high-speed, and infrared videos of the 13 full-scale hydrogen dispersion and burning experiments.  The videos show different burning dynamics of various hydrogen/air mixtures in the presence and absence of vehicles parked inside the garage.","language":["en"],"title":"Dispersion and Burning Behavior of Hydrogen Released in a Full-Scale Residential Garage in the Presence and Absence of Conventional Automobiles Supplemental Video Materials","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1500893%20","format":"text/url","description":"DOI Access to Dispersion and Burning Behavior of Hydrogen Released in a Full-Scale Residential Garage in the Presence and Absence of Conventional Automobiles Supplemental Video Materials","title":"DOI Access to Dispersion and Burning Behavior of Hydrogen Released in a Full-Scale Residential Garage in the Presence and Absence of Conventional Automobiles Supplemental Video Materials"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-04-17","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://www.nist.gov/el/fire-research-division-73300/dispersion-and-burning-behavior-hydrogen-released-full-scale","accrualPeriodicity":"irregular","theme":["Fire:Fire risk reduction","Fire:Fire detection","Fire:Fire dynamics and science","Fire:Fire fighting"],"keyword":["fire safety","fuel-cell","garage","hydrogen"]},{"identifier":"6A4A339C5C091C09E053245706817F211916","accessLevel":"public","contactPoint":{"hasEmail":"mailto:bryan.barnes@nist.gov","fn":"Bryan Barnes"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/6A4A339C5C091C09E053245706817F211916","description":"An in-house developed finite-difference time-domain (FDTD) code has been used to simulate certain patterned defects as found in the semiconductor industry.  Intrinsic to FDTD is the establishment of a simulation domain, a 3-D matrix of some arbitrary size (X, Y, Z) comprised of smaller cells (in our case, cubic with side length x), with each cell indexed to a material (including the vacuum) to form the geometry.  Although the specific text files used as inputs to the in-house FDTD engine are provided, such files are likely incompatible with external FDTD solutions for the replication of our results.  Therefore, entire 3-D matrices for our simulations have been reduced to single-vector, readable ASCII data files indexing the geometry and materials of the system, accompanied by text files that supply the optical constants used in the simulation as well as cross-sectional images that allow verification by others of their reconstruction of the 3-D matrix from the supplied 1-D ASCII data files.","language":["en"],"title":"Geometries and material properties for simulating semiconductor patterned bridge defects using the finite-difference time-domain (FDTD) method","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/6A4A339C5C091C09E053245706817F211916/README.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6A4A339C5C091C09E053245706817F211916/README.txt.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1500937","format":"text/html","description":"DOI Access to Geometries and material properties for simulating semiconductor patterned bridge defects using the finite-difference time-domain (FDTD) method","title":"DOI Access to Geometries and material properties for simulating semiconductor patterned bridge defects using the finite-difference time-domain (FDTD) method"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-04-20","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Metrology:Dimensional metrology","Manufacturing:Process measurement and control","Nanotechnology:Nanoelectronics"],"keyword":["finite-difference time-domain","FDTD","electromagnetic simulation","pattered defect inspection","defects","simulation input"]},{"identifier":"6A4D752E493C3B19E053245706819BFD1917","accessLevel":"public","references":["https://doi.org/10.1016/j.atmosenv.2018.11.013","https://doi.org/10.5194/acp-17-8313-2017"],"contactPoint":{"hasEmail":"mailto:anna.karion@nist.gov","fn":"Anna Karion"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/6A4D752E493C3B19E053245706819BFD1917","description":"Dataset accompanying publication:Martin, C. R., Zeng, N., Karion, A., Mueller, K., Ghosh, S., Lopez-Coto, I., Gurney, K. R., Oda, T., Prasad, K., Liu, Y., Dickerson, R. R., and Whetstone, J.: Investigating sources of variability and error in simulations of carbon dioxide in an urban region, Atmos. Environ., https://doi.org/10.1016/j.atmosenv.2018.11.013, 2018.Data files presented here include both carbon dioxide (CO2) mole fraction observations from NIST-Earth Networks tower stations in the region surrounding Washington, D.C. and Baltimore, MD and modeled mole fractions for these same sites.  Please refer to the publication above for all details, including locations of sites, and abbreviations of models and site codes.  Observations from SNP are not included, as they can be obtained from NOAA/ESRL.Model Output Files:All files are comma-delimited text files.  The filename indicates the site (three letter code: SNP, ARL, NDC, HAL), followed by the inlet height above ground (in meters), followed by the name of the inventory (Vulcan, ODIAC, ODIACFIX, EDGAR, FFDAS, VEGAS), or Back, indicating the incoming CO2 mole fraction background.  The total modeled CO2 mole fractions, for example, for FFDAS, are the sum of Back, VEGAS, and FFDAS tracers.Example: HAL_29_FFDAS.csv contains the CO2 mole fraction contribution at HAL at 29m from the FFDAS inventory.  The sum shown in the paper is HAL_29_FFDAS + HAL_29_Back + HAL_29_VEGAS.Model output files contain two columns:The date-time stamp (UTC), and the CO2 mole fraction in micromoles of CO2 per mole of dry air, or ppm.Observation Files:CO2 Measurements are reported on the WMO X2007 (CO2) scales. Refer to Verhulst et al, 2017 for measurement and calibration details.Verhulst, K. R., A. Karion, J. Kim, P. K. Salameh, R. F. Keeling, S. Newman, J. Miller, C. Sloop, T. Pongetti, P. Rao, C. Wong, F. M. Hopkins, V. Yadav, R. F. Weiss, R. M. Duren and C. E. Miller (2017), Carbon dioxide and methane measurements from the Los Angeles Megacity Carbon Project Part 1: calibration, urban enhancements, and uncertainty estimates, Atmos. Chem. Phys., 17(13), 8313-8341, doi:10.5194/acp-17-8313-2017.Each file name is encoded with the site, year, revision number and date, as follows:For all sites, all file names follow this naming scheme:[site]-[year]-[gas]-[measurement height]-[averaging-interval]-[revision date].csvExample:  ARL-2016-co2-100m-1-hour-20160504.csvColumn Headers (with description) for observation CO2 files:datetime_UTC (date, dd-mm-yyyy HH:MM:SS)dec_year_UTC (decimal year)yyyy (year)mm (month)dd (day)HH (hour, UTC)co2_ppm (dry air mole fraction, ppm, averaged over the hour following the time stamp)co2_SD (standard deviation of air data in 1 hour)co2_n_minutes (number of 1-minute values in each 1-hour average)co2_min (ppm, minimum 1-min value in each 1-hour)co2_max (ppm, maximum 1-min value in each 1-hour)co2_uncertainty (ppm, 1-sigma measurement uncertainty)co2_flags (data quality flag: \"..P\" indicates preliminary data, which might change if a final calibration is applied at a later date)","language":["en"],"title":"Carbon Dioxide Mole Fraction Measurements and Model Output in the Northeast Corridor: Baltimore/Washington for February 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This database contains images of the 10 rolled fingerprint impressions, the two four-finger slap impressions (finger positions 13 and 14), the two thumb slap impressions (finger positions 11 and 12) and the segmented impressions from the slap images (13,14). The database also includes the coordinates that were used to segment the impressions from the slap fingerprint images.The cards were scanned at three different resolutions: 500, 1,000, and 2,000 pixels per inch (PPI). All three resolutions were scanned in grayscale at a depth of 8 bits-per pixel.Data available as of July 2018 is Special Database 300a, in 500 ppi with PNG formatted impressions.  Data at other resolutions, in other image formats, and in other record types may be forthcoming.","language":["en"],"title":"NIST Special Database 300 Uncompressed Plain and Rolled Images from Fingerprint Cards","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1502472","format":"text/html","description":"DOI Access to NIST Special Database 300 Uncompressed Plain and Rolled Images from Fingerprint Cards","title":"DOI Access to NIST Special Database 300 Uncompressed Plain and Rolled Images from Fingerprint Cards"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Use of this data requires users agree to be bound by the following terms and conditions. The database will only be used for biometrics related research. The database will not be further distributed, published, copied, or disseminated in any way or form","modified":"2018-06-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Biometrics"],"keyword":["fingerprint","rolled","plain","segmentation","ink cards","biometrics","friction ridge"]},{"identifier":"6BB8BD4125224EBFE05324570681E8171920","accessLevel":"public","references":["https://dx.doi.org/10.1021/acs.macromol.8b00011"],"contactPoint":{"hasEmail":"mailto:tyler.martin@nist.gov","fn":"Tyler Martin"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/6BB8BD4125224EBFE05324570681E8171920","description":"Polymer Reference Interaction Site Model (PRISM) theory describes the equilibrium spatial-correlations of liquid-like polymer systems including melts, blends, solutions, block copolymers, ionomers, liquid crystal forming polymers and nanocomposites. Using PRISM theory, one can calculate thermodynamic (e.g., second virial coefficients, Flory-Huggins interaction parameters, potentials of mean force) and structural (eg., pair correlation functions, structure factors) information for these macromolecular materials. pyPRISM is a Python-based, open-source framework for conducting PRISM theory calculations. This framework aims to simplify PRISM-based studies by providing a user-friendly scripting interface for setting up and numerically solving the PRISM equations. pyPRISM also provides data structures, functions, and classes that streamline PRISM calculations, allowing pyPRISM to be extended for use in other tasks such as the coarse-graining of atomistic simulation force-fields or the modeling of experimental scattering data. The goal of this framework is to reduce the barrier to correctly and appropriately using PRISM theory and to provide a platform for rapid calculations of the structure and thermodynamics of polymeric fluids and nanocomposites.","language":["en"],"title":"pyPRISM: A Computational Tool for Liquid State Theory Calculations of Macromolecular Materials","distribution":[{"accessURL":"https://github.com/usnistgov/pyPRISM","format":"GitHub Repository","description":"Polymer Reference Interaction Site Model (PRISM) theory describes the equilibrium spatial-correlations of liquid-like polymer systems including melts, blends, solutions, block copolymers, ionomers, liquid crystal forming polymers and nanocomposites. Using PRISM theory, one can calculate thermodynamic (e.g., second virial coefficients, Flory-Huggins interaction parameters, potentials of mean force) and structural (eg., pair correlation functions, structure factors) information for these macromolecular materials. pyPRISM is a Python-based, open-source framework for conducting PRISM theory calculations. This framework aims to simplify PRISM-based studies by providing a user-friendly scripting interface for setting up and numerically solving the PRISM equations. pyPRISM also provides data structures, functions, and classes that streamline PRISM calculations, allowing pyPRISM to be extended for use in other tasks such as the coarse-graining of atomistic simulation force-fields or the modeling of experimental scattering data. The goal of this framework is to reduce the barrier to correctly and appropriately using PRISM theory and to provide a platform for rapid calculations of the structure and thermodynamics of polymeric fluids and nanocomposites.","title":"pyPRISM GitHub Repository"},{"accessURL":"http://pyprism.readthedocs.io/en/latest/","description":"Documentation for the pyPRISM Software","title":"pyPRISM Documentation"},{"accessURL":"http://pyprism.readthedocs.io/en/latest/tutorial/tutorial.html","description":"Static tutorial materials for the pyPRISM Software","title":"pyPRISM Static Tutorial"},{"accessURL":"https://mybinder.org/v2/gh/usnistgov/pyprism/master?filepath=tutorial","description":"Live, online tutorial materials for the pyPRISM Software","title":"pyPRISM Live Binder Tutorial"},{"accessURL":"https://doi.org/10.18434/T4/1500864","description":"DOI Access to pyPRISM: A Computational Tool for Liquid State Theory Calculations of Macromolecular Materials","title":"DOI Access to pyPRISM: A Computational Tool for Liquid State Theory Calculations of Macromolecular Materials"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-05-08","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Thermodynamics","Physics:Condensed matter","Chemistry:Chemical thermodynamics and chemical properties","Materials:Composites","Materials:Modeling and computational material science","Mathematics and Statistics:Numerical methods and software","Materials:Polymers","Chemistry:Theoretical chemistry and modeling"],"keyword":["polymer","theory","liquid-state theory","Python","polymer nanocomposite","polymer solution","X-ray scattering","neutron scattering","software","tool","computation"]},{"identifier":"6BC6DDA74AD3283CE053245706819E221921","accessLevel":"public","references":["http://dx.doi.org/10.6028/NIST.IR.8198r1","http://dx.doi.org/10.6028/NIST.IR.8180"],"contactPoint":{"hasEmail":"mailto:cheok@nist.gov","fn":"Geraldine Cheok"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/el/intelligent-systems-division-73500/peg-hole-data","description":"A method was developed to reduce the point-based registration error by restoring the rigid body condition (RRBC method).  Registration is the process of transforming one coordinate frame to another coordinate frame.  The coordinate frame from which points are transformed is called the working frame and the coordinate frame to which points are transformed is called the destination frame.  The RRBC method can be used to reduce the uncertainty of a hole location and thus, improve the success rate for insertion tasks.  Peg-in-hole experiments were conducted to quantify the level of improvement.\n\nHow the RRBC method works:  A grid of points is measured in two different coordinate frames - working and destination.  The basic premise of the RRBC method is that the distance between any two points should be same in the working and destination frames - a fundamental concept for rigid bodies.  However, due to systematic and/or random measurement error, the distances are not exactly the same in the two frames. Using the two sets of measured points (fiducials), the RRBC method calculates the corrections to the fiducials in the working frame so that the rigid body condition is restored.  For points that are measured only in the working frame (targets), corrections for these points are linearly interpolated from the closest corrected fiducials. \n\nData and results from three peg-in-hole experiments may be downloaded from this site.","language":["en"],"title":"Data and Results from Three Peg-in-Hole Experiments for Improving Insertion Tasks","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1500929","format":"text/url","description":"DOI Access to Data and Results from Three Peg-in-Hole Experiments for Improving Insertion Tasks","title":"DOI Access to Data and Results from Three Peg-in-Hole Experiments for Improving Insertion Tasks"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-05-08","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Robotics in manufacturing"],"keyword":["assembly","insertion task","manufacturing","peg-in-hole experiments","rigid-body","registration","registration error","target registration error."]},{"identifier":"6CBEE783B6972F21E0532457068129011925","accessLevel":"public","contactPoint":{"hasEmail":"mailto:zachary.levine@nist.gov","fn":"Zachary Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/6CBEE783B6972F21E0532457068129011925","description":"We present the data supporting \"Experimental test of the intrinsic dimensionality of Hounsfield unit measurements\" (In preparation).  In this study, we passed 34 different substances in separate vials through a computed tomography (CT) scanner at 4 different voltages.  At each voltage, we obtained 1824 images (in DICOM format) depicting a sequence of slices through the vials.  All 7296 images are provided here.  In addition, we provide a table of the substances, their masses, and their positions in the sequence.This dataset deprecates the earlier release of this data (ark:/88434/mds019bfm9).  The image and substance table data are exactly the same; however, the image data has been re-arranged to make browsing and downloading more convenient.","language":["en"],"title":"Experimental test of the intrinsic dimensionality of Hounsfield unit measurements: the CT data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0001-0100.zip","description":"A zip file of the 100 DICOM images depicting slices 1-100 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1-100 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0001-0100.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0101-0200.zip","description":"A zip file of the 100 DICOM images depicting slices 101-200 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 101-200 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0101-0200.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1201-1300.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1301-1400.zip","description":"A zip file of the 100 DICOM images depicting slices 1301-1400 at 140kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1301-1400 at 140kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1301-1400.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1401-1500.zip","description":"A zip file of the 100 DICOM images depicting slices 1401-1500 at 140kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1401-1500 at 140kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1401-1500.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1501-1600.zip","description":"A zip file of the 100 DICOM images depicting slices 1501-1600 at 140kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1501-1600 at 140kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1501-1600.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1601-1700.zip","description":"A zip file of the 100 DICOM images depicting slices 1601-1700 at 140kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1601-1700 at 140kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1601-1700.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1701-1800.zip","description":"A zip file of the 100 DICOM images depicting slices 1701-1800 at 140kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1701-1800 at 140kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1701-1800.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0201-0300.zip","description":"A zip file of the 100 DICOM images depicting slices 201-300 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 201-300 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0201-0300.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0301-0400.zip","description":"A zip file of the 100 DICOM images depicting slices 301-400 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 301-400 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0301-0400.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1801-1824.zip","description":"A zip file of the 24 DICOM images depicting slices 1801-1824 at 140kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1801-1824 at 140kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/140kV/slices1801-1824.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/README.txt","format":"plain text","description":"A summary of the data of this publication","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/README.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/ctBaltimore20170914_02.jpg.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0401-0500.zip","description":"A zip file of the 100 DICOM images depicting slices 401-500 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 401-500 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0401-0500.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0501-0600.zip","description":"A zip file of the 100 DICOM images depicting slices 501-600 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 501-600 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0501-0600.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0601-0700.zip","description":"A zip file of the 100 DICOM images depicting slices 601-700 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 601-700 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0601-0700.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0701-0800.zip","description":"A zip file of the 100 DICOM images depicting slices 701-800 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 701-800 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0701-0800.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/ctBaltimoreB20170914.csv","description":"A table listing the substances scanned, their positions, and the masses of the samples.","mediaType":"text/csv","title":"Substances Table"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/ctBaltimoreB20170914.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/slices.tsv","description":"A table describing each image slice giving the image filename, slice number, CT voltage, and slice position.","mediaType":"text/tab-separated-values","title":"Image Slices Table"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/slices.tsv.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M3M956","format":"text/html","description":"DOI Access to Experimental test of the intrinsic dimensionality of Hounsfield unit measurements","title":"DOI Access to Experimental test of the intrinsic dimensionality of Hounsfield unit measurements"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/fig4TheoryExpt.tsv","description":"The experimental values (illustrated in Fig. 4 in the paper) for the molar Hounsfield unit potency is given for both theory and experiment.","mediaType":"text/tab-separated-values","title":"Figure 4 Data"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/fig4TheoryExpt.tsv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/ctBaltimore20170914_02.jpg","description":"A photo of the arrangement of the vials in the CT scanner.  Slice 1 is at the \"foot\", closest to the viewer.","mediaType":"image/jpeg","title":"The experimental setup"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0801-0900.zip","description":"A zip file of the 100 DICOM images depicting slices 801-900 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 801-900 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0801-0900.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0901-1000.zip","description":"A zip file of the 100 DICOM images depicting slices 901-1000 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 901-1000 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices0901-1000.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1001-1100.zip","description":"A zip file of the 100 DICOM images depicting slices 1001-1100 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1001-1100 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1001-1100.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1101-1200.zip","description":"A zip file of the 100 DICOM images depicting slices 1101-1200 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1101-1200 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1101-1200.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1201-1300.zip","description":"A zip file of the 100 DICOM images depicting slices 1201-1300 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1201-1300 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1201-1300.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1301-1400.zip","description":"A zip file of the 100 DICOM images depicting slices 1301-1400 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1301-1400 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1301-1400.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1401-1500.zip","description":"A zip file of the 100 DICOM images depicting slices 1401-1500 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1401-1500 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1401-1500.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1501-1600.zip","description":"A zip file of the 100 DICOM images depicting slices 1501-1600 at 80kV.  The file also contains a brief README file, a tiled preview of all the images, and a file listing the SHA256 checksum values for each of the images.","mediaType":"application/zip","title":"Image slices 1501-1600 at 80kV"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1501-1600.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6CBEE783B6972F21E0532457068129011925/Compounds/080kV/slices1601-1700.zip","description":"A zip file of the 100 DICOM images depicting slices 1601-1700 at 80kV.  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For each stage of degradation, micrometer measurements were taken of the degradation zone, and IMU and laser-based reference data were also collected. For each stage, fifty (50) runs of IMU data were collected bidirectionally at slow (0.02 m/s), moderate (0.1 m/s), and fast (0.5 m/s) axis speeds over a travel range of 322 mm, following the method described in Vogl et al. (https://www.nist.gov/publications/diagnostics-geometric-performance-machine-tool-linear-axes). The axis position from the motor encoder was also collected simultaneously during each motion of the linear axis. Afterwards, ten (10) runs of laser-based reference data were collected bidirectionally at finite positions of travel, specifically every 1 mm between travel positions 1 mm and 321 mm.","language":["en"],"title":"Linear Axis Testbed - Rail Degradation Experiment 01","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170418.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170418.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170419.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170419.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170420.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170420.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170421.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170421.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170424.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170424.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170425.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170425.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170501.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170501.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170502.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170502.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170504.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170504.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170508.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170508.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170510.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170510.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170512.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170512.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170517.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170517.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170518.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/20170518.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1502585","format":"text/html","description":"DOI Access to Linear Axis Testbed - Rail Degradation Experiment 01","title":"DOI Access to Linear Axis Testbed - Rail Degradation Experiment 01"},{"downloadURL":"https://data.nist.gov/od/ds/6EF435207EF17114E0532457068155831934/README.pdf","mediaType":"application/pdf"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-06-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Process improvement","Manufacturing:Machining"],"keyword":["manufacturing","machining","machine tool","linear axis","error motion","degradation","accelerometer","rate gyroscope","inertial measurement unit"]},{"identifier":"6F0C74FF99CC53D5E05324570681FA761935","accessLevel":"public","references":["https://doi.org/10.1007/s40192-020-00170-8"],"contactPoint":{"hasEmail":"mailto:public-access@nist.gov","fn":"Brandon Lane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/6F0C74FF99CC53D5E05324570681FA761935","description":"These measurements were performed as part of the 2018 Additive Manufacturing Benchmark Test Series (AM-Bench). This dataset and the associated experiments are part of a continuing series of controlled benchmark tests, in conjunction with a conference series, with two initial goals, 1) to allow modelers of Additive Manufacturing processes to test their simulations against rigorous, highly controlled additive manufacturing benchmark test data, and 2) to encourage additive manufacturing practitioners to develop novel mitigation strategies for challenging build scenarios. More information regarding the AMBench 2018 study can be found at www.nist.gov/ambench. For this year's challenge, numerous metal parts of the same geometry were created using an identical processing condition using a commercial powder bed fusion machine. The eight parts in total were manufactured in two builds. In situ thermal measurements of a select region on one of the parts within each build were acquired at 1800 frames per second. The part is a bridge structure geometry that has 12 legs of varying size (5 mm x 5 mm, 5 mm x 2.5 mm, and 0.5 mm x 5 mm), each leg is 5 mm tall, then uses a 45-degree overhang to transition into the bridge structure with a constant cross section. Each part is manufactured using 0.02 mm layer thickness, a programmed laser power of 195 W traveling at a scan speed of 800 mm/s, and the hatch spacing is 0.1 mm. The part is manufactured in 624 layers and the total build time nearly 9.5 hours. Details on the experiment can be found at www.nist.gov/ambench/amb2018-01-description, while related post-process measurement results can be found at www.nist.gov/ambench/benchmark-test-data.This dataset consists of thermal videos and MATLAB data structures for each layer. These are provided for each layer of the build and are grouped ten layers at a time in the provided zip files. The thermal videos provide an overview of the radiant temperature (not accounting for emissivity) measured during each layer, while the MATLAB structures contain the measurement data along with information on the camera timing, calibration, and process information. Two MATLAB functions are also provided. The first allows the measured radiant temperature to be converted into true temperature based on an assumed emissivity correction factor. The second function recreates the thermal video files. The second MATLAB function helps to provides context on how to interact with the MATLAB structures.For a detailed description of the dataset, please refer to the NIST Journal of Research publication, \"Thermography of the Metal Bridge Structures Fabricated for the 2018 Additive Manufacturing Benchmark Test Series (AM-Bench 2018).\" (in press)","language":["en"],"title":"In situ thermography of the metal bridge structures fabricated for the 2018 Additive Manufacturing Benchmark Test Series (AM-Bench 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Modeling and simulation research"],"keyword":["Additive manufacturing","benchmark tests","AMBench","AMBench 2018","powder bed fusion","nickel super alloy 625","IN625","thermography","cooling rate","temperature"]},{"identifier":"6F1714704711023AE053245706818C1A1936","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2002"],"contactPoint":{"hasEmail":"mailto:karen.marshall@nist.gov","fn":"Karen Marshall"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/6F1714704711023AE053245706818C1A1936","description":"In April 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a dry run for the data collection portion of its Nail to Nail (N2N) Fingerprint Challenge. This data collection event was designed to ensure that the real data collection event held in September 2017 would be successful. To this end, biometric data from unhabituated individuals needed to be collected. That data is now released by NIST as Special Database 301.In total, 14 fingerprint sensors were deployed during the data collection, amassing a series of rolled and plain images. The devices include rolled fingerprints captured by skilled experts from the Federal Bureau of Investigation (FBI) Biometric Training Team. Captures of slaps, palms, and other plain impression fingerprint impressions were additionally recorded.   NIST also partnered with the FBI and Schwarz Forensic Enterprises to design activity scenarios in which subjects would likely leave fingerprints on different objects. The activities and associated objects were chosen in order to use a number of latent print development techniques and simulate the types of objects often found in real law enforcement case work. NIST also collected some mugshot-style face and iris images of the subjects who participated in the dry run. These data are also available for download.","language":["en"],"title":"NIST Special Database 301 Nail to Nail (N2N) Fingerprint Challenge Dry Run","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1502474","format":"text/html","description":"DOI Access to NIST Special Database 301 Nail to Nail (N2N) Fingerprint Challenge Dry Run","title":"DOI Access to NIST Special Database 301 Nail to Nail (N2N) Fingerprint Challenge Dry Run"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Use of this data requires users agree to be bound by the following terms and conditions. The database will only be used for biometrics related research. The database will not be further distributed, published, copied, or disseminated in any way or form wh","modified":"2018-06-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Biometrics"],"keyword":["latent","fingerprints","rolled","plain","palm","friction ridge","biometrics"]},{"identifier":"6FCA2C44E87B3E49E05324570681DCB11939","accessLevel":"public","references":["https://dx.doi.org/10.1109/CVPRW.2016.137"],"contactPoint":{"hasEmail":"mailto:javier.bernal@nist.gov","fn":"Javier Bernal"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/6FCA2C44E87B3E49E05324570681DCB11939","description":"This is a software suite for computing optimal diffeomorphisms for elastic registration of curves. Algorithm adapt-DP is based on DP (dynamic programming) restricted to an adapting strip which is able to perform this computation in linear time. Description of Algorithm adapt-DP can be found in \"Fast Dynamic Programming for Elastic Registration of Curves\", Proceedings of the 2nd International Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16) in conjunction with Computer Vision Pattern Recognition Conference (CVPR) 2016, Las Vegas, Nevada, June 26-July 1, 2016. The zip file Fast_Dynamic_Programming.zip contains copies of implementation of Algorithm adapt-DP as Fortran files (a Matlab Fortran mex file and a Python compatible Fortran file) for execution with Matlab/Python, Matlab/Python test files for executing adapt-DP Matlab Fortran mex file and Python compatible Fortran file, respectively, example data files, usage instructions in README files, etc.","language":["en"],"title":"Fast Dynamic Programming for Elastic Registration of Curves","distribution":[{"downloadURL":"https://math.nist.gov/~JBernal/Fast_Dynamic_Programming.zip","format":"zip file","description":"zip file with copies of implementation of Algorithm adapt-DP as Fortran files (a Matlab Fortran mex file and a Python compatible Fortran file) for execution with Matlab/Python, Matlab/Python test file for executing adapt-DP Matlab Fortran mex file and Python compatible Fortran file, respectively, example data files, usage intructions in README files, etc. Algorithm adapt-DP is based on DP (dynamic programming) restricted to an adapting strip for computing in linear time optimal diffeomorphisms for elastic registration of curves. Description of Algorithm adapt-DP can be found in \"Fast Dynamic Programming for Elastic Registration of Curves\", Proceedings of the 2nd International Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16) in conjunction with Computer Vision Pattern Recognition Conference (CVPR) 2016, Las Vegas, Nevada, June 26-July 1, 2016.","mediaType":"application/pdf","title":"Fast_Dynamic_Programming.zip"},{"downloadURL":"https://data.nist.gov/od/ds/6FCA2C44E87B3E49E05324570681DCB11939/Fast_Dynamic_Programming.zip.sha256","format":"SHA256","description":"Hash of the data file","mediaType":"text/plain","title":"SHA256 Hash"},{"downloadURL":"https://data.nist.gov/od/ds/6FCA2C44E87B3E49E05324570681DCB11939/Fast_Dynamic_Programming.zip","format":"zip archive","description":"zip file with copies of implementation of Algorithm adapt-DP as Fortran files (a Matlab Fortran mex file and a Python compatible Fortran file) for execution with Matlab/Python, Matlab/Python test file for executing adapt-DP Matlab Fortran mex file and Python compatible Fortran file, respectively, example data files, usage intructions in README files, etc. Algorithm adapt-DP is based on DP (dynamic programming) restricted to an adapting strip for computing in linear time optimal diffeomorphisms for elastic registration of curves. Description of Algorithm adapt-DP can be found in \"Fast Dynamic Programming for Elastic Registration of Curves\", Proceedings of the 2nd International Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16) in conjunction with Computer Vision Pattern Recognition Conference (CVPR) 2016, Las Vegas, Nevada, June 26-July 1, 2016.","mediaType":"application/zip","title":"Fast_Dynamic_Programming.zip"},{"accessURL":"https://doi.org/10.18434/T4/1502501","format":"text/html","description":"DOI Access to Fast Dynamic Programming for Elastic Registration of Curves","title":"DOI Access to Fast Dynamic Programming for Elastic Registration of Curves"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-06-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Image and signal processing"],"keyword":["dynamic programming","shape analysis","elastic registration","adapting strip"]},{"identifier":"6FF921428B10408FE053245706817D241941","accessLevel":"public","contactPoint":{"hasEmail":"mailto:richard.candell@nist.gov","fn":"Rick Candell Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/6FF921428B10408FE053245706817D241941","description":"Wireless technology is a key enabler of the vision of the future factory work-cell. Such work-cell will operate autonomously with a high degree of mobility enabled by wireless technology.  This model describes the work-cell using the Systems Modeling Language (SysML).  Using SysML the structural and parametric characteristics of the work-cell are described. Our model provides the architectural components and performance constraints of the work-cell in which wireless is used for a significant portion of connectivity. It identifies the structural components, interfaces, and data flows. Parametric characteristics that impact work-cell performance are included in the model.  Using this model, industrial wireless networking requirements and work-cell behaviors may be developed and performance limits may be evaluated. Note: This dataset is stored in MagicDraw XML format. To open the XML file, MagicDraw 18.4 or higher with the SysML plugin is required.  An HTML report is included. To view the HTML report, and browser that supports ActiveX is required.","language":["en"],"title":"Model of the Wireless Factory Work-cell using the Systems Modeling Language","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1502475","format":"text/html","description":"DOI Access to Model of the Wireless Factory Work-cell using the Systems Modeling Language","title":"DOI Access to Model of the Wireless Factory Work-cell using the Systems Modeling Language"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-07-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Factory communications"],"keyword":["industrial wireless","industrial communication","wireless sensor networks","factory communication","industrial control","manufacturing","cyber-physical systems","SysML"]},{"identifier":"70BA2D6BE8C167ABE053245706812F0F1943","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2007"],"contactPoint":{"hasEmail":"mailto:karen.marshall@nist.gov","fn":"Karen Marshall"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/70BA2D6BE8C167ABE053245706812F0F1943","description":"In September 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a data collection as part of its Nail to Nail (N2N) Fingerprint Challenge. Participating Challengers deployed devices designed to collect an image of the full nail to nail surface area of a fingerprint equivalent to a rolled fingerprint from an unacclimated user, without assistance from a trained operator. Traditional operator-assisted live-scan rolled fingerprints were also captured, along with assorted other friction ridge live-scan and latent captures.In this data collection, study participants needed to have their fingerprints captured using traditional operator-assisted techniques in order to quantify the performance of the Challenger devices. IARPA invited members of the Federal Bureau of Investigation (FBI) Biometric Training Team to the data collection to perform this task. Each study participant had N2N fingerprint images captured twice, each by a different FBI expert, resulting in two N2N baseline datasets.To ensure the veracity of recorded N2N finger positions in the baseline datasets, Challenge test staff also captured plain fingerprint impressions in a 4-4-2 slap configuration. This capture method refers to simultaneously imaging the index, middle, ring, and little fingers on the right hand, then repeating the process on the left hand, and finishing with the simultaneous capture of the left and right thumbs. This technique is a best practice to ensure finger sequence order, since it is physically challenging for a study participant to change the ordering of fingers when imaging them simultaneously. There were four baseline (two rolled and two slap), eight challenger and ten auxiliary fingerprint sensors deployed during the data collection, amassing a series of rolled and plain images. It was required that the baseline devices achieve 100% acquisition rate, in order to verify the recorded friction ridge generalized positions (FRGPs) and study participant identifiers for other devices. There were no such requirements for Challenger devices. Not all devices were able to achieve 100% acquisition rate.Plain, rolled, and touch-free impression fingerprints were captured from a multitude of devices, as well as sets of plain palm impressions. NIST also partnered with the FBI and Schwarz Forensic Enterprises (SFE) to design activity scenarios in which subjects would likely leave fingerprints on different objects. The activities and associated objects were chosen in order to use a number of latent print development techniques and simulate the types of objects often found in real law enforcement case work.","language":["en"],"title":"NIST Special Database 302 Nail to Nail (N2N) Fingerprint Challenge","distribution":[{"accessURL":"https://doi.org/10.18434/M31943","title":"DOI Access for NIST Special Database 302 Nail to Nail (N2N) Fingerprint Challenge"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Use of this data requires users agree to be bound by the following terms and conditions. The database will only be used for biometrics related research. The database will not be further distributed, published, copied, or disseminated in any way or form wh","modified":"2018-06-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Biometrics"],"issued":"2019-12-18","keyword":["latent","fingerprints","rolled","plain","palm","friction ridge","biometrics","segmentation","slap"]},{"identifier":"70BBD5692D6F3F6AE0532457068108311944","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8220"],"contactPoint":{"hasEmail":"mailto:fernando.cintron@nist.gov","fn":"Fernando Cintron"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/70BBD5692D6F3F6AE0532457068108311944","description":"This dataset represents the results from conducted simulations to evaluate the performance of Device-to-Device (D2D) communications in LTE when the User Equipment (UEs) are operating in UE-scheduled mode (Mode 2) with frequency hopping enabled. Different scenarios were simulated and communication performance was evaluated for each frequency hopping scheme defined in the LTE standard. The communication performance metrics include the successful transmission-reception ratio of transport blocks (TBs) at the physical layer, and the probability mass funciton (PMF) of consecutive TBs lost. Simulations were run multiple times with different random variable seeds, therefore, the mean and standard deviation of each metric is reported.","language":["en"],"title":"LTE Device-to-Device Out-of-Coverage Communication with Frequency Hopping Resource Scheduling Performance Metrics Evaluation Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/70BBD5692D6F3F6AE0532457068108311944/20180723_NISTIR8220_D2dFrequencyHopping.rar.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1502506","format":"text/html","description":"DOI Access to LTE Device-to-Device Out-of-Coverage Communication with Frequency Hopping Resource Scheduling Performance Metrics Evaluation Data","title":"DOI Access to LTE Device-to-Device Out-of-Coverage Communication with Frequency Hopping Resource Scheduling Performance Metrics Evaluation Data"},{"downloadURL":"https://data.nist.gov/od/ds/70BBD5692D6F3F6AE0532457068108311944/20180723_NISTIR8220_D2dFrequencyHopping.rar","format":"RAR","mediaType":"application/octet-stream"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-07-24","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Public Safety:Public safety communications research"],"keyword":["D2D communication","transport block success ratio (received/transmitted)","PMF of transport block consecutive losses (loss spurt)."]},{"identifier":"71EEB3C6E4EA667BE05324570681EB601945","accessLevel":"public","references":["https://www.nist.gov/publications/sensor-placement-and-detection-coverage-spectrum-sharing-35-ghz-band"],"contactPoint":{"hasEmail":"mailto:thao.t.nguyen@nist.gov","fn":"Thao Nguyen"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/71EEB3C6E4EA667BE05324570681EB601945","description":"This dataset contains the results of simulated deployments of Environmental Sensing Capability (ESC) sensors along the coasts of the contiguous United States in the 3.5 GHz band. The Federal Communications Commission rules for spectrum sharing operation in this band require the ESC sensors to detect the presence of a federal incumbent shipborne radar for interference protection. The results include sensor locations, detection coverage, probability of outage, and probability of false alarm for each Dynamic Protection Area (DPA), i.e., a predefined geographic area inside which radar may experience harmful interference.","language":["en"],"title":"Sensor Placement and Detection Coverage for Spectrum Sharing in the 3.5 GHz Band Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/71EEB3C6E4EA667BE05324570681EB601945/west_coast_analysis.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/71EEB3C6E4EA667BE05324570681EB601945/west_coast_analysis.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/71EEB3C6E4EA667BE05324570681EB601945/west_coast_antenna_cover.kml","mediaType":"application/vnd.google-earth.kml+xml"},{"downloadURL":"https://data.nist.gov/od/ds/71EEB3C6E4EA667BE05324570681EB601945/west_coast_antenna_cover.kml.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/71EEB3C6E4EA667BE05324570681EB601945/east_coast_analysis.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/71EEB3C6E4EA667BE05324570681EB601945/east_coast_analysis.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/71EEB3C6E4EA667BE05324570681EB601945/east_coast_antenna_cover.kml","mediaType":"application/vnd.google-earth.kml+xml"},{"downloadURL":"https://data.nist.gov/od/ds/71EEB3C6E4EA667BE05324570681EB601945/east_coast_antenna_cover.kml.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1503336","format":"text/html","description":"DOI Access to Sensor Placement and Detection Coverage for Spectrum Sharing in the 3.5 GHz Band","title":"DOI Access to Sensor Placement and Detection Coverage for Spectrum Sharing in the 3.5 GHz Band"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-08-21","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Electronics:Sensors","Advanced Communications:Wireless (RF)"],"keyword":["Spectrum sharing","3.5 GHz band","Environmental Sensing Capability (ESC) sensors","Dynamic Protection Areas (DPAs)."]},{"identifier":"725039AC054648F9E0532457068156CB1948","accessLevel":"public","references":["https://pages.nist.gov/REFPROP-docs/"],"contactPoint":{"hasEmail":"mailto:marcia.huber@nist.gov","fn":"Marcia Huber"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/srd/refprop","description":"REFPROP is an acronym for REFerence fluid PROPerties. This program, developed by the National Institute of Standards and Technology (NIST), calculates the thermodynamic and transport properties of industrially important fluids and their mixtures. These properties can be displayed in Tables and Plots through the graphical user interface; they are also accessible through spreadsheets or user-written applications accessing the REFPROP dll. REFPROP is based on the most accurate pure fluid and mixture models currently available. It implements three models for the thermodynamic properties of pure fluids: equations of state explicit in Helmholtz energy, the modified Benedict-Webb-Rubin equation of state, and an extended corresponding states (ECS) model. Mixture calculations employ a model that applies mixing rules to the Helmholtz energy of the mixture components; it uses a departure function to account for the departure from ideal mixing. Viscosity and thermal conductivity are modeled with either fluid-specific correlations, an ECS method, or in some cases the friction theory method.","language":["en"],"title":"NIST Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) Version 10 - SRD 23","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1502528","format":"text/html","description":"DOI Access to REFPROP version 10 - SRD 23","title":"DOI Access to REFPROP version 10 - SRD 23"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-06-05","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://www.nist.gov/sites/default/files/documents/2018/05/23/refprop10a.pdf","accrualPeriodicity":"irregular","theme":["Standards:Reference data"],"keyword":["AGA-8; Benedict Webb Rubin; CFCs; Excels; FORTRAN; GERG 2004; GERG 2008; Gibbs; Gruneisen; HCFCs; HFCs; Joule-Thomson; Lennard Jones; MBWR; Pitzer; Second cross virial coefficient; acentric factors; adiabatics; air; air conditioning; air conductivities; air densities; air viscosities; alternative refrigerants; azeotropes; binaries; binary mixtures; biodiesels; biofuels; boiling points; boilings; butenes; calorific values; chemical engineering; chemical potentials; chemicals; chemistry; chlorofluorocarbons; compressed natural gases; conductivity functions; cooling equipment; critical flow factors; criticals; cryogen; cryogenics; energies; equations of state; excess values; extended corresponding states; fatty acid methyl esters; fluids; fluorinated; freons; fugacity coefficients; fundamentals; gas and oil; gas phases; gaseous; gases; graphical interfaces; greenhouse gases; gross heating values; heats; heavy waters; hydrocarbons; hydrochlorofluorocarbons; hydrofluorocarbons; hydrogen fuel cells; interaction parameters; liquid equilibria; liquified natural gases; mechanical engineering; mixtures; modelings; molar masses; natural gases; net heating values; normal hydrogens; nuclear magnetic resonance; orthohydrogens; ozone depletions; parahydrogens; phase boundaries; phase diagrams; phase equilibria; physical; physics; pseudo pure fluids; pure fluids; reference states; refrigerants; refrigerations; saturated; siloxanes; software; sound speeds; specific heat inputs; states; substances; supercritical CO2; thermal; thermo chemistry; thermo physical; thermochemical data; thermodynamic data; thermodynamic properties; thermodynamics; thermophysical data; thermophysical properties; thermophysics; thermos; transport equations; transport properties; transport property data; triples; vapor; vapor compression cycles; water densities; water properties; water vapor pressures"]},{"identifier":"7394D482FB305D2BE053245706814ED41954","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2006"],"contactPoint":{"hasEmail":"mailto:william.luecke@nist.gov","fn":"William E. Luecke"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/7394D482FB305D2BE053245706814ED41954","description":"Data from NIST TN 2006 'Interlaboratory mechanical-property study for Cobalt-Chromium alloy made by laser powder-bed-fusion additive manufacturing' DOI: 10.6028/NIST.TN.2006.  Data from Figures 2, 3, 4,  and 6, and Table 4.","language":["en"],"title":"Data from \"NIST TN 2006 'Interlaboratory mechanical-property study for Cobalt-Chromium alloy made by laser powder-bed-fusion additive manufacturing'\" DOI: 10.6028/NIST.TN.2006","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-ILS-results-attributes.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-ILS-results-attributes.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-Figure-6-data.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-Figure-6-data.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-Figure-6-data-attributes.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-Figure-6-data-attributes.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-Figure-4-data.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-Figure-4-data.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-Figure-4-data-attributes.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-Figure-4-data-attributes.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-stress-strain.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-stress-strain.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-stress-strain-attributes.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-stress-strain-attributes.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-ILS-results.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/TN2006-ILS-results.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/10.18434_T4_10502566readme.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7394D482FB305D2BE053245706814ED41954/10.18434_T4_10502566readme.txt.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1502566","description":"DOI Access to Data from NIST TN 2006 'Interlaboratory mechanical-property study for Cobalt-Chromium alloy'","title":"DOI Access to Data from NIST TN 2006"},{"accessURL":"https://doi.org/10.18434/T4/1502566","description":"DOI Access to Data from NIST TN 2006 Interlaboratory mechanical-property study for Cobalt-Chromium alloy","title":"DOI Access to Data from NIST TN 2006"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2016-01-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing","Materials:Metals"],"keyword":["additive manufacturing; ASTM F75; laser-powder-bed fusion; yield strength."]},{"identifier":"73AE20D288CF02F1E05324570681502B1955","accessLevel":"public","contactPoint":{"hasEmail":"mailto:simon.frechette@nist.gov","fn":"Simon P. Frechette"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://smstestbed.nist.gov/tdp/mtc","description":"To better understand and address the challenges faced in linking all stages of a manufacturing and design process, an investigative fabrication process was designed and enacted as part of a collaboration between National Institute of Standards and Technology (NIST) and Manufacturing Technology Centre (MTC). This collaboration sought to test selected open standards' ability to integrate the product-lifecycle stages of engineering design, manufacturing, and quality assurance through a thorough implementation of a small scale model-based enterprise (MBE). This dataset was generated as a result of the collaboration.","language":["en"],"title":"Design, Manufacturing, and Inspection Data for a Box Assembly","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/73AE20D288CF02F1E05324570681502B1955/mtc.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/73AE20D288CF02F1E05324570681502B1955/mtc.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1503154","format":"text/html","description":"DOI Access to Design, Manufacturing, and Inspection Data for a Box Assembly","title":"DOI Access to Design, Manufacturing, and Inspection Data for a Box Assembly"},{"accessURL":"https://smstestbed.nist.gov/","description":"Homepage for accessing all information and data from the SMS Test Bed","title":"SMS Test Bed Homepage"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-08-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Factory operations planning and control","Manufacturing:Machining","Manufacturing:Systems integration","Manufacturing:Product data","Manufacturing:Interoperability in manufacturing"],"keyword":["manufacturing data","product assembly","computer-aided design (CAD)","MTConnect","Quality Information Framework (QIF)"]},{"identifier":"74851861FBF13DA5E05324570681F8EC1961","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ashley.russell@nist.gov","fn":"Ashley Russell"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/74851861FBF13DA5E05324570681F8EC1961","description":"Liquid chromatography tandem mass spectrometry allows for the measurement of steroid hormone suites in the blubber of marine mammals.  By combining this technology with remote biopsy collection, endocrine profiles can be assessed, allowing for studies of hormonal profile variation over time.  In this study, we explored associations among different steroidogenic pathways and seasonal differences in blubber hormone profiles of free-ranging common bottlenose dolphins along the coast of South Carolina, USA.  We found that male dolphins experience a peak in testosterone, androstenedione, progesterone, and 17-hydroxyprogesterone in the spring, likely related to an upregulation of the androgen steroidogenic pathway during mating season.  We also observed increased cortisol concentrations not related to blubber lipid percentages during summer compared to other months.  Though the sample size of females was insufficient to describe female seasonal patterns, there was an increase in androstenedione among females with elevated progesterone concentrations indicative of pregnancy, highlighting another potential endocrine marker for pregnancy in free-ranging dolphins. This work emphasizes the importance of selecting the appropriate season for studies on endocrine status to effectively uncover physiological variation or disruption in free-ranging cetaceans.","language":["en"],"title":"Data to support Seasonal blubber steroid profiles of free-ranging bottlenose dolphins (Tursiops truncatus)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/74851861FBF13DA5E05324570681F8EC1961/data_dictionary.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/74851861FBF13DA5E05324570681F8EC1961/data_dictionary.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/74851861FBF13DA5E05324570681F8EC1961/dolphin_blubber_hormones.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/74851861FBF13DA5E05324570681F8EC1961/dolphin_blubber_hormones.csv.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1503309","format":"text/html","description":"DOI Access to Data to support Seasonal blubber steroid profiles of free-ranging bottlenose dolphins (Tursiops truncatus)","title":"DOI Access to Data to support Seasonal blubber steroid profiles of free-ranging bottlenose dolphins (Tursiops truncatus)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-08-28","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Environment:Marine science","Environment:Environmental health","Chemistry:Analytical chemistry"],"keyword":["Liquid chromatography tandem mass spectrometry","hormones","stress","reproduction","percent lipid","cetacean"]},{"identifier":"754A77D9DA1E771AE0532457068179851962","accessLevel":"public","references":["https://doi.org/10.1016/j.jmsy.2018.04.004"],"contactPoint":{"hasEmail":"mailto:guixiu.qiao@nist.gov","fn":"Helen Qiao"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/el/intelligent-systems-division-73500/degradation-measurement-robot-arm-position-accuracy","description":"The dataset contains both the robot's high-level tool center position (TCP) health data and controller-level components' information (i.e., joint positions, velocities, currents, temperatures, currents). The datasets can be used by users (e.g., software developers, data scientists) who work on robot health management (including accuracy) but have limited or no access to robots that can capture real data. The datasets can support the:\n\n- Development of robot health monitoring algorithms and tools\n- Research of technologies and tools to support robot monitoring, diagnostics, prognostics, and health management (collectively called PHM)\n- Validation and verification of the industrial PHM implementation. For example, the verification of a robot's TCP accuracy after the work cell has been reconfigured, or whenever a manufacturer wants to determine if the robot arm has experienced a degradation.\n\nFor data collection, a trajectory is programmed for the Universal Robot (UR5) approaching and stopping at randomly-selected locations in its workspace. The robot moves along this preprogrammed trajectory during different conditions of temperature, payload, and speed. The TCP (x,y,z) of the robot are measured by a 7-D measurement system developed at NIST. Differences are calculated between the measured positions from the 7-D measurement system and the nominal positions calculated by the nominal robot kinematic parameters. The results are recorded within the dataset. Controller level sensing data are also collected from each joint (direct output from the controller of the UR5), to understand the influences of position degradation from temperature, payload, and speed. Controller-level data can be used for the root cause analysis of the robot performance degradation, by providing joint positions, velocities, currents, accelerations, torques, and temperatures. For example, the cold-start temperatures of the six joints were approximately 25 degrees Celsius. After two hours of operation, the joint temperatures increased to approximately 35 degrees Celsius. Control variables are listed in the header file in the data set (UR5TestResult_header.xlsx). \n\nIf you'd like to comment on this data and/or offer recommendations on future datasets, please email guixiu.qiao@nist.gov.","language":["en"],"title":"Degradation Measurement of Robot Arm Position Accuracy","distribution":[{"accessURL":"https://doi.org/10.18434/M31962","title":"DOI Access for Degradation Measurement of Robot Arm Position Accuracy"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-09-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Robotics in manufacturing"],"issued":"2019-09-23","keyword":["Manufacturing","Robotics in manufacturing","Sensing and perception","accuracy degradation","PHM"]},{"identifier":"7589389138C4687EE0532457068193C91963","accessLevel":"public","references":["https://dx.doi.org/10.1103/PhysRevApplied.13.044026"],"contactPoint":{"hasEmail":"mailto:aaron.hagerstrom@nist.gov","fn":"Aaron Hagerstrom"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Included here are figures and other relevant data from the paper \"Measurements of nonlinear polarization dynamics in the tens of GHz\" to be published in Physical Review Applied.Abstract: Frequency-dependent linear permittivity measurements are commonplace in the literature, providing key insights into the structure of dielectric materials. These measurements describe a material's dynamic response to a small applied electric field. In contrast, nonlinear dielectric materials are widely used for their responses to large applied fields, including switching in ferroelectric materials, and field-tuning of the permittivity in paraelectric materials. These behaviors are described by nonlinear permittivity. Nonlinear permittivity measurements are fraught with technical challenges because of the complex electrical coupling between a sample and its environment. Here, we describe a technique for measuring the complex nonlinear permittivity that circumvents many of the difficulties associated with other approaches. We validate this technique by measuring a the nonlinear permittivity of a tunable Ba0.5Sr0.5TiO thin film up to 40 GHz and comparing our results with a phenomenological model. These measurements provide insight into the dynamics of nonlinear dielectric materials down to picosecond timescales.","language":["en"],"title":"Data for: ?Measurements of nonlinear polarization dynamicsin the tens of gigahertz?","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/fig8_eps2_V_dep_midas.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Voltage-dependent data for Figure 8"},{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/fig8_eps2_V_dep_midas.csv","format":"Column data, comma delimited","description":"Voltage-dependent data shown in Figure 8 of \"Measurements of nonlinear polarization dynamics in the tens of GHz\"","mediaType":"text/csv","title":"Voltage-dependent data for Figure 8"},{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/readme.txt","description":"Readme file describing data.","mediaType":"text/plain","title":"readme"},{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/fig6_Y2_midas.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Data for figure 6"},{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/fig7_eps_midas.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Data for figure 7"},{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/fig6_Y2_midas.csv","format":"column data, comma-delimited.","description":"Data shown in Figure 6 of the paper \"Measurements of nonlinear polarization dynamics in the tens of GHz\"","mediaType":"text/csv","title":"Data for Figure 6"},{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/fig7_eps_midas.csv","format":"Column data, comma delimited","description":"Data shown in Figure 6 of the paper \"Measurements of nonlinear polarization dynamics in the tens of GHz\"","mediaType":"text/csv","title":"Data for Figure 7"},{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/fig2_calculation_midas.py.sha256","mediaType":"text/plain","title":"SHA256 File for Script to generate plots in Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/fig2_calculation_midas.py","format":"text file containing python script","description":"Python script to generate plots in Figure 2.","mediaType":"text/plain","title":"Script to generate plots in Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/fig8_eps2_freq_dep_midas.csv","format":"Column data, comma delimited","description":"Frequency-dependent data shown in Figure 8 of the paper \"Measurements of nonlinear polarization dynamics in the tens of GHz\"","mediaType":"text/csv","title":"Frequency-dependent data for Figure 8"},{"downloadURL":"https://data.nist.gov/od/ds/7589389138C4687EE0532457068193C91963/fig8_eps2_freq_dep_midas.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Frequency-dependent data for Figure 8"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-02-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology:Electrical/electromagnetic metrology","Materials:Materials characterization"],"issued":"2024-07-19","keyword":["Microwave Materials","permittivity","loss","tunability","nonlinear measurements"]},{"identifier":"758A5E930B207E05E05324570681A1181964","accessLevel":"public","contactPoint":{"hasEmail":"mailto:samuel.ray@nist.gov","fn":"Samuel Ray"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/758A5E930B207E05E05324570681A1181964","description":"This experimental Android application was developed by NIST PSCR to enable public safety personnel to measure general LTE coverage quality using a standard smartphone.  Source code is published on https://github.com/usnistgov/ as an SDK to allow developers to enhance the application or use it in other applications.\n\nThe application and SDK enable first responders and public safety personnel to survey and evaluate environments.  While accuracy of the data from UE measurements is much lower than that of specialized equipment, the application provides subjective assessments of coverage quality, using UEs which are already available to agencies and personnel.\n","language":["en"],"title":"LTE Coverage Tool","distribution":[{"accessURL":"https://github.com/usnistgov/LTECoverageTool","format":"text/html","description":"GitHub repository","title":"LTE Coverage Tool"},{"accessURL":"https://doi.org/10.18434/T4/1503069","format":"text/html","description":"DOI Access to LTE Coverage Tool","title":"DOI Access to LTE Coverage Tool"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-09-26","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)","Public Safety:Public safety communications research"],"keyword":["LTE","Public Safety","LTE Coverage"]},{"identifier":"75AF41FBDA802C07E0532457068152421966","accessLevel":"public","references":["http://dx.doi.org/10.6028/NIST.NSRDS.64"],"contactPoint":{"hasEmail":"mailto:angela.lee@nist.gov","fn":"Angela Y. Lee"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://srdata.nist.gov/srd64","description":"Note that this SRD supersedes SRD 64 Version 3.2. The NIST Electron Elastic-Scattering Cross-Section Database provides values of differential elastic-scattering cross sections, total elastic-scattering cross sections, phase shifts, and transport cross sections in electron-atom scattering for elements with atomic numbers from 1 to 96 and for electron energies between 50 eV and 300 keV (in steps of 1 eV). Knowledge of elastic-scattering effects is important for the development of theoretical models for quantitative analysis by Auger-electron spectroscopy, X-ray photoelectron spectroscopy, electron microprobe analysis, and analytical electron microscopy. These data are also needed for modeling of electron transport in radiation dosimetry, electron-beam lithography, and interactions of ionizing radiation with matter. The database is designed to facilitate simulations of electron transport for these and similar applications in which electron energies from 50 eV to 300 keV are utilized. An analysis of available elastic-scattering cross-section data has been published [A. Jablonski, F. Salvat, and C. J. Powell, J. Phys. Chem. Ref. Data 33, 409 (2004)].","language":["en"],"title":"NIST Electron Elastic-Scattering Cross-Section Database - SRD 64","distribution":[{"downloadURL":"https://srdata.nist.gov/srd64/","description":"The NIST Electron Elastic-Scattering Cross-Section Database provides values of differential elastic-scattering cross sections, total elastic-scattering cross sections, phase shifts, and transport cross sections in electron-atom scattering for elements with atomic numbers from 1 to 96 and for electron energies between 50 eV and 300 keV (in steps of 1 eV). Knowledge of elastic-scattering effects is important for the development of theoretical models for quantitative analysis by Auger-electron spectroscopy, X-ray photoelectron spectroscopy, electron microprobe analysis, and analytical electron microscopy. These data are also needed for modeling of electron transport in radiation dosimetry, electron-beam lithography, and interactions of ionizing radiation with matter. The database is designed to facilitate simulations of electron transport for these and similar applications in which electron energies from 50 eV to 300 keV are utilized.An analysis of available elastic-scattering cross-section data has been published [A. Jablonski, F. Salvat, and C. J. Powell, J. Phys. Chem. Ref. Data 33, 409 (2004)].","mediaType":"text/html","title":"Home page of NIST Electron Elastic-Scattering Cross-Section Database - SRD 64 Version 4.0"},{"accessURL":"https://doi.org/10.18434/T4/1502642","format":"text/html","description":"DOI Access to NIST Electron Elastic-Scattering Cross-Section Database SRD 64, Version 4.0","title":"DOI Access to NIST Electron Elastic-Scattering Cross-Section Database SRD 64, Version 4.0"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2012-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Spectroscopy","Materials","Standards:Reference data"],"keyword":["Auger electron spectroscopy","analytical electron microscopy","cross-section","elastic scattering","electron scattering","electron spectroscopy","electron transport","electron-probe microanalysis","surface analysis","x-ray photoelectron spectroscopy","x-ray spectroscopy"]},{"identifier":"75C6467F8B205670E05324570681995A1967","accessLevel":"public","references":["https://doi.org/10.1016/j.chemolab.2016.12.010","https://doi.org/10.1007/s00216-018-1240-2"],"contactPoint":{"hasEmail":"mailto:david.sheen@nist.gov","fn":"David Sheen"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/75C6467F8B205670E05324570681995A1967","description":"A python module for consensus analysis in interlaboratory studies","language":["en"],"title":"Interlab: A python module for consensus analysis in interlaboratory studies","distribution":[{"accessURL":"https://pages.nist.gov/interlab_py/","format":"Python module","description":"A python module for consensus analysis in interlaboratory studies","title":"Interlab"},{"accessURL":"https://doi.org/10.18434/T4/1503310","format":"text/html","description":"DOI Access to Interlab: A python module for consensus analysis in interlaboratory studies","title":"DOI Access to Interlab: A python module for consensus analysis in interlaboratory studies"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-09-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Software research","Mathematics and Statistics"],"keyword":["interlaboratory study","uncertainty analysis","outlier detection"]},{"identifier":"7619E70B50E70FE5E05324570681A1921968","accessLevel":"public","contactPoint":{"hasEmail":"mailto:eric.marksz@nist.gov","fn":"Eric Marksz"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/7619E70B50E70FE5E05324570681A1921968","description":"Included here are figures and other relevant data from the paper \"Targeted Chemical Pressure Yields Tunable Millimeter-Wave 5G Dielectric with Unparalleled Performance\" published online in Nature Materials on 23 December 2019 (https://doi.org/10.1038/s41563-019-0564-4). Abstract: Epitaxial strain can unlock enhanced properties in oxide materials but restricts substrate choice and maximum film thickness, above which lattice relaxation and property degradation occur. Here we employ a chemical alternative to epitaxial strain by providing targeted chemical pressure, distinct from random doping, to induce a ferroelectric instability with the strategic introduction of barium into today's best millimeter-wave tunable dielectric, the epitaxially strained 50 nm thick n = 6 (SrTiO3)nSrO Ruddlesden-Popper grown on (110) DyScO3. The defect mitigating nature of (SrTiO3)nSrO results in unprecedented low loss at frequencies up to 125 GHz. No barium-containing Ruddlesden-Popper titanates are known, but this atomically-engineered superlattice material, (SrTiO3)n?m(BaTiO3)mSrO, enables low-loss, tunable dielectric properties to be achieved with lower epitaxial strain and a 200 % improvement in the figure of merit at commercially-relevant millimeter-wave frequencies. As tunable dielectrics are key constituents for emerging millimeter-wave high-frequency devices in telecommunications our findings could lead to higher performance adaptive and reconfigurable electronics at these frequencies.","language":["en"],"title":"Data for \"Targeted Chemical Pressure Yields Tunable Millimeter-Wave Dielectric \"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/README.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/README.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/Fig1_DFT_BSTO_RP_structures_CIF.tgz","format":"CIF","description":"A .TGZ compressed folder containing the information necessary to reproduce the Density Functional Theory (DFT) simulations of the Ba-containing STO Ruddlesden-Popper structures. The files are included as .CIF files, for the paraelectric/ferroelectric films spanning from n=2 to n=6. Information in this folder can be extracted via archiving tools such as Gzip.","mediaType":"application/gzip","title":"Figure 1: DFT files for the Ba-containing STO Ruddlesden-Popper structures"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/Fig2_X-ray_Diffraction_50_nm_samples.csv","format":"CSV","description":"This CSV file contains the raw data for the X-ray diffraction curves shown in Figure 2. These curves show the relationship between diffracted angle 2theta (deg) and detector count intensity (a.u.)  for the 50 nm Ba-containing STO Ruddlesden-Popper thin films spanning n=2 to n=6.","mediaType":"application/vnd.ms-excel","title":"Figure 2: Data for the X-Ray Diffraction curves of the Ba-containing STO Ruddlesden-Popper films (n=2-6)"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/Fig3a_Dielectric_Const_vs_Temp_Freq_50_nm_samples.csv","format":"CSV","description":"This CSV file contains the raw data for the dielectric constant (K11) vs. temperature curves shown in Figure 3(a) for the Ba-containing STO Ruddlesden-Popper films from n=2-6. Data is included for the 10 kHz, 100 kHz, and 1 MHz traces.","mediaType":"application/vnd.ms-excel","title":"Figure 3(a): Data for the dielectric constant (K11) vs. temperature curves for the Ba-containing STO Ruddlesden-Popper films from n=2-6"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/Fig3b_Tc_Comparison.csv","format":"CSV","description":"This CSV file contains the raw data for the ferroelectric transition temperature (Tc) vs. series number (n) plots shown in Figure 3(b) for the Ba-containing STO Ruddlesden-Popper films from n=2-6.","mediaType":"application/vnd.ms-excel","title":"Figure 3(b): Data for the ferroelectric transition temperature (Tc) vs. series number (n) plots for the Ba-containing STO Ruddlesden-Popper films from n=2-6"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/Fig3c_lattice_w_wo_ba.csv","format":"CSV","description":"This CSV file contains the raw data for the lattice parameter (a) / strain vs. series number (n) plot shown in Figure 3(c) for the Ba-containing STO Ruddlesden-Popper films from n=2-6.","mediaType":"application/vnd.ms-excel","title":"Figure 3(c): Data for the lattice parameter (a) / strain vs. series number (n) plot for the Ba-containing STO Ruddlesden-Popper films from n=2-6"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/Fig3d_potential_well_n246.csv","format":"CSV","description":"This CSV file contains the raw data for the energy vs. total ionic distortion curves shown in Figure 3(d) for the Ba-containing STO Ruddlesden-Popper films from n=2-6. These curves map out the \"double potential well\" of the materials. \"Total ionic distortion\" can be thought of as a proxy for polarization.","mediaType":"application/vnd.ms-excel","title":"Figure 3(d): Data for the energy vs. total ionic distortion curves for the Ba-containing and Ba-free STO Ruddlesden-Popper films with n = 2,4,6"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/Fig4a_Complex_permittivity_100_nm_n6.csv","format":"CSV","description":"This CSV file contains the raw data for the complex dielectric constant (K11) vs. frequency curves shown in Figure 4(a) for the Ba-containing STO Ruddlesden-Popper films from n=2-6.","mediaType":"application/vnd.ms-excel","title":"Figure 4(a): Data for the complex dielectric constant (K11) vs. frequency curves for the 100 nm Ba-containing STO Ruddlesden-Popper films with n=6"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/Fig4a_inset_Loss_tangent_100_nm_n6.csv","format":"CSV","description":"This CSV file contains the raw data for the loss tangent vs. frequency curves shown in the inset of Figure 4(a) for the 100 nm Ba-containing STO Ruddlesden-Popper films with n=6.","mediaType":"application/vnd.ms-excel","title":"Figure 4(a)[inset]: Data for the loss tangent vs. frequency curve for the 100 nm Ba-containing STO Ruddlesden-Popper films with n=6"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/Fig4b_Film_Tunability_100_nm_n6.csv","format":"CSV","description":"This CSV file contains the raw data for the dielectric constant tunability vs applied bias electric field curves shown in Figure 4(b) for the 100 nm Ba-containing STO Ruddlesden-Popper films with n=6. Data is included for 5 GHz, 20 GHz, and 40 GHz traces.","mediaType":"application/vnd.ms-excel","title":"Figure 4(b): Data for the dielectric constant tunability vs. applied bias electric field curves for the 100 nm  Ba-containing STO Ruddlesden-Popper films from n=6"},{"downloadURL":"https://data.nist.gov/od/ds/7619E70B50E70FE5E05324570681A1921968/Fig4c_FOM_100_nm_n6.csv","format":"CSV","description":"This CSV file contains the raw data for the figure of merit (FOM) vs. frequency curve shown in the inset of Figure 4(c) for the 100 nm Ba-containing STO Ruddlesden-Popper films with n=6.","mediaType":"application/vnd.ms-excel","title":"Figure 4(c): Data for the figure of merit (FOM) vs. frequency curves for the 100 nm Ba-containing STO Ruddlesden-Popper films with n=6"},{"accessURL":"https://doi.org/10.18434/M31968","title":"DOI Access for Data for \"Targeted Chemical Pressure Yields Tunable Millimeter-Wave Dielectric \""}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-11-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Electronics:Optoelectronics","Advanced Communications:Wireless (RF)","Physics:Condensed matter","Metrology:Electrical/electromagnetic metrology","Materials:Materials characterization","Materials:Ceramics","Electronics:Thin-film electronics","Electronics:Electromagnetics"],"issued":"2019-11-22","keyword":["microwave","millimeter-wave","5G","targeted chemical pressure","materials","dielectric constant","loss tangent","permittivity","low loss","tunability","frequency-agile","filters","resonators","physical vapor","deposition","molecular beam epitaxy","strain engineering","barium","strontium","titanate","superlattice","ruddlesden-popper","DFT","density functional theory"]},{"identifier":"762AD4DA63D05927E05324570681D36C1970","accessLevel":"public","contactPoint":{"hasEmail":"mailto:arlin.stoltzfus@nist.gov","fn":"Arlin B. Stoltzfus"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/762AD4DA63D05927E05324570681D36C1970","description":"McCandlish and Stoltzfus gathered data from deep mutational scanning experiments on 12 proteins, comprising 56641 distinct amino acid replacement mutations.  By converting fitnesses to within-study quantiles, they combined results from all studies to draw general conclusions about distributions of fitness effects for the 380 different types of possible amino acid changes in proteins.  They found that most replacements are neither conservative nor radical, but barely different from the background distribution.  The shapes of these distributions can be approximated by a maximum-entropy model with only 1 parameter. This data package makes it possible to reproduce the main calculations used by Stoltzfus and McCandlish.  The data also may be useful to researchers carrying out meta-analyses of mutation-scanning experiments or DFE experiments.","language":["en"],"title":"Supplementary data for \"Distributions of fitness effects for amino acid changes from high-throughout mutagenesis experiments\" (McCandlish and Stoltzfus, 2018)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/README.md.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/figure_s1.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/figure_s1.pdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/ms2018_calculations.nb","mediaType":"application/mathematica"},{"downloadURL":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/ms2018_calculations.nb.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/all_obs.tsv","mediaType":"text/tab-separated-values"},{"downloadURL":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/all_obs.tsv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/raw_obs.tsv","mediaType":"text/tab-separated-values"},{"downloadURL":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/raw_obs.tsv.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M31970","title":"DOI Access for Supplementary data for \"Distributions of fitness effects for amino acid changes from high-throughout mutagenesis experiments\" (McCandlish and Stoltzfus, 2018)"},{"downloadURL":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/README.md","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-09-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://data.nist.gov/od/ds/762AD4DA63D05927E05324570681D36C1970/README.md","theme":["Mathematics and Statistics:Numerical methods and software","Chemistry:Molecular characterization","Bioscience:Engineering/synthetic biology"],"keyword":["mutation","deep mutational scanning","fitness","protein"]},{"identifier":"764045B99D9B3B84E0532457068164541971","accessLevel":"public","references":["https://www.nist.gov/el/net-zero-energy-residential-test-facility/nzertf-publications","https://doi.org/10.6028/jres.122.014"],"contactPoint":{"hasEmail":"mailto:william.healy@nist.gov","fn":"William Healy"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pages.nist.gov/netzero/","description":"This dataset presents measurements from the NIST Net-Zero Energy Residential Test Facility (NZERTF) for a full year of operation.  The NZERTF is a laboratory built in the form of a single-family house that serves as a testbed for technologies and operational strategies to achieve energy efficient performance while maintaining a healthy and comfortable indoor environment.  The facility is equipped with well-instrumented heating, cooling, ventilation, water heating, lighting, and electrical subsystems as would be present in an efficient home.  A photovoltaic array on the roof provides onsite renewable energy.  The dataset includes nearly 400 measured parameters for a full year of operation on a minutely basis.  Those parameters include indoor temperatures and humidities, outdoor weather conditions, electrical consumption by all devices, hot water consumption, photovoltaic energy generation, and operational schedules of the virtual occupants of the facility.  The data presented here are for the first year of operation which occurred from July 1, 2013 through June 31, 2014","language":["en"],"title":"Net Zero Energy Residential Test Facility Instrumented Data;  Year 1","distribution":[{"accessURL":"https://pages.nist.gov/netzero/data.html","format":"Comma-Separated Values (CSV) text","description":"Minutely data for the entire year is downloaded by subsystem or for the entire facility.","mediaType":"text/csv","title":"NIST Net-Zero - Data Page"},{"accessURL":"https://doi.org/10.18434/T4/1503134","format":"text/html","description":"DOI Access to Net Zero Energy Residential Test Facility Instrumented Data; Year 1","title":"DOI Access to Net Zero Energy Residential Test Facility Instrumented Data; Year 1"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-09-19","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://pages.nist.gov/netzero/data.html#data_dictionary","accrualPeriodicity":"irregular","theme":["Heating and cooling equipment","Energy:Alternative energy","Buildings and Construction:Indoor air quality","Energy:Energy efficiency"],"spatial":"Latitude:  39.138 N \n Longitude:  77.219 W","keyword":["residential energy use; energy conservation; photovoltaics; net-zero energy; heating","ventilation","and air conditioning; water heating;"],"temporal":"2013-07-01/2014-06-30"},{"identifier":"76544CA76ADE200DE05324570681FE0E1973","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2020"],"contactPoint":{"hasEmail":"mailto:stanley.gilbert@nist.gov","fn":"Stanley W. Gilbert"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/76544CA76ADE200DE05324570681FE0E1973","description":"This  data set contains estimates of the percentage smoke detector utilization at the Census Tract level for the United States. Development of this data set is described in NIST TN 2020 (see references below).\n\nThe zip file contains the data in shapefile format. Each record is a single census tract (using the 2013 Tiger files for census tracts) with associated data. \n\nFields contained in the data set are:\n\ngeoid: Geographic ID of the census tract. Format is '14000USXXYYYZZZZZZ', where XX is the FIPS code for the state, YYY is the FIPS code for the county, and ZZZZZZ is the census tract number. This field serves as a unique ID for the dataset.\n\nstate: FIPS code for the state.\n\ncounty: FIPS code for the county.\n\ntract: Tract number.\n\nsmsa: Standard Metropolitan Statistical Area as used in the American Housing Survey.\n\nPUMA: Public Use Microdata Area ID.\n\nregion: Census region.\n\ndtctrs: Estimated fraction of households in the census tract with smoke detectors installed.","language":["en"],"title":"Smoke Alarm Distribution","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/76544CA76ADE200DE05324570681FE0E1973/smoke_detectors.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/76544CA76ADE200DE05324570681FE0E1973/smoke_detectors.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M31973","title":"DOI Access for Smoke Alarm Distribution"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-09-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Fire detection","Fire:Fire risk reduction","Fire:Emergency building evacuation"],"issued":"2019-10-07","keyword":["smoke detector utilization;smoke alarm utilization;small area estimation;NIST TN 2020;Spatial Distribution"]},{"identifier":"76F046F18CCA46FBE05324570681CB301977","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jeffrey.hudgens@nist.gov","fn":"Jeffrey Hudgens"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/76F046F18CCA46FBE05324570681CB301977","description":"The spreadsheet file reported herein provides centroid data, descriptive of deuterium uptake, for the Fab Fragment of NISTmAb (PDB: 5K8A) reference material, as measured by the bottom-up hydrogen-deuterium exchange mass spectrometry (HDX-MS) method. The protein sample was incubated in deuterium-rich solutions under uniform pH and salt concentrations between 3.6 degrees C and 25.4 degrees C for seven intervals ranging (0 to 14,400) s plus a control sample that simulates a Fab Fragment immersed for infinite time in D2O. The deuterium content of peptic peptide fragments were measured by mass spectrometry. These data were reported by fifteen laboratories, which conducted the measurements using orbitrap and Q-TOF mass spectrometers. The cohort reported about 78,900 centroids for 430 proteolytic peptide sequences of the heavy and light chains of NISTmAb, providing nearly 100 % coverage. The instrumentation and physical and chemical conditions under which these data were acquired are documented.","language":["en"],"title":"Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS) Centroid Data measured between 3.6 degrees C and 25.4 degrees C for the Fab Fragment of NISTmAb","distribution":[{"accessURL":"https://doi.org/10.18434/8SX3-NQ49","format":"text/html","description":"DOI Access to Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS) Centroid Data measured between 3.6 degrees C and 25.4 degrees C for the Fab Fragment of NISTmAb","title":"DOI Access to Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS) Centroid Data measured between 3.6 degrees C and 25.4 degrees C for the Fab Fragment of NISTmAb"},{"downloadURL":"https://data.nist.gov/od/ds/76F046F18CCA46FBE05324570681CB301977/Data%20Document%20NIST%20HDX-MS%20IC%20Project.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"HDX-MS centroid data for NISTmAb"},{"downloadURL":"https://data.nist.gov/od/ds/76F046F18CCA46FBE05324570681CB301977/Data%20Document%20NIST%20HDX-MS%20IC%20Project.xlsx.sha256","mediaType":"text/plain","title":"SHA-256 for HDX-MS centroid data for NISTmAb"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-09-28","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology:Amount of substance","Chemistry:Molecular characterization","Chemistry:Analytical chemistry","Bioscience:Cell biology","Bioscience:Proteomics","Bioscience:Biomaterials","Chemistry:Chemical thermodynamics and chemical properties","Manufacturing:Biomanufacturing","Metrology:Mass metrology"],"keyword":["hydrogen-deuterium exchange","hydrogen exchange","HDX-MS","interlaboratory comparison","mass spectrometry","peptide","precision","proteomics","reference material","repeatability","reproducibility"]},{"identifier":"77D21EEB60F37A2BE053245706819FDB1978","accessLevel":"public","contactPoint":{"hasEmail":"mailto:william.osborn@nist.gov","fn":"William Osborn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/77D21EEB60F37A2BE053245706819FDB1978","description":"These are the unprocessed electron back scatter diffraction patterns (EBSPs) collected from the mesas of electron beam induced deposition (EBID) material, in addition to the Mathematica notebook used to process the images.  Each of the 12 EBID mesa has 10 EBSPs collected at 20 kV and 10 kV, in addition to several longer line scans that step from the silicon substrate onto the EBID mesa.","language":["en"],"title":"Supporting Data for Selected Area Electron Beam Induced Deposition of Pt and W for EBSD Backgrounds","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/20kV-mesas.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/20kV-mesas.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/afmOfMesas.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/10kV-lineScans.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/10kV-mesas.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/10kV-mesas.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/20kV-lineScans.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/20kV-lineScans.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/readme.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/readme.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/10kV-lineScans.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/fibColoring.nb","mediaType":"application/mathematica"},{"downloadURL":"https://data.nist.gov/od/ds/77D21EEB60F37A2BE053245706819FDB1978/fibColoring.nb.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1503158","format":"text/html","description":"DOI Access to Supporting Data for Selected Area Beam Induced Deposition of Pt and W for EBSD Background","title":"DOI Access to Supporting Data for Selected Area Beam Induced Deposition of Pt and W for ..."}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-10-17","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization","Nanotechnology:Nanomechanics","Nanotechnology:Nanometrology","Electronics:Semiconductors"],"keyword":["Electron Back Scatter Diffraction","EBSD","flat field","background subtraction","Electron Beam Induced Deposition","EBID","Pt","W"]},{"identifier":"789B2FE7AA4E46E5E05324570681AEDD1979","accessLevel":"public","references":["https://www.nist.gov/publications/35-ghz-federal-incumbent-protection-algorithms"],"contactPoint":{"hasEmail":"mailto:michael.souryal@nist.gov","fn":"Michael Souryal"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/789B2FE7AA4E46E5E05324570681AEDD1979","description":"Simulated Citizens Broadband Radio Service device deployments, calculated federal incumbent protection move lists, and calculated aggregate interference statistics.\n\nThis data is associated with publication, \"3.5 GHz Federal Incumbent Protection Algorithms,\" M. R. Souryal, T. T. Nguyen, and N. J. LaSorte, in Proc. IEEE DySPAN 2018, Oct. 2018.","language":["en"],"title":"3.5 GHz CBRS Federal Incumbent Protection Move Lists and Aggregate Interference Statistics","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/789B2FE7AA4E46E5E05324570681AEDD1979/dyspan2018_data.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/789B2FE7AA4E46E5E05324570681AEDD1979/dyspan2018_data.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1503311","format":"text/html","description":"DOI Access to 3.5 GHz CBRS Federal Incumbent Protection Move Lists and Aggregate Interference Statistics","title":"DOI Access to 3.5 GHz CBRS Federal Incumbent Protection Move Lists and Aggregate Interference Statistics"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-10-19","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"keyword":["3.5 GHz","aggregate interference","CBRS","federal incumbent","move list"]},{"identifier":"78E7C9CE7CEE3E1CE05324570681AA561980","accessLevel":"public","references":["https://doi.org/10.1002/srin.201800370"],"contactPoint":{"hasEmail":"mailto:adam.creuziger@nist.gov","fn":"Adam Creuziger"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/78E7C9CE7CEE3E1CE05324570681AA561980","description":"This data set supports the technical paper and supporting information documents accepted to Steel Research International (https://doi.org/10.1002/srin.201800370). The technical paper presents transformation potential calculations for two different martensitic transformation paths and compares these to experimentally measured textures in a SAE 201 stainless steel.  The data set provides: scripts used to calculate martensitic transformations, analyze data, and create figures; and pole figure data files used to calculate orientation distribution functions (ODF) included in the paper. The authors hope that providing this data set will facilitate reuse and expansion, as well as making research more reproducible.","language":["en"],"title":"Data Set: Assessment of Martensitic Transformation Paths Based on Transformation Potential 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Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Modeling and computational material science","Transportation:Automotive","Materials:Metals","Neutron Research","Materials:Materials characterization"],"keyword":["martensitic phase transformation","texture","neutron diffraction","austenitic steels","modeling"]},{"identifier":"7A924A67E9460B0FE0532457068144CD1986","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2039"],"contactPoint":{"hasEmail":"mailto:ryan.falkenstein-smith@nist.gov","fn":"Ryan Falkenstein-Smith"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/7A924A67E9460B0FE0532457068144CD1986","description":"This data set reports measurements of the drag coefficient and geometric parameters of different types of vegetation. The vegetation chosen for this work was a Bakers Blue Spruce (Picea pungens `Bakeri'), an Evergreen Distylium (Distylium `PIIDIST-I'), a Gold Rider Leyland Cypress (Cupressocyparis leylandii `Gold Rider'), a Kimberly Queen Fern (Nephrolepis obliterata `Kimberly Queen'), a Blue Shag Eastern White Pine (Pinus strobus `Blue Shag'), and a Robin Red Holly (Ilex opaca). Each sample was chosen based on its local availability.  Wind resistance measurements are made in a wind tunnel with a 2.0 m test section and 0.5 m by 0.5 m cross-section. The drag coefficient is inferred via measurement of the pressure drop across the sample at wind speeds ranging from 2 m/s to 8 m/s. The data set also reports the white fraction, the absorption coefficient (Kappa,) and the solid fraction of each vegetation sample. The white fraction and absorption coefficients were determined via photography. The solid fractions were determined from water displacement testing. A detailed description of this work can be found in Ref. [1].\n\n[1]  R. Falkenstein-Smith, K. McGrattan, B. Toman, and M. Fernandez. Measurement of the Flow Resistance of Vegetation. Technical report, National Institute of Standards and Technology, April 2019.","language":["en"],"title":"Data set of Flow Resistance Measurements of Vegetation","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Evergreen%20Distylium.csv","mediaType":"application/vnd.ms-excel"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Evergreen%20Distylium.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Gold%20Rider%20Leyland%20Cypress.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Gold Rider Leyland Cypress"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Gold%20Rider%20Leyland%20Cypress.csv","format":"CSV File","description":"Drag, White Fraction, Kappa, and Solid Fraction Data for Gold Rider Leyland Cypress. Uncertainties for this data are also included.","mediaType":"application/vnd.ms-excel","title":"Gold Rider Leyland Cypress"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Kimberly%20Queen%20Fern.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Kimberly Queen Fern"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Kimberly%20Queen%20Fern.csv","format":"CSV File","description":"Drag, White Fraction, Kappa, and Solid Fraction Data for Kimberly Queen Fern. Uncertainties for this data are also included.","mediaType":"application/vnd.ms-excel","title":"Kimberly Queen Fern"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Robin%20Red%20Holly.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Robin Red Holly"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Robin%20Red%20Holly.csv","format":"CSV File","description":"Drag, White Fraction, Kappa, and Solid Fraction Data for the Robin Red Holly. Uncertainties for this data are also included in this file.","mediaType":"application/vnd.ms-excel","title":"Robin Red Holly"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Summary_Table.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Summary_Table.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Bakers%20Blue%20Spruce.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Bakers Blue Spruce"},{"downloadURL":"https://data.nist.gov/od/ds/7A924A67E9460B0FE0532457068144CD1986/Bakers%20Blue%20Spruce.csv","format":"CSV File","description":"Drag, White Fraction, Absorption Coefficient, Solid Fraction Data for Bakers Blue Spruce. 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Uncertainties are also included in this data.","mediaType":"application/vnd.ms-excel","title":"Blue Shag Eastern White Pine Data"},{"accessURL":"https://doi.org/10.18434/M31986","title":"DOI Access for Data set of Flow Resistance Measurements of Vegetation"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-09-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Wildland urban interface fire"],"issued":"2019-09-23","keyword":["Vegetation Canopy; Drag Coefficient; Wind Resistance"]},{"identifier":"7B0767A00BDB25A5E0532457068151011987","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.7880-48"],"contactPoint":{"hasEmail":"mailto:david.duewer@nist.gov","fn":"David Duewer"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/7B0767A00BDB25A5E0532457068151011987","description":"The Micronutrients Measurement Quality Assurance Program (MMQAP) was coordinated by the Chemical Sciences Division and supported measurement technology for selected fat- and water-soluble vitamins and carotenoids in human serum. This program was initiated in 1984 by the National Cancer Institute Division of Cancer Prevention and Control to ensure the long-term reliability of the measurements made while studying the possible cancer chemoprevention roles of these compounds. This program provided participants with measurement comparability assessment through use of interlaboratory comparison studies, Standard Reference Materials and control materials, and methods development and validation. The MMQAP concluded in 2017, and parts of the MMQAP community will now be served through the NIST Health Assessment Measurements Quality Assurance Program (HAMQAP).","language":["en"],"title":"NIST Micronutrients Measurement Quality Assurance Program Winter and Summer 2017 Comparability Studies","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/7B0767A00BDB25A5E0532457068151011987/FSV_RR82.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/7B0767A00BDB25A5E0532457068151011987/FSV_RR82.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7B0767A00BDB25A5E0532457068151011987/FSV_RR81.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/7B0767A00BDB25A5E0532457068151011987/FSV_RR81.xlsx.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1503374","format":"text/html","description":"DOI Access to NIST Micronutrients Measurement Quality Assurance Program Winter and Summer 2017 Comparability Studies","title":"DOI Access to NIST Micronutrients Measurement Quality Assurance Program Winter and Summer 2017 Comparability Studies"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-01-01","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Standards:Conformity assessment","Chemistry:Analytical chemistry","Standards:Reference materials"],"keyword":["interlaboratory comparisons","Biosciences and Health","Energy","Environment and Climate","Food and Nutrition","Fat-Soluble Vitamins","Carotenoids","Vitamin C","Human Serum","Interlaboratory Study"]},{"identifier":"7C25917BBFA96466E05324570681F6ED1994","accessLevel":"public","references":["https://www.nist.gov/publications/detection-incumbent-radar-35-ghz-cbrs-band"],"contactPoint":{"hasEmail":"mailto:raied.caromi@nist.gov","fn":"Raied Caromi"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/7C25917BBFA96466E05324570681F6ED1994","description":"This data is for ROC curve plots from the publication,  \"Detection of Incumbent Radar in the 3.5 GHz CBRS Band\", Raied Caromi, Michael Souryal, and Wen-Bin Yang, IEEE GlobalSIP, Nov. 2018, Anaheim, CA.\nThe ROC curves represent performance of  federal incumbent radar detection in the 3.5 GHz CBRS band using field-measured waveforms. The data is for the following figures:\n\nFig. 2: Measured signal as template; signal with added WGN, Fig. 3: Synthetic template; signal with added WGN, Fig. 4: Synthetic template; signal with LTE interference, Fig. 5: Synthetic template; signal with measured adjacent-band radar emissions.","language":["en"],"title":"DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1503328","format":"text/html","description":"DOI Access to DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND","title":"DOI Access to DETECTION OF INCUMBENT RADAR IN THE 3.5 GHZ CBRS BAND"},{"downloadURL":"https://data.nist.gov/od/ds/7C25917BBFA96466E05324570681F6ED1994/AllFiguresMetaData.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7C25917BBFA96466E05324570681F6ED1994/AllFiguresMetaData.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7C25917BBFA96466E05324570681F6ED1994/Measured%20signal%20as%20template%3B%20signal%20with%20added%20WGN.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7C25917BBFA96466E05324570681F6ED1994/Measured%20signal%20as%20template%3B%20signal%20with%20added%20WGN.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7C25917BBFA96466E05324570681F6ED1994/Synthetic%20template%3B%20signal%20with%20added%20WGN.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7C25917BBFA96466E05324570681F6ED1994/Synthetic%20template%3B%20signal%20with%20added%20WGN.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7C25917BBFA96466E05324570681F6ED1994/Synthetic%20template%3B%20signal%20with%20LTE%20interference.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7C25917BBFA96466E05324570681F6ED1994/Synthetic%20template%3B%20signal%20with%20LTE%20interference.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/7C25917BBFA96466E05324570681F6ED1994/Synthetic%20template%3B%20signal%20with%20measured%20adjacent-band%20radar%20emissions.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7C25917BBFA96466E05324570681F6ED1994/Synthetic%20template%3B%20signal%20with%20measured%20adjacent-band%20radar%20emissions.csv.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-09-14","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"keyword":["3.5 GHz","CBRS","detection","environmental sensing capability","radar"]},{"identifier":"7C39465E8B0D52E4E0532457068153821996","accessLevel":"public","contactPoint":{"hasEmail":"mailto:yishen.sun@nist.gov","fn":"Yishen Sun"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/7C39465E8B0D52E4E0532457068153821996","description":"Summary of analytical estimates and simulation results that are used to generate figures in a paper titled \"Access Time Analysis of MCPTT Off-Network Mode over LTE\".  The paper was submitted to the special issue \"Emergency Networks and Future Public Safety Systems\" of journal \"Wireless Communications and Mobile Computing\" on November 30, 2018.","language":["en"],"title":"Access Time Analysis of MCPTT Off-Network Mode over LTE","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/7C39465E8B0D52E4E0532457068153821996/AccessTime_dataset_graph_final.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/7C39465E8B0D52E4E0532457068153821996/AccessTime_dataset_graph_final.xlsx.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1503329","format":"text/html","description":"DOI Access to Access Time Analysis of MCPTT Off-Network Mode over LTE","title":"DOI Access to Access Time Analysis of MCPTT Off-Network Mode over LTE"},{"downloadURL":"https://data.nist.gov/od/ds/7C39465E8B0D52E4E0532457068153821996/AccessTime_dataset_graph_final_updt.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/7C39465E8B0D52E4E0532457068153821996/AccessTime_dataset_graph_final_updt.xlsx.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-11-30","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Public Safety:Public safety communications research"],"keyword":["Mission Critical Push to Talk (MCPTT)","Access Time","Proximity Services","Off-Network Mode","Direct Mode"]},{"identifier":"7C718D35BA0F1229E053245706810AD22004","accessLevel":"public","contactPoint":{"hasEmail":"mailto:peter.bajcsy@nist.gov","fn":"Peter Bajcsy"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://isg.nist.gov/deepzoomweb/data/RPEimplants","description":"Induced Retinal Pigment Epithelial (iRPE) implants were developed from two healthy donors (Healthy-1 and Healthy-2). Live iRPE were imaged using quantitative bright-field absorbance microscopy (QBAM) every week during the in vitro maturation process. The data are organized by the culture plate, imaging date and time, color filter used to capture the images, and finally images that include the well ID and grid position in the name of the image. Both Healthy-1 and Healthy-2 iRPE were cultured in 12-well plates as outlined in Figure 1, with only half of the 12-well plate containing cells (green circles, Figure 1). The Blank Well (blue circles) was filled with culture medium but contained no cells and was used for benchmarking and calibration protocols that are part of the QBAM process. The grid in each well indicates that a 4x3 grid of overlapping images (~10-15% overlap) were captured for each well. One unique characteristic of this dataset is that each plate contains iRPE treated maturation inhibitors (negative control, HPI4), maturation promotors (positive control, Aphidicolin), or neither. The imaging parameters and functional data collected for each dataset for Healthy-1 and Healthy-2 were different, and the details of data collection and contents are included in the DataSummary.txt file included in each subfolder.","language":["en"],"title":"2D Measurement of Retinal Pigment Epithelium Function Using Quantitative Bright-Field Microscopy","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1503229","format":"text/html","description":"DOI Access to 2D Measurement of Retinal Pigment Epithelium Function Using Quantitative Bright-Field Microscopy","title":"DOI Access to 2D Measurement of Retinal Pigment Epithelium Function Using Quantitative Bright-Field Microscopy"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-12-07","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Cell biology","Information Technology:Computational science","Information Technology:Data and informatics"],"keyword":["computational science in biological metrology","artificial intelligence based modeling","convolutional neural networks","image processing","bright-field microscopy","retinal pigment epithelial implants"]},{"identifier":"7CAEA9D04EC628DEE05324570681AF372008","accessLevel":"public","contactPoint":{"hasEmail":"mailto:aric.sanders@nist.gov","fn":"Aric Sanders"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/7CAEA9D04EC628DEE05324570681AF372008","description":"Data used to generate figures in Large-Signal-Network-Analyzer Phase Calibration on an Arbitrary Grid.","language":["en"],"title":"Large-Signal-Network-Analyzer Phase Calibration on an Arbitrary Grid","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/7CAEA9D04EC628DEE05324570681AF372008/AWG_LSNA_25KHz.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/7CAEA9D04EC628DEE05324570681AF372008/AWG_LSNA_25KHz.csv.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32008","title":"DOI Access for Large-Signal-Network-Analyzer Phase Calibration on an Arbitrary Grid"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-12-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"issued":"2020-07-16","keyword":["phase calibration","oscilloscope","arbitrary waveform generator","vector network analyzer"]},{"identifier":"7CE906EBFAD7231EE0532457068153892009","accessLevel":"public","contactPoint":{"hasEmail":"mailto:r.kline@nist.gov","fn":"Regis Kline"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/mml/materials-science-and-engineering-division/polymers-processing-group/x-ray-metrology","description":"Video tutorials from NIST workshop on X-ray metrology for the semiconductor industry","language":["en"],"title":"X-ray Metrology for the Semiconductor Industry Tutorial","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1503330","format":"text/html","description":"DOI Access to X-ray Metrology for the Semiconductor Industry Tutorial","title":"DOI Access to X-ray Metrology for the Semiconductor Industry Tutorial"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-12-01","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Nanotechnology:Nanoelectronics","Metrology:Dimensional metrology","Electronics:Semiconductors","Nanotechnology:Nanometrology"],"keyword":["Semiconductor metrology","dimensional metrology","small angle x-ray scattering"]},{"identifier":"7CEBE79CD26E2467E0532457068151622010","accessLevel":"public","references":["https://doi.org/10.6028/jres.124.017"],"contactPoint":{"hasEmail":"mailto:raphael.barbau@nist.gov","fn":"Raphael Barbau"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/7CEBE79CD26E2467E0532457068151622010","description":"The translator implements the SysML Extension for Physical Interaction and Signal Flow Simulation (SysPhS). It can generate Modelica or Simulink/Simscape files from SysML models extended according to the SysPhS specification. This dataset binary and source code for the translator, as well as a set of SysML models that can be translated into simulation models. Disclaimer: Certain commercial equipment, instruments, or materials are identified in this dataset to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.","language":["en"],"title":"Translator from Extended SysML to Physical Interaction and Signal Flow Simulation Platforms","distribution":[{"accessURL":"https://doi.org/10.18434/M32010","title":"DOI Access for Translator from Extended SysML to Physical Interaction and Signal Flow Simulation Platforms"},{"downloadURL":"https://data.nist.gov/od/ds/7CEBE79CD26E2467E0532457068151622010/sysphs1.0.zip","mediaType":"application/x-zip-compressed","title":"Translator from Extended SysML to Physical Interaction and Signal Flow Simulation Platforms"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-05-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Systems engineering","Manufacturing:Interoperability in manufacturing","Manufacturing:Manufacturing systems design and analysis","Manufacturing:Systems integration"],"conformsTo":"https://www.omg.org/spec/SysPhS/1.0/","keyword":["lumped-parameter simulation","model-based systems engineering","model transformation","SysML"]},{"identifier":"7D6696ECDA1445A2E0532457068131DE2013","accessLevel":"public","contactPoint":{"hasEmail":"mailto:david.flater@nist.gov","fn":"David Flater"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/Metrology/tree/master/Reaper","description":"Reaper is a Chrome (or Chromium) browser extension that terminates browser processes when they use too much CPU.","language":["en"],"title":"The Chrome Reaper","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1503404","format":"text/html","description":"DOI Access to: The Chrome Reaper","title":"DOI Access to: The Chrome Reaper"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-12-18","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research"],"keyword":["software performance"]},{"identifier":"7D66D45C42152995E05324570681B8F92014","accessLevel":"public","contactPoint":{"hasEmail":"mailto:joseph.conny@nist.gov","fn":"Joseph Conny"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/7D66D45C42152995E05324570681B8F92014","description":"This project involved the analysis and optical modeling of atmospheric dust particles collected at Mauna Loa Observatory in Hawaii. Dust from the Asian mainland was identified in filter samples with computer-controlled scanning electron microscopy (CC-SEM). Individual Asian dust and marine background air particles were further examined using focused ion-beam (FIB) SEM and energy dispersive x-ray spectroscopy (EDX). The particles were heterogeneous in that they had multiple mineral phases, including an iron-containing phase which was compositionally minor but optically important. FIB tomography was used to produce 3-D reconstructions of the particles. The discrete dipole approximation (DDA) method for calculating optical properties was used to determine the light-scattering behavior of the particles based on the 3-D reconstructions. The dataset includes the following:\n1) Tabulated optical property results for all selected particles in a single Excel file\n2) Folder with CC-SEM data files\n3) Folder and subfolders with particle SEM images and EDX element maps\n4) Folder with Excel files with element composition, oxide composition, mineral composition, and mineral phase volumes for each particle\n5) Folder and subfolders with optical modeling parameter files\n6) Folder and subfolders with input and output files from the DDA program DDSCAT ver. 7.3.\n7) Folder with Excel files with backscatter fraction calculations","language":["en"],"title":"Analysis and Optical Modeling of Individual Heterogeneous Asian Dust Particles Collected at Mauna Loa Observatory","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/7D66D45C42152995E05324570681B8F92014/NISTdataset_ModelingAsianDustFromMLO.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/7D66D45C42152995E05324570681B8F92014/NISTdataset_ModelingAsianDustFromMLO.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/T4/1503344","format":"Persistent identifier for Analysis and Optical Modeling of Individual Heterogeneous Asian Dust Particles Collected at Mauna Loa Observatory","description":"DOI Access for Analysis and Optical Modeling of Individual Heterogeneous Asian Dust Particles Collected at Mauna Loa Observatory","title":"DOI Access for Analysis and Optical Modeling of Individual Heterogeneous Asian Dust Particles Collected at Mauna Loa Observatory"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-12-18","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Environment:Air / water / soil quality"],"keyword":["atmospheric aerosol","mineral dust","scanning electron microscopy","energy dispersive X-ray spectroscopy","focused ion-beam tomography","FIB-SEM","discrete dipole approximation","extinction","scattering","back scattering"]},{"identifier":"808B2042D1696C0BE05324570681AB642018","accessLevel":"public","references":["https://doi.org/10.5194/acp-2018-736"],"contactPoint":{"hasEmail":"mailto:anna.karion@nist.gov","fn":"Anna Karion"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/808B2042D1696C0BE05324570681AB642018","description":"Please refer to the publication, particularly the information in the Supplementary Information, for details on the model configuration.Footprint files:Footprints generated every 30 seconds along flight paths using different models are archived as netCDF files.  Each tar.gz file contains all the netCDF footprints for a particular flight and model combination, and is named after the model combination and flight date (YYYYMMDD). For example the WRF-HYSPLIT combination for the flight on 10/19/2013 referenced in the paper is in:WRF-HYSPLIT/WRFHYS_Footprints_20131019.tar.gzMost flight footprints were run for 24 hours back in time but there are a few exceptions, so the files must be opened to see how far back the footprints go.  A few flights use shorter times, and on 10/28/2013 WRF-HYSPLIT was run for 36 hours.  The HYSPLIT footprint files are numbered consecutively in order of time.  The location and time of the receptor is in the NetCDF file itself, not in the filename.  The STILT filenames have location and time information but only to the nearest minute, so the numerical order in the filename indicates their time order, because there are two footprints each minute.  STILT and HYSPLIT footprints all also contain particle trajectories.  The receptor location and time is in the NetCDF file as origutctime, origlat, origlong, etc.The files include footprints from basic model runs referenced in Figures 4 and 7, that is NAMS-HYSPLIT, WRF-HYSPLIT, and WRF-STILT.  WRF-STILT for flights on 3/25, 3/27, 3/30 and 10/16 uses instantaneous wind output; WRF-STILT for 10/19, 10/20, 10/25, and 10/28 flights uses averaged wind fields (see the SI in the manuscript for details).  For WRF2-Flexpart footprints, please contact Wayne.Angevine@noaa.gov.  For CarbonTracker Lagrange WRF-STILT footprints, contact Arlyn.Andrews@noaa.gov. For WRF-LPDM footprints, contact Thomas Lauvaux (thomas.lauvaux@lsce.ipsl.fr).WRF-Chem output files:WRF-Chem output for 4 flights in October 2013 is also included here, both in native NetCDF format and in ARL format.  These contain 3-km resolution, hourly WRF-Chem output.  ARL files can be used to run HYSPLIT or STILT.  The NetCDF formatted files include the tracer variables for CH4 from both the EPA inventory (tracer_1 variable) and the EDF inventory used in the paper (tracer_2 variable).  The third tracer (tracer_3) is using the EPA inventory but masked so that emissions outside the domain of the EDF inventory are zero (so that they can be directly compared).  Only tracer_2 is used in the manuscript.  The NetCDF output files are combined into tar.gz files.  Please contact Thomas Lauvaux (thomas.lauvaux@lsce.ipsl.fr) for details regarding WRF-Chem.Observations:Observations from the Barnett flight campaign are made available by NOAA/ESRL, please contact Colm.Sweeney@noaa.gov.","language":["en"],"title":"Model output associated with \"Inter-comparison of Atmospheric Trace Gas Dispersion Models: Barnett Shale Case Study\", Atmospheric Chemistry and Physics, 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The experiment methodically varied part position and orientation relative to the build plate and recoater blade and details of the experiment and dataset are available in a Journal of Research at NIST article.","language":["en"],"title":"Variation of Surface Topography in Laser Powder Bed Fusion Additive Manufacturing of Nickel Super Alloy 625","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/Orientation%20and%20Sample%20Data.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/Orientation%20and%20Sample%20Data.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV2.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV2.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV1.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV1.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV3.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV3.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV9.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV9.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV4.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV4.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV5.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV5.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV6.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV6.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV7.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV7.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV8.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/809B1360E1345A54E0532457068169A42020/STV8.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32020","title":"DOI Access for Variation of Surface Topography in Laser Powder Bed Fusion Additive Manufacturing of Nickel Super Alloy 625"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-08-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Additive manufacturing"],"keyword":["Additive manufacturing","focus variation","IN625","laser powder bed fusion","nickel super alloy 625","surface texture","surface topography"]},{"identifier":"80AFCBD8D53C1969E053245706818B502021","accessLevel":"public","contactPoint":{"hasEmail":"mailto:alan.zheng@nist.gov","fn":"Xiaoyu Alan Zheng"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://tsapps.nist.gov/NRBTD/","description":"2D image and 3D topography measurements of fired bullets and cartridge cases. Regions of interest include land impressions, breechface, firing pins, and aperture shears.","language":["en"],"title":"NIST Ballistics Toolmark Research Database","distribution":[{"accessURL":"https://tsapps.nist.gov/NRBTD/","format":"ISO 25178-72 X3P format for 3D data. 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Bernstein"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/81DDA0CEDF474150E0532457068141292027","description":"This dataset represents a reference implementation of the Unit Manufacturing Process (UMP) information model presented in ASTM E3012, Standard Guide for Characterizing Environmental Aspects of Manufacturing Processes.  A version of this schema is used in the UMP Builder, a web-based toolkit for recording and storing UMP models.","language":["en"],"title":"A Reference Schema for the Unit Manufacturing Process Information Model","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/81DDA0CEDF474150E0532457068141292027/UMPSchema.xsd","mediaType":"application/xml"},{"downloadURL":"https://data.nist.gov/od/ds/81DDA0CEDF474150E0532457068141292027/UMPSchema.xsd.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32027","title":"DOI Access for A Reference Schema for the Unit Manufacturing Process Information Model"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-02-13","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Systems integration","Manufacturing:Sustainable manufacturing","Manufacturing:Manufacturing systems design and analysis","Manufacturing:Systems engineering"],"conformsTo":"https://www.astm.org/Standards/E3012.htm","keyword":["Sustainable Manufacturing","ASTM E3012","Information Modeling","Manufacturing Process Models","Systems Engineering"]},{"identifier":"848DBEBF057D6362E053245706813C122041","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2038"],"contactPoint":{"hasEmail":"mailto:matthew.hoehler@nist.gov","fn":"Matthew Hoehler"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/el/fire-research-division-73300/national-fire-research-laboratory-73306/influence-fire-lateral-0","description":"Twenty-two 2.7 m by 3.7 m shear wall specimens consisting of 150 mm wide CFS framing were tested.  All walls were designed to achieve a 1-hour fire-resistance rating per American Society for Testing and Materials (ASTM) standard E119. Heating was applied to the side of the wall opposite to the side where the shear-resisting elements are located; it is assumed that the shear-resisting elements line an egress corridor and the fire occurs in a room adjacent to the corridor.  The walls are designed using Allowable Stress Design nominally following American Iron and Steel Institute (AISI) standards S400-15/S1-16 and AISI S100-16.","language":["en"],"title":"Data from Influence of Fire on the Lateral Resistance of Cold-Formed Steel Shear Walls - Phase 2","distribution":[{"accessURL":"https://doi.org/10.18434/M32041","title":"DOI Access to Dataset from Influence of Fire on the Lateral Resistance of Cold-Formed Steel Shear Walls - Phase 2"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-03-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Fire:Structural fire resistance"],"keyword":["fire","shear wall","cold-formed steel","earthquake","NFRL","oriented strand board","strap braced","steel sheathed"]},{"identifier":"84ADF76F1D1F6427E05324570681129F2042","accessLevel":"public","contactPoint":{"hasEmail":"mailto:douglas.thomas@nist.gov","fn":"Douglas Thomas"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/services-resources/software/monte-carlo-tool","description":"This tool is used to implement Monte Carlo analysis, which uses probabilistic sensitivity analysis to account for uncertainty. This tool is developed to follow the simulation segment of ASTM E1369. This technique involves a method of model sampling. Specification involves defining which variables are to be simulated, the distribution of each of these variables, and the number of iterations performed. The software then randomly samples from the probabilities for each input variable of interest. Three common distributions that are used include triangular, normal, and uniform.\n\nTo illustrate, consider a situation where a firm has to purchase 100 ball bearings at $10 each; however, the price can vary plus or minus $2. In order to address this situation, one can use a Monte Carlo analysis where the price is varied using a triangular distribution with $12 being the maximum, $8 being the minimum, and $10 being the most likely. Moreover, the anticipated results should have a low value of approximately $800 (i.e., 100 ball bearings at $8 each) and a high value of approximately $1200 (i.e., 100 ball bearings at $12 each). The triangular distribution would make it so the $8 price and $12 price have lower likelihoods. For a Monte Carlo analysis, one must select the number of iterations that the simulation will run. Each iteration is similar to rolling a pair of dice, albeit, with the probabilities having been altered. In this case, the dice determine the price of the bearings. The number of iterations is the number of times this simulation is calculated (i.e., the number of times the dice is rolled). \n","language":["en"],"title":"Monte Carlo Tool","distribution":[{"accessURL":"https://doi.org/10.18434/M32042","title":"DOI Access for Monte Carlo Tool"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-03-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Lean manufacturing","Manufacturing:Manufacturing systems design and analysis","Manufacturing:Factory operations planning and control"],"issued":"2019-08-02","keyword":["monte carlo","simulation","manufacturing","cost","economics"]},{"identifier":"84F1B343E11C27CCE0532457068154222043","accessLevel":"public","contactPoint":{"hasEmail":"mailto:demian.riccardi@nist.gov","fn":"Demian Riccardi"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/84F1B343E11C27CCE0532457068154222043","description":"Combustion calorimetry is the main method for the determination of enthalpies of formation for organic compounds. Rigorous application of the method uses a 100-step procedure, sometimes called Washburn corrections, to convert the experimental results into standard thermodynamic quantities. Because every laboratory uses its own in-house software implementing this procedure, which is often not available for verification or testing, it is difficult to fully assess experimental results. Furthermore, these programs often use obsolete reference values of thermodynamic properties. This Standard Reference Database (SRD) introduces a standard procedure for this conversion. All experimental data used in this procedure (second virial coefficients of gas mixtures, densities, solubilities of gases in water and electrolyte solutions, etc.) have been reviewed by NIST personnel and the most reliable values have been recommended. The working equations were revised where necessary. Consistent with the NIST efforts on developing publication standards, this SRD also provides a resource essential to reproducible publications and interlaboratory exchange of the combustion calorimetry results. The primary users are thermochemical laboratories worldwide. This SRD will also benefit current practitioners in industry and future investigators through incorporation into university coursework.  Please see the supporting publication for details: doi:10.1016/j.jct.2021.106425","language":["en"],"title":"Combustion Calorimetry Tool - NIST Standard Reference Database 206","distribution":[{"accessURL":"https://trc.nist.gov/cctool/","title":"Combustion Calorimetry Tool"},{"accessURL":"https://doi.org/10.18434/M32043","title":"DOI Access for Combustion Calorimetry Tool - NIST Standard Reference Database 206"},{"accessURL":"https://doi.org/10.1016/j.jct.2021.106425","title":"Supporting Publication: Corrections to standard state in combustion calorimetry: an update and a web-based tool"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference data","Chemistry:Chemical thermodynamics and chemical properties","Chemistry:Thermochemical properties","Physics:Thermodynamics"],"keyword":["Thermochemistry","Combustion Calorimetry","Organosulfur Compounds","Standard-State Corrections","Washburn Corrections"]},{"identifier":"85196AB9232E7202E053245706813DFA2044","accessLevel":"public","references":["https://doi.org/10.6028/jres.124.033","https://doi.org/10.1016/j.addma.2020.101383"],"contactPoint":{"hasEmail":"mailto:brandon.lane@nist.gov","fn":"Brandon Lane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/85196AB9232E7202E053245706813DFA2044","description":"This dataset includes the files pertaining to a 3D additive manufacturing build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung on July 8, 2018. 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How much do manufacturers spend on lighting?\n","language":["en"],"title":"Manufacturing Cost Guide","distribution":[{"accessURL":"https://doi.org/10.18434/M32048","title":"DOI Access for Manufacturing Cost Guide"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-03-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Manufacturing systems design and analysis","Manufacturing:Supply chain","Manufacturing:Lean manufacturing","Manufacturing:Sustainable manufacturing"],"issued":"2019-08-02","keyword":["manufacturing","cost","input output","economics","labor","maintenance","energy","value added","supply chain","environmental impact","environmental sustainability"]},{"identifier":"857B42B5ABEE3B2FE053245706817AD02049","accessLevel":"public","contactPoint":{"hasEmail":"mailto:anirudha.sahoo@nist.gov","fn":"Anirudha Sahoo"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/857B42B5ABEE3B2FE053245706817AD02049","description":"This dataset contains results of our simulation experiments carried out for the Optimal Dynamic Spectrum Access Scheme to utilize white space in LTE Systems.  The results were published in the proceedings of IEEE WCNC 2019 with the title \"Optimal Dynamic Spectrum Access Scheme for Utilizing White Space in LTE Systems\".  One set of data belongs to output of experiments run against LTE data captured in our lab and corresponds to various configurations used in our experiment (please see the paper for different configuration). Python scripts are provided to process theses data files and plot the graphs published in the paper. Another set of data corresponds to the experiments run against LTE data collected in the metro Philadelphia area with different configuration (please refer to the paper for different configurations used with these experiments). Python scripts are also provided to process the data files and obtain the graphs presented in the paper.","language":["en"],"title":"Optimal Dynamic Spectrum Access Scheme for Utilizing White Space in LTE 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications"],"keyword":["LTE; dynamic spectrum access; white space utilization; probability of interference"]},{"identifier":"857DE783740035BDE0532457068163002050","accessLevel":"public","contactPoint":{"hasEmail":"mailto:anirudha.sahoo@nist.gov","fn":"Anirudha Sahoo"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/857DE783740035BDE0532457068163002050","description":"This dataset contains the result from simulation experiments carried out for our study of analytical modeling of white space utilization for a Dynamic Spectrum Access (DSA) system. It contains the data for the results obtained while comparing analytical and simulated white space utilization (WSU) for synthetic data.  It also contains Q-Q plot data which was used to show that the data collected from the metro Philadelphia area is approximately exponentially distributed. It has the histogram of channel 15 and 16 of Philadelphia LTE uplink data. Finally, it has the data that corresponds to the results of comparing analytical and simulated WSU for channel 15 and 16 of Philadelphia LTE data. Also included are the python scripts that processes the data files and plots the graphs.","language":["en"],"title":"Analytical Modeling of White Space Utilization for a Dynamic Spectrum Access 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Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Atomic, molecular, and quantum"],"keyword":["quantum physics","quantum enhanced metrology","squeezing"]},{"identifier":"861B3A62A69C595FE05324570681235F2055","accessLevel":"public","references":["https://www.nist.gov/publications/practical-approach-place-coastal-sensors-spectrum-sharing-35-ghz-band"],"contactPoint":{"hasEmail":"mailto:thao.t.nguyen@nist.gov","fn":"Thao T. 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The results include sensor location, antenna height, probability of detection coverage, probability of false alarm for each Dynamic Protection Area (DPA), i.e., a predefined geographic area inside which radar may experience harmful interference.","language":["en"],"title":"A Practical Approach to Placing Coastal Sensors for Spectrum Sharing in the 3.5 GHz Band","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/861B3A62A69C595FE05324570681235F2055/East_results.npz","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/861B3A62A69C595FE05324570681235F2055/East_results.npz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/861B3A62A69C595FE05324570681235F2055/West_results.npz","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/861B3A62A69C595FE05324570681235F2055/West_results.npz.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32055","title":"DOI Access for A Practical Approach to Placing Coastal Sensors for Spectrum Sharing in the 3.5 GHz Band"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-04-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Electronics:Sensors","Advanced Communications:Wireless (RF)"],"issued":"2019-10-07","keyword":["3.5 GHz; CBRS; Environmental Sensing Capability (ESC) sensor; radar; Dynamic Protection Area (DPA)"]},{"identifier":"8620C5D3EB475986E0532457068118922056","accessLevel":"public","references":["https://arxiv.org/abs/1902.02891"],"contactPoint":{"hasEmail":"mailto:yong.wan@nist.gov","fn":"Yong Wan"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/8620C5D3EB475986E0532457068118922056","description":"Data for \"Quantum gate teleportation between separated qubits in a trapped-ion processor\"","language":["en"],"title":"Data for \"Quantum gate teleportation between separated qubits in a trapped-ion processor\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/8620C5D3EB475986E0532457068118922056/data_science.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/8620C5D3EB475986E0532457068118922056/data_science.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/8620C5D3EB475986E0532457068118922056/Tomography%20Data%20Set%201_2018_06_01-04.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/8620C5D3EB475986E0532457068118922056/Tomography%20Data%20Set%202_2018_06_06-07.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32056","title":"DOI Access for Data for \"Quantum gate teleportation between separated zones of a trapped-ion processor\""},{"downloadURL":"https://data.nist.gov/od/ds/8620C5D3EB475986E0532457068118922056/Tomography%20Data%20Set%201_2018_06_01-04.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/8620C5D3EB475986E0532457068118922056/Tomography%20Data%20Set%202_2018_06_06-07.zip","mediaType":"application/zip"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-04-09","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"keyword":["quantum gate teleportation","trapped ions","scalable quantum computing"]},{"identifier":"862EB42004B00A97E05324570681ABB22059","accessLevel":"public","contactPoint":{"hasEmail":"mailto:gregory.moille@nist.gov","fn":"Gregory Moille"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/862EB42004B00A97E05324570681ABB22059","description":"We present the development of pyLLE, a freely accessible Lugiato-Lefever equation solver programmed in Python and Julia and optimized for the simulation of microresonator frequency combs. 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Dataset accompanying publication \"Seasonally resolved excess urban methane emissions from the Baltimore/Washington, DC metropolitan region\" by Huang, Y., Kort, E. A., Gourdji, S., Karion, A., Mueller, K., and Ware, J.: Seasonally Resolved Excess Urban Methane Emissions from the Baltimore/Washington, DC Metropolitan Region, Environ. Sci. Technol., 10.1021/acs.est.9b02782, 2019. Data is described further in the publication and readme document.","language":["en"],"title":"Methane mole fraction measurements in the Northeast Corridor: Baltimore/Washington DC: Jan 1 2016 - Dec 31 2016","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/86A6B37B6BFE2E61E0532457068108D72062/ARL-2016-ch4-92m-1-hour-20180223.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/86A6B37B6BFE2E61E0532457068108D72062/ARL-2016-ch4-92m-1-hour-20180223.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/86A6B37B6BFE2E61E0532457068108D72062/BUC-2016-ch4-75m-1-hour-20180223.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/86A6B37B6BFE2E61E0532457068108D72062/BUC-2016-ch4-75m-1-hour-20180223.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/86A6B37B6BFE2E61E0532457068108D72062/HAL-2016-ch4-58m-1-hour-20180223.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/86A6B37B6BFE2E61E0532457068108D72062/HAL-2016-ch4-58m-1-hour-20180223.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/86A6B37B6BFE2E61E0532457068108D72062/NDC-2016-ch4-91m-1-hour-20180223.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/86A6B37B6BFE2E61E0532457068108D72062/NDC-2016-ch4-91m-1-hour-20180223.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/86A6B37B6BFE2E61E0532457068108D72062/NEC_Data_Readme.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/86A6B37B6BFE2E61E0532457068108D72062/NEC_Data_Readme.txt.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32062","title":"DOI Access for Methane mole fraction measurements in the Northeast Corridor: Baltimore/Washington DC: Jan 1 2016 - Dec 31 2016"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-02-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Environment:Greenhouse gas measurements"],"spatial":"-78.4, 38.4, -76.2, 39.6","keyword":["Greenhouse gas","methane","urban emissions"],"temporal":"2016-01-01/2016-12-31"},{"identifier":"86C039BC7729668CE05324570681CDA42064","accessLevel":"public","contactPoint":{"hasEmail":"mailto:charles.camp@nist.gov","fn":"Charles Camp Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pages.nist.gov/pyMCR","description":"pyMCR is an open-source software library for performing multivariate curve resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a chemometric method for elucidating signatures of analytes and their relative abundance from a series of mixture measurements, without any knowledge of these values a priori. This software library, written in Python, enables users to perform MCR analysis with their choice of constraints, regressors, and error functions to minimize. Further, users can apply different constraints and regressors for signature and abundance calculations. Finally, this library enables users to develop their own constraints, regressors, and error functions or import them from existing libraries.","language":["en"],"title":"pyMCR: A Python Library for Multivariate Curve Resolution Analysis.","distribution":[{"downloadURL":"https://github.com/usnistgov/pyMCR","format":"Python source code","description":"pyMCR is a small package for performing multivariate curve resolution. Currently, it implements a simple alternating regression scheme (MCR-AR).","mediaType":"text/plain","title":"pyMCR: Multivariate Curve Resolution for Python"},{"accessURL":"https://doi.org/10.18434/M32064","title":"DOI Access for pyMCR: A Python Library for Multivariate Curve Resolution Analysis."}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-04-19","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1M","theme":["Mathematics and Statistics:Numerical methods and software","Mathematics and Statistics:Image and signal processing","Chemistry:Analytical chemistry"],"keyword":["chemometrics","endmember extraction","multivariate curve resolution","quantitative analysis","spectral unmixing"]},{"identifier":"86CF87E90A931F6FE05324570681D1922065","accessLevel":"public","references":["https://dx.doi.org/10.1021/acscombsci.8b00158"],"contactPoint":{"hasEmail":"mailto:brian.decost@nist.gov","fn":"Brian DeCost"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/86CF87E90A931F6FE05324570681D1922065","description":"The open dataset accompanying An Inter-Laboratory Study of Zn-Sn-Ti-O Thin Films using High-throughput Experimental Methods (https://pubs.acs.org/doi/10.1021/acscombsci.8b00158).High-throughput experimental (HTE) techniques are an increasingly important way to accelerate the rate of materials research and development for many technological applications. However, there are very few publications on the reproducibility of the HTE results obtained across different laboratories for the same materials system, and on the associated sample and data exchange standards. Here, we report a comparative study of Zn-Sn-Ti-O thin films materials using high-throughput experimental methods at National Institute of Standards and Technology (NIST) and National Renewable Energy Laboratory (NREL). The thin film sample libraries were synthesized by combinatorial physical vapor deposition (co-sputtering and pulsed laser deposition), and characterized by spatially-resolved techniques for composition, structure, thickness, optical and electrical properties. The results of this study indicate that all these measurement techniques performed at two different laboratories show excellent qualitative agreement. The quantitative similarities and differences vary by measurement type, with 95% confidence interval of 0.1-0.2 eV for the band gap, 24-29 nm for film thickness, and 0.08 to 0.37 orders of magnitude for sheet resistance. Overall, this work serves as a case study for the feasibility of a High-Throughput Experimental Materials Collaboratory (HTE-MC) by demonstrating the exchange of high-throughput sample libraries, workflows and data.","language":["en"],"title":"Dataset: An Inter-Laboratory Study of Zn-Sn-Ti-O Thin Films using High-throughput Experimental Methods","distribution":[{"accessURL":"https://doi.org/10.18434/M32065","title":"DOI Access for Dataset: An Inter-Laboratory Study of Zn-Sn-Ti-O Thin Films using High-throughput Experimental Methods"},{"accessURL":"https://github.com/usnistgov/NIST-NREL-Interlaboratory-Figures","title":"NIST-NREL-Interlaboratory-Figures"},{"downloadURL":"https://data.nist.gov/od/ds/86CF87E90A931F6FE05324570681D1922065/round-robin-data-update.zip","mediaType":"application/zip","title":"round-robin-data-update.zip"},{"downloadURL":"https://data.nist.gov/od/ds/86CF87E90A931F6FE05324570681D1922065/README.TXT","mediaType":"text/plain","title":"README.TXT"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-03-19 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry","Materials"],"keyword":["high throughput","collaboratory","thin film","open data","diffraction","phase mapping","band gap"]},{"identifier":"884B718C01370FAAE05324570681F31F2067","accessLevel":"public","references":["https://www.nist.gov/publications/overview-smart-manufacturing-system-readiness-assessment","https://www.nist.gov/publications/improvement-strategies-manufacturers-using-mesa-mom-capability-maturity-model","https://www.nist.gov/publications/maturity-models-and-tools-enabling-smart-manufacturing-systems-comparison-and","https://www.nist.gov/publications/towards-platform-smart-manufacturing-improvement-planning","https://www.nist.gov/publications/analysis-technologies-and-standards-designing-smart-manufacturing-systems","https://doi.org/10.6028/jres.121.021","https://www.nist.gov/publications/activity-model-factory-design-and-improvement","https://doi.org/10.1080/09537287.2016.1237686"],"contactPoint":{"hasEmail":"mailto:boonserm.kulvatunyou@nist.gov","fn":"Boonserm Kulvatunyou"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/services-resources/software/smart-manufacturing-systems-readiness-level-smsrl-tool","description":"The Smart Manufacturing Systems Readiness Level (SMSRL) focuses on evaluating the readiness (also can be viewed as maturity) for a factory to undergo improvements, particularly related to the data intensive smart manufacturing technology deployment in a factory. The SMSRL uses the Factory Design and Improvement (FDI) activity model as a guide for indicating actions and things that should be in place for performing a successful smart factory transformation at the operational level. In the SMSRL, activities employed from the FDI are subdivided into their applicability at the various control levels of ISA-88: Enterprise, Site, Area, Process Cell, Unit, Equipment Module, and Control Module. Each activity is measured under multiple dimensions such as Activity Management, Designated Personnel, Software System, Output Data Format, KPIs and KPI relationship, all of which are grouped into 4 measurement categories (C1: Organizational Maturity, C2: IT Applications Maturity, C3: Performance Management Maturity and C4: Information Connectivity Maturity). Each of the measurement categories has its own calculation method to quantify the maturity level; and it is used for deriving customized factory improvement plans.\n\nNote: The tool only works with Windows-based Microsoft Excel.","language":["en"],"title":"Smart Manufacturing Systems Readiness Level (SMSRL) Tool","distribution":[{"accessURL":"https://doi.org/10.18434/M32067","title":"DOI access to Smart Manufacturing Systems Readiness Level (SMSRL) Tool"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-11-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Factory operations planning and control","Manufacturing:Interoperability in manufacturing","Manufacturing:Manufacturing systems design and analysis"],"issued":"2020-04-16","keyword":["factory design","manufacturing operation","improvement","smart manufacturing","maturity assessment","readiness assessment"]},{"identifier":"884C42D607634FA1E053245706811BE62068","accessLevel":"public","references":["https://www.nist.gov/publications/overview-smart-manufacturing-system-readiness-assessment","https://www.nist.gov/publications/improvement-strategies-manufacturers-using-mesa-mom-capability-maturity-model","https://www.nist.gov/publications/maturity-models-and-tools-enabling-smart-manufacturing-systems-comparison-and","https://www.nist.gov/publications/towards-platform-smart-manufacturing-improvement-planning","https://www.nist.gov/publications/analysis-technologies-and-standards-designing-smart-manufacturing-systems","https://doi.org/10.6028/jres.121.021","https://www.nist.gov/publications/activity-model-factory-design-and-improvement","https://doi.org/10.1080/09537287.2016.1237686"],"contactPoint":{"hasEmail":"mailto:boonserm.kulvatunyou@nist.gov","fn":"Boonserm Kulvatunyou"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/services-resources/software/mesa-manufacturing-operation-management-maturity-assessment-tool","description":"MESA MOM/CMM (Manufacturing Operations Management / Capability Maturity Model) Assessment Tool is a questionnaire-based tool designed to help evaluate maturity and readiness of manufacturing enterprises from the factory operation management perspective. Based on level 3 of ISA-95: Part 1 MOM processes, MOM/CMM defines evaluation criteria for four operational areas namely, productions operations management, quality operations management, inventory operations management and maintenance operations management. Each operational area consists of a set of activities including detailed scheduling, dispatching, execution management, resource management, definition management, data collection and tracking and performance analysis. With the tool, users can assess their maturity level (0 to 5) of each activity independently, i.e., they can pick and choose activities and operational areas they would like to assess in any order.\n\nNote: The tool only works with Windows-based Microsoft Excel.","language":["en"],"title":"MESA Manufacturing Operation Management Maturity Assessment Tool","distribution":[{"accessURL":"https://doi.org/10.18434/M32068","title":"DOI access to MESA Manufacturing Operation Management Maturity Assessment Tool"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-11-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Factory operations planning and control","Manufacturing:Interoperability in manufacturing","Manufacturing:Manufacturing systems design and analysis"],"issued":"2020-04-16","keyword":["manufacturing operation management","MESA","ISA-95","IEC 62264","maturity model","assessment"]},{"identifier":"88525F0C8F94562FE05324570681843C2069","accessLevel":"public","contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/itl/iad/sharp-shape-analysis-research-project","description":"We have applied 3D shape-based retrieval to various disciplines such as computer vision, CAD/CAM, computer graphics, molecular biology and 3D anthropometry. We have organized two workshops on 3D shape retrieval and two shape retrieval contests. We also have developed 3D shape benchmarks, performance evaluation software and prototype 3D retrieval systems. We have developed a robotic map quality assessment tool in collaboration with MEL) We also have developed different shape descriptors to represent 3D human bodies and heads efficiently and other work related to 3D anthropometry. Finally, we also have done some in a Structural Bioinformatics, Bio-Image analysis and retrieval.","language":["en"],"title":"SHARP - Shape Analysis Research Project","distribution":[{"accessURL":"https://doi.org/10.18434/M32069","title":"DOI Access for SHARP - Shape Analysis Research Project"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-05-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Visualization research"],"keyword":["3D Shape Retrieval","search technology"]},{"identifier":"891A0F41384D739AE05324570681A6422071","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8183A-2-draft"],"contactPoint":{"hasEmail":"mailto:cheeyee.tang@nist.gov","fn":"CheeYee Tang"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/891A0F41384D739AE05324570681A6422071","description":"The Cybersecurity Framework Manufacturing Profile Low Security Level Example Implementations Guide provides example proof-of-concept solutions demonstrating how open-source and commercial off-the-shelf (COTS) products that are currently available today can be implemented in manufacturing environments to satisfy the requirements in the Cybersecurity Framework (CSF) Manufacturing Profile [8] Low Security Level. Example proof-of-concept solutions for a process-based manufacturing environment and a discrete-based manufacturing environment are included in the guide. Depending on factors like size, sophistication, risk tolerance, and threat landscape, manufacturers should make their own determinations about the breadth of the proof-of-concept solutions they may voluntarily implement. The dataset records the Key Performance Indicator (KPI) for the example implementation of the process-based manufacturing system use case.","language":["en"],"title":"Cybersecurity Framework Manufacturing Profile Low Security Level Example Implementations for Process-based Manufacturing System Datasets","distribution":[{"accessURL":"https://doi.org/10.18434/M32071","title":"DOI Access for Cybersecurity Framework Manufacturing Profile Low Security Level Example Implementations for Process-based Manufacturing System Datasets"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-05-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Cybersecurity","Manufacturing:Manufacturing systems design and analysis","Manufacturing:Process measurement and control"],"keyword":["cybersecurity","smart manufacturing","industrial control system","process control system"]},{"identifier":"896E2306582D5362E0532457068134D12072","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8183A-3-draft"],"contactPoint":{"hasEmail":"mailto:timothy.zimmerman@nist.gov","fn":"Timothy Zimmerman"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/896E2306582D5362E0532457068134D12072","description":"The Cybersecurity Framework Manufacturing Profile Low Security Level Example Implementations Guide provides example proof-of-concept solutions demonstrating how open-source and commercial off-the-shelf (COTS) products that are currently available today can be implemented in manufacturing environments to satisfy the requirements in the Cybersecurity Framework (CSF) Manufacturing Profile Low Security Level. Example proof-of-concept solutions for a process-based manufacturing environment and a discrete-based manufacturing environment are included in the guide. Depending on factors like size, sophistication, risk tolerance, and threat landscape, manufacturers should make their own determinations about the breadth of the proof-of-concept solutions they may voluntarily implement. The dataset includes all of the raw and processed measurement data for the example implementation of the discrete-based manufacturing system use case.","language":["en"],"title":"Cybersecurity Framework Manufacturing Profile Low Security Level Example Implementations for Discrete-based Manufacturing System Datasets","distribution":[{"accessURL":"https://www.nist.gov/el/intelligent-systems-division-73500/cybersecurity-framework-manufacturing-profile-example","title":"Cybersecurity Framework Manufacturing Profile Example Implementations Datasets"},{"accessURL":"https://doi.org/10.18434/M32072","title":"DOI Access for Cybersecurity Framework Manufacturing Profile Low Security Level Example Implementations for Discrete-based Manufacturing System Datasets"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-05-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Cybersecurity"],"keyword":["cybersecurity","smart manufacturing","industrial control system","discrete manufacturing","robotics"]},{"identifier":"89811452D29952B5E05324570681C0AF2073","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jeeseong.hwang@nist.gov","fn":"Jeeseong C. Hwang"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/89811452D29952B5E05324570681C0AF2073","description":"This work involves only data processing software and does not involve data to publish. Data are acquired by the users of this software by the following methods. Two methods can be used to acquire images of agar plates with bacterial colonies grown. In the first, plates were placed on a flatbed scanner, covered by soft gloss paper (HP LaserJet soft gloss paper), and 8-bit grayscale images of the plates were obtained at 300 dpi (dots per inch; 118 dots per cm), 600 dpi (236 dots per cm), and 1,200 dpi (472 dots per cm). In the second approach, images are acquired with a digital camera. Images are acquired at the maximum resolution of the camera with no zoom. More details are described in the manual for the software.","language":["en"],"title":"NIST's Integrated Colony Enumerator","distribution":[{"accessURL":"https://github.com/usnistgov/NICE-Public","format":"Demo images in TIFF and code in MATLAB 2016b","description":"This work involves only data processing software and does not involve data to publish. Data are acquired by the users of this software by the following methods. Two methods can be used to acquire images of agar plates with bacterial colonies grown. In the first, plates were placed on a flatbed scanner, covered by soft gloss paper (HP LaserJet soft gloss paper), and 8-bit grayscale images of the plates were obtained at 300 dpi (dots per inch; 118 dots per cm), 600 dpi (236 dots per cm), and 1,200 dpi (472 dots per cm). In the second approach, images are acquired with a digital camera. Images are acquired at the maximum resolution of the camera with no zoom. More details are described in the manual for the software.","title":"NIST's integrated colony enumerator source code repository"},{"accessURL":"https://doi.org/10.18434/M32073","title":"DOI Access for NIST's Integrated Colony Enumerator"},{"downloadURL":"https://data.nist.gov/od/ds/89811452D29952B5E05324570681C0AF2073/Demo%20Images.zip","mediaType":"application/x-zip-compressed","title":"Demo Images for Tutorial"},{"downloadURL":"https://data.nist.gov/od/ds/89811452D29952B5E05324570681C0AF2073/Demo%20Images.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Demo Images for Tutorial"},{"downloadURL":"https://data.nist.gov/od/ds/89811452D29952B5E05324570681C0AF2073/NICE_1.4_Essential.zip","mediaType":"application/x-zip-compressed","title":"NICE essential files V1.4"},{"downloadURL":"https://data.nist.gov/od/ds/89811452D29952B5E05324570681C0AF2073/NICE_1.4_Essential.zip.sha256","mediaType":"text/plain","title":"SHA256 File for NICE essential files V1.4"},{"downloadURL":"https://data.nist.gov/od/ds/89811452D29952B5E05324570681C0AF2073/NICE_Manual_April_2020.zip","mediaType":"application/x-zip-compressed","title":"NICE Manual V1.4 April 2020"},{"downloadURL":"https://data.nist.gov/od/ds/89811452D29952B5E05324570681C0AF2073/NICE_Manual_April_2020.zip.sha256","mediaType":"text/plain","title":"SHA256 File for NICE Manual V1.4 April 2020"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-06-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Bioscience","Information Technology:Software research"],"keyword":["bacteria colony counting; pneumonia; pneumococcus; vaccine; counting software; fieldable imaging; multiplexed opsonophagocytic killing assay;"]},{"identifier":"8A113AEB4BC110BCE05324570681DBE22076","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jennifer.lynch@nist.gov","fn":"Jennifer Lynch"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/8A113AEB4BC110BCE05324570681DBE22076","description":"Supporting information documents for Brignac et al manuscript entitled, \"Marine Debris Polymers on Main Hawaiian Island Beaches, Sea Surface, and Seafloor.\"  Files include raw data tables of each debris item that was categorized and measured as well as additional figures of interpreted data.","language":["en"],"title":"Brignac et al. Marine Debris Polymers on Main Hawaiian Island Beaches, Sea Surface, and Seafloor Supporting Information","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/8A113AEB4BC110BCE05324570681DBE22076/Brignac%20et%20al_Supporting%20info%20Tables%205-29-19.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Brignac et al Supporting Information Tables"},{"downloadURL":"https://data.nist.gov/od/ds/8A113AEB4BC110BCE05324570681DBE22076/Brignac%20et%20al_Supporting%20info%20Tables%205-29-19.xlsx","format":"Microsoft Excel","description":"Tables of raw data concerning Hawaiian marine debris type, size, weight, color, and polymer identifications.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Brignac et al Supporting Information Tables"},{"downloadURL":"https://data.nist.gov/od/ds/8A113AEB4BC110BCE05324570681DBE22076/Brignac%20et%20al_Supporting%20info%20Figures%205-29-2019.docx.sha256","mediaType":"text/plain","title":"SHA256 File for Brignac et al Supporting Information Figures"},{"downloadURL":"https://data.nist.gov/od/ds/8A113AEB4BC110BCE05324570681DBE22076/Brignac%20et%20al_Supporting%20info%20Figures%205-29-2019.docx","format":"Microsoft Word","description":"Additional figures about plastic marine debris found in Hawaii to support Brignac et al manuscript, entitled, \"Marine Debris Polymers on Main Hawaiian Island Beaches, Sea Surface, and Seafloor.\"","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"Brignac et al Supporting Information Figures"},{"accessURL":"https://doi.org/10.18434/M32076","title":"DOI Access for Brignac et al. Marine Debris Polymers on Main Hawaiian Island Beaches, Sea Surface, and Seafloor Supporting Information"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-05-29 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"spatial":"Main Hawaiian Islands","issued":"2020-01-13","keyword":["plastic","polymers","pollution","marine debris","Hawaii","environment","ocean"]},{"identifier":"8A35AAA0486B3B7AE0532457068113532077","accessLevel":"public","references":["https://www.nist.gov/publications/wireless-interference-estimation-using-machine-learning-robotic-force-seeking-scenario"],"contactPoint":{"hasEmail":"mailto:richard.candell@nist.gov","fn":"Rick Candell Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/8A35AAA0486B3B7AE0532457068113532077","description":"Cyber-physical systems are systems governed by the laws of physics that are tightly controlled by computer-based algorithms and network-based sensing and actuation. Wireless communication technology is envisioned to play a primary role in conducting the information flows within such systems. A practical industrial wireless use case involving a robot manipulator control system, an integrated wireless force-torque sensor, and a remote vision-based observer is constructed and the performance of the cyber-physical system is examined. The resulting data from the experiments conducted are included in the dataset.","language":["en"],"title":"Measurement Data for a Wireless Force Seeking Apparatus","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/8A35AAA0486B3B7AE0532457068113532077/Force_Seeking_Dataset_V2.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Dataset for a Wireless Force Seeking Apparatus Measurement Apparatus"},{"downloadURL":"https://data.nist.gov/od/ds/8A35AAA0486B3B7AE0532457068113532077/Force_Seeking_Dataset_V2.zip","format":"ZIP file","mediaType":"application/zip","title":"Dataset for a Wireless Force Seeking Apparatus Measurement Apparatus"},{"accessURL":"https://doi.org/10.18434/M32077","title":"DOI Access for Measurement Data for a Wireless Force Seeking Apparatus"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-05-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Factory operations planning and control","Manufacturing:Factory communications","Advanced Communications:Wireless (RF)"],"keyword":["industrial wireless","802.11","factory communications","cyber-physical systems","wireless networking","robotics"]},{"identifier":"8B3BFE0480BD4943E053245706812C692078","accessLevel":"public","references":["https://www.jove.com/video/58991/cutting-procedures-tensile-testing-ageing-flexible-unidirectional","http://dx.doi.org/10.3791/58991"],"contactPoint":{"hasEmail":"mailto:amy.engelbrecht-wiggans@nist.gov","fn":"Amy Engelbrecht-Wiggans"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/8B3BFE0480BD4943E053245706812C692078","description":"Flexible Unidirectional (UD) composite laminates are commonly being used for ballistic-resistant body armor. These laminates comprise UD layers, each constructed by laminating thin layers of high-performance fibers held in place using binder resins, with the fibers in each layer oriented parallel to each other. As these materials are used in body armor, it is important to investigate their long-term reliability, particularly with regards to exposure to temperature and humidity as these are known causes of degradation in other commonly used body armor materials. This work investigates the tensile behavior of a poly(p-phenylene terephthalamide), reffered to as PPTA, flexible UD laminate, both unaged and aged for up to 150 d at accelerated conditions of 70 °C and 76 % relative humidity (RH). Tests on aged specimens were performed at three different crosshead displacement rates and three different gauge lengths. Unaged characterization additionally included three different widths and two other configurations.\n\nCertain commercial equipment, instruments, or materials are identified in this dataset in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.","language":["en"],"title":"Aged and unaged flexible unidirectional composite laminate tensile testing for soft body armor 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Access for Aged and unaged flexible unidirectional composite laminate tensile testing for soft body armor applications"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-08-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Composites","Materials"],"keyword":["Composite laminate","flexible composite","strip tensile testing","body armor","aramid","hydrothermal ageing"]},{"identifier":"8C18479716ED3F67E053245706818A952080","accessLevel":"public","contactPoint":{"hasEmail":"mailto:adam.pintar@nist.gov","fn":"Adam L. Pintar"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/SLP_data_code","description":"The data and code that accompanies the book chapter Bayesian Hierarchical Models for Service Life Prediction of Polymers.","language":["en"],"title":"Bayesian Hierarchical Models for Service Life Prediction of Polymers Data and Code","distribution":[{"accessURL":"https://doi.org/10.18434/M32080","title":"DOI access to 'Bayesian Hierarchical Models for Service Life Prediction of Polymers Data and  Code'"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-06-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Polymers","Mathematics and Statistics:Modeling and simulation research"],"issued":"2020-04-10","keyword":["Service Life Prediction","Bayesian Hierarchical Model","Polymer Material","Reliability"]},{"identifier":"8C401B5C708647B8E05324570681610A2081","accessLevel":"public","contactPoint":{"hasEmail":"mailto:javier.bernal@nist.gov","fn":"Javier Bernal"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/8C401B5C708647B8E05324570681610A2081","description":"The programs here compute 3x3 matrices of maximal trace over rotation matrices from input matrices. A dxd matrix M is of maximal trace over rotation matrices if given any dxd rotation matrix U, the trace of UM does not exceed that of M. Given a dxd matrix M, the problem of finding among all dxd rotation matrices U one such that UM is of maximal trace over rotation matrices is intricately related to the so-called constrained orthogonal Procrustes problem which is the least-squares problem that calls for a rotation matrix that optimally aligns two corresponding sets of points in d-dimensional Euclidean space. It is well known that computing an optimal U can be achieved with a method based on the computation of the singular value decomposition (SVD) of M.\n\nThe package consists of a Fortran program, a Matlab program, a Matlab mex file of the Fortran program, a compilation of the mex file, and a sample data file of 3x3 matrices. If a 3x3 matrix M is symmetric, without the SVD, the Fortran program computes an optimal 3x3 rotation matrix U for M with an approach based on a trigonometric identity. Otherwise, if M is not symmetric, part of the approach can still be used to compute U with the SVD. However, the option exists in the program to execute a procedure based on the Cayley transform and Newton's method that for each input matrix M computes a rotation matrix U such that UM is symmetric. If the procedure is successful (Newton's method didn't fail) a rotation matrix R (without the SVD) is computed as described above such that RUM is of maximal trace over rotation matrices. If Newton's method fails, then using the SVD, the program computes a rotation matrix R such that RM is of maximal trace over rotation matrices.\n\nNote the following: (a) The option exists in the Fortran code to do everything using the SVD only. (b) If using the Cayley-Newton procedure in the Fortran code, an integer variable named ITEX is set to the maximum number of allowed iterations per input matrix of Newton's method. (c) The Matlab program can be used for the same purposes. The option also exists in the Matlab code to do everything using the Matlab version of the SVD method only. Otherwise the Matlab mex file of the Fortran program is used by the Matlab code. This has the effect of executing the Fortran code as described above from the Matlab code.","language":["en"],"title":"Computation of 3-d matrices of maximal trace over rotations","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/8C401B5C708647B8E05324570681610A2081/Maximal_Trace.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Computation of 3x3 matrices of maximal trace over rotations"},{"downloadURL":"https://data.nist.gov/od/ds/8C401B5C708647B8E05324570681610A2081/Maximal_Trace.zip","description":"Programs and sample input data for computing 3x3 matrices of maximal trace over rotations","mediaType":"application/zip","title":"Computation of 3x3 matrices of maximal trace over rotations"},{"accessURL":"https://doi.org/10.18434/M32081","title":"DOI Access for Computation of 3-d matrices of maximal trace over rotations"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-06-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Image and signal processing"],"keyword":["eigen value","eigen vector","maximal trace","rotation","singular value decomposition"]},{"identifier":"8C40CFA7931709DAE0532457068179072082","accessLevel":"public","references":["https://doi.org/10.1080/00393630.2019.1666467"],"contactPoint":{"hasEmail":"mailto:joshua.taillon@nist.gov","fn":"Joshua Taillon"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This dataset contains X-ray EDS (energy dispersive spectroscopy), FTIR (Fourier transform infrared spectroscopy), and DART-MS (Direct analysis in real time mass spectrometry) data and analysis files that\ncan be used to reproduce the results from the paper:\n\nChristine P. Romano, Thomas Lam, G. Asher Newsome, Joshua A. Taillon, Nicole C. Little, and Jia-sun Tsang. \"Characterization of zinc carboxylates on an oil paint test panel,\" Studies in Conservation, (2019), available at https://doi.org/10.1080/00393630.2019.1666467\n\nFor a detailed description of the files contained within this dataset, please extract the \"README.zip\" file into the root of the dataset folder, and open the \"README.html\" that is placed in the root folder afterwards. This top-level file can be opened with any web browser, and contains links to all the README files in the folders within the dataset. Each of those files contains a more complete description of the data contained within.\n","language":["en"],"title":"Characterization of zinc carboxylates in an oil paint test panel 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Spectroscopy","Materials:Polymers","Materials:Materials characterization","Chemistry:Analytical chemistry"],"keyword":["EDS","EDX","FTIR","DART-MS","Energy dispersive x-ray spectroscopy","fourier transform infrared spectroscopy","direct analysis in real time mass spectrometry","conservation","Zn soaps"]},{"identifier":"8C669FDFEA76120CE0532457068104992083","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8275"],"contactPoint":{"hasEmail":"mailto:alison.kahn@nist.gov","fn":"Alison Kahn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2083","description":"Data collected includes audio device characterization (latency/delay). End to end access time of Land Mobile Radios (LMR) including direct mode operation and trunked mode operation.","language":["en"],"title":"MCV QoE End to End Access Time Measurement Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/8C669FDFEA76120CE0532457068104992083/Access_Time_Data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Example Access Delay Measurement Data"},{"downloadURL":"https://data.nist.gov/od/ds/8C669FDFEA76120CE0532457068104992083/Access_Time_Data.zip","format":"The data is provided as wav files for the audio clips used, and results are presented as csv files.","description":"Example measurement results from the Mission Critical Voice (MCV) Quality of Experience (QoE) access delay measurement system. Access delay and mouth-to-ear latency combine to describe end-to-end access time. In particular access delay describes the minimum amount of time a transmitting user must wait between pressing PTT on a device and starting to speak to ensure that the start of their message is not lost.","mediaType":"application/x-zip-compressed","title":"Example Access Delay Measurement Data"},{"downloadURL":"https://doi.org/10.18434/M32085","format":"Git repository","description":"The purpose of this software is to measure the access delay of a push-to-talk network. Access time generally describes time associated with the initialization and assignment of channels upon user request to speak and has been identified as a key component of quality of experience (QoE) in communications. NIST?s PSCR division developed a method to measure and quantify the access time of any push to talk (PTT) communication system.","mediaType":"application/http","title":"accessTime Repository"},{"accessURL":"https://nist.force.com/pdr?@id=8C669FDFEA76120CE0532457068104992083","description":"This page provides a registration form that must be completed before downloading the data.","title":"Gateway for Registered Data Access"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Purchase is not required for data downloading. Users must complete registration form to download data.","modified":"2019-06-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)","Public Safety:Public safety communications research"],"issued":"2019-10-07","keyword":["Access Delay; Access Time; Articulation Band Correlation Modified Rhyme Test (ABC-MRT); Audio; Communications; Delay; Device to Device (D2D); Direct Mode; Handset;Key Performance Indicator (KPI); Land Mobile Radio (LMR); Latency; Mission CriticalPush  To  Talk  (MCPTT);  Mission  Critical  Voice  (MCV);  Modified  Rhyme  Test  (MRT);Mouth-to-Ear (M2E); Project 25 (P25); Proximity Services (ProSe); Public Safety; PushTo Talk (PTT); Quality of Experience (QoE); Quality of Service (QoS); Repeater;  RootMean Square Error (RMSE); Sidelink; Trunked Mode."]},{"identifier":"8CB5A3BD7FAE7B33E0532457068152C52085","accessLevel":"public","contactPoint":{"hasEmail":"mailto:alison.kahn@nist.gov","fn":"Alison Kahn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2085","description":"This codeset was developed for the QoE MCV end to end access time measurement methodology.","language":["en"],"title":"MCV QoE End to End Access Time Software Code","distribution":[{"accessURL":"https://github.com/usnistgov/accessTime","format":"web site","title":"The accessTime software repository at GitHub"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-07-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research"],"issued":"2019-10-10","keyword":["Access Delay; Access Time; Articulation Band Correlation Modified Rhyme Test (ABC-MRT); Audio; Communications; Delay; Device to Device (D2D); Direct Mode; Handset;Key Performance Indicator (KPI); Land Mobile Radio (LMR); Latency; Mission CriticalPush  To  Talk  (MCPTT);  Mission  Critical  Voice  (MCV);  Modified  Rhyme  Test  (MRT);Mouth-to-Ear (M2E); Project 25 (P25); Proximity Services (ProSe); Public Safety; PushTo Talk (PTT); Quality of Experience (QoE); Quality of Service (QoS); Repeater;  RootMean Square Error (RMSE); Sidelink; Trunked Mode."]},{"identifier":"8CB5C3610F6B39EAE053245706816A662086","accessLevel":"public","contactPoint":{"hasEmail":"mailto:alison.kahn@nist.gov","fn":"Alison Kahn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/8CB5C3610F6B39EAE053245706816A662086","description":"Code for the TI MSP4030F5529LP board for PTT application with MCV QoE software","language":["en"],"title":"MCV QoE Microcontroller Firmware","distribution":[{"accessURL":"https://doi.org/10.18434/M32086","title":"DOI Access for MCV QoE Microcontroller Firmware"},{"accessURL":"https://github.com/usnistgov/MCV-QoE-firmware","description":"This Repository is for the microcontroller utilized in the PSCR MCV QoE measurement system. This code is utilized by the measurement systems described in NIST IR 8206 and NIST IR 8275. The code is written for the TI MSP430F5529 processor and was developed using a MSP-EXP430F5529LP LaunchPad board. The code was compiled and loaded using TI Code Composer Studio (CCS) IDE, available here: http://www.ti.com/tool/CCSTUDIO.","title":"Radio Interface Firmware"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-07-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research"],"issued":"2019-10-10","keyword":["Ti Launch Pad","firmware","code Push to Talk"]},{"identifier":"8CB8DBD57C1E5C68E05324570681B2C42087","accessLevel":"public","contactPoint":{"hasEmail":"mailto:rosemary.astheimer@nist.gov","fn":"Rosemary Astheimer"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2087","description":"The QIF PMI Report (QPR) software generates a spreadsheet from a QIF (Quality Information Framework) file containing Product and Manufacturing Information (PMI).   QIF is a unified XML framework standard for computer-aided quality QIF systems, available free to all implementers.  QIF enables the capture, use, and re-use of metrology-related information throughout the Product Lifecycle Management (PLM) and Product Data Management (PDM) domains.  QIF was created by the Digital Metrology Standards Consortium. PMI consists of annotations and attributes that define product geometry and product specifications. PMI includes annotations to specify Geometric Dimensioning and Tolerancing (GD&T), as well as non-geometric data such as surface texture specifications, finish requirements, process notes, material specifications, and welding symbols. GD&T is a symbolic language used to communicate tolerances on manufactured parts.  PMI in QIF is defined by the QIF MBD (Model-based Definition). The spreadsheet that QPR generates creates a visual presentation of the PMI from its semantic definition in the QIF file.  Measurements and QPids are also reported.  QPR does not consider any of the graphical PMI in a QIF file.","language":["en"],"title":"QIF PMI Report Software","distribution":[{"accessURL":"https://www.nist.gov/services-resources/software/qif-pmi-report-software","format":"ZIP file","title":"QIF PMI Report Software"},{"accessURL":"https://doi.org/10.18434/M32087","title":"DOI Access for QIF PMI Report Software"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-01-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Quality assurance","Manufacturing:Product data","Manufacturing:Interoperability in manufacturing"],"issued":"2019-08-02","keyword":["manufacturing","QIF","PMI","interoperability","quality"]},{"identifier":"8E0EBB30A2CA18B8E053245706813B342089","accessLevel":"public","contactPoint":{"hasEmail":"mailto:richard.rouil@nist.gov","fn":"Richard A. Rouil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/8E0EBB30A2CA18B8E053245706813B342089","description":"The data in this document corresponds to the results presented in the paper entitled \"System Level Evaluation of UE-to-Network Relays in D2D-enabled LTE Networks\", to appear in the proceeding of the 2019 IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (IEEE CAMAD 2019)","language":["en"],"title":"System Level Evaluation of UE-to-Network Relays in D2D-enabled LTE Networks","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/8E0EBB30A2CA18B8E053245706813B342089/PaperCamad2019-ResultsData.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for System Level Evaluation of UE-to-Network Relays in D2D-enabled LTE Networks dataset"},{"downloadURL":"https://data.nist.gov/od/ds/8E0EBB30A2CA18B8E053245706813B342089/PaperCamad2019-ResultsData.xlsx","description":"Dataset for publication \"System Level Evaluation of UE-to-Network Relays in D2D-enabled LTE Networks\"","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"System Level Evaluation of UE-to-Network Relays in D2D-enabled LTE Networks dataset"},{"accessURL":"https://doi.org/10.18434/M32089","title":"DOI Access for System Level Evaluation of UE-to-Network Relays in D2D-enabled LTE Networks"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-07-19 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research","Advanced Communications:Wireless (RF)","Information Technology:Networking","Information Technology:Mobile"],"keyword":["public safety communication","device-to-device","D2D","UE-to-Network relay","wireless communication"]},{"identifier":"8E5FC500E0A4777CE0532457068151792090","accessLevel":"public","references":["https://doi.org/10.18434/M32090"],"contactPoint":{"hasEmail":"mailto:robert.mcmichael@nist.gov","fn":"Robert D. McMichael"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/8E5FC500E0A4777CE0532457068151792090","description":"Python module \"optbayesexpt\" uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters.  Given a parametric model - analogous to a fitting function - Bayesian inference uses each measurement \"data point\" to refine model parameters.  Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties.   A TCP socket interface allows the software to be used from experimental control software written in other programming languages. Code is developed in python and shared via GitHub's USNISTGOV organization.","language":["en"],"title":"Optimal Bayesian Experimental Design","distribution":[{"accessURL":"https://doi.org/10.18434/M32090","title":"DOI access to Optimal Bayesian Experimental Design"},{"accessURL":"https://pages.nist.gov/optbayesexpt/","title":"Documentation for Optimal Bayesian Experimental Design"},{"downloadURL":"https://github.com/usnistgov/optbayesexpt","format":"Python source code, documentation in jupyter notebook, markdown and rst formats","description":"Python module \"optbayesexpt\" uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters.  Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement \"data point\" to refine model parameters.  Using this information, the software suggests measurement settings that are likely to efficeiently reduce uncertainties.   A TCP socket interface allows the software to be used from experimental control software written in other programming languages. Code is developed in python, and shared via GitHub's USNISTGOV organization.","mediaType":"text/plain","title":"Optimal Bayesian Experimental Design v. 0.1.8"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-07-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Experiment design","Mathematics and Statistics:Numerical methods and software","Physics:Magnetics"],"issued":"2020-04-13","keyword":["GitHub pages template","experimental design","Bayesian","optbayesexpt","python","measurement"]},{"identifier":"ark:/88434/mds2-2100","accessLevel":"public","references":["https://www.nist.gov/publications/impact-wireless-communications-controlling-two-dimensional-gantry-system"],"contactPoint":{"hasEmail":"mailto:richard.candell@nist.gov","fn":"Rick Candell Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2100","description":"This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions. The wireless channel impact is produced through the RF channel emulator. First, we consider the benchmark channel with free-space log-distance path loss and ideal channel impulse response (CIR) which has no multi-path. Second, we consider a measured delay profile of an industrial environment where the CIR is experimentally measured and processed to be deployed using the channel emulator and to reflect the industrial environment impact. Moreover, time-varying log-normal shadowing is introduced due to the fluctuations in the signal level because of obstructions. The variance of zero-mean log-normal shadowing is set through the emulator. In order to collect the position information of the gantry system tool, we used a vision tracking system. In this dataset, we attached a meta_data.csv file to map various files to their corresponding parameters.  A README.doc file is included to describe the measurement apparatus.","language":["en"],"title":"Measurement Dataset for A Wireless Gantry System","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2100/Dataset_Gantry_System_Edited.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Experimental Wireless Data Set for 2D Gantry System"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2100/Dataset_Gantry_System_Edited.zip","format":"ZIP File","description":"A typical two-dimensional gantry system is controlled by a local controller which receives G-code commands wirelessly over a Wi-Fi network. The industrial wireless channel is replicated using a radio frequency (RF) channel emulator where various scenarios are considered, and various wireless channel parameters are studied. The movement of the gantry system tool is tracked using a vision tracking system to quantify the impact of the wireless channel on the system performance.","mediaType":"application/x-zip-compressed","title":"Experimental Wireless Data Set for 2D Gantry System"},{"accessURL":"https://doi.org/10.18434/M32100","title":"DOI Access for Measurement Dataset for A Wireless Gantry System"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-08-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Factory communications","Advanced Communications:Wireless (RF)"],"issued":"2020-06-29","keyword":["wireless","manufacturing","cyber-physical systems","robotics","gantry"]},{"identifier":"ark:/88434/mds2-2106","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2059"],"contactPoint":{"hasEmail":"mailto:john.pagliaro@nist.gov","fn":"John L. Pagliaro"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"A bilateral study to compare guarded-hot-plate measurements at extended temperatures between laboratories at the National Institute of Standards and Technology (NIST) and the National Physical Laboratory (NPL) is presented.  Measurements were conducted in accordance with standardized test methods (ISO 8302 or ASTM C177) over a temperature range from 20 °C to 160 °C (293 K to 433 K).  Following a blind round-robin format, specimens of non-woven fibrous glass mat, approximately 22 mm thick and having a nominal bulk density of 200 kg/m3, were prepared and studied.  Results of the study show that the thermal conductivity measurements agree over the temperature range of interest to within ±1.0 %, or less. See also related \"Data from: Collaborative Guarded-Hot-Plate Tests between the Laboratoire national de métrologie et d'essais and the National Institute of Standards and Technology,\" accessible at https://doi.org/10.18434/T4XK5G","language":["en"],"title":"Data from: Collaborative Guarded-Hot-Plate Tests between the National Institute of Standards and Technology and the National Physical Laboratory","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2106/Readme.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Data Dictionary for NIST/NPL Interlaboratory Comparison"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2106/Readme.txt","description":"Data dictionary for intercomparison data from guarded-hot-plate laboratories at the National Institute of Standards and Technology and at the National Physical Laboratory","mediaType":"text/plain","title":"Data Dictionary for NIST/NPL Interlaboratory Comparison"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2106/NIST_NPL_InterlabData2019.csv.sha256","mediaType":"text/plain","title":"SHA256 File for NIST/NPL Interlaboratory Guarded-Hot-Plate Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2106/NIST_NPL_InterlabData2019.csv","format":"comma-separated values file","description":"A description of the guarded-hot-plate data is in the meta-data file Readme.txt.","mediaType":"application/vnd.ms-excel","title":"NIST/NPL Interlaboratory Guarded-Hot-Plate Data"},{"accessURL":"https://doi.org/10.18434/M32106","title":"DOI Access for Data from: Collaborative Guarded-Hot-Plate Tests between the National Institute of Standards and Technology and the National Physical Laboratory"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-08-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Buildings and Construction:Building materials","Materials:Materials characterization"],"issued":"2019-12-31","keyword":["bilateral","comparison","fibrous glass mat","guarded hot plate","industrial insulation","interlaboratory","thermal conductivity"]},{"identifier":"ark:/88434/mds2-2112","accessLevel":"public","contactPoint":{"hasEmail":"mailto:brandon.lane@nist.gov","fn":"Brandon Lane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2112","description":"This document provides details on the experiment and associated measurement files available for download in the dataset ?In Situ Thermography During Laser Powder Bed Fusion of a Nickel Superalloy 625 Artifact with Various Overhangs and Supports.? The measurements were acquired during the fabrication of a small nickel superalloy 625 (IN625) artifact using a commercial laser powder bed fusion (LPBF) system. The artifact consists of two half-arch features with increasing degrees of overhangs, from 5° to 85°, in increments of 10°. The artifact geometry and process are controlled to ensure consistent processing along the overhang geometry, thus enabling the effect due to overhang geometry and support structures to be isolated from effects due to inter-layer scan-strategy variations that are typical in commercial LPBF processes. The measurements include high-speed thermography of each layer, from which radiant temperature, cooling rate, and melt pool length are calculated. The objective of this experiment and data dissemination is twofold. First, to provide data for the modeling community for model validation to ensure that their models are accurately accounting for the effect of overhang geometries and support structures in thermal models. The second objective is to provide fundamental insight into these effects for researchers and process designers.","language":["en"],"title":"In Situ Thermography During Laser Powder Bed Fusion of a Nickel Superalloy 625 Artifact with Various Overhangs and 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing","Manufacturing:Process measurement and control"],"issued":"2020-07-29","keyword":["3D build; Additive manufacturing; IN625; nickel super alloy 625; powder bed fusion; temperature measurement; thermography; overhangs; reference artifact; model validation"]},{"identifier":"ark:/88434/mds2-2113","accessLevel":"public","contactPoint":{"hasEmail":"mailto:david.lavan@nist.gov","fn":"David A. LaVan"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2113","description":"Four files are included in this data set for mask generation that can be used to produce photolithography contact masks to create NIST nanocalorimeters.  Two different designs are included, each in AutoCAD *.dwg format and a universal *.dxf format.  The masks are intended to be printed on 5 inch masks blanks used to pattern 100 mm (4 inch) wafers.  The front drawings are used to pattern the metal layer that defines the contact pads and the combined heater / thermometer and includes alignment marks at the center of the mask and at approximately plus and minus 1 inch along the X-axis for front to back alignment.  The rear drawings have already been mirrored and define the KOH etch windows used to create individual die and create the windows in the center of the nanocalorimeter.  The dimensions of the windows was defined for a standard, nominal, 525 micron thick 100-mm wafer.\nPlease cite the related paper \"Guide to the Design, Fabrication and Calibration of NIST Nanocalorimeters\" by  Feng Yi, Michael D. Grapes, and David A. LaVan in the Journal of Research of the National Institute of Standards and Technology,  Volume 124 (in press).","language":["en"],"title":"NIST Nanocalorimeter DWG and DXF drawings for microfabrication mask generation","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2113/Nanocalorimeter_mask_back_KOH_etch_mirrored.dxf.sha256","mediaType":"text/plain","title":"SHA256 File for Nanocalorimeter mask file backside"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2113/Nanocalorimeter_mask_back_KOH_etch_mirrored.dxf","format":"universal DXF CAD format","description":"Nanocalorimeter mask file backside in DXF format","mediaType":"application/octet-stream","title":"Nanocalorimeter mask file backside"},{"accessURL":"https://doi.org/10.18434/M32113","title":"DOI Access for NIST Nanocalorimeter DWG and DXF drawings for microfabrication mask generation"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2113/Nanocalorimeter_mask_back_KOH_etch_mirrored.dwg.sha256","mediaType":"text/plain","title":"SHA256 File for Nanocalorimeter Mask Back Drawing"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2113/Nanocalorimeter_mask_back_KOH_etch_mirrored.dwg","format":"AutoCAD DWG format","description":"Nanocalorimeter Mask Back Drawing in DWG format","mediaType":"application/octet-stream","title":"Nanocalorimeter Mask Back Drawing"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2113/Nanocalorimeter_mask_top_metal.dwg.sha256","mediaType":"text/plain","title":"SHA256 File for Nanocalorimeter mask drawing top metal layer"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2113/Nanocalorimeter_mask_top_metal.dwg","format":"AutoCAD DWG format","description":"Nanocalorimeter mask drawing top metal layer in DWG format","mediaType":"application/octet-stream","title":"Nanocalorimeter mask drawing top metal layer"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2113/Nanocalorimeter_mask__top_metal.dxf.sha256","mediaType":"text/plain","title":"SHA256 File for Nanocalorimeter mask drawing top metal layer"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2113/Nanocalorimeter_mask__top_metal.dxf","format":"universal DXF CAD format","description":"Nanocalorimeter mask drawing top metal layer in DXF format","mediaType":"application/octet-stream","title":"Nanocalorimeter mask drawing top metal layer"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-08-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization"],"issued":"2019-08-28","keyword":["nanocalorimeter","nanocalorimetry","microfabrication","photolithography","mask","NIST"]},{"identifier":"ark:/88434/mds2-2114","accessLevel":"public","contactPoint":{"hasEmail":"mailto:david.lavan@nist.gov","fn":"David A. LaVan"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2114","description":"3D Solid Model of NIST Nanocalorimeter created in Solidworks 2019.  The files include an assembly of three layers represented as part files - the silicon die layer, the silicon nitride membrane layer and the platinum metal layer. Please cite the related paper \"Practical Guide to the Design, Fabrication and Calibration of NIST Nanocalorimeters\" by  Feng Yi, Michael D. Grapes, and David A. LaVan in the Journal of Research of the National Institute of Standards and Technology,  Volume 124 (in press).\n","language":["en"],"title":"3D solid model of NIST Nanocalorimeter","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2114/3D%20model%20NIST%20Nanocalorimeter%20sensor%20die%20layer.SLDPRT.sha256","mediaType":"text/plain","title":"SHA256 File for Solidworks part drawing of silicon die layer for NIST Nanocalorimeter"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2114/3D%20model%20NIST%20Nanocalorimeter%20sensor%20die%20layer.SLDPRT","format":"solidworks part, SLDPRT","description":"Solidworks part drawing of silicon die layer for NIST Nanocalorimeter","mediaType":"application/octet-stream","title":"Solidworks part drawing of silicon die layer for NIST Nanocalorimeter"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2114/3D%20model%20NIST%20Nanocalorimeter%20sensor%20silicon%20nitride%20layer.SLDPRT.sha256","mediaType":"text/plain","title":"SHA256 File for Solidworks part drawing of silicon nitride layer for NIST Nanocalorimeter"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2114/3D%20model%20NIST%20Nanocalorimeter%20sensor%20silicon%20nitride%20layer.SLDPRT","format":"solidworks part, SLDPRT","description":"Solidworks part drawing of silicon nitride layer for NIST Nanocalorimeter","mediaType":"application/octet-stream","title":"Solidworks part drawing of silicon nitride layer for NIST Nanocalorimeter"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2114/3D%20model%20NIST%20Nanocalorimeter%20sensor%20top%20metal%20layer.SLDPRT.sha256","mediaType":"text/plain","title":"SHA256 File for Solidworks part drawing of top metal layer for NIST Nanocalorimeter"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2114/3D%20model%20NIST%20Nanocalorimeter%20sensor%20top%20metal%20layer.SLDPRT","format":"solidworks part, SLDPRT","description":"Solidworks part drawing of top metal layer for NIST Nanocalorimeter","mediaType":"application/octet-stream","title":"Solidworks part drawing of top metal layer for NIST Nanocalorimeter"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2114/NIST%20Nanocalorimeter%20single%20die%203D%20model.SLDASM.sha256","mediaType":"text/plain","title":"SHA256 File for Solidworks assembly drawing of all layers for NIST Nanocalorimeter"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2114/NIST%20Nanocalorimeter%20single%20die%203D%20model.SLDASM","format":"solidworks assembly, SLDASM","description":"Solidworks assembly drawing of all layers for NIST Nanocalorimeter","mediaType":"application/octet-stream","title":"Solidworks assembly drawing of all layers for NIST Nanocalorimeter"},{"accessURL":"https://doi.org/10.18434/M32114","title":"DOI Access for 3D solid model of NIST Nanocalorimeter"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-08-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization"],"issued":"2019-08-28","keyword":["nanocalorimeter","nanocalorimetry","drawing","solid model","NIST"]},{"identifier":"ark:/88434/mds2-2115","accessLevel":"public","contactPoint":{"hasEmail":"mailto:david.lavan@nist.gov","fn":"David A. LaVan"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2115","description":"Nanocalorimeter calibration data.  The file format is Origin Pro* project files, which include multiple data worksheets and derived graphs.  \n\n*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.\n\nPlease cite the related paper \"Practical Guide to the Design, Fabrication and Calibration of NIST Nanocalorimeters\" by  Feng Yi, Michael D. Grapes, and David A. LaVan in the Journal of Research of the National Institute of Standards and Technology,  Volume 124 (in press).\n","language":["en"],"title":"Nanocalorimeter calibration data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2115/multiple%20cycles%20room%20temperature%20resistance%20measurements%20Fig%202%20to%20Fig%205.opju.sha256","mediaType":"text/plain","title":"SHA256 File for mutiple cycles of room temperature calibration data for NIST nanocalorimeters"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2115/multiple%20cycles%20room%20temperature%20resistance%20measurements%20Fig%202%20to%20Fig%205.opju","format":"Origin Project, OPJU","description":"mutiple cycles of room temperature calibration data for NIST nanocalorimeters with graphs in Origin project file format","mediaType":"application/octet-stream","title":"mutiple cycles of room temperature calibration data for NIST nanocalorimeters"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2115/elevated%20temperature%20calibration%20measurements%20100%20random%20nanocalorimeters%20Fig%206%20to%20Fig%2010.opju.sha256","mediaType":"text/plain","title":"SHA256 File for elevated temperature nanocalorimeter calibration data from 100 randomly selected devices"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2115/elevated%20temperature%20calibration%20measurements%20100%20random%20nanocalorimeters%20Fig%206%20to%20Fig%2010.opju","format":"Origin project, OPJU","description":"elevated temperature nanocalorimeter calibration data from 100 randomly selected devices with derived graphs.  data and graphs are in Origin project format","mediaType":"application/octet-stream","title":"elevated temperature nanocalorimeter calibration data from 100 randomly selected devices"},{"accessURL":"https://doi.org/10.18434/M32115","title":"DOI Access for Nanocalorimeter calibration data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology","Materials:Materials characterization"],"issued":"2019-08-28","keyword":["nanocalorimeter","nanocalorimetry","calibration","room temperature","pyrometer","resistance"]},{"identifier":"ark:/88434/mds2-2116","accessLevel":"public","contactPoint":{"hasEmail":"mailto:raied.caromi@nist.gov","fn":"Raied Caromi"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2116","description":"The RF dataset can be used to develop and test detection algorithms for the 3.5 GHz CBRS or similar bands where the primary users of the band are federal incumbent radar systems. The dataset consists of synthetically generated radar waveforms with added white Gaussian noise. The RF dataset is suitable for development and testing of machine/deep learning detection algorithms. A large number of parameters of the waveforms are randomized across the dataset. Due to its large size, the dataset is divided into groups, and each group consists of multiple files. For more information about the dataset, refer to: R. Caromi, M. Souryal, and T. Hall \"RF Dataset of Incumbent Radar Signals in the 3.5 GHz CBRS Band,\" J Res Natl Inst Stan 124:124038, 2019. available online at: https://doi.org/10.6028/jres.124.038. In addition, the metadata of the dataset is summarized in \"Data Dictionary of 3.5 GHz Radar Waveforms\" [pdf] accompanying the data. For more information about the motivation behind this RF dataset, refer to:  T. Hall, R. Caromi, M. Souryal, and A. Wunderlich, \"Reference Datasets for Training and Evaluating RF Signal Detection and Classification Models,\" in Proc. IEEE GLOBECOM Workshop on Advancements in Spectrum Sharing, Dec. 2019, available online at: https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928336.","language":["en"],"title":"RF Dataset of Incumbent Radar Systems in the 3.5 GHz CBRS Band","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_15.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_16.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_16.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_17.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_17.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_18.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_18.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_19.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_19.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_2.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_2.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_20.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_20.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_21.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_21.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_22.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_22.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_23.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_23.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_24.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/Instructions%20to%20download%20the%20RF%20dataset.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/Instructions%20to%20download%20the%20RF%20dataset.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_1.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_1.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_10.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_10.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_11.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_11.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_12.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_12.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_13.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_13.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_14.mat","mediaType":"application/x-matlab-data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_14.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2116/SimulatedRadarWaveforms/Group2/group2_subset_15.mat","mediaType":"application/x-matlab-data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-08-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)"],"issued":"2019-11-21","keyword":["3.5 GHz; CBRS; incumbent radar detection; RF dataset; classification; deep learning; machine learning; spectrum sharing"]},{"identifier":"ark:/88434/mds2-2117","accessLevel":"public","contactPoint":{"hasEmail":"mailto:david.lavan@nist.gov","fn":"David A. LaVan"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2117","description":"These software are used to control an instrument that makes measurements and calculates calibration coefficients for NIST nanocalorimeters. Please cite the related paper \"Practical Guide to the Design, Fabrication and Calibration of NIST Nanocalorimeters\" by  Feng Yi, Michael D. Grapes, and David A. LaVan in the Journal of Research of the National Institute of Standards and Technology,  Volume 124 (in press).\n","language":["en"],"title":"NIST nanocalorimeter calibration virtual instruments","distribution":[{"accessURL":"https://doi.org/10.18434/M32117","title":"DOI Access for NIST nanocalorimeter calibration virtual instruments"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2117/Nanocalorimeter%20Calibration.zip","format":"virtual instruments (VI) that run under LabVIEW environment on Windows 7.  zipped into an archive","description":"A virtual instrument that runs under the National Instruments LabView  environment running on a computer with Windows 7, and controls an Optitherm III infrared thermometer temperature measurement system and a NI PXI source meter. Note: Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.","mediaType":"application/x-zip-compressed","title":"nanocalorimeter calibration virtual instrument"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2117/Nanocalorimeter%20Calibration.zip.sha256","mediaType":"text/plain","title":"SHA256 File for nanocalorimeter calibration virtual instrument"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2117/TCR%20Generation.zip.sha256","mediaType":"text/plain","title":"SHA256 File for virtual instrument to calculate TCR coefficients as part of nanocalorimeter"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2117/TCR%20Generation.zip","format":"virtual instrument that runs under National Instruments LabVIEW environment on windows 7, zipped into one file","description":"virtual instrument to calculate TCR coefficients as part of nanocalorimeter calibration.  Works with other virtual instruments in this record.","mediaType":"application/x-zip-compressed","title":"virtual instrument to calculate TCR coefficients as part of nanocalorimeter"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2117/SubVIs.zip.sha256","mediaType":"text/plain","title":"SHA256 File for sub-VI's needed for nanocalorimeter calibration"},{"format":"sub-virtual instruments that run in the National Instruments LabVIEW environment on Windows 7, zipped into one file","downloadURL":"https://data.nist.gov/od/ds/mds2-2117/SubVIs.zip","description":"sub-virtual instruments used by the nanocalorimeter virtual instruments for nanocalorimeter calibration","mediaType":"application/x-zip-compressed","title":"sub-VI's needed for nanocalorimeter calibration"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-08-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization","Metrology","Materials"],"issued":"2019-08-30","keyword":["nanocalorimeter","nanocalorimetry","calibration","software","virtual instrument"]},{"identifier":"ark:/88434/mds2-2119","accessLevel":"public","references":["https://doi.org/10.1109/TMTT.2020.2983358"],"contactPoint":{"hasEmail":"mailto:dylan.williams@nist.gov","fn":"Dylan Williams"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2119","description":"The data used to generate the graphs in figures 1-9 of the paper \"Evaluating Uncertainty of Microwave Calibrations from Regression Residuals\".The full citation is D. F. Williams, B. F. Jamroz, J. D. Rezac and R. D. Jones, \"Evaluating Uncertainty of Microwave Calibration Models With Regression Residuals,\" in IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 6, pp. 2454-2467, June 2020, doi: 10.1109/TMTT.2020.2983358.The files are named as follows: 1. CI_figX.plt - Contains EasyPlot V 4.0.4 file used to create the plot, columns used in each file, legend, etc. 2. FigX_FY_name - Contains TAB-delimited data file Y used to construct figure X with original file name \"name\".  First two lines repeats key information found in EasyPlot file.  First line specifies columns used in EasyPlot column-selection format.  Second line contains original location.EasyPlot column-selection format is as follows: \"xyiiyy\"  or \"xy..yy\"  means that column 1 was used for x axis, column 2 for first curve y values, column 5 for second curve y values, column 6 for third curve y values","language":["en"],"title":"Evaluating Uncertainty of Microwave Calibration Models from Regression Residuals","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2119/Figures1-9.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Data for Figures 1-9"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2119/Figures1-9.zip","format":"EasyPlot files and tab-deliminted text files with data plotted in paper.","description":"Data required to reproduce figures 1-9.","mediaType":"application/x-zip-compressed","title":"Data for Figures 1-9"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-09-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)","Mathematics and Statistics:Uncertainty quantification"],"issued":"2022-04-29","keyword":["Confidence intervals","coupling corrections","on-wafer measurement","prediction intervals."]},{"identifier":"ark:/88434/mds2-2120","accessLevel":"public","references":["https://doi.org/10.1007/s00216-018-1240-2","http://dx.doi.org/10.1080/1062936X.2016.1238010"],"contactPoint":{"hasEmail":"mailto:david.sheen@nist.gov","fn":"David Sheen"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2120","description":"This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.","language":["en"],"title":"ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models","distribution":[{"accessURL":"https://pages.nist.gov/ml_uncertainty_py/","format":"Python scripts and Jupyter notebooks","description":"This software is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.","title":"Machine Learning Uncertainty Estimation Toolbox"},{"accessURL":"https://doi.org/10.18434/M32120","title":"DOI Access for ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-06-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Uncertainty quantification","Mathematics and Statistics:Numerical methods and software","Information Technology:Data and informatics"],"issued":"2020-01-21","keyword":["uncertainty analysis","machine learning","model calibration"]},{"identifier":"ark:/88434/mds2-2121","accessLevel":"public","contactPoint":{"hasEmail":"mailto:daniel.kuester@nist.gov","fn":"Dan Kuester"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2121","description":"This is a collection of data sets acquired for measurements of noise figure and receive system noise of wireless/radio frequency receivers and transceivers. These data include tabular data that list1) Inputs: calibrated input signal and excess noise levels, and2) Outputs: summary statistics for each type of user data collected for each DUT.The experiments that produced these data were meant to be used to assess noise measurands, but the data are generic and could be applied to other problems if desired.The structure of each zip archive dataset is as follows:| Root|-- (Anonymized DUT name 1)|---- Data file 1|---- Data file 2|---- ...Data file N|---- DUT-README.txt|-- (Anonymized DUT name 2)|---- Data file 1|---- Data file 2|---- ...Data file N|---- DUT-README.txt| (etc.)Data tables in each archive are provided as comma-separated values (.csv), and the descriptive text files are ASCII (.txt). Detailed discussion of the test conditions and data formatting is given by the DUT-README.txt for each DUT.","language":["en"],"title":"Blind Measurements of Receiver System Noise","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2121/GPS-2019-09.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Dataset: GPS L1 DUT"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2121/GPS-2019-09.zip","format":"zip archive","description":"This dataset was originally collected on a commercial off-the-shelf GPS L1 receiver in 2017. It includes input conditions calibrated at the DUT input, as well as verification tests performed in cascade with a calibrated reference LNA. Tabular data are given as .csv files. These are documented in accompanying DUT-README.txt files, which also detail relevant experimental parameters.","mediaType":"application/x-zip-compressed","title":"Dataset: GPS L1 DUT"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2121/WLAN-2019-09.zip.sha256","mediaType":"text/plain","title":"SHA256 File for WLAN dataset (2019)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2121/WLAN-2019-09.zip","format":"zip archive","description":"This dataset was originally collected on a commercial off-the-shelf IEEE 802.11 WLAN (\"WiFi\") equipment in 2019. The DUTs were 1 access point (AP) and 1 client, operating at 5.3 GHz. The measurements were performed for each DUT at both 1) its input and 2) in cascade with a calibrated reference LNA to verify the test method. Tabular data are given as .csv files. These are documented in accompanying DUT-README.txt files, which also detail relevant experimental parameters.","mediaType":"application/x-zip-compressed","title":"WLAN dataset (2019)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-05-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Statistical analysis","Mathematics and Statistics:Experiment design","Metrology:Electrical/electromagnetic metrology","Information Technology:Mobile","Electronics:Sensors","Electronics:Electromagnetics"],"issued":"2022-04-21","keyword":["Noise","electromagnetics","wireless spectrum","RF systems"]},{"identifier":"ark:/88434/mds2-2122","accessLevel":"public","contactPoint":{"hasEmail":"mailto:daniel.kuester@nist.gov","fn":"Dan Kuester"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2122","description":"This is the source code package for the labbench python module, version 0.20, which is its first public release.  The purpose of labbench is to streamline and organize complicated laboratory automation tasks that involve large-scale benchtop automation, concurrency, and/or data management. It is built around a system of wrappers that facilitate robust, concise exception handling, type checking, API conventions, and synchronized device connection through python context blocks. The wrappers also provide convenient new functionality, such as support for automated status displays in jupyter notebooks, simplified threaded concurrency, and automated, type-safe logging to relational databases.Together, these features help to minimize the amount of \"copy-and-paste\" code that can make your lab automation scripts error-prone and difficult to maintain.The python code that results can be clear, concise, reusable and maintainable, and provide consistent formatting for stored data. The result helps researchers to meet NIST's open data obligations, even for complicated, large, and heterogeneous datasets.Several past and ongoing projects in the NIST Communication Technology Laboratory (CTL) published data that were acquired by automation in labbench. We release it here both for transparency and to invite public use and feedback. Ongoing updates to this source code will be maintained on the NIST github page at https://github.com/usnistgov/labbench.The code was developed in python, documented with the python sphinx package and markdown, and shared through the USNISTGOV organization on GitHub.INSTALLATIONlabbench can run on any computer that supports python 3.6. The hardware requirements are discussed here:    https://docs.anaconda.com/anaconda/install/#requirements1. Install your favorite distribution of a python version 3.6 or greater2. In a command prompt, pip install git+https://gitlab.nist.gov/gitlab/ssm/labbench3. (Optional) install an NI VISA [1] runtime, for example this one for windows.USAGEThe source distribution contains detailed information including* README.md - documentation to get started using labbench* LICENSE.md - license and redistribution information* doc/labbench-api.pdf - complete listing of the module and documentation","language":["en"],"title":"Source code for labbench 0.20 release","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2122/README.txt","format":"text","description":"getting started guide; links to documentation and installation","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2122/LICENSE.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Licensing information"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2122/LICENSE.txt","format":"text","description":"The source code license for this package and dependencies (on its original 2019 release)","mediaType":"text/plain","title":"Licensing information"},{"accessURL":"https://doi.org/10.18434/M32122","title":"DOI Access for Source code for labbench 0.20 release"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2122/labbench-0.20.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Source code and documentation"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2122/labbench-0.20.zip","format":"compressed zip file","description":"This zip file contains version 0.20 of the source code and documentation for labbench.","mediaType":"application/x-zip-compressed","title":"Source code and documentation"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2122/labbench-api.pdf.sha256","mediaType":"text/plain","title":"SHA256 File for API Reference - documentation for each function, object, and method"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2122/labbench-api.pdf","format":"pdf documentation file","mediaType":"application/pdf","title":"API Reference - documentation for each function, object, and method"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2122/README.txt.sha256","mediaType":"text/plain","title":"SHA256 File for README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-09-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Computational science","Information Technology:Data and informatics","Information Technology:Interoperability testing","Information Technology:Mobile","Information Technology:Software research","Advanced Communications:Wireless (RF)"],"issued":"2019-10-07","keyword":["Laboratory automation"]},{"identifier":"ark:/88434/mds2-2123","accessLevel":"public","references":["http://dx.doi.org/10.3791/58124"],"contactPoint":{"hasEmail":"mailto:amy.engelbrecht-wiggans@nist.gov","fn":"Amy Engelbrecht-Wiggans"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2123","description":"Traditionally, soft body armor has been made from materials such as poly(p-phenylene terephthalamide) (PPTA) and ultra-high molar mass polyethylene (UHMMPE). However, to diversify the fiber choices in the United States body armor market, copolymer fibers based on the combination of 5-amino-2-(p-aminophenyl) benzimidazole (PBIA) and PPTA were introduced. Little is known regarding the long-term stability of PBIA fibers, but as condensation polymers, they have potential sensitivity to moisture and humidity.  Ballistic resistance and other critical structural properties of these fibers are predicated on their superior mechanical properties.  Therefore, it is important to characterize the strength of these materials and understand their vulnerability to environmental conditions to evaluate their use lifetime in safety applications.  Three PBIA-based fibers were selected for the study. The fibers were thoroughly washed to remove an organic coating, which held the individual fibers in each yarn bundle together, allowing for the disentangling of single fibers for mechanical testing.  Molecular spectroscopy and single fiber tensile testing were performed on the fibers to characterize changes in their chemical structure, tensile strength, and strain to failure as a function of exposure time to four different hydrothermal ageing conditions.","language":["en"],"title":"Aged and unaged PBIA-based copolymer testing for soft body armor applications","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2123/copolymer_PPTA_readme.txt.sha256","mediaType":"text/plain","title":"SHA256 File for copolymer_PPTA_readme"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2123/copolymer_PPTA_readme.txt","format":"a text file","description":"This is the readme file that describes the dataset and the file structure","mediaType":"text/plain","title":"copolymer_PPTA_readme"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2123/Moisture%20data.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2123/Moisture%20data.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2123/Tensile%20data.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2123/Tensile%20data.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2123/FTIR%20data.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2123/FTIR%20data.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2123/copolymer_PPTA_readme.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2123/copolymer_PPTA_readme.txt.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32123","title":"DOI Access for Aged and unaged PBIA-based copolymer testing for soft body armor applications"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-09-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization"],"issued":"2019-10-07","keyword":["5-amino-2-(p-aminophenyl) benzimidazole","PBIA","PPTA","body armor","single fiber tensile testing","ballistic materials","aramid fibers","environmental degradation","strength","mechanical testing","hydrothermal ageing"]},{"identifier":"ark:/88434/mds2-2124","accessLevel":"public","references":["https://doi.org/10.1063/1.555845","http://www.worldcat.org/oclc/220243010"],"contactPoint":{"hasEmail":"mailto:janiel.reed@nist.gov","fn":"Janiel J. Reed"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2124","description":"Recommended values are provided for chemical thermodynamic properties of inorganic substances and for organic substances usually containing only one or two carbon atoms.  Where available, values are given for the enthalpy of formation, Gibbs energy of formation, entropy, and heat capacity at 298.15 K (25 degrees C), the enthalpy difference between 298.15 and 0 K and the enthalpy of formation at 0 K.  All values are given in SI units and are for a standard state pressure of 100 000 pascal.  This volume is a new collective edition of \"Selected Values of Chemical Thermodynamic Properties,\" which was issued serially as National Bureau of Standards Technical Notes 270-1 (1965) to 270-8 (1981).  Values are given for properties of gaseous, liquid and crystalline substances, for solutions in water, and for mixed aqueous and organic solutions.  Values are not given for alloys or other solid solutions, fused salts or for substances of undefined composition.  Compounds of the transuranium elements are not included.","language":["en"],"title":"The NBS Tables of Chemical Thermodynamic Properties: Selected Values for Inorganic and C1 and C2 Organic Substances in SI Units","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2124/NBS_Tables_with_ErratumValues.csv.sha256","mediaType":"text/plain","title":"SHA256 File for NBS Tables with Corrected Values"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2124/NBS_Tables_with_ErratumValues.csv","format":"CSV file encoded with UTF-8","description":"NBS Tables with corrected values, with html superscripts.  Subscripts are not noted in this file.","mediaType":"text/csv","title":"NBS Tables with Corrected Values"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2124/NBS_Tables%20Library.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for NBS Tables"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2124/NBS_Tables%20Library.xlsx","format":"Microsoft Excel Worksheet","description":"Excel file without corrected values; contains all original values.","mediaType":"application/vnd.ms-excel","title":"NBS Tables"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2124/NBS_Tables%20Library_with_ErratumValues.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for NBS Tables with Erratum"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2124/NBS_Tables%20Library_with_ErratumValues.xlsx","format":"Microsoft Excel Worksheet","description":"Excel file with corrected NBS Tables values.","mediaType":"application/vnd.ms-excel","title":"NBS Tables with Erratum"},{"accessURL":"https://doi.org/10.18434/M32124","title":"DOI Access for The NBS Tables of Chemical Thermodynamic Properties: Selected Values for Inorganic and C1 and C2 Organic Substances in SI Units"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"1989-09-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Chemical thermodynamics and chemical properties"],"issued":"2020-01-13","keyword":["NBS Tables","chemical thermodynamics","enthalpy","entropy","Gibbs energy","inorganic chemistry","thermochemistry","evaluated data"]},{"identifier":"ark:/88434/mds2-2125","accessLevel":"public","contactPoint":{"hasEmail":"mailto:anirudha.sahoo@nist.gov","fn":"Anirudha Sahoo"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2125","description":"This research work studied the effect of timing constraint and overloading of Spectrum Access System (SAS) on the SAS-CBSD protocol. Specifically, it studies how Heartbeat and Grant Request fail as number of CBSDs served by a SAS becomes large for a given service rate. It also looks at the time taken by CBSDs to vacate a channel (on which an incumbent has appeared) at different Heartbeat Interval. These study results are captured in the following files.\n\n(1) Number of CBSD vs number of Heartbeat timeout when SAS service rate is 40 requests/sec\n(2) Number of CBSD vs number of Heartbeat timeout when SAS service rate is 60 requests/sec\n(3) Number of CBSD vs number of failed grants when SAS service rate is 40 requests/sec\n(4) Number of CBSD vs number of failed grants when SAS service rate is 60 requests/sec\n(5) CDF of duration of CBSDs vacating a channel when number of CBSD=700, mean heartbeat interval = 90 s\n(6) CDF of duration of CBSDs vacating a channel when number of CBSD=1200, mean heartbeat interval = 150 s\n(7) CDF of duration of CBSDs vacating a channel when number of CBSD=1500, mean heartbeat interval = 220 s","language":["en"],"title":"A Study of Timing Constraints and SAS Overload of SAS-CBSD Protocol in the CBRS Band","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cbsd_vs_hb_timeout_mu40.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Number of CBSD vs number of Heartbeat timeout when SAS service rate is 40 req/s"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cbsd_vs_hb_timeout_mu40.dat","description":"Number of CBSD vs number of Heartbeat timeout when SAS service rate is 60 req/s","mediaType":"application/octet-stream","title":"Number of CBSD vs number of Heartbeat timeout when SAS service rate is 40 req/s"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cbsd_vs_hb_timeout_mu60.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Number of CBSD vs number of Heartbeat timeout when SAS service rate is 60 req/s"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cbsd_vs_hb_timeout_mu60.dat","description":"Number of CBSD vs number of Heartbeat timeout when SAS service rate is 60 req/s","mediaType":"application/octet-stream","title":"Number of CBSD vs number of Heartbeat timeout when SAS service rate is 60 req/s"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cbsd_vs_grant_timeout_mu40.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Number of CBSD vs number of Grant timeout when SAS service rate is 40 req/s"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cbsd_vs_grant_timeout_mu40.dat","description":"Number of CBSD vs number of Grant timeout when SAS service rate is 40 req/s","mediaType":"application/octet-stream","title":"Number of CBSD vs number of Grant timeout when SAS service rate is 40 req/s"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cbsd_vs_grant_timeout_mu60.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Number of CBSD vs number of Grant timeout when SAS service rate is 60 req/s"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cbsd_vs_grant_timeout_mu60.dat","description":"Number of CBSD vs number of Grant timeout when SAS service rate is 60 req/s","mediaType":"application/octet-stream","title":"Number of CBSD vs number of Grant timeout when SAS service rate is 60 req/s"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cdf_timeout_midas_0.dat.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of duration of CBSD vacating a channel"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cdf_timeout_midas_0.dat","description":"CDF of duration of CBSD vacating a channel when the number of CBSD=700, mean Heartbeat Interval = 90 s","mediaType":"application/octet-stream","title":"CDF of duration of CBSD vacating a channel"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cdf_timeout_midas_1.dat.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of duration of CBSD vacating a channel"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cdf_timeout_midas_1.dat","description":"CDF of duration of CBSD vacating a channel when the number of CBSD=1200, mean Heartbeat Interval = 150 s","mediaType":"application/octet-stream","title":"CDF of duration of CBSD vacating a channel"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cdf_timeout_midas_2.dat.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of duration of CBSD vacating a channel"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2125/cdf_timeout_midas_2.dat","description":"CDF of duration of CBSD vacating a channel when the number of CBSD=1500, mean Heartbeat Interval = 220 s","mediaType":"application/octet-stream","title":"CDF of duration of CBSD vacating a channel"},{"accessURL":"https://doi.org/10.18434/M32125","title":"DOI Access for A Study of Timing Constraints and SAS Overload of SAS-CBSD Protocol in the CBRS Band"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-09-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)","Information Technology:Mobile"],"issued":"2019-11-12","keyword":["3.5 GHz; CBRS; SAS-CBSD protocol","Heartbeat Interval;"]},{"identifier":"ark:/88434/mds2-2126","accessLevel":"public","references":["https://doi.org/10.5194/essd-2019-206","https://doi.org/10.5194/essd-12-699-2020"],"contactPoint":{"hasEmail":"mailto:anna.karion@nist.gov","fn":"Anna Karion"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2126","description":"NOTE: please see the latest (March 2025) update at https://doi.org/10.18434/mds2-3765.  Hourly observations of CO2 and CH4 from tower-based sites in the NIST Northeast Corridor network (currently 23, but more may be added in future updates); CO observations from one site. Data files are comma delimited (CSV). Measurements of each species may be from two or more different heights above ground. Site locations, heights, and other information is in a separate ascii (CSV) file (NEC_sites.csv). Data is currently reported for the years 2015-2019, with planned annual updates. An ASCII Readme file (NEC_Readme_10052020) is also posted, along with an Updates_10052020.txt file that includes information on updates.  Current update: Nov 11, 2020. See also associated publication (Karion et al., Earth System Science Data, 2020): https://doi.org/10.5194/essd-12-699-2020","language":["en"],"title":"Observations of CO2, CH4, and CO mole fractions from the NIST Northeast Corridor urban 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Payne II"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/el/energy-and-environment-division-73200/hvacr-equipment-performance-group/fault-detection-and","description":"Work by Purdue and NIST has shown that different system types behave differently when exposed to the same type of fault at the same intensity; therefore, a better understanding of fault effects on the performance of different systems is needed.  The data referenced here contains measurements of the fault-free and fault performance of two different heat pumps; a 14 SEER heat pump and a 16 SEER  air-conditioner.  The data represents the cooling mode performance of the two systems with and without various faults imposed while operating at steady-state over a wide range of indoor and outdoor air temperatures and humidities.  \n","language":["en"],"title":"Residential Heat Pump Fault Detection and Diagnosis Research Data","distribution":[{"accessURL":"https://doi.org/10.18434/M32132","title":"DOI Access for Residential Heat Pump Fault Detection and Diagnosis Research Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-09-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Energy:Energy efficiency","Energy:Alternative energy","Energy:Conventional energy"],"issued":"2020-10-23","keyword":["Fault detection and diagnosis data","heat pump data","air-conditioner data","cooling mode data"]},{"identifier":"ark:/88434/mds2-2135","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2082"],"contactPoint":{"hasEmail":"mailto:ryan.falkenstein-smith@nist.gov","fn":"Ryan Falkenstein-Smith"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2135","description":"This dataset documents a series of time-averaged gas species measurements made along the centerline of methanol, ethanol, and acetone pool fires steadily burning in a quiescent environment. All gas species measurements are obtained using a Gas Chromatograph/ Mass Spectrometer System (GC/MS). Measurements were made at different heights along the centerline of the fire and repeated at least twice for each location. Gas species volume fractions were determined via the GC/MS using predetermined calibration factors. Soot mass fractions are simultaneously measured during the gas sampling process. The gas species volume and soot mass fractions are compared at different heights within the fire and across a variety of different fuels. Other fire parameters are measured as well, including time-averaged temperature measurements and mass burning rates. The dataset provided here are CSV files listing the volume fractions and temperature measurements made in 30 cm diameter Acetone, Ethanol, and Methanol pool fires.  A technical note (https://doi.org/10.6028/NIST.TN.2082) describes the collection and analysis of these datasets in further detail.","language":["en"],"title":"Volume Fraction and Temperature Measurements of 30 cm Acetone, Ethanol, Methanol, Pool Fires","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Acetone_30_cm.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 30 cm Acetone Pool Fire Measurements with Uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Acetone_30_cm.csv","mediaType":"application/vnd.ms-excel","title":"30 cm Acetone Pool Fire Measurements with Uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Ethanol_30_cm.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 30 cm Ethanol Pool Fire Measurements with Uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Ethanol_30_cm.csv","mediaType":"application/vnd.ms-excel","title":"30 cm Ethanol Pool Fire Measurements with Uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Methanol_30_cm.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 30 cm Methanol Pool Fire Measurements with Uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2135/Methanol_30_cm.csv","mediaType":"application/vnd.ms-excel","title":"30 cm Methanol Pool Fire Measurements with Uncertainty"},{"accessURL":"https://doi.org/10.18434/M32135","title":"DOI Access for Volume Fraction and Temperature Measurements of 30 cm Acetone, Ethanol, Methanol, Pool Fires"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-09-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Fire modeling","Fire:Fire dynamics and science"],"issued":"2020-09-04","keyword":["Acetone","Ethanol","Gas species measurements","Methanol","Liquid Pool fires"]},{"identifier":"ark:/88434/mds2-2137","accessLevel":"public","references":["https://nvlpubs.nist.gov/nistpubs/TechnicalNotes/NIST.TN.2060.pdf"],"contactPoint":{"hasEmail":"mailto:oliver.borchert@nist.gov","fn":"Oliver Borchert"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2137","description":"The NIST BGP Secure Routing Extension (BGP-SRx) prototype is an open source reference implementation and research platform for investigating emerging BGP security extensions and supporting protocols such as RPKI Origin Validation and BGPSec Path Validation. BGP-SRx is designed in such to minimize the dependencies on and the impact on specific router implementations. As a result, much functionality is provided by the stand-alone SRx server module. The prototype is also designed to support experimentation with various deployment architectures. As a result, the SRx module can run on the router, the validating cache, or on a completely separate platform.","language":["en"],"title":"BGP Secure Routing Extension (BGP-SRx): Reference Implementation and Test Tools for Emerging BGP Security Standards","distribution":[{"accessURL":"https://github.com/usnistgov/NIST-BGP-SRx","format":"Source Code in C - Linux","description":"The NIST BGP Secure Routing Extension (SRx) is an open source reference implementation and research platform for investigating emerging BGP security extensions and supporting protocols such as RPKI Origin Validation and BGPsec Path Validation.","title":"NIST-BGP-SRx GitHub Repository"},{"accessURL":"https://www.nist.gov/services-resources/software/bgp-secure-routing-extension-bgp-srx-prototype","format":"Webpage","title":"BGP Secure Routing Extension (BGP?SRx)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-09-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Cybersecurity","Information Technology:Networking","Standards:Documentary standards"],"conformsTo":"https://rfc-editor.org/rfc/rfc8205.txt","issued":"2021-09-16","keyword":["Border Gateway Protocol (BGP) security","BGP origin validation (BGP-OV)","BGP path validation (BGP-PV)","BGPsec","Internet infrastructure security","Resource Public Key Infrastructure (RPKI)","Routing security","and robustness."]},{"identifier":"ark:/88434/mds2-2139","accessLevel":"public","contactPoint":{"hasEmail":"mailto:joel.helton@nist.gov","fn":"Joel Helton"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2139","description":"A series of neutron spectroscopy measurements were performed on two single crystal samples of Co3V2O8 using the Disk Chopper Spectrometer (DCS) time-of-flight neutron spectrometer at the NIST Center for Neutron Research. In the low-temperature ferromagnetic phase spin waves are observed throughout the Brillouin zone. Modelling the spin wave dispersion reveals significant magnetic interaction between buckled kagome planes, contrary to prior work which treated the compound as quasi-two-dimensional. Further, the spin wave branches show evidence of a surprisingly large Dzyaloshinskii-Moriya interaction. At T=9.2 K the sample is antiferromagnetically aligned and well-defined spin waves are not observed.  Rather, most of the spectral weight is observed in broad diffuse scattering centered at the (0 0.5 0) antiferromagnetic Bragg peak.\n\nThe raw data files and parameter files from these experiments are provided here. The data can be analyzed using the DCS Reduction (Mslice) capabilities of the DAVE software package available at https://www.ncnr.nist.gov/dave/.","language":["en"],"title":"Spin Excitations in Co3V2O8. Raw data files from neutron spectroscopy experiments using the DCS spectrometer at the NCNR to probe the spin fluctuation spectrum of Co3V2O8.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2139/Spin%20Excitations%20in%20Co3V2O8.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Spin Excitations in Co3V2O8"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2139/Spin%20Excitations%20in%20Co3V2O8.zip","description":"Raw data files and parameter files from a neutron spectroscopy experiment using the DCS time-of-flight spectrometer.","mediaType":"application/x-zip-compressed","title":"Spin Excitations in Co3V2O8"},{"accessURL":"https://doi.org/10.18434/M32139","title":"DOI Access for Spin Excitations in Co3V2O8. Raw data files from neutron spectroscopy experiments using the DCS spectrometer at the NCNR to probe the spin fluctuation spectrum of Co3V2O8."}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-10-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Condensed matter"],"issued":"2019-10-24","keyword":["Neutron spectroscopy data","frustrated magnetism","kagome staircase lattice","Co3V2O8","spin waves","Dzyaloshinskii-Moriya interaction"]},{"identifier":"ark:/88434/mds2-2142","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2068"],"contactPoint":{"hasEmail":"mailto:harrison.skye@nist.gov","fn":"Harrison M. Skye"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2142","description":"These data from the laboratory tests of a prototype residential liquid-to-air ground-source air conditioner (GSAC) using CO2 as the refrigerant.  The data collection and processing methods are described in detail in this report:\n\nReport Title:  \"Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner\", NIST Technical Note 2068\nPublication Date: October 2019\nDOI: https://doi.org/10.6028/NIST.TN.2068\nAuthors: Harrison Skye, Wei Wu\n\nThe tests were performed in an environmental chamber and followed the ISO 13256-1 standard for rating GSHPs.  The CO2 GSAC operated either in a subcritical or a transcritical cycle, depending on the entering liquid temperature (ELT).  The test results included the coefficient of performance (COP), capacity, sensible heat ratio (SHR), and pressures.  The system incorporated a liquid-line/suction-line heat exchanger (LLSL-HX), which was estimated to cause a COP penalty of (0 to 2) % for ELTs ranging (10 to 25) °C, and benefit of (0 to 5) % for ELTs ranging (30 to 39) °C.  With ELTs ranging (10 to 39) °C the CO2 system cooling COP ranged (7.3 to 2.4).  At the standard rating condition (ELT 25 °C), the CO2 GSAC cooling COP was 4.14, and at part-load conditions (ELT 20 °C) the system had a COP of 4.92.","language":["en"],"title":"Data from Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2142/CO2_GSAC_Lab_Tests%20%28TN%202068%29%20-%20Appendix%20data.csv","format":"Comma Separated Values (CSV)","description":"This data is from the appendix of the publication. The data are for the system at steady state, and are an average of 120 samples taken over 30 minutes.","mediaType":"application/vnd.ms-excel","title":"Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner, NIST Technical Note 2068"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2142/README.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Readme"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2142/README.txt","format":".txt","description":"Description of the dataset","mediaType":"text/plain","title":"Readme"},{"accessURL":"https://doi.org/10.18434/M32142","title":"DOI Access for Data from Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2142/CO2_GSAC_Lab_Tests%20%28TN%202068%29%20-%20Appendix%20data.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner, NIST Technical Note 2068"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2142/CO2_GSAC_Lab_Tests%20%28TN%202068%29%20-%20Appendix%20data.xlsx","format":"Microsoft Excel (*.xlsx)","description":"This data is from the appendix of the publication. The data are for the system at steady state, and are an average of 120 samples taken over 30 minutes.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner, NIST Technical Note 2068"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2142/CO2_GSAC_Lab_Tests%20%28TN%202068%29%20-%20Appendix%20data.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner, NIST Technical Note 2068"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-10-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Energy:Energy efficiency"],"issued":"2020-01-13","keyword":["Air conditioner","carbon dioxide","CO2","ground-source heat pump","subcritical and transcritical cycles"]},{"identifier":"ark:/88434/mds2-2145","accessLevel":"public","contactPoint":{"hasEmail":"mailto:amy.engelbrecht-wiggans@nist.gov","fn":"Amy Engelbrecht-Wiggans"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2145","description":"To improve the reliability and design of armor, it is imperative to understand the failure modes and the degradation rates of the materials used in armor. Despite the best efforts of manufacturers, some vulnerability of armor materials to ageing due to hydrolytic or oxidative environments is expected and may result in the degradation of material properties such as tensile strength. In this work, p-aramid yarns from two manufacturers were exposed to environmental conditions of various fixed temperature and humidity combinations. The maximum temperature and humidity condition was 70 °C and 76 % RH. Tensile tests were performed on specimens extracted at several different timepoints over the course of at least one year to determine the change in ultimate tensile strength and failure strain as a function of time, temperature, and humidity. These materials were found to be generally resistant to degradation at most conditions, showing changes of less than 10 % only at the highest temperature and humidity conditions.\n\nThis data set contains failure load and failure strain values for three different aramid yarns, exposed to various conditions. It also includes Fourier Transform Infrared (FTIR) spectroscopy spectra for two of the aramids.","language":["en"],"title":"Ageing of High Strength p-Aramid Fibers Used in Body Armor","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w118%2055C%2060RH%20Aramid%20B%201-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w118%2055C%2060RH%20Aramid%20B%202-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w118%2055C%2060RH%20Aramid%20B%202-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w118%2055C%2060RH%20Aramid%20B%203-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w118%2055C%2060RH%20Aramid%20B%203-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w50%2070C%2076RH%20Aramid%20A%201-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w50%2070C%2076RH%20Aramid%20A%201-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w50%2070C%2076RH%20Aramid%20A%202-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w50%2070C%2076RH%20Aramid%20A%202-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w50%2070C%2076RH%20Aramid%20A%203-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w50%2070C%2076RH%20Aramid%20A%203-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w50%2070C%2076RH%20Aramid%20B%201-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w50%2070C%2076RH%20Aramid%20B%201-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w50%2070C%2076RH%20Aramid%20B%203-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w50%2070C%2076RH%20Aramid%20B%203-3.0.dpt.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32145","title":"DOI Access for Ageing of High Strength p-Aramid Fibers Used in Body Armor"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/Tensile%20data/AramidA.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/Tensile%20data/AramidA.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/Tensile%20data/AramidB.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/Tensile%20data/AramidB.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/Tensile%20data/AramidC.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/Tensile%20data/AramidC.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/Aramid_yarns_readme.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/Aramid_yarns_readme.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20A%201-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20A%201-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20A%202-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20A%202-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20A%203-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20A%203-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20B%201-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20B%201-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20B%202-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20B%202-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20B%203-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/unaged%20Aramid%20B%203-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w105%2055C%2060RH%20Aramid%20A%202-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w105%2055C%2060RH%20Aramid%20A%202-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w105%2055C%2060RH%20Aramid%20A%203-3.0.dpt","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w105%2055C%2060RH%20Aramid%20A%203-3.0.dpt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2145/FTIR%20data/w118%2055C%2060RH%20Aramid%20B%201-3.0.dpt","mediaType":"application/octet-stream"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-10-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Polymers","Materials:Materials characterization","Materials"],"issued":"2019-10-24","keyword":["mechanical properties","tensile testing","yarn testing","body armor","aramid","PPTA","environmental ageing"]},{"identifier":"ark:/88434/mds2-2146","accessLevel":"public","contactPoint":{"hasEmail":"mailto:william.bernstein@nist.gov","fn":"William Z. Bernstein"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2146","description":"This dataset provides reference ontologies that were translated from product design and inspection data from the National Institute of Standards and Technology (NIST) Smart Manufacturing Systems (SMS) Test Bed.  The examples represents a three-component assembly of a box, machined from Aluminum, and has a technical data package available on the SMS Test Bed website.  The use of the ontologies aims to integrate the product lifecycle data of engineering design represented in the STEP AP242 format, which is described in the ISO 10303 series, as well as quality assurance data, representing in the Quality Information Framework (QIF) standard.","language":["en"],"title":"Reference Knowledge Graphs of STEP and QIF Data for a Three-Part Box Assembly","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2146/qif-ontology-dataset-V2.zip.sha256","mediaType":"text/plain","title":"SHA256 File for QIF and STEP instance data of three-component box assembly"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2146/qif-ontology-dataset-V2.zip","mediaType":"application/x-zip-compressed","title":"QIF and STEP instance data of three-component box assembly"},{"accessURL":"https://doi.org/10.18434/M32146","title":"DOI Access for Reference Knowledge Graphs of STEP and QIF Data for a Three-Part Box Assembly"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-10-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://qifstandards.org","theme":["Manufacturing:Systems integration","Manufacturing:Product data","Manufacturing:Quality assurance","Manufacturing:Machining","Manufacturing:Interoperability in manufacturing","Manufacturing"],"conformsTo":"https://qifstandards.org","issued":"2021-07-09","keyword":["digital thread","inspection","product data","smart manufacturing","quality information framework","STEP AP242","QIF","ontology"]},{"identifier":"ark:/88434/mds2-2147","accessLevel":"restricted public","references":["https://doi.org/10.1021/ci00038a003","https://doi.org/10.6028/NBS.TN.1229","https://doi.org/10.1107/S0108768102006948"],"contactPoint":{"hasEmail":"mailto:igor.levin@nist.gov","fn":"Igor Levin"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2147","description":"Materials discovery and development necessarily begins with the preparation and identification of product phase(s). Crystalline compounds can be identified by their characteristic diffraction patterns using X-rays, neutrons, and or electrons. An estimated 20,000 X-ray diffractometers and a comparable number of electron microscopes are used daily in materials research and development laboratories for this purpose. Access to crystal structure data is a critical step in solving research and applications problems in materials researches, and these data are of interest to analysts in areas such as materials design, property prediction and compound identification.\n\nNIST Crystallographic Data Center, in collaboration with partners all over the world, evaluates and disseminates chemical, physical and crystallographic information published on these materials. NIST Standard Reference Database 3: NIST Inorganic Crystal Structure Database (NIST ICSD) is a comprehensive collection of crystal structure data of nonorganic compounds (including inorganics, ceramics, minerals, pure elements, metals, and intermetallic systems) containing over 210,000 entries and covering the literature from 1913.\n\nNIST ICSD includes entries that fall into the following categories: full structure data from experimental refinement or derived from their iso-structural structure types, theoretically predicted structures from computer simulations, as well as partially characterized structures.\n\nThe NIST ICSD web application provides materials researchers with a user-friendly interface to search the database based on bibliographic information, chemistry, unit cell, space group, experimental settings, mineral name/group and other derived data from expert evaluation. In addition, it also provides users with functions to easily create and examine results from various crystallographic computations, such as reduced cell, bond distance/angle, calculated powder diffraction data, and structure standardization.","language":["en"],"title":"NIST Inorganic Crystal Structure Database (ICSD)","distribution":[{"accessURL":"https://icsd.nist.gov","description":"Search, display, and export the crystal structure data, or running scientific calculations using NIST ICSD.","title":"NIST Inorganic Crystal Structure Database"},{"accessURL":"https://doi.org/10.18434/M32147","title":"DOI Access for NIST Inorganic Crystal Structure Database (ICSD)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-10-19 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://icsd.nist.gov/fields.html","theme":["Materials:Materials characterization","Materials:Modeling and computational material science"],"issued":"2020-02-18","keyword":["chemical structures","crystallography","crystal structures","diffraction","disorder","electrons","identification","inorganic","neutrons","magnetic","metals","minerals","materials","Rietveld","synchrotron","twinned","x-rays","Advanced Materials","Manufacturing","Safety","Security and Forensics"]},{"identifier":"ark:/88434/mds2-2149","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.7760","https://www.nist.gov/publications/analysis-three-different-regression-models-estimate-ballistic-performance-new-and"],"contactPoint":{"hasEmail":"mailto:amanda.forster@nist.gov","fn":"Amanda L. Forster"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2149","description":"The performance standard for ballistic-resistant body armor published by the National Institute of Justice (NIJ), NIJ Standard 0101.06, recommends estimating the perforation performance of body armor by performing a statistical analysis on V50 ballistic limit testing data.  The first objective of this study is to evaluate and compare the estimations of the performance provided by different statistical methods applied to ballistic data generated in the laboratory. Three different distribution models are able to describe the relationship between the projectile velocity and the probability of perforation are considered: the logistic, the probit and the complementary log-log response models. A secondary objective of this study is to apply the different methods to a new body armor model with unusual ballistic limit results, leading one to suspect that it may not be best described by a symmetric model, to determine if this data can be better fitted by a model other than the logistic model.  This work has been published as NISTIR 7760, \"Analysis of Three Different Regression Models to Estimate the Ballistic Performance of New and Environmentally Conditioned Body Armor.\"  The raw data (ballistic limit data) associated with this prior publication is archived in this dataset.","language":["en"],"title":"Ballistic test results for several different soft body armor systems","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2149/V50.zip.sha256","mediaType":"text/plain","title":"Ballistic test results for several different soft body armor systems"},{"accessURL":"https://doi.org/10.18434/M32149","title":"DOI Access for Ballistic test results for several different soft body armor systems"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2149/V50.zip","format":"This is a zipped complication of six .csv files and one .txt readme file.","description":"There are six .csv files, all contain V50 ballistic limit data, one for each of the armors tested. Each .csv has two columns, one for velocity in (m/s) and one for result.  The results are coded as a 1 if the armor was perforated by the projectile and a 0 if the armor stopped the projectile. A readme file gives additional information on the dataset.","mediaType":"application/x-zip-compressed","title":"Ballistic test results for several different soft body armor systems"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2011-02-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Forensics"],"issued":"2019-11-12","keyword":["ballistic limit","logistic regression","c-log-log","probit"]},{"identifier":"ark:/88434/mds2-2150","accessLevel":"public","references":["https://doi.org/10.29173/iq967"],"contactPoint":{"hasEmail":"mailto:regina.avila@nist.gov","fn":"Regina L. Avila"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2150","description":"Data from an exercise conducted by a group of federal library and data information managers who are part of the interagency CENDI Data Curation Discussion Group (DCDG). The exercise examined a cross-section of literature and other resources to reveal common reproducibility issues faced by stakeholders regardless of subject area or focus. The data include a variety of issues identified as reproducibility barriers, the solutions to such barriers, and a reflection on how researchers and information professionals can act to address the 'reproducibility crisis.'","language":["en"],"title":"CENDI-DCDG Reproducibility Resource Analysis Data","distribution":[{"accessURL":"https://doi.org/10.18434/M32150","title":"DOI Access for CENDI-DCDG Reproducibility Resource Analysis Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2150/DCDG-Reproducibility_data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for CENDI-DCDG Reproducibility Resource Analysis Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2150/DCDG-Reproducibility_data.zip","format":"zipped collection of .csv files","description":"Bibliographic and metric data used in analysis of resources on reproducibility.","mediaType":"application/zip","title":"CENDI-DCDG Reproducibility Resource Analysis Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-10-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Computational science","Information Technology:Data and informatics"],"issued":"2019-11-22","keyword":["reproducibility","reproducibility crisis","replicability","research data","landscape analysis","culture shift"]},{"identifier":"ark:/88434/mds2-2151","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jeffrey.hudgens@nist.gov","fn":"Jeffrey W. Hudgens"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2151","description":"This tutorial provides  mechanical drawings, electrical schematics, parts lists, STL files for 3D-printed parts, IGS files, and instructions for automated machining necessary for construction of a dual-protease, subzero, liquid chromatography system for hydrogen-deuterium exchange mass spectrometry. Electrical schematics for construction of a multi-zone temperature controller that regulate to ±0.1 oC are also included in this tutorial.","language":["en"],"title":"Construction of a Dual-enzyme, Subzero (-30 degree C) Chromatography System and Multi-channel Precision Temperature Controller for Hydrogen-Deuterium Exchange Mass Spectrometry","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/T-controller%20%28ver%202%29.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/T-controller%20%28ver%202%29.pdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/ADDENDUM%20for%20HDX.docx","format":"word processing document","description":"This addendum provides information for configuring the instrument with an 8-port valve in the Valve #2 location. The 8-port valve offers the ability to backflush the protease, trap, and analytical columns, which allows more rapid removal of protease aggregates from the top of each column.","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"ADDENDUM for HDX-MS Analytical Instrument for Incorporation on a Robotic Rail, Featuring Dual Enzyme Chambers and HPLC Separations at -30 oC"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/Multi-Channel%20T-controller.pdf","description":"This file presents mechanical drawings, electrical schematics, parts lists, STL file inventory for 3D-printed parts, and instructions for construction of the Multi-channel Temperature Controller","mediaType":"application/pdf","title":"Construction information for the Multi-channel Temperature Controller"},{"accessURL":"https://doi.org/10.18434/M32151","title":"DOI Access for Construction of a Dual-enzyme, Subzero (-30 degree C) Chromatography System and Multi-channel Precision Temperature Controller for Hydrogen-Deuterium Exchange Mass Spectrometry"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/HDX-MS%20Analytical%20Instrument.pdf","description":"This file presents mechanical drawings, electrical schematics, parts lists, lists of STL files for 3D-printed parts, lists of IGS files for automated machining, and instructions for construction of the HDX-MS Analytical Instrument for Incorporation on a Robotic Rail, Featuring Dual Enzyme Chambers and HPLC Separations at -30 degree C","mediaType":"application/pdf","title":"HDX-MS Analytical Instrument for Incorporation on a Robotic Rail, Featuring Dual Enzyme Chambers and HPLC Separations at -30 degree C"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/Multi-Channel%20Valve%20Controller.pdf","description":"This file presents mechanical drawings, electrical schematics, parts lists, STL file inventory for 3D-printed parts, and instructions for construction of the Multi-Channel Valve Controller.","mediaType":"application/pdf","title":"Multi-Channel Valve Controller"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/Multi-Channel%20Valve%20Controller%20STL%20files.7z","description":"This file contains the STL files for the 3D-printed parts used to construct the Multi-Channel Valve Controller","mediaType":"application/octet-stream","title":"Multi-Channel Valve Controller STL files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/ADDENDUM%20for%20HDX.docx.sha256","mediaType":"text/plain","title":"SHA256 File for ADDENDUM for HDX-MS Analytical Instrument for Incorporation on a Robotic Rail, Featuring Dual Enzyme Chambers and HPLC Separations at -30 oC"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/Multi-Channel%20T-controller.pdf.sha256","mediaType":"text/plain","title":"SHA256 File for Construction information for the Multi-channel Temperature 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Controller"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/HDX-MS%20Analytical%20Instrument%20STL-IGS%20Files.7z.sha256","mediaType":"text/plain","title":"SHA256 File for STL and IGS files that accompany the file, HDX-MS Analytical Instrument.pdf"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/Multi-Channel%20T-controller%20STL%20Files.7z","format":"7Z","description":"STL Files for parts used to construct the Multi-channel Temperature Controller","mediaType":"application/octet-stream","title":"STL Files for parts used to construct the Multi-channel Temperature Controller"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2151/HDX-MS%20Analytical%20Instrument%20STL-IGS%20Files.7z","format":"7Z","description":"This is the compressed file containing the STL and IGS files that accompany the file, HDX-MS Analytical Instrument.pdf.","mediaType":"application/octet-stream","title":"STL and IGS files that accompany the file, HDX-MS Analytical Instrument.pdf"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-02-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Bioscience:Bioprocessing","Chemistry:Analytical chemistry","Physics:Biological physics","Bioscience:Proteomics","Metrology:Amount of substance"],"issued":"2020-04-14","keyword":["liquid chromatography","hydrogen-deuterium exchange","mass spectrometry","precision","peptide","protein","proteolysis","proteomics","reference material","temperature control."]},{"identifier":"ark:/88434/mds2-2153","accessLevel":"public","references":["https://dx.doi.org/10.1016/j.calphad.2016.12.004","https://dx.doi.org/10.1063/1.4942634"],"contactPoint":{"hasEmail":"mailto:boris.wilthan@nist.gov","fn":"Boris Wilthan"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2153","description":"The NIST Alloy data web application (https://trc.nist.gov/metals_data) provides access to thermophysical property data with a focus on unary, binary, and ternary metal systems.","language":["en"],"title":"NIST alloy data","distribution":[{"accessURL":"https://trc.nist.gov/metals_data/","title":"NIST alloy data"},{"accessURL":"https://doi.org/10.18434/M32153","title":"DOI Access for NIST alloy data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-10-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Thermodynamics","Physics:Condensed matter","Mathematics and Statistics:Uncertainty quantification","Materials:Superconductors","Materials:Metals","Materials:Materials characterization","Manufacturing:Additive manufacturing","Chemistry:Thermochemical properties","Chemistry:Chemical thermodynamics and chemical properties","Chemistry:Chemical engineering and processing"],"issued":"2020-01-23","keyword":["thermophysical property","elements","binary","ternary","alloy","metal","calphad","material development","enthalpy","heat capacity","density","resistivity","phase","viscosity","surface tension","solid","liquid","crystal","temperature dependent","phase transition","conductivity","diffusivity"]},{"identifier":"ark:/88434/mds2-2154","accessLevel":"public","contactPoint":{"hasEmail":"mailto:donald.burgess@nist.gov","fn":"Donald R. Burgess"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2154","description":"This version of NIST Standard Reference Database 46 is a major enhancement to this widely used database which provides comprehensive coverage of interactions for aqueous systems of organic and inorganic ligands with protons and various metal ions and is based on the six-volume Critical Stability Constants by Martell and Smith. The new version contains 225 additional ligands, new data, data printing, rapid bibliography searching and more streamlined commands. New literature has resulted in revision and upgrading of 30% of previous data. Entire contents are critically selected for accuracy and consistency.","language":["en"],"title":"NIST SRD 46. Critically Selected Stability Constants of Metal Complexes: Version 8.0 for Windows","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2154/SRD%2046%20Manual.pdf.sha256","mediaType":"text/plain","title":"SHA256 File for Users' Guide for NIST SRD 46.  Critically Selected Stability Constants of Metal Complexes Database"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2154/SRD%2046%20Manual.pdf","format":"PDF file","description":"Version 8.0 is a major enhancement to this widely used database which provides comprehensive coverage of interactions for aqueous systems of organic and inorganic ligands with protons and various metal ions and is based on the six-volume Critical Stability Constants by Martell and Smith. The new version contains 225 additional ligands, new data, data printing, rapid bibliography searching and more streamlined commands. New literature has resulted in revision and upgrading of 30% of previous data. Entire contents are critically selected for accuracy and consistency.","mediaType":"application/pdf","title":"Users' Guide for NIST SRD 46.  Critically Selected Stability Constants of Metal Complexes Database"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2154/SRD%2046%20Install.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Install executable for SRD 46"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2154/SRD%2046%20Install.zip","format":"zipped exe executable","description":"Install executable for SRD 46","mediaType":"application/x-zip-compressed","title":"Install executable for SRD 46"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2154/SRD%2046%20SQL.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SQL files for NIST SRD 46 Stability Constants"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2154/SRD%2046%20SQL.zip","format":"zipped sql and text files","description":"SQL files for NIST SRD 46 Stability Constants","mediaType":"application/x-zip-compressed","title":"SQL files for NIST SRD 46 Stability Constants"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2154/SRD%2046%20README.txt.sha256","mediaType":"text/plain","title":"SHA256 File for SRD 46 README file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2154/SRD%2046%20README.txt","format":"text file","description":"SRD 46 README file.  Contains an overview, descriptions of the files attached, description of operating system  under which it can operate, a disclaimer, and a Fair Use policy.","mediaType":"text/plain","title":"SRD 46 README file"},{"accessURL":"https://doi.org/10.18434/M32154","title":"DOI Access for NIST SRD 46. Critically Selected Stability Constants of Metal Complexes: Version 8.0 for Windows"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2004-05-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Chemical thermodynamics and chemical properties"],"issued":"2020-01-13","keyword":["NIST","database","stability constants","metal complexes"]},{"identifier":"ark:/88434/mds2-2155","accessLevel":"public","contactPoint":{"hasEmail":"mailto:peter.bajcsy@nist.gov","fn":"Peter Bajcsy"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2155","description":"This web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes. The motivation behind this web validation system is to prepare a large number of pixel-level multi-class annotated microscopy images for training artificial intelligence (AI) based segmentation models (U-Net and SegNet models). While the purpose of the AI models is to predict accurately four damage labels, such as, paste damage, aggregate damage, air voids, and no-damage, our goal is to assert trust in such predictions (a) by using contextual labels and (b) by enabling visual validations of predicted damage labels.","language":["en"],"title":"2D Segmentation of Concrete Samples for Training AI Models","distribution":[{"accessURL":"https://isg.nist.gov/deepzoomweb/data/concreteScoring","format":"TIFF file format","description":"his web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes.","title":"2D Segmentation of Concrete Samples for Training AI Models"},{"accessURL":"https://github.com/usnistgov/WIPP-unet-train-plugin","title":"UNet CNN Semantic-Segmentation Training plugin"},{"accessURL":"https://github.com/usnistgov/WIPP-unet-inference-plugin","title":"UNet CNN Semantic-Segmentation Inference plugin"},{"accessURL":"https://doi.org/10.18434/M32155","title":"DOI Access for 2D Segmentation of Concrete Samples for Training AI Models"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-11-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Computational science"],"issued":"2019-12-31","keyword":["CS-MET computational metrology"]},{"identifier":"ark:/88434/mds2-2156","accessLevel":"public","contactPoint":{"hasEmail":"mailto:anirudha.sahoo@nist.gov","fn":"Anirudha Sahoo"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2156","description":"This research work is a performance study of GAA-GAA coexistence in the Citizens Broadband Radio Service (CBRS) band. The Wireless Innovation Forum (WINNForum), which has developed standards for commercial operations in this band, has recommended three approaches to GAA-GAA coexistence. This work studies one of the approaches (called Approach 1) . We provide the results of this study through the dataset archived here. There are few configurations considered in the study.  We chose two geographical locations: Virginia Beach (VB) and San Diego (SD). In one deployment scenario all the CBRS devices (CBSDs) were chosen to be Category A (Cat A)  CBSDs and in the other, it was a mix of Cat A and Cat B. CBSD deployment densities of 3, 10, 30, 50 per square kilometers were considered. Two propagation models, namely ITM and Hybrid models were used in the evaluation.","language":["en"],"title":"Performance Study of a GAA-GAA Coexistence Scheme in the CBRS Band","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_13_sd_both_bwix.txt","description":"This data shows how average BW allocation to CBSDs change when Average Interference Per CBSD per Channel per Grid (AIPCCG) increases  at SD when a mix of Cat A and Cat B CBSDs is deployed. Data for different deployment densities and ITM and Hybrid propagation models are included.","mediaType":"text/plain","title":"Average BW vs average interference at SD with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_12_sd_cata_bwix.txt","description":"This data shows how average BW allocation to CBSDs change when Average Interference Per CBSD per Channel per Grid (AIPCCG) increases  at SD when all the CBSDs are Cat A. Data for different deployment densities and ITM and Hybrid propagation models are included.","mediaType":"text/plain","title":"Average BW vs average interference at SD with only Cat A CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_15_vb_both_bwix.txt","description":"This data shows how average BW allocation to CBSDs change when Average Interference Per CBSD per Channel per Grid (AIPCCG) increases  at VB when a mix of Cat A and Cat B CBSDs is deployed. Data for different deployment densities and ITM and Hybrid propagation models are included.","mediaType":"text/plain","title":"Average BW vs average interference at VB with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_14_vb_cata_bwix.txt","description":"This data shows how average BW allocation to CBSDs change when Average Interference Per CBSD per Channel per Grid (AIPCCG) increases  at VB when all the CBSDs are Cat A. Data for different deployment densities and ITM and Hybrid propagation models are included.","mediaType":"text/plain","title":"Average BW vs average interference at VB with only Cat A CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_5_sd_both_bwcdf.txt","description":"This data contains the cumulative distribution function of bandwidth  allocated to CBSDs at SD with a mix of Cat A and Cat B CBSDs at different deployment densities and when ITM and Hybrid propagation models are used.","mediaType":"text/plain","title":"CDF of BW allocation at SD with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_4_sd_cata_bwcdf.txt","description":"This data contains the cumulative distribution function of bandwidth  allocated to CBSDs at SD with only Cat A CBSDs at different deployment densities and when ITM and Hybrid propagation models are used.","mediaType":"text/plain","title":"CDF of BW allocation at SD with only Cat A CBSD"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_7_vb_both_bwcdf.txt","description":"This data contains the cumulative distribution function of bandwidth  allocated to CBSDs at VB with a mix of Cat A and Cat B CBSDs at different deployment densities and when ITM and Hybrid propagation models are used.","mediaType":"text/plain","title":"CDF of BW allocation at VB with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_6_vb_cata_bwcdf.txt","description":"This data contains the cumulative distribution function of bandwidth  allocated to CBSDs at VB with only Cat A CBSDs at different deployment densities and when ITM and Hybrid propagation models are used","mediaType":"text/plain","title":"CDF of BW allocation at VB with only Cat A CBSD"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_9_sd_both_ixcdf_w.txt","description":"This data represents the CDF of Average Interference Per unit Area  at SD with a mix of Cat A and Cat B CBSDs. Data for different deployment densities and ITM and Hybrid propagation models are included.","mediaType":"text/plain","title":"CDF of average interference at SD with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_8_sd_cata_ixcdf_w.txt","description":"This data represents the CDF of Average Interference Per unit Area  at SD when all the CBSDs are Cat A. Data for different deployment densities and ITM and Hybrid propagation models are included.","mediaType":"text/plain","title":"CDF of average interference at SD with only Cat A CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_11_vb_both_ixcdf_w.txt","description":"This data represents the CDF of Average Interference Per unit Area  at VB with a mix of Cat A and Cat B CBSDs. Data for different deployment densities and ITM and Hybrid propagation models are included.","mediaType":"text/plain","title":"CDF of average interference at VB with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_10_vb_cata_ixcdf_w.txt","description":"This data represents the CDF of Average Interference Per unit Area  at VB when all the CBSDs are Cat A. Data for different deployment densities and ITM and Hybrid propagation models are included.","mediaType":"text/plain","title":"CDF of average interference at VB with only Cat A CBSDs"},{"accessURL":"https://doi.org/10.18434/M32156","title":"DOI Access for Performance Study of a GAA-GAA Coexistence Scheme in the CBRS Band"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_13_sd_both_bwix.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Average BW vs average interference at SD with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_12_sd_cata_bwix.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Average BW vs average interference at SD with only Cat A CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_15_vb_both_bwix.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Average BW vs average interference at VB with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_14_vb_cata_bwix.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Average BW vs average interference at VB with only Cat A CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_5_sd_both_bwcdf.txt.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of BW allocation at SD with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_4_sd_cata_bwcdf.txt.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of BW allocation at SD with only Cat A CBSD"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_7_vb_both_bwcdf.txt.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of BW allocation at VB with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_6_vb_cata_bwcdf.txt.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of BW allocation at VB with only Cat A CBSD"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_9_sd_both_ixcdf_w.txt.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of average interference at SD with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_8_sd_cata_ixcdf_w.txt.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of average interference at SD with only Cat A CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_11_vb_both_ixcdf_w.txt.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of average interference at VB with mix of Cat A and Cat B CBSDs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2156/fig_10_vb_cata_ixcdf_w.txt.sha256","mediaType":"text/plain","title":"SHA256 File for CDF of average interference at VB with only Cat A CBSDs"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-11-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Networking"],"issued":"2019-11-22","keyword":["3.5 GHz; CBRS; Coexistence","GAA"]},{"identifier":"ark:/88434/mds2-2157","accessLevel":"public","contactPoint":{"hasEmail":"mailto:strbase@nist.gov","fn":"Katherine Gettings"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2157","description":"This dataset consists of single source and mixture samples which were genotyped/sequenced with kits targeting Forensic DNA markers. More information specific to the kit and or method used can be found in the README text files included in each zipped file.The CE-STR kits reported for the single source samples include:    Applied Biosystems GlobalFiler,     Applied Biosystems Y-Filer Plus,     Promega PowerPlex Fusion 6C,     Promega PowerPlex Y23The CE profiles for single source samples are also included in a spreadsheet.The following CE-STR kit is reported for the mixture samples:     Promega PowerPlex Fusion 6CThe sequencing kits reported for the mixture and single source samples include:     Verogen ForenSeq DNA Signature Prep Kit,     Promega PowerSeq 46GY,     Thermo Fisher Applied Biosystems Precision ID GlobalFiler NGS STR Panel v2The single source samples only are reported for:    Promega PowerSeq CRM Nested SystemThis data was produced with approval from the NIST Research Protections Office. It is intended for research, training, and educational purposes only and could potentially contain errors due to limited review prior to uploading. This data should not be used to identify the donor of the profile or uploaded/searched versus public or law enforcement DNA databases. Certain commercial equipment, instruments, or materials are identified in this dataset in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.","language":["en"],"title":"Forensic DNA Open Dataset","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/CE-STR_Assays/Single_Source.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/CE-STR_Assays/Single_Source.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/CE-STR_Assays/Mixture_Profiles.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/CE-STR_Assays/Mixture_Profiles.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/NGS_Assays/Promega_PowerSeq_46GY.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/NGS_Assays/Promega_PowerSeq_46GY.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/NGS_Assays/Verogen_ForenSeq_DNA_Signature_Prep_Kit.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/NGS_Assays/Verogen_ForenSeq_DNA_Signature_Prep_Kit.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/NGS_Assays/ThermoFisher_Precision_ID_GlobalFiler_NGS_STR_Panel_v2.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/NGS_Assays/ThermoFisher_Precision_ID_GlobalFiler_NGS_STR_Panel_v2.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/NGS_Assays/Promega_PowerSeq_CRM_Nested_System.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/NGS_Assays/Promega_PowerSeq_CRM_Nested_System.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/README_Forensic_DNA_Open_Dataset.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2157/README_Forensic_DNA_Open_Dataset.txt.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2025-02-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Forensics:DNA and biological evidence"],"keyword":["Forensic","DNA","Sequence","Capillary Electrophoresis","STR","SNP","mtDNA"]},{"identifier":"ark:/88434/mds2-2158","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jeanne.quimby@nist.gov","fn":"Jeanne Quimby"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2158","description":"This project is anticipated to generate Working Data, Derived Data, and Published Results. Working Data will comprise raw measurement information from channel sounder instruments or oscilloscopes. Our channel sounder records data in National Instruments proprietary binary (.tdms) format. Oscilloscope measurements will be recorded as ASCII spreadsheet (.csv) formats. Also among working data will be human-readable files containing experimental configurations and conditions for the collection of channel sounding data. These data are stored as spreadsheet (.xlsx) files. Working Data will be processed into Derived Data using a set of Matlab scripts (hereafter \"data processing codes\"), which apply timing and statistical calculations to the Working Data. These Derived Data will be saved in Matlab's proprietary storage format (.mat) files. Each file will contain data structures which group experimental conditions and results together. Published Results may comprise charts or tables, which will demonstrate associations between derived results and experimental configurations or environmental conditions. The data contained in these charts or tables will comprise data from the Derived Data (.mat) files, as well as calculated statistical information such as confidence intervals. A set of Matlab scripts (hereafter \"plotting codes\") allows for selection of specific experiments from among the Derived Data, and for representation of these results in various plots. Any published chart may be reproduced provided three sets of information: (1) the Derived Data (.mat) file, (2) the Matlab plotting codes, and (3) a series of commands to the Matlab plotting codes to select the experimental data to be plotted. Special data requirements: These data may require appropriate software to access. National Instruments (.tdms) data files require software from National Instruments, or a third-party converter, to be viewable. Matlab (.mat) files may require Matlab to access contained data.","language":["en"],"title":"Timing Offset and Timing Stability for a Dual-Clock Channel Sounder","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2158/metadata.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Metadata File"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2158/metadata.txt","format":"Plain text","description":"A human readable description of the data contained in the .mat datafile.","mediaType":"text/plain","title":"Metadata File"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2158/channel_sounder_timing_uncertainty_20200724.mat.sha256","mediaType":"text/plain","title":"SHA256 File for Data file for publication"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2158/channel_sounder_timing_uncertainty_20200724.mat","format":"Matlab .mat data file","description":"A Matlab .mat file containing variables for all plots in the publications.","mediaType":"application/octet-stream","title":"Data file for publication"},{"accessURL":"https://doi.org/10.18434/M32158","title":"DOI Access for Timing Offset and Timing Stability for a Dual-Clock Channel Sounder"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2018-12-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)"],"issued":"2021-01-19","keyword":["5G technology; channel sounder; millimeter-wave wireless communication; propagation channel; system timing","Time Allan Deviation; timing errors; timing noise; timing offset; wireless system."]},{"identifier":"ark:/88434/mds2-2160","accessLevel":"public","contactPoint":{"hasEmail":"mailto:tyler.diamond@nist.gov","fn":"Tyler Diamond"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2160","description":"Software to simulate performing a selfish mining attack against certain cryptocurrencies' difficulty algorithms. Allows tweaking parameters of the attack and the difficulty algorithms themselves.","language":["en"],"title":"Selfish Mining Simulator for Cryptocurrencies","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2160/SelfishMiningSim-master.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Selfish mining simulator"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2160/SelfishMiningSim-master.zip","description":"The code to run the selfish mining simulator. Requires the \"go\" language. The researchers' results were included in this download (\"all_results.json\"), and new results will be saved to \"results.json\"","mediaType":"application/zip","title":"Selfish mining simulator"},{"accessURL":"https://github.com/usnistgov/SelfishMiningSim","title":"GitHub URL for Selfish Mining Simulator for Cryptocurrencies"},{"accessURL":"https://doi.org/10.18434/M32160","title":"DOI Access for Selfish Mining Simulator for Cryptocurrencies"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-11-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Cybersecurity"],"issued":"2019-12-10","keyword":["Selfish mining","bitcoin","blockchain","mining","cryptocurrency","cryptocurrency mining"]},{"identifier":"ark:/88434/mds2-2161","accessLevel":"public","contactPoint":{"hasEmail":"mailto:alden.dima@nist.gov","fn":"Alden A. Dima"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2161","description":"Training and test-related data to accompany \"Keyphrase Extraction for Technical Language Processing\" by Alden Dima and Aaron Massey (in press). The subdirectories \"keyphrase-extraction-jct-train\" and \"keyphrase-extraction-jct-test\" contain a total of 1153 ThermoML files which are each associated with a corresponding Journal of Chemical Thermodynamics (JCT) article. These ThermoML files contain information about these papers in extensible markup language (XML) format including the title, authors, abstract, digital object identifier (DOI) and keywords. They also contain thermophysical property data unrelated to the keyphrase extraction study. These files were obtained from the National Institute of Standard and Technology (NIST) Thermodynamics Research Center (TRC) in Boulder, Colorado (https://trc.nist.gov/). Readers wishing to replicate this work will also need to obtain the original JCT articles which can be obtained from https://www.sciencedirect.com/journal/the-journal-of-chemical-thermodynamics.","language":["en"],"title":"Training and Test-Related Data for Keyphrase Extraction for Technical Language Processing","distribution":[{"accessURL":"https://doi.org/10.18434/M32161","title":"DOI Access for Training and Test-Related Data for Keyphrase Extraction for Scientific Registries"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2161/keyphrase-extraction-jct-data-20191212.tgz.sha256","mediaType":"text/plain","title":"Hash File for Training and Test-Related Data for Keyphrase Extraction for Scientific Registries"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2161/keyphrase-extraction-jct-data-20191212.tgz","format":".tgz file of data","description":"Download link for data","mediaType":"application/gzip","title":"Training and Test-Related Data for Keyphrase Extraction for Scientific Registries"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-12-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Software research","Information Technology:Data and informatics"],"issued":"2019-12-20","keyword":["MGI informatics"]},{"identifier":"ark:/88434/mds2-2162","accessLevel":"public","references":["https://doi.org/10.1016/j.addma.2017.06.011","http://dx.doi.org/10.6028/jres.119.019"],"contactPoint":{"hasEmail":"mailto:felix.kim@nist.gov","fn":"Felix Kim"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2162","description":"This data contains X-ray computed tomography (XCT) reconstructed slices of additively manufactured cobalt chrome samples produced with varying laser powder bed fusion (LPBF) processing parameters (scan speed and hatch spacing). A constant laser power of 195 W and a layer thickness of 20 µm were used. Unoptimized processing parameters created defects in these parts. The as-built CoCr disks were 40 mm in diameter and 10 mm in height, with no post-processing step (e.g. heat treatment or hot isostatic pressing) used. Five mm diameter cylinders were cored out of each disk, and regions of interests (ROIs) within the cylinders were measured with XCT. The voxel size is approximately 2.5 µm, and approximately 1000 x 1000 x 1000 voxel three-dimensional images were obtained, for an actual volume of about  (pi/4) x (2.5 mm)^3 in case of the approximately 2.5 µm voxel data sets. The data set contains two folders ('raw' and 'segmented') with 5 zipped tiff image folders, one for each sample. The images in the 'raw' folder are the original 16-bit XCT reconstructed images. The images in the 'segmented' folder are the segmented images. 'setn' in the file name represents the sample set and 'samplen' represents the sample number.  The final trailing -n represents the number of the image in the stack where higher number is toward the top of the sample.","language":["en"],"title":"High-Resolution X-ray computed tomography (XCT) image data set of additively manufactured cobalt chrome samples produced with varying laser powder bed fusion processing parameters","distribution":[{"accessURL":"https://doi.org/10.18434/M32162","title":"DOI Access for High-Resolution X-ray computed tomography (XCT) image data set of additively manufactured cobalt chrome samples produced with varying laser powder bed fusion processing parameters"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/Readme.docx","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/Readme.docx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/raw/set1sample2raw.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/raw/set1sample2raw.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/raw/set1sample3raw.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/raw/set1sample3raw.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/raw/set1sample4raw.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/raw/set1sample4raw.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/raw/set1sample5raw.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/raw/set1sample5raw.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/raw/set1sample6raw.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/raw/set1sample6raw.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/segmented/set1sample2segmented.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/segmented/set1sample2segmented.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/segmented/set1sample3segmented.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/segmented/set1sample3segmented.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/segmented/set1sample4segmented.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/segmented/set1sample4segmented.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/segmented/set1sample5segmented.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/segmented/set1sample5segmented.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/segmented/set1sample6segmented.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2162/segmented/set1sample6segmented.zip.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-12-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing","Materials:Metals","Mathematics and Statistics:Image and signal processing"],"issued":"2020-01-06","keyword":["Additive Manufacturing; defect; X-ray computed tomography; Laser powder bed fusion;"]},{"identifier":"ark:/88434/mds2-2164","accessLevel":"public","references":["https://dx.doi.org/10.1088/1361-6455/aa6c4a"],"contactPoint":{"hasEmail":"mailto:marcus.mendenhall@nist.gov","fn":"Marcus Mendenhall"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2164","description":"This data set consist of two representations of the electron-induced copper k-alpha x-ray emission spectrum.  One file contains rebinned counting data, and is close to 'raw'.  The second file contains a least-squares spline of the data, which should be a highly useful representation for computational purposes. \n\n","language":["en"],"title":"Copper k-alpha xray emission spectrum","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2164/all_spectrum_sliced.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Copper K-alpha spectrum rebinned counting data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2164/all_spectrum_sliced.dat","format":"dadta columns as described above","description":"3-column text file.  The first column is photon energy in eV.  The sxceond column is count rate in couts/second.  The third column is the standard uncertainty on this count rate, derived from its Poisson statistics.","mediaType":"text/plain","title":"Copper K-alpha spectrum rebinned counting data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2164/spline_table.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Spline table representation of Cu k-alpha emission spectrum"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2164/spline_table.dat","format":"3-column text file, delimited with spaces.  First column is photon energy in eV.  Second column is intensity.  Third column is the spline coefficient, as described in the paper.","description":"This is a least-squares spline of the data presented in the paper.  It should be a faithful representation of the the spectrum, in that the spline is consistent with the standard uncertainty of the raw data.","mediaType":"text/plain","title":"Spline table representation of Cu k-alpha emission spectrum"},{"accessURL":"https://doi.org/10.18434/M32164","title":"DOI Access for Copper k-alpha xray emission spectrum"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-04-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology:Ionizing radiation metrology","Physics:Atomic, molecular, and quantum","Physics:Spectroscopy","Standards:Reference data"],"issued":"2019-12-23","keyword":["x-ray","xray","copper","k-alpha","emission spectrum","SI","traceable"]},{"identifier":"ark:/88434/mds2-2165","accessLevel":"public","references":["https://doi.org/10.1088/1361-6455/ab45d6"],"contactPoint":{"hasEmail":"mailto:marcus.mendenhall@nist.gov","fn":"Marcus Mendenhall"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2165","description":"This data set consist of rebinned data, with and without efficiency corrections, from the kl, km, and kn emission lines of the molybdenum x-ray spectrum.","language":["en"],"title":"Molybdenum k-shell xray emission spectrum","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2165/supplementary%20data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for molybdenum k-shell emission spectra"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2165/supplementary%20data.zip","format":"space-delimited 8-column text file with 6 lines of comments at the beginning. format is :# data set structure # column    1: energy, eV # column    2: system efficiency # column 3, 4: raw intensity and standard error # column 5, 6: raw background and standard error # column 7, 8: efficiency corrected net intensity and standard error","description":"A zip file containing text files of the kl, km, and kn emission spectra of molybdenum.","mediaType":"application/zip","title":"molybdenum k-shell emission spectra"},{"accessURL":"https://doi.org/10.18434/M32165","title":"DOI Access for Molybdenum k-shell xray emission spectrum"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-04-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Standards:Reference data","Physics:Spectroscopy","Physics:Atomic, molecular, and quantum","Metrology:Ionizing radiation metrology"],"issued":"2019-12-23","keyword":["x-ray","xray","molybdenum","k-alpha","emission spectrum","SI","traceable"]},{"identifier":"ark:/88434/mds2-2166","accessLevel":"public","contactPoint":{"hasEmail":"mailto:benjamin.caplins@nist.gov","fn":"Benjamin Caplins"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/SEDcorr","description":"SEDCORR is an open-source Python module designed to correct for the systematic energy deficits in atom probe mass spectra of electrically insulating samples.  The assumption of the algorithm is that the mass spectrum for a dataset is conserved throughout the dataset and that any changes to the peak positions arise from an unknown slowly-fluctuating accelerating voltage.  For computational speed, the unknown accelerating voltage is determined using a template matching FFT-based cross correlation method.  The Python source code and an example dataset is available on the home page: https://github.com/usnistgov/SEDcorr","language":["en"],"title":"SEDCORR: An Algorithm for Correcting Systematic Energy Deficits in the Atom Probe Mass Spectra","distribution":[{"accessURL":"https://doi.org/10.18434/M32166","title":"DOI Access for SEDCORR: An Algorithm for Correcting Systematic Energy Deficits in the Atom Probe Mass Spectra"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-12-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization"],"issued":"2020-04-21","keyword":["atom probe microscopy","insulator","mass spectra","energy deficit correction","Python","FFT"]},{"identifier":"ark:/88434/mds2-2167","accessLevel":"public","references":["https://dx.doi.org/10.6028/NIST.TN.1913","https://dx.doi.org/10.6028/NIST.TN.1896","https://nvlpubs.nist.gov/nistpubs/jres/122/jres.122.040.pdf"],"contactPoint":{"hasEmail":"mailto:william.healy@nist.gov","fn":"William M. Healy"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pvdata.nist.gov","description":"One-minute averaged values and one-second instantaneous values for 2015 through 2018 for three grid-connected photovoltaic arrays on the NIST campus in Gaithersburg, Maryland USA.  The arrays are built from monocrystalline silicon modules and range from 73 kW to 217 kW. Each array has a different tilt, orientation, and configuration. Irradiance, temperature, wind, and electrical measurements are recorded at each array. One-minute average data and one-second instantaneous data from a nearby weather station and images of the sky and arrays at one-hour and five-minute intervals are also included.","language":["en"],"title":"NIST Campus Photovoltaic (PV) Arrays and Weather Station Data Sets","distribution":[{"accessURL":"https://doi.org/10.18434/M3S67G","title":"DOI Access for NIST Campus Photovoltaic (PV) Arrays and Weather Station Data Sets"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-12-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://www.nist.gov/file/391591","accrualPeriodicity":"irregular","theme":["Energy:Electric power / smart grid","Energy:Alternative energy","Metrology:Environmental metrology"],"spatial":"Canopy Array: 39.1385 N, -77.2155 E \nGround Array: 39.1319 N, -77.2141 E \nRoof Array: 39.1354 N, -77.2156 E \nWeather Station: 39.1374, -77.2187 E","issued":"2020-02-07","keyword":["photovoltaic","PV","weather","solar","array","meteorology"],"temporal":"2015-01-01/2018-12-31"},{"identifier":"ark:/88434/mds2-2168","accessLevel":"public","contactPoint":{"hasEmail":"mailto:vladimir.orkin@nist.gov","fn":"Vladimir L. Orkin"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2168","description":"Partial lifetimes of chemical compounds in the atmosphere calculated by GSFC2D model (years).","language":["en"],"title":"Atmospheric Lifetimes of Halogenated Hydrocarbons: Improved Estimations from an Analysis of Modeling Results","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2168/Supporting%20Information%20%28Lifetimes%20from%20GSFC2D%29%20with%20molecular%20formulas.docx.sha256","mediaType":"text/plain","title":"SHA256 File for Supporting Information (Lifetimes from GSFC2D)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2168/Supporting%20Information%20%28Lifetimes%20from%20GSFC2D%29%20with%20molecular%20formulas.docx","description":"Supporting Data Information for 'Atmospheric Lifetimes of Halogenated Hydrocarbons: Improved Estimations from an Analysis of Modeling Results.' by Vladimir L. Orkin, Michael J. Kurylo, Eric L. Fleming","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"Supporting Information (Lifetimes from GSFC2D)"},{"accessURL":"https://doi.org/10.18434/M32168","title":"DOI Access for Atmospheric Lifetimes of Halogenated Hydrocarbons: Improved Estimations from an Analysis of Modeling Results"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-06-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Environment:Air / water / soil quality"],"issued":"2020-06-15","keyword":["atmospheric lifetime","partial lifetime","atmosphere","troposphere","stratosphere","photolysis","OH","hydroxyl"]},{"identifier":"ark:/88434/mds2-2169","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8290-upd1","https://doi.org/10.4242/BalisageVol23.Lubell01"],"contactPoint":{"hasEmail":"mailto:joshua.lubell@nist.gov","fn":"Joshua Lubell"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2169","description":"SCAP Composer is a software application for creating Security Content Automation Protocol (SCAP) source data stream collections from Extensible Markup Language (XML) documents valid with respect to schemas defined in SCAP component specifications. SCAP Composer's limited scope and small footprint make it easy to install, use, and integrate with other SCAP content development tools. SCAP Composer uses the DITA Open Toolkit, an open source publishing engine for content authored in the Darwin Information Typing Architecture (DITA). SCAP Composer may be used with the National Institute of Standards and Technology (NIST) SCAP Content Validation Tool to check the conformance of SCAP source data stream components to content requirements and recommendations.","language":["en"],"title":"SCAP Composer","distribution":[{"downloadURL":"https://github.com/usnistgov/sctools/releases","description":"Downloadable zip file. Unzip, and install  following instructions in the User Guide.","mediaType":"application/zip","title":"SCAP Composer"},{"accessURL":"https://doi.org/10.18434/M32169","title":"DOI Access for SCAP Composer"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Cybersecurity","Manufacturing:Systems integration","Standards:Conformity assessment"],"conformsTo":"https://doi.org/10.6028/NIST.SP.800-126r3","issued":"2020-02-25","keyword":["SCAP","cybersecurity","Security Content Automation Protocol","source data stream","Darwin Information Typing Architecture","software","DITA Open Toolkit","SCAP content validation"]},{"identifier":"ark:/88434/mds2-2170","accessLevel":"public","references":["https://doi.org/10.1109/DySPAN.2019.8935639"],"contactPoint":{"hasEmail":"mailto:michael.souryal@nist.gov","fn":"Michael Souryal"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2170","description":"Federal Communications Commission rules for the 3.5 GHz citizens broadband radio service (CBRS) permit commercial systems to share 150 MHz of bandwidth with federal and other incumbents. To protect the federal incumbents, a spectrum coordinator?the spectrum access system (SAS)?uses a standardized algorithm to suspend some commercial transmissions when a nearby incumbent (e.g., a shipborne radar) becomes active. Using propagation models based on those employed by the SAS for interference management, the data herein was used to  quantify the impact of federal incumbent activity on commercial service, in terms of both the numbers of transmissions affected and their service area. These metrics are also used to examine the tradeoff between commercial coverage and immunity to incumbent activity as a function of commercial base station antenna height. The data include Simulated Citizens Broadband Radio Service device deployments, calculated federal incumbent protection move lists, and calculated coverage contours. The data is associated with the publication, \"Effect of Federal Incumbent Activity on CBRS Commercial Service,\" M. R. Souryal and T. T. Nguyen, in Proc. IEEE DySPAN, Nov. 2019.","language":["en"],"title":"3.5 GHz CBRS Federal Incumbent Protection Move Lists with a Hybrid ITM/extended Hata Propagation Model","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2170/dyspan2019_data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for DOI Access to 3.5 GHz CBRS Federal Incumbent Protection Move Lists with a Hybrid ITM/extended Hata Propagation Model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2170/dyspan2019_data.zip","format":"zip-compressed file of JSON and NumPy data files","description":"DOI Access to 3.5 GHz CBRS Federal Incumbent Protection Move Lists with a Hybrid ITM/extended Hata Propagation Model","mediaType":"application/x-zip-compressed","title":"DOI Access to 3.5 GHz CBRS Federal Incumbent Protection Move Lists with a Hybrid ITM/extended Hata Propagation Model"},{"accessURL":"https://doi.org/10.18434/M32170","title":"DOI Access for 3.5 GHz CBRS Federal Incumbent Protection Move Lists with a Hybrid ITM/extended Hata Propagation Model"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-05-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)"],"issued":"2020-01-14","keyword":["3.5 GHz; CBRS; federal incumbent; move list"]},{"identifier":"ark:/88434/mds2-2171","accessLevel":"public","references":["https://doi.org/10.1007/s10694-021-01112-2","https://doi.org/10.1016/j.firesaf.2020.103043","https://www.nfpa.org/-/media/Files/News-and-Research/Resources/Research-Foundation/Symposia/2019-SUPDET/Presentations/SUPDET19MenschAbstract.ashx?la=en","https://www.nist.gov/publications/development-detection-algorithm-kitchen-cooktop-ignition-prevention"],"contactPoint":{"hasEmail":"mailto:amy.mensch@nist.gov","fn":"Amy Mensch"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2171","description":"This data set includes the time series data from 16 electrochemical, optical, temperature and humidity sensors in 60 experiments to characterize the conditions preceding cooktop ignition compared to the conditions of normal cooking. The sensors are placed in the exhaust duct above a mock-up kitchen cooktop. Experiments cover a broad range of conditions, including both unattended cooking and normal cooking scenarios, where 39 experiments led to auto-ignition. The experiments involve a variety of cooking oils and foods and were conducted using either an electric coil cooktop, gas-fueled cooktop, or electric oven.","language":["en"],"title":"Time Series Data from Sensors in the Duct Above a Kitchen Cooktop During Normal Cooking and Ignition Conditions","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2171/all%20data4-10-20.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Time Series Data and Supplemental Material"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2171/all%20data4-10-20.xlsx","format":"Excel spreadsheet with 4 tabs","description":"Schematic of experiments with photos, list of experiments and conditions, description of each column in the data tab (including information on the sensors), and time series data for all experiments.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Time Series Data and Supplemental Material"},{"accessURL":"https://doi.org/10.18434/M32171","title":"DOI Access for Time Series Data from Sensors in the Duct Above a Kitchen Cooktop During Normal Cooking and Ignition Conditions"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-04-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Fire detection","Information Technology:Internet of Things (IoT)"],"issued":"2020-04-24","keyword":["cooktop ignition","sensor analysis","neural networks","ignition prevention"]},{"identifier":"ark:/88434/mds2-2172","accessLevel":"public","references":["https://doi.org/10.1109/TMTT.2020.3005170"],"contactPoint":{"hasEmail":"mailto:dylan.williams@nist.gov","fn":"Dylan Williams"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2172","description":"The data used to generate the graphs in figures 2, 4, 5 and 6 of the paper \"Evaluating Uncertainty of Nonlinear Microwave Calibrations from Regression Residuals\". The full reference is D. F. Williams, B. Jamroz and J. D. Rezac, \"Evaluating Uncertainty of Nonlinear Microwave Calibration Models With Regression Residuals,\" in IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 9, pp. 3776-3782, Sept. 2020, doi: 10.1109/TMTT.2020.3005170. 0. The file with data for Fig. X is named FigX.zip. 1. The file Fig X guide.txt in the top directory of each zip file describes which EasyPlot file was used to create the graph(s) in the figure. 2. The EasyPlot files were made with EasyPlot V 4.0.4, and document the data locations, legends, etc. EasyPlot column-selection format is as follows: \"xyiiyy\"  or \"xy..yy\"  means that column 1 was used for x axis, column 2 for first curve y values, column 5 for second curve y values, column 6 for third curve y values","language":["en"],"title":"Evaluating Uncertainty of Nonlinear Microwave Calibration Models from Regression Residuals","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2172/Fig5.zip","description":"Data used to generate Figure 5","mediaType":"application/x-zip-compressed","title":"Fig. 5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2172/Fig6.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Figure 6"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2172/Fig6.zip","description":"Data for Figure 6","mediaType":"application/x-zip-compressed","title":"Figure 6"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2172/Fig5.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Figure 5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2172/Fig5.zip","description":"Data for Figure 5","mediaType":"application/x-zip-compressed","title":"Figure 5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2172/Fig2.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2172/Fig2.zip","description":"Data for Figure 2","mediaType":"application/x-zip-compressed","title":"Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2172/Fig4.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Figure 4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2172/Fig4.zip","description":"Data for Figure 4","mediaType":"application/x-zip-compressed","title":"Figure 4"},{"accessURL":"https://nist.force.com/pdr?@id=ark:/88434/mds2-2172","description":"This page provides a registration form that must be completed before downloading the data.","title":"Gateway for Registered Data Access"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Purchase is not required for data downloading. Users must complete registration form to download data.","modified":"2020-01-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)","Mathematics and Statistics:Uncertainty quantification"],"issued":"2022-03-16","keyword":["Confidence intervals","coupling corrections","on-wafer measurement","prediction intervals."]},{"identifier":"ark:/88434/mds2-2174","accessLevel":"public","references":["http://dx.doi.org/10.6028/jres.119.012"],"contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"John L. Pagliaro"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Thermal conductivity data acquired previously for the establishment of Standard Reference Material (SRM) 1450, Fibrous Glass Board, as well as subsequent renewals 1450a, 1450b, 1450c, and 1450d, are re-analyzed collectively and as individual data sets.  Additional data sets for proto-1450 material lots are also included in the analysis (eleven data sets in total). The data cover the years 1958 to 2009; 52 years of activity by the National Institute of Standards and Technology (NIST) in developing and providing thermal insulation SRMs, specifically high-density molded fibrous-glass board, to the public. Collectively, the data sets cover two nominal thicknesses of 13 mm and 25 mm, bulk densities from 60 kg/m3 to 180 kg/m3, and mean temperatures from 100 K to 340 K.  The prevailing generic model for the majority of data sets is the bilinear model in density and temperature.  The regression equations are not intended to be, and cannot be, used to \"re-certify\" any of these previous SRMs. The results of this analysis, instead, aim to enhance our understanding of the original certificate equations derived by previous NIST (formerly the National Bureau of Standards) researchers as well as to improve the development and modeling of future thermal insulation SRMs.","language":["en"],"title":"SRM 1450 Fibrous Glass Board: Retrospective Analysis","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2013-11-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization","Buildings and Construction:Building materials","Standards:Reference materials"],"issued":"2020-04-01","keyword":["bulk density","certified reference material","fit","guarded hot plate","high density molded fibrous glass board","model","regression analysis","standard reference material","thermal conductivity","thermal insulation"]},{"identifier":"ark:/88434/mds2-2175","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2175","description":"This dataset was an initial test harness infrastructure test for the TrojAI program. It should not be used for research. Please use the more refined datasets generated for the other rounds. The data being generated and disseminated is training, validation, and test data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 200 trained, human level, image classification AI models using the following architectures (Inception-v3, DenseNet-121, and ResNet50). The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Challenge Round 0 (Dry Run) Test Dataset","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2175/trojai-round0-dataset.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2175/trojai-round0-dataset.tar.gz.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32175","title":"DOI Access for Challenge Round 0 (Dry Run) Test Dataset"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-02-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Computational science","Information Technology:Software research"],"issued":"2020-02-07","keyword":["Trojan Detection","Artificial Intelligence","AI","Machine Learning","Adversarial Machine Learning"]},{"identifier":"ark:/88434/mds2-2176","accessLevel":"public","references":["https://doi.org/10.1016/j.bpj.2017.12.019","https://doi.org/10.1074/jbc.RA117.001569","https://doi.org/10.1039/C6NR08145B","https://doi.org/10.1103/PhysRevLett.111.248301","https://doi.org/10.1103/PhysRevE.86.011921","https://doi.org/10.1038/nphys1230"],"contactPoint":{"hasEmail":"mailto:david.hoogerheide@nist.gov","fn":"David Hoogerheide"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2176","description":"Web calculator for polypeptide motion through nanopores. The calculation is done in the framework of conditional first passage times based on the Smoluchowski transport equation. These calculations are quite complex in general, but this calculator allows the piecewise construction of an energy landscape that reflects the average forces on a polymer confined in a nanopore based on its primary sequence. The calculator is written in HTML and javascript.","language":["en"],"title":"PPdiffuse: Web-based (html and javascript) calculator for voltage-driven transport of polypeptides in nanopores","distribution":[{"accessURL":"https://github.com/usnistgov/ppdiffuse","format":"Github","description":"Github site for PPdiffuse software","title":"PPdiffuse Github site"},{"accessURL":"https://doi.org/10.18434/M32176","title":"DOI Access for PPdiffuse: Web-based (html and javascript) calculator for voltage-driven transport of polypeptides in nanopores"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-01-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Nanotechnology:Nanobiotechnology","Chemistry:Molecular characterization","Physics:Biological physics"],"issued":"2020-04-01","keyword":["diffusion","protein","nanopore","polypeptide","calculator"]},{"identifier":"ark:/88434/mds2-2177","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ulf.griesmann@nist.gov","fn":"Ulf Griesmann"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"An open-source numerical toolbox , written in the Octave language, for the creation of computer-generated binary and multi-level holograms used in interferometric form error measurements of complex aspheric and free-form precision surfaces and optical wavefronts. In a typical measurement setup for this type of surface, a hologram is used to generate a test wavefront that has the design shape of the surface, which is then compared to a fabricated part using an imaging laser interferometer.","language":["en"],"title":"A Toolbox for Isophase-Curvature Guided Computation of Metrology Holograms","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/cgh-toolbox-181.zip.sha256","mediaType":"text/plain","title":"SHA256 File for CGH Toolbox"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/cgh-toolbox-181.zip","format":"Compressed archive of Octave .m files","description":"Toolbox for the calculation of metrology holograms needed in precision surface metrology with optical interferometry","mediaType":"application/x-zip-compressed","title":"CGH Toolbox"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/cgh-toolbox_manual-23.zip.sha256","mediaType":"text/plain","title":"SHA256 File for User Manual for the CGH Toolbox"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/cgh-toolbox_manual-23.zip","format":"Compressed file archive","description":"A user manual describing the key functions of the CGH toolbox and a set of examples that illustrate the use of the toolbox.","mediaType":"application/x-zip-compressed","title":"User Manual for the CGH Toolbox"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/polygon-toolbox-6.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Polygon toolbox"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/polygon-toolbox-6.zip","format":"Compressed archive","description":"An Octave / Matlab toolbox with polygon related functions. Required by the CGH toolbox.","mediaType":"application/x-zip-compressed","title":"Polygon toolbox"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/gdsii-toolbox-145.zip.sha256","mediaType":"text/plain","title":"SHA256 File for GDSII Toolbox"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/gdsii-toolbox-145.zip","format":"Compressed archive.","description":"An Octave / Matlab toolbox of functions to generate lithographic layout files suitable for transmission to a fabrication foundry. Required by the CGH toolbox.","mediaType":"application/x-zip-compressed","title":"GDSII Toolbox"},{"accessURL":"https://doi.org/10.18434/M32177","title":"DOI Access for A Toolbox for Isophase-Curvature Guided Computation of Metrology Holograms"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/CGH_toolbox_manual_27apr2020.pdf.sha256","mediaType":"text/plain","title":"SHA256 File for CGH Toolbox Manual"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/CGH_toolbox_manual_27apr2020.pdf","format":"Portable Document Format (PDF)","description":"Manual version of April 27, 2020","mediaType":"application/pdf","title":"CGH Toolbox Manual"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/cgh-toolbox_manual-24.zip.sha256","mediaType":"text/plain","title":"SHA256 File for CGH Toolbox Manual and Examples"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2177/cgh-toolbox_manual-24.zip","format":"Compressed archive in .zip format","description":"Manual version of April 27, 2020 with example code","mediaType":"application/zip","title":"CGH Toolbox Manual and Examples"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-02-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Optical physics","Metrology:Dimensional metrology","Metrology:Optical, photometry, and laser metrology"],"issued":"2020-02-18","keyword":["computer-generated hologram","diffractive optics","optical interferometry","surface metrology","aspheric surfaces","freeform surfaces","lithographic layout"]},{"identifier":"ark:/88434/mds2-2179","accessLevel":"public","contactPoint":{"hasEmail":"mailto:amanda.forster@nist.gov","fn":"Amanda L. Forster"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2179","description":"We present correlations found between the tensile strengths of yarns extracted from armor and the V50 ballistic limit when significant degradation of the mechanical properties of the extracted yarns was observed. These studies provided the basis for a validation dataset from which we compare the experimentally measured V50 ballistic limit results to the theoretically predicted V50 results. This approach is promising for the development of a tool for fielded-armor-performance surveillance relying upon mechanical testing of armor coupon samples. \n\nThere are 3 datasets included. The first dataset consists of failure strength and failure strain for yarns extracted from aged ballistic vests, where the vests were aged in laboratory accelerated ageing conditions. The second dataset consists of failure strength for yarns extracted from aged vests, where the vests were aged in the field. The third dataset consists of V50 measurements on vests that were aged using an accelerated laboratory protocol.","language":["en"],"title":"Aged Body Armor- Linking Theory to Practice by Predicting Ballistic Performance from Mechanical Properties","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2179/Fieldaged_tensile_results.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2179/Fieldaged_tensile_results.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2179/Labaged_tensile_results.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2179/Labaged_tensile_results.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2179/Labaged_V50_results.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2179/Labaged_V50_results.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2179/Labaging_Conditions.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2179/Labaging_Conditions.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2179/V50_data_readme.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2179/V50_data_readme.txt.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32179","title":"DOI Access for Aged Body Armor- Linking Theory to Practice by Predicting Ballistic Performance from Mechanical Properties"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2012-05-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Polymers","Materials:Materials characterization"],"issued":"2020-02-18","keyword":["body armor","field aging","artificial aging","ballistic resistance"]},{"identifier":"ark:/88434/mds2-2180","accessLevel":"public","references":["https://arxiv.org/abs/1911.12808"],"contactPoint":{"hasEmail":"mailto:chin-wen.chou@nist.gov","fn":"Chin-wen Chou"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2180","description":"These data files contain the data for the measured transition frequencies shown in Table I and the traces in Figure 3 of the publication \"Frequency-comb spectroscopy on pure quantum states of a single molecular ion,\" accessible at https://arxiv.org/abs/1911.12808. In this publication we use generally applicable quantum-logic techniques to prepare a trapped molecular ion in a single quantum state, drive terahertz rotational transitions with an optical frequency comb, and read out the molecular state non-destructively, leaving the molecule ready for further manipulation. \n\nOne file contains data For Table 1. In the measurement of rotational transition frequencies, the intensities of the comb beams are varied to characterize the effect of AC Stark shift, while the intensity ratio between the sigma and pi polarized beams are kept at close to 2. The average intensity of the  sigma-polarized comb beam is quantified by measuring the resultant Stark shift,  fSS_sigma, on the 729 nm transition of the Ca+ ion, with the Ca+ ion where the CaH+ ion would be during rotational spectroscopy experiments. \n\nThe other file contains data for Figure 3, (a) Spectra for the J = 4 to 2 transition: 40CaH+ is prepared in J = 2, followed by a pulse train from the comb Raman beams probing the J = 2 to J = 4 transition. After the probe pulse train, projective measurements of both initial and final states are performed and the state occupation probability is determined. The probe time is ~1.6 ms. The frequency shows the offset of the Raman difference frequency from the resonant value. (b) Rabi flopping on the J = 4 to J = 2 transition: Starting in J = 4, with the comb Raman pulse detuning set to resonance, the state of the 40CaH+ ion is driven coherently to J = 2  by a pulse train of variable duration from the comb Raman beams. The center wavelength of the frequency comb was ~800 nm for these spectra and Rabi flopping traces. The error bars stand for ±1 standard deviation. \n","language":["en"],"title":"Data for \"Frequency-comb spectroscopy on pure quantum states of a single molecular ion\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2180/Data%20for%20Figure%203.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Data for figure 3 of \"frequency-comb spectroscopy on pure quantum state of a single molecular ion\""},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2180/Data%20for%20Figure%203.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Data for figure 3 of \"frequency-comb spectroscopy on pure quantum state of a single molecular ion\""},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2180/Data%20for%20measured%20transition%20frequencies.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Data for measured transition frequencies for \"Frequency-comb spectroscopy on pure quantum states of a single molecular ion\""},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2180/Data%20for%20measured%20transition%20frequencies.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Data for measured transition frequencies for \"Frequency-comb spectroscopy on pure quantum states of a single molecular ion\""},{"accessURL":"https://doi.org/10.18434/M32180","title":"DOI Access for Data for \"Frequency-comb spectroscopy on pure quantum states of a single molecular ion\""}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-02-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Atomic, molecular, and quantum"],"issued":"2020-02-25","keyword":["ions","molecules","spectroscopy","quantum logic","frequency comb"]},{"identifier":"ark:/88434/mds2-2181","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2054","https://doi.org/10.6028/NIST.TN.2055"],"contactPoint":{"hasEmail":"mailto:lisa.choe@nist.gov","fn":"Lisa Choe"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/el/fire-research-division-73300/national-fire-research-laboratory-73306/steel-concrete-composite-0","description":"Experimental fire tests were conducted on four 12.8 m long steel-concrete composite floor beam assemblies with varying end support conditions, including two types of simple shear connections (shear tabs or welded-bolted double angles) and slab end continuity. Each specimen was constructed as a partially-composite beam, consisting of a lightweight concrete slab on formed metal decking supported by a W18x35 steel beam acting compositely via 19 mm diameter headed stud anchors spaced 305 mm. While resisting an ASCE 7-specified gravity load using hydraulic actuators, each specimen was exposed to a structurally significant  fire that was produced using three natural gas-fueled burners distributed on the floor of the test compartment with the maximum heat release rate of 4 MW. Imposed mechanical loading and fire characteristics, thermal and structural responses, and structural failure modes of the specimens were studied.","language":["en"],"title":"Dataset from Steel-Concrete Composite Floor Systems Subject to Fire - Phase 1","distribution":[{"accessURL":"https://doi.org/10.18434/M32181","title":"DOI Access for Dataset from Steel-Concrete Composite Floor Systems Subject to Fire - Phase 1"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-01-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Buildings and Construction:Structural engineering","Fire:Fire risk reduction","Fire:Structural fire resistance"],"issued":"2020-03-31","keyword":["compartment fires","composite beams","steel connections","Temperature effects","NFRL"]},{"identifier":"ark:/88434/mds2-2182","accessLevel":"public","contactPoint":{"hasEmail":"mailto:samantha.gamboaquintiliani@nist.gov","fn":"Samantha Gamboa Quintiliani"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2182","description":"This dataset corresponds to the results presented in the paper entitled \"UE-to-Network Relay Discovery in ProSe-enabled LTE Networks\", to appear in the proceeding of the 2020 International Conference on Computing, Networking and Communications (ICNC 2020).","language":["en"],"title":"UE-to-Network Relay Discovery in ProSe-enabled LTE Networks","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2182/icnc2020-relaydiscovery-DataSet.zip.sha256","mediaType":"text/plain","title":"SHA256 File for UE-to-Network Relay Discovery in ProSe-enabled LTE Networks"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2182/icnc2020-relaydiscovery-DataSet.zip","mediaType":"application/x-zip-compressed","title":"UE-to-Network Relay Discovery in ProSe-enabled LTE Networks"},{"accessURL":"https://doi.org/10.18434/M32182","title":"DOI Access for UE-to-Network Relay Discovery in ProSe-enabled LTE Networks"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-10-16 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research","Advanced Communications:Wireless (RF)","Information Technology:Networking","Information Technology:Mobile"],"issued":"2020-07-16","keyword":["public safety communication","device-to-device","D2D","wireless communication","direct discovery","LTE","ProSe","UE-to-Network relay"]},{"identifier":"ark:/88434/mds2-2183","accessLevel":"public","contactPoint":{"hasEmail":"mailto:hung.trinh@nist.gov","fn":"Hung Trinh"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2183","description":"Sample purchasing data containing information on suppliers, the products they provide, and the projects those products are used for. Data created or adapted from publicly available sources.","language":["en"],"title":"Sample Purchasing / Supply Chain Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2183/Sample_Data_Sets.zip","format":"comma separated files in a compressed zip file","description":"Sample files for suppliers, products, and related projects.","mediaType":"application/zip","title":"Sample Purchasing Data Set"},{"accessURL":"https://doi.org/10.18434/M32183","title":"DOI Access for Sample Purchasing / Supply Chain Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-02-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Supply chain","Information Technology:Cybersecurity"],"issued":"2022-01-10","keyword":["supply chain; purchasing; supplier"]},{"identifier":"ark:/88434/mds2-2184","accessLevel":"public","references":["https://doi.org/10.1029/2019GL086344"],"contactPoint":{"hasEmail":"mailto:david.long@nist.gov","fn":"David Long"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2184","description":"Data files for the publication \"High Accuracy Near-infrared Carbon Dioxide Intensity Measurements to Support Remote Sensing\" in Geophysical Research Letters","language":["en"],"title":"High Accuracy Near-infrared Carbon Dioxide Intensity Measurements to Support Remote Sensing","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2184/FigureS1.csv","format":".csv","mediaType":"application/vnd.ms-excel","title":"FigureS1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2184/FigureS2.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 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30014_CO2_626_expData"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2184/30014_CO2_626_expData_Final.csv","format":".csv","mediaType":"application/vnd.ms-excel","title":"30014_CO2_626_expData"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2184/Figure1.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Figure1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2184/Figure1.csv","format":".csv","mediaType":"application/vnd.ms-excel","title":"Figure1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2184/Figure2.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Figure2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2184/Figure2.csv","format":".csv","mediaType":"application/vnd.ms-excel","title":"Figure2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2184/FigureS1.csv.sha256","mediaType":"text/plain","title":"SHA256 File for FigureS1"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-02-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Spectroscopy","Environment:Greenhouse gas measurements"],"issued":"2020-03-19","keyword":["greenhouse gases","carbon dioxide","remote sensing"]},{"identifier":"ark:/88434/mds2-2185","accessLevel":"public","references":["https://doi.org/10.6028/NIST.SP.1197","https://www.nist.gov/topics/community-resilience/planning-guide"],"contactPoint":{"hasEmail":"mailto:jennifer.helgeson@nist.gov","fn":"Jennifer Helgeson"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://edges.nist.gov","description":"Designed to support community-level resilience planning, the powerful online EDGe$ Tool, Version 1.0 assists in selecting cost-effective community resilience projects. Produced by the National Institute of Standards and Technology (NIST), the EDGe$ platform-independent app can help community planners and resilience officers, as well as economic development, budget, and public works officials.\n\nEDGe$ provides a standard economic methodology for evaluating investment decisions required to improve the ability of communities to adapt to, withstand, and quickly recover from natural, technology, and human-caused disruptive events. The tool helps the user to identify and compare the relevant present and future resilience costs and benefits associated with new capital investment versus maintaining a community?s status-quo. Benefits include cost savings and damage loss avoidance because enhancing resilience on a community scale creates value, including co-benefits, even if a hazard event does not strike.\n\nEDGe$ is based on the process found in NIST?s Community Resilience Economic Decision Guide for Buildings and Infrastructure Systems (EDG). 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Errata: This dataset had a software bug in the trigger embedding code that caused 4 models trained for this dataset to have a ground truth value of 'poisoned' but which did not contain any triggers embedded. These models should not be used. 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The four materials include two steel alloys, DP980 for Benchmark 1 and DP1180 for Benchmark 2, and two 6000 series aluminum alloys, AA6xxx-T4 for Benchmark 1 and AA6xxx-T81 for Benchmark 2, that will be referred to here as BM1-DP980, BM2-DP1180, BM1-6xxx-T4, and BM2-6xxx-T81, respectively. The testing reported here was requested by the Numisheet 2020 Benchmark Committee and was performed by the NIST Center for Automotive Lightweighting (NCAL) at the National Institute of Standards and Technology in Gaithersburg, MD. The tests were performed at NCAL from October 2019 through January 2020.","language":["en"],"title":"Data for Numisheet 2020 uniaxial tensile and tension/compression 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Metals"],"issued":"2020-04-14","keyword":["Mechanical testing","stress","strain","Digital Image Correlation","Steel","Aluminum"]},{"identifier":"ark:/88434/mds2-2203","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8262"],"contactPoint":{"hasEmail":"mailto:conrad.bock@nist.gov","fn":"Conrad Bock"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2203","description":"System models and model-based engineering methods have the promise of transforming the way that industrial engineers interact with production and logistics systems. Model-based methods play a role in improving communication between stakeholders, interoperability between systems, automated access to consistent analysis models, and multi-disciplinary design methods for complex systems. However, there remains a need for a foundation for modeling these kinds of systems -- a foundation that tailors the methods and tools developed in other engineering domains to the unique concepts and semantics of the production and logistics domain. This necessary foundation is the topic of these models. This repository contains model libraries for modeling discrete event logistics systems (DELS), an abstraction that covers manufacturing plants, material handling and transportation systems, warehouses, supply chains, etc. The DELS abstraction was created by abstracting and modeling the commonalities across the kinds of systems that industrial engineers typically encounter, and the analysis models that they use to analyze those system.The models are implemented in the Systems Modeling Language (SysML) developed using MagicDraw 18.5 by No Magic.  This software data item is created using SysML 1.4 and stored as Extensible Markup Language (XML) Model Interchange (XMI) models and Hypertext Markup Language (HTML) reports. MagicDraw 18.5 or later with SysML plugin is required to edit the model (also provided in proprietary mdzip format). The HTML documentation is provided for readers without the MagicDraw software. 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Currently, vaporized hydrogen peroxide (VHP) mask disinfection systems are being deployed at multiple locations throughout the United States, for example in shipping containers and in small rooms. Such enclosures will be of a different size, have different surface materials and have varying air change rates; all these parameters will impact the VHP dose that the masks receive. This spreadsheet tool can be used to estimate the VHP concentration in air of such a room. The tool employs a single-zone mass balance analysis that accounts for room size, VHP losses to surfaces and air change rate. This tool is intended to support VHP disinfection efforts by providing estimates of VHP concentrations in the room being employed, but it does not describe or provide guidance on VHP disinfection applications.\n","language":["en"],"title":"Tool for Evaluation of Vaporized Hydrogen Peroxide Disinfection of N95 Masks in Rooms","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2205/VHP%20Mask%20Disinfection%20Tool%20V6.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Tool for Evaluation of Vaporized Hydrogen Peroxide Disinfection of N95 Masks in Rooms"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2205/VHP%20Mask%20Disinfection%20Tool%20V6.xlsx","description":"The SARS-CoV-2 virus has put a strain on the supply and distribution of N95 masks, with some hospitals electing to reuse masks after disinfection. Currently, vaporized hydrogen peroxide (VHP) mask disinfection systems are being deployed at multiple locations throughout the United States, for example in shipping containers and in small rooms. Such enclosures will be of a different size, have different surface materials and have varying air change rates; all these parameters will impact the VHP dose that the masks receive. This spreadsheet tool can be used to estimate the VHP concentration in air of such a room. The tool employs a single-zone mass balance analysis that accounts for room size, VHP losses to surfaces and air change rate. This tool is intended to support VHP disinfection efforts by providing estimates of VHP concentrations in the room being employed, but it does not describe or provide guidance on VHP disinfection applications.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Tool for Evaluation of Vaporized Hydrogen Peroxide Disinfection of N95 Masks in Rooms"},{"accessURL":"https://doi.org/10.18434/M32205","title":"DOI access for 'Tool for Evaluation of Vaporized Hydrogen Peroxide Disinfection of N95 Masks in Rooms'"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-04-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Environment:Air / water / soil quality"],"issued":"2020-04-16","keyword":["vaporized hydrogen peroxide (VHP)","N95","mask","disinfection systems","surface deposition"]},{"identifier":"ark:/88434/mds2-2206","accessLevel":"public","references":["https://arxiv.org/abs/2003.02923"],"contactPoint":{"hasEmail":"mailto:takuma.nakamura@nist.gov","fn":"Takuma Nakamura"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2206","description":"This data was used for main results for the paper entitled \"Coherent Optical Clock Down-Conversion for Microwave Frequencies with 10-18 Instability\".\nWe could calculate relative phase fluctuation and Allan deviation for both Yb optical clocks and 10 GHz microwaves. Uncertainty of our down-conversion system was also calculated from this.","language":["en"],"title":"Data for \"Coherent Optical Clock Down-Conversion for Microwave Frequencies with 10-18 Instability\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2206/10GHz_phase%28mrad%29%20vs%20time.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 10GHz microwave phase"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2206/10GHz_phase%28mrad%29%20vs%20time.csv","description":"first column is time, second is relative phase (mrad)","mediaType":"application/vnd.ms-excel","title":"10GHz microwave phase"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2206/Yb_Clock_phase%28rad%29%20vs%20time.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Yb clock phase"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2206/Yb_Clock_phase%28rad%29%20vs%20time.csv","description":"Time, Phase(rad)","mediaType":"application/vnd.ms-excel","title":"Yb clock phase"},{"accessURL":"https://doi.org/10.18434/M32206","title":"DOI access to Data for 'Coherent Optical Clock Down-Conversion for Microwave Frequencies with 10-18 Instability'"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-04-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Metrology:Time and frequency metrology"],"issued":"2020-04-09","keyword":["frequency comb","optical clock","optical frequency division","low noise microwave generation"]},{"identifier":"ark:/88434/mds2-2207","accessLevel":"public","references":["https://doi.org/10.1007/s00371-012-0746-4"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2207","description":"This benchmark aims to provide tools to evaluate 3D Interest Point Detection Algorithms with respect to human generated ground truth.\n\nUsing a web-based subjective experiment, human subjects marked 3D interest points on a set of 3D models. The models were organized in two datasets: Dataset A and Dataset B. Dataset A consists of 24 models which were hand-marked by 23 human subjects. Dataset B is larger with 43 models, and it contains all the models in Dataset B. The number of human subjects who marked all the models in this larger set is 16.\n\nSome of the models are standard models that are widely used in 3D shape research; and they have been used as test objects by researchers working on the best view problem.\n\nWe have compared five 3D Interest Point Detection algorithms. The interest points detected on the 3D models of the dataset can be downloaded from the link below. Please refer to README for details in the download.\n\nMesh saliency [Lee et al. 2005] : Interest points by mesh saliency \n\nSalient points [Castellani et al. 2008] : Interest points by salient points \n\n3D-Harris [Sipiran and Bustos, 2010] : Interest points by 3D-Harris \n\n3D-SIFT [Godil and Wagan, 2011] : Interest points by 3D-SIFT   (Please note that some models in the dataset are not watertight, hence their volumetric representations could not be generated. Therefore, 3D-SIFT algorithm wasn't able to detect interest points for those models.)\n\nScale-dependent corners [Novatnack and Nishino, 2007] : Interest points by SD corners \n\nHKS-based interest points [Sun et al. 2009] : Interest points by HKS method \n\nPlease Cite the Paper:\n\nHelin Dutagaci, Chun Pan Cheung, Afzal Godil, ?Evaluation of 3D interest point detection techniques via human-generated ground truth?, The Visual Computer, 2012. \n\nReferences:\n\n[Lee et al. 2005] Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. In: ACM SIGGRAPH 2005, pp. 659?666 (2005)\n\n[Castellani et al. 2008] Castellani, U., Cristani, M., Fantoni, S., Murino, V.: Sparse points matching by combining 3D mesh saliency with statistical descriptors. Comput. Graph. Forum 27(2), 643?652 (2008)\n\n[Sipiran and Bustos, 2010] Sipiran, I., Bustos, B.: A robust 3D interest points detector based on Harris operator. In: Eurographics 2010 Workshop on 3D Object Retrieval (3DOR?10), pp. 7?14 (2010)\n\n[Godil and Wagan, 2011] Godil, A., Wagan, A.I.: Salient local 3D features for 3D shape retrieval. In: 3D Image Processing (3DIP) and Applications II, SPIE (2011)\n\n[Novatnack and Nishino, 2007] Novatnack, J., Nishino, K.: Scale-dependent 3D geometric features. In: ICCV, pp. 1?8, (2007)\n\n[Sun et al. 2009] Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Eurographics Symposium on Geometry Processing (SGP), pp. 1383?1392 (2009)","language":["en"],"title":"A Benchmark for 3D Interest Point Detection Algorithms","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2207/3DInterestPoint.zip.sha256","mediaType":"text/plain","title":"SHA256 File for A Benchmark for 3D Interest Point Detection Algorithms"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2207/3DInterestPoint.zip","description":"The zip file contains the 3D models for Dataset A and B in MAT files, 3D Interest Point Detection algorithms, Human subject interest points and the Evaluation code. Also the documentation is included.","mediaType":"application/zip","title":"A Benchmark for 3D Interest Point Detection Algorithms"},{"accessURL":"https://doi.org/10.18434/M32207","title":"DOI access to A Benchmark for 3D Interest Point Detection Algorithms"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2012-03-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Data and informatics"],"issued":"2020-04-14","keyword":["3D interest points","D salient points","3D shape analysis","Evaluation of 3D interest point"]},{"identifier":"ark:/88434/mds2-2208","accessLevel":"public","references":["https://doi.org/10.1007/s00371-012-0746-4"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2208","description":"This benchmark aims to provide tools to evaluate 3D Interest Point Detection Algorithms with respect to human generated ground truth. Please refer to the paper for more information about this benchmark: \"Helin Dutagaci, Chun Pan Cheung, Afzal Godil: Evaluation of 3D interest point detection techniques via human-generated ground truth\", The Visual Computer, 2012.Using a web-based subjective experiment, human subjects marked 3D interest points on a set of 3D models. The models were organized in two datasets: Dataset A and Dataset B. Dataset A consists of 24 models which were hand-marked by 23 human subjects. Dataset B is larger with 43 models, and it contains all the models in Dataset B. The number of human subjects who marked all the models in this larger set is 16.We have compared five 3D Interest Point Detection algorithms. The interest points detected on the 3D models of the dataset can be downloaded from the link next to the corresponding algorithm. Please refer to README for details.Mesh saliency [Lee et al. 2005] : Interest points by mesh saliency Salient points [Castellani et al. 2008] : Interest points by salient points 3D-Harris [Sipiran and Bustos, 2010] : Interest points by 3D-Harris 3D-SIFT [Godil and Wagan, 2011] : Interest points by 3D-SIFT    (Please note that some models in the dataset are not watertight, hence their volumetric representations could not be generated. Therefore, 3D-SIFT algorithm wasn?t able to detect interest points for those models.)Scale-dependent corners [Novatnack and Nishino, 2007] : Interest points by SD corners  HKS-based interest points [Sun et al. 2009] : Interest points by HKS method Please Cite the Paper: Dutagaci, Helin, Chun Pan Cheung, and Afzal Godil. \"Evaluation of 3D interest point detection techniques via human-generated ground truth.\" The Visual Computer 28.9 (2012): 901-917.","language":["en"],"title":"Evaluation of 3D Interest Point Detection Techniques via Human-generated Ground Truth","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2208/3DInterestPoint.zip.sha256","mediaType":"text/plain","title":"SHA256 File for A Benchmark for 3D Interest Point Detection Algorithms"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2208/3DInterestPoint.zip","description":"The zip files contains the 3D models for dataset A and B in MAT files, the code for five 3D Interest Point Detection algorithms, the evaluation code and documentation.","mediaType":"application/zip","title":"A Benchmark for 3D Interest Point Detection Algorithms"},{"accessURL":"https://doi.org/10.18434/M32208","title":"DOI access to Evaluation of 3D Interest Point Detection Techniques via Human-generated Ground Truth"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2012-03-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Data and informatics","Mathematics and Statistics:Image and signal processing"],"issued":"2020-04-14","keyword":["3D Interest Points","3D Salient Points","3D Shape Analysis","Evaluation of 3D Interest Point Detection","Measurement Science"]},{"identifier":"ark:/88434/mds2-2209","accessLevel":"public","references":["https://doi.org/10.1145/1877808.1877819"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2209","description":"BEST VIEW SELECTION corresponds to the task of automatically selecting the most representative view of a 3D model. \n\nThis benchmark aims to provide tools to evaluate automatic best view selection algorithms. It consists of the preferred viewpoints of 68 models selected by 26 human subjects, and a code to calculate \"View selection error\". The subjective viewpoints were collected using a web-based subjective experiment where the users were asked to select the most informative view of a 3D model.\n\nThe 3D object dataset consists of 68 triangular meshes. Some of the models are standard models that are widely used in 3D shape research; and they have been used as test objects by researchers working on the best view problem.\n\nA web-based interface is used in the experiments. The user is shown the 68 3D models one at a time. Each model is initially rendered with a random pose. The user is asked to rotate the model via dragging the mouse into a view that he/she thinks is the best, and then to click on the submit button. 26 participants have submitted their preferred best views for the 68 models. For each model, we provide an ASCII file containing the view-points selected by the human subjects.\n\nOur evaluations take into account the symmetry sets of the viewpoints. We have used the Fourier-Mellin image matching technique to determine symmetries in the object with respect to a particular view-point.\n\nPlease Cite the Paper: \n\nDutagaci, Helin, Chun Pan Cheung, and Afzal Godil. \"A benchmark for best view selection of 3D objects.\" Proceedings of the ACM workshop on 3D Object Retrieval. 2010. https://doi.org/10.1145/1877808.1877819\n","language":["en"],"title":"A Benchmark for Automatic Best View Selection of 3D Objects","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2209/3D_BestView.zip.sha256","mediaType":"text/plain","title":"SHA256 File for A Benchmark for Best View Selection of 3D Objects"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2209/3D_BestView.zip","description":"The 3D Models, 3D evaluation code, and Baseline code","mediaType":"application/zip","title":"A Benchmark for Best View Selection of 3D Objects"},{"accessURL":"https://doi.org/10.18434/M32209","title":"DOI Access for A Benchmark for Automatic Best View Selection of 3D Objects"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2010-04-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Visualization research","Mathematics and Statistics:Image and signal processing"],"issued":"2020-04-22","keyword":["Best view selection","3D shape analysis","Best view evaluation"]},{"identifier":"ark:/88434/mds2-2210","accessLevel":"public","references":["http://dx.doi.org/10.2312/3DOR/3DOR09/069-076"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2210","description":"There are two objectives of this shape retrieval contest:\n\na) To evaluate partial similarity between query and target objects and retrieve complete 3D models that are relevant to a partial query object.\n\nb) To retrieve 3D models those are relevant to a query depth map. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.\n\nTask description\n\nIn response to a given set of queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query. The set of queries either consists of partial 3D models or of range images. The participants may present ranked lists for either of the query sets or both. There is no obligation to submit rank lists for both of the query sets.\n\nDataset\n\nThe first query set consists of 20 3D partial models which are obtained by cutting parts from complete models. The objective is to retrieve the models which the query part may belong to. The file format to represent the partial query models is the ASCII Object File Format (*.off).\n\nThis second query set is composed of 20 range images, which are acquired by capturing range data of 20 models from arbitrary view directions. The range images are captured using a desktop 3D scanner. The file format is in the ASCII Object File Format (*.off) representing the scan in a triangular mesh.\n\nThe target database is the same for both of the query sets and it contains 720 complete 3D models, which are categorized into 40 classes. In each class there are 18 models. The file format to represent the 3D models is the ASCII Object File Format (*.off).D Models, Classification files, Evaluation software, Images\n\nPaper: Dutagaci, Helin, Godil, Afzal, et al. \"SHREC'09 track: querying with partial models.\" Proceedings of the 2nd Eurographics conference on 3D Object Retrieval. Eurographics Association, 2009. https://doi.org/10.5555/2381128.2381144","language":["en"],"title":"SHREC'09 track: querying with partial models","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2210/SHREC2009_Partial.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SHREC 2009 - Shape Retrieval Contest of Partial 3D Models"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2210/SHREC2009_Partial.zip","description":"Target Set is 3D Models / Query Set is partial 3D models / Evaluation code","mediaType":"application/zip","title":"SHREC 2009 - Shape Retrieval Contest of Partial 3D Models"},{"accessURL":"https://doi.org/10.18434/M32210","title":"DOI Access for SHREC'09 track: querying with partial models"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2009-02-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Data and informatics","Mathematics and Statistics:Image and signal processing"],"issued":"2020-04-22","keyword":["Information Search and Retrieval","3D Shape Retrieval","Partial 3D Models"]},{"identifier":"ark:/88434/mds2-2211","accessLevel":"public","references":["http://dx.doi.org/10.2312/3DOR/3DOR09/069-076"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2211","description":"The objectives of this shape retrieval contest are to evaluate the performance of 3D shape retrieval approaches on a new generic 3D shape benchmark.\n\nTask description: In response to a given set of queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query.\n\nData set: In this new generic benchmark there are 800 3D models. The target database contains 720 complete 3D models, which are categorized into 40 classes. In each class there are 18 models. The file format to represent the 3D models is the ASCII Object File Format (*.off).\n\nEvaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2).\n\nPaper: Godil, A., Dutagaci, H., Akgül, C.B., Axenopoulos, A., Bustos, B., Chaouch, M., Daras, P., Furuya, T., Kreft, S., Lian, Z. and Napoleon, T., 2009, March. SHREC'09 Track: Generic shape retrieval. In 3DOR (pp. 61-68). http://dx.doi.org/10.2312/3DOR/3DOR09/061-068","language":["en"],"title":"SHREC'09 Track: Generic Shape Retrieval","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2211/SHREC2009_Generic.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SHREC'09 Track: Generic shape retrieval"},{"accessURL":"https://doi.org/10.18434/M32211","title":"DOI Access for SHREC'09 Track: Generic Shape Retrieval"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2211/SHREC2009_Generic.zip","description":"3D models, evaluation code","mediaType":"application/zip","title":"SHREC'09 Track: Generic shape retrieval"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2009-02-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Data and informatics","Mathematics and Statistics:Image and signal processing"],"issued":"2020-04-22","keyword":["3D Shape Retrieval","3D Models","Generic Shape Retrieval","Evaluation"]},{"identifier":"ark:/88434/mds2-2212","accessLevel":"public","contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2212","description":"Non-rigid 3D objects are commonly seen in our surroundings. However, previous efforts have been mainly devoted to the retrieval of rigid 3D models, and thus comparing non-rigid 3D shapes is still a challenging problem in content-based 3D object retrieval. Therefore, we organize this track to promote the development of non-rigid 3D shape retrieval.\n\nThe objective of this track is to evaluate the performance of 3D shape retrieval approaches on the subset of a publicly available non-rigid 3D models database----McGill Articulated Shape Benchmark database.\n\nTask description: The task is to evaluate the dissimilarity between every two objects in the database and then output the dissimilarity matrix.\n\nData set: The McGill Articulated Shape Benchmark database consists of 255 non-rigid 3D models which are classified into 10 categories. The maximum number of the objects in a class is 31, while the minimum number is 20.\n200 models are selected (or modified) to generate our test database to ensure that every class contains equal number of models. The models are represented as watertight triangle meshes and the file format is selected as the ASCII Object File Format (*.off). The original database is publicly available on the website: http://www.cim.mcgill.ca/~shape/benchMark/\n\nEvaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2).\n\nPlease Cite the paper: SHREC'10 Track: Non-rigid 3D Shape Retrieval., Z. Lian, A. Godil, T. Fabry, T. Furuya, J. Hermans, R. Ohbuchi, C. Shu, D. Smeets, P. Suetens, D. Vandermeulen, S. Wuhrer \nIn: M. Daoudi, T. Schreck, M. Spagnuolo, I. Pratikakis, R. Veltkamp (eds.), Proceedings of the Eurographics/ACM SIGGRAPH Symposium on 3D Object Retrieval, 2010.\n","language":["en"],"title":"SHREC'10 Track: Non-rigid 3D Shape Retrieval","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2212/SHREC2011_NonRigid.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SHREC'10 Track: Non-rigid 3D Shape Retrieval"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2212/SHREC2011_NonRigid.zip","description":"3D Models, Classification file, Images","mediaType":"application/zip","title":"SHREC'10 Track: Non-rigid 3D Shape Retrieval"},{"accessURL":"https://doi.org/10.18434/M32212","title":"DOI Access for SHREC'10 Track: Non-rigid 3D Shape Retrieval"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2010-02-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Image and signal processing","Information Technology:Data and informatics"],"issued":"2020-04-22","keyword":["Nonrigid 3D Shape Retrieval","Nonrigid 3D Models","3D Shape Analysis"]},{"identifier":"ark:/88434/mds2-2213","accessLevel":"public","contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2213","description":"The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a Generic 3D shape benchmark based on the Google 3D Warehouse.\n\nIntroduction: With the increasing number of 3D models are created everyday and stored in databases. Effectively searching a 3D repository for 3D shapes which are similar to a given 3D query model has become an important area of research. Benchmarking allows researchers to evaluate the quality of results of different 3D shape retrieval approaches. Here, we propose a new publicly available 3D shape benchmark based on the Google 3D Warehouse to advance the state of art in 3D shape retrieval\n\nTask description: The task is to evaluate the dissimilarity between every two objects in the database mentioned above and then output the dissimilarity matrix.\n\nData set: All the 3D models in the generic shape benchmark were acquired by a web crawler from the Google 3D Warehouse. To classify the 3D shape models into a ground truth database, one person based on the Google tags has classified objects into ground truth categories based mainly on visual similarity. In this benchmark, there will be over three thousand 3D models. The file format used to represent the 3D models will be the ASCII Object File Format (*.off).\n\nEvaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2).\n\nPlease Cite the Paper: SHREC'10 Track: Generic 3D Warehouse., T.P. Vanamali, A. Godil, H. Dutagaci,T. Furuya, Z. Lian, R. Ohbuchi, In: M. Daoudi, T. Schreck, M. Spagnuolo, I. Pratikakis, R. Veltkamp (eds.), Proceedings of the Eurographics/ACM SIGGRAPH Symposium on 3D Object Retrieval, 2010.\n","language":["en"],"title":"SHREC'10 Track: Generic 3D Warehouse","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2213/SHREC2010_Generic3DWarehouse.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SHREC'10 Track: Generic 3D Warehouse"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2213/SHREC2010_Generic3DWarehouse.zip","description":"3D models, Classification file, Images","mediaType":"application/zip","title":"SHREC'10 Track: Generic 3D Warehouse"},{"accessURL":"https://doi.org/10.18434/M32213","title":"DOI Access for SHREC'10 Track: Generic 3D Warehouse"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2010-02-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Data and informatics","Mathematics and Statistics:Image and signal processing"],"issued":"2020-04-21","keyword":["3D Shape Retrieval","3D Models","3D Shape Analysis","Evaluation and Measurement Science"]},{"identifier":"ark:/88434/mds2-2214","accessLevel":"public","references":["http://dx.doi.org/10.2312/3DOR/3DOR10/109-115"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2214","description":"The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.\n\nTask description: In response to a given set of queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query. The set of query consists of range images.\n\nData set: The query set is composed of 120 range images, which are acquired by capturing 3 range scans of 40 models from arbitrary view directions. The range images are captured using a Minolta Laser Scanner. The file format is in the ASCII Object File Format (*.off) representing the scan in a triangular mesh.\nThe target database contains 800 complete 3D models, which are categorized into 40 classes. In each class there are 20 models. The file format to represent the 3D models is the ASCII Object File Format (*.off).\n\nEvaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2).\n\nPlease Cite the Paper: \nDutagaci H, Godil A, Cheung CP, Furuya T, Hillenbrand U, Ohbuchi R. SHREC'10 Track: Range Scan Retrieval. In3DOR 2010 May 2 (pp. 109-115). http://dx.doi.org/10.2312/3DOR/3DOR10/109-115\n\n","language":["en"],"title":"SHREC'10 track: Range scan retrieval","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2214/SHREC2010_RangeScans.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SHREC'10 Track: Range Scan Retrieval"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2214/SHREC2010_RangeScans.zip","description":"3D Models, 3D Range Scans, Images","mediaType":"application/zip","title":"SHREC'10 Track: Range Scan Retrieval"},{"accessURL":"https://doi.org/10.18434/M32214","title":"DOI Access for SHREC'10 track: Range scan retrieval"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2010-02-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Image and signal processing","Information Technology:Data and informatics"],"issued":"2020-04-22","keyword":["3D Shape Retrieval","3D Models","3D Range Scans","Evaluation"]},{"identifier":"ark:/88434/mds2-2215","accessLevel":"public","references":["http://dx.doi.org/10.2312/3DOR/3DOR11/079-088"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2215","description":"Non-rigid 3D objects are commonly seen in our surroundings. However, previous efforts have been mainly devoted to the retrieval of rigid 3D models, and thus comparing non-rigid 3D shapes is still a challenging problem in content-based 3D object retrieval. Therefore, we organize this track to promote the development of non-rigid 3D shape retrieval.\nThe objective of this track is to evaluate the performance of 3D shape retrieval approaches on a large-scale database of non-rigid 3D watertight meshes generated by our group.\n\nTask description: The task is to evaluate the dissimilarity between every two objects in the database and then output the dissimilarity matrix.\n\nData set: Our large-scale database consists of 600 non-rigid 3D objects (see the figure for some examples) that are created by our group using some modeling software and our own codes. We classified these models properly to make sure that every class contains equal number of models. The models are represented as watertight triangle meshes and the file format is selected as the ASCII Object File Format (*.off).\n(Note that: Some of these models we recreated and modified with permission are originally from several publicly available databases: such as McGill database, TOSCA shapes, Princeton Shape Benchmark, etc.)\n\nEvaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2).\n\nPlease Cite the paper : SHREC'11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes, Z. Lian, A. Godil, B. Bustos, M. Daoudi, J. Hermans, S. Kawamura, Y. Kurita, G. Lavou�, H.V. Nguyen, R. Ohbuchi, Y. Ohkita, Y. Ohishi, F. Porikli, M. Reuter, I. Sipiran, D. Smeets, P. Suetens, H. Tabia, and D. Vandermeulen , In: H. Laga and T. Schreck, A. Ferreira, A. Godil, I. Pratikakis, R. Veltkamp (eds.), Proceedings of the Eurographics/ACM SIGGRAPH Symposium on 3D Object Retrieval, 2011. http://dx.doi.org/10.2312/3DOR/3DOR11/079-088\n","language":["en"],"title":"SHREC'11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2215/SHREC2011_NonRigid.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SHREC'11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2215/SHREC2011_NonRigid.zip","description":"Non-rigid 3D models, Shape Retrieval on Non-rigid 3D Models","mediaType":"application/zip","title":"SHREC'11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes"},{"accessURL":"https://doi.org/10.18434/M32215","title":"DOI Access for SHREC'11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2011-02-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Image and signal processing","Information Technology:Data and informatics"],"issued":"2020-04-22","keyword":["3D Non-rigid Shape Retrieval","3D Non-rigid Models","3D Shape Analysis","Evaluation"]},{"identifier":"ark:/88434/mds2-2216","accessLevel":"public","contactPoint":{"hasEmail":"mailto:thomas.germer@nist.gov","fn":"Thomas A. Germer"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/pySCATMECH","description":"SCATMECH is a library of object-oriented C++ computer codes originally developed for disseminating models for polarized light scattering from surfaces and aerosols and for diffraction from gratings. The pySCATMECH package has been developed as an interface to the SCATMECH library, simplifying use of the codes and allowing for more rapid development of software for these applications.","language":["en"],"title":"pySCATMECH: A Python interface to the SCATMECH C++ library of polarized light scattering codes","distribution":[{"accessURL":"https://doi.org/10.18434/M32216","title":"DOI Access for pySCATMECH: A Python interface to the SCATMECH C++ library of polarized light scattering codes"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-04-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Modeling and simulation research","Materials:Materials characterization","Metrology:Optical, photometry, and laser metrology","Physics:Spectroscopy","Physics:Optical physics"],"issued":"2020-07-31","keyword":["aerosol","bidirectional reflectance","BRDF","diffuse","gratings","Mie scattering","modeling","Mueller matrix","polarization","Python","roughness","scatter","surface"]},{"identifier":"ark:/88434/mds2-2217","accessLevel":"public","references":["http://dx.doi.org/10.2312/3dor.20151069"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2217","description":"The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.\n\nTask description:\nIn response to a given set of range scan queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query.\n\nData set: \nThe query set is composed of at least 180 range images, which are acquired by capturing 3 or 4 range scans of 60 models from arbitrary view directions. The range images are captured using a Minolta Laser Scanner. The file format is in the ASCII Object File Format (*.off) representing the scan in a triangular mesh.\nThe target database contains 1200 complete 3D models, which are categorized into 60 classes. In each class there are 20 models. The file format to represent the 3D models is the ASCII Object File Format (*.off).\n\nEvaluation Methodology:\nWe will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2).\n\nPlease Cite the Paper:  Godil A, Dutagaci H, Bustos B, Choi S, Dong S, Furuya T, Li H, Link N, Moriyama A, Meruane R, Ohbuchi R. SHREC'15: range scans based 3D shape retrieval. In Proceedings of the Eurographics Workshop on 3D Object Retrieval, Zurich, Switzerland 2015 May 3 (pp. 2-3). https://doi.org/10.5555/2852282.2852312","language":["en"],"title":"SHREC'15: Range Scans based 3D Shape Retrieval","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2217/SHREC2015_Range.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SHREC'15: Range Scans based 3D Shape Retrieval"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2217/SHREC2015_Range.zip","description":"3D Models, RangeData, Classification file, Images, etc.","mediaType":"application/zip","title":"SHREC'15: Range Scans based 3D Shape Retrieval"},{"accessURL":"https://doi.org/10.18434/M32217","title":"DOI Access for SHREC'15: Range Scans based 3D Shape Retrieval"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2015-02-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Image and signal processing","Information Technology:Data and informatics"],"issued":"2020-04-22","keyword":["Range Scans","3D Shape Retrieval","3D Models","Evaluation and Measurement Science"]},{"identifier":"ark:/88434/mds2-2218","accessLevel":"public","references":["https://doi.org/10.1016/j.cviu.2014.10.006","https://doi.org/10.2312/3dor.20141058"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2218","description":"The objective of SHREC'14 Track is to evaluate the performances of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a generic 3D model dataset.\n\nSketch-based 3D model retrieval is to retrieve relevant 3D models using sketch(es) as input. This scheme is intuitive and convenient for users to learn and search for 3D models. It is also popular and important for related applications such as sketch-based modeling and recognition, as well as 3D animation production via 3D reconstruction of a scene of 2D storyboard. However, most existing 3D model retrieval algorithms target the Query-by-Model framework which uses existing 3D models as queries. \n\nMuch less research work has been done regarding the Query-by-Sketch framework. Large scale sketch-based 3D shape retrieval has received more and more attentions in the community of content-based 3D object retrieval. The objective of this track is to evaluate the performance of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a comprehensive 3D model dataset. The benchmark contains 12,680 sketches and 8,987 3D models, divided into 171 distinct classes. In this track, 12 runs were submitted by 4 groups and their retrieval performance was evaluated using 7 commonly used retrieval performance metrics. We hope that this benchmark, the comparative evaluation results and the corresponding evaluation code will further promote the progress of this research direction for the 3D model retrieval community.\n\nEvaluation Method:\nThe performance is evaluated by Precision-Recall (PR) graph, Nearest Neighbor (NN), First Tier (FT), Second Tier (ST), E-Measures (E), Discounted Cumulated Gain (DCG) and Average Precision (AP)\n\nPlease cite the papers:\n\n[1] Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono, Martin Burtscher, Qiang Chen, Nihad Karim Chowdhury, Bin Fang, Hongbo Fu, Takahiko Furuya, Haisheng Li, Jianzhuang Liu, Henry Johan, Ryuichi Kosaka, Hitoshi Koyanagi, Ryutarou Ohbuchi, Atsushi Tatsuma, Yajuan Wan, Chaoli Zhang, Changqing Zou. A Comparison of 3D Shape Retrieval Methods Based on a Large-scale Benchmark Supporting Multimodal Queries. Computer Vision and Image Understanding, November 4, 2014. \n\n[2] Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono, Martin Burtscher, Hongbo Fu, Takahiko Furuya, Henry Johan, Jianzhuang Liu, Ryutarou Ohbuchi, Atsushi Tatsuma, Changqing Zou. SHREC' 14 Track: Extended Large Scale Sketch-Based 3D Shape Retrieval. Eurographics Workshop on 3D Object Retrieval 2014 (3DOR 2014): 121-130, 2014.","language":["en"],"title":"SHREC'14 Track: Extended Large Scale Sketch-Based 3D Shape Retrieval","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2218/SHREC2014_SBR.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2218/SHREC2014_SBR.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32218","title":"DOI access to SHREC'14 Track: Extended Large Scale Sketch-Based 3D Shape Retrieval"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2014-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Data and informatics","Mathematics and Statistics:Image and signal processing"],"issued":"2020-04-14","keyword":["3D Shape Retrieval","3D Models","Sketches","Evaluation"]},{"identifier":"ark:/88434/mds2-2219","accessLevel":"public","references":["https://www.nist.gov/publications/comparison-3d-shape-retrieval-methods-based-large-scale-benchmark-supporting-multimodal","http://dx.doi.org/10.1016/j.cviu.2014.10.006"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2219","description":"Objective:\nThe objective of this track is to evaluate the performance of 3D shape retrieval approaches on a large-sale comprehensive 3D shape database which contains different types of models, such as generic, articulated, CAD and architecture models.\n\nIntroduction:\nWith the increasing number of 3D models created every day and stored in databases, the development of effective and scalable 3D search algorithms has become an important research area. In this contest, the task will be retrieving 3D models similar to a complete 3D model query from a new integrated large-scale comprehensive 3D shape benchmark including various types of models. Owing to the integration of the most important existing benchmarks to date, the newly created benchmark is the most exhaustive to date in terms of the number of semantic query categories covered, as well as the variations of model types. The shape retrieval contest will allow researchers to evaluate results of different 3D shape retrieval approaches when applied on a large scale comprehensive 3D database. \n\nThe benchmark is motivated by a latest large collection of human sketches built by Eitz et al. [1]. To explore how human draw sketches and human sketch recognition, they collected 20,000 human-drawn sketches, categorized into 250 classes, each with 80 sketches. This sketch dataset is exhaustive in terms of the number of object categories. Thus, we believe that a 3D model retrieval benchmark based on their object categorizations will be more comprehensive and appropriate than currently available 3D retrieval benchmarks to more objectively and accurately evaluate the real practical performance of a comprehensive 3D model retrieval algorithm if implemented and used in the real world. \n\nConsidering this, we build a SHREC'14 Large Scale Comprehensive Track Benchmark (SHREC14LSGTB) by collecting relevant models in the major previously proposed 3D object retrieval benchmarks. Our target is to find models for as many as classes of the 250 classes and find as many as models for each class. These previous benchmarks have been compiled with different goals in mind and to date, not been considered in their sum. Our work is the first to integrate them to form a new, larger benchmark corpus for comprehensive 3D shape retrieval. \n\nDataset:\nSHREC'14 Large Scale Comprehensive Retrieval Track Benchmark has 8,987 models, categorized into 171 classes. We adopt a voting scheme to classify models. For each classification, we have at least two votes. If these two votes agree each other, we confirm that the classification is correct, otherwise, we perform a third vote to finalize the classification. All the models are categorized according to the classifications in Eitz et al. [1], based on visual similarity. \n\nEvaluation Method:\nTo have a comprehensive evaluation of the retrieval algorithm, we employ seven commonly adopted performance metrics in 3D model retrieval technique.\n\nPlease cite the papers: \n\n[1] Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono, Martin Burtscher, Qiang Chen, Nihad Karim Chowdhury, Bin Fang, Hongbo Fu, Takahiko Furuya, Haisheng Li, Jianzhuang Liu, Henry Johan, Ryuichi Kosaka, Hitoshi Koyanagi, Ryutarou Ohbuchi, Atsushi Tatsuma, Yajuan Wan, Chaoli Zhang, Changqing Zou. A Comparison of 3D Shape Retrieval Methods Based on a Large-scale Benchmark Supporting Multimodal Queries. Computer Vision and Image Understanding, November 4, 2014. \n\n[2] Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono, Qiang Chen, Nihad Karim Chowdhury, Bin Fang, Takahiko Furuya, Henry Johan, Ryuichi Kosaka, Hitoshi Koyanagi, Ryutarou Ohbuchi, Atsushi Tatsuma. SHREC' 14 Track: Large Scale Comprehensive 3D Shape Retrieval. Eurographics Workshop on 3D Object Retrieval 2014 (3DOR 2014): 131-140, 2014.\n","language":["en"],"title":"SHREC'14 Track: Large Scale Comprehensive 3D Shape Retrieval","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2219/SHREC2014_Generic3D.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SHREC'14 Track: Large Scale Comprehensive 3D Shape Retrieval"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2219/SHREC2014_Generic3D.zip","description":"3D Models, Classification file, Images, etc.","mediaType":"application/zip","title":"SHREC'14 Track: Large Scale Comprehensive 3D Shape Retrieval"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2014-01-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Data and informatics","Mathematics and Statistics:Image and signal processing"],"issued":"2020-04-14","keyword":["3D shape retrieval","Large-scale benchmark","Multimodal queries","Performance evaluation","Query-by-Model","Query-by-Example","Evaluation and measurement science."]},{"identifier":"ark:/88434/mds2-2220","accessLevel":"public","references":["https://www.nist.gov/publications/shrec14613-track-large-scale-sketch-based-3d-shape-retrieval","http://dx.doi.org/10.1016/j.cviu.2013.11.008"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2220","description":"Objective: \nThe objective of this track is to evaluate the performance of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset for querying from a generic 3D model dataset.\n\nIntroduction:\nSketch-based 3D model retrieval is focusing on retrieving relevant 3D models using sketch(es) as input. This scheme is intuitive and convenient for users to learn and search for 3D models. It is also popular and important for related applications such as sketch-based modeling and recognition, as well as 3D animation production via 3D reconstruction of a scene of 2D storyboard.\n\nPlease cite the papers:\n\n[1] B. Li, Y. Lu, Afzal Godil, Tobias Schreck, Masaki Aono, Henry Johan, Jose M. Saavedra, S. Tashiro, In: S. Biasotti, I. Pratikakis, U. Castellani, T. Schreck, A. Godil, and R. Veltkamp (eds.), SHREC'13 Track: Large Scale Sketch-Based 3D Shape Retrieval, Eurographics Workshop on 3D Object Retrieval 2013 (3DOR 2013): 89-96, 2013. \n\n[2] B. Li, Y. Lu, A. Godil, T. Schreck, B. Bustos, A. Ferreira, T. Furuya, M.J. Fonseca, H. Johan, T. Matsuda, R. Ohbuchi, P.B. Pascoal, J.M. Saavedra, A comparison of methods for sketch-based 3D shape retrieval, Computer Vision and Image Understanding (2013), doi: http://dx.doi.org/10.1016/j.cviu.2013.11.008.","language":["en"],"title":"SHREC'13 Track: Large Scale Sketch-Based 3D Shape Retrieval","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2220/SHREC2013_SBR.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SHREC'13 Track: Large Scale Sketch-Based 3D Shape Retrieval"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2220/SHREC2013_SBR.zip","description":"3D models, Sketches, Classification file, Evaluation code","mediaType":"application/zip","title":"SHREC'13 Track: Large Scale Sketch-Based 3D Shape Retrieval"},{"accessURL":"https://doi.org/10.18434/M32220","title":"DOI access to SHREC'13 Track: Large Scale Sketch-Based 3D Shape Retrieval"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2013-01-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Image and signal processing","Information Technology:Data and informatics"],"issued":"2020-04-14","keyword":["Sketch-based 3D model retrieval","Evaluation","SHREC contest","Large-scale Benchmark"]},{"identifier":"ark:/88434/mds2-2221","accessLevel":"public","references":["http://dx.doi.org/10.2312/3DOR/3DOR12/119-126"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2221","description":"Objective: The objective of this track is to evaluate the performance of 3D shape retrieval approaches on Generic 3D Dataset.\n\nIntroduction: With the increasing number of 3D models are created every day and stored in databases, effectively searching a 3D repository for 3D shapes which are similar to a given 3D query model has become an important area of research. Benchmarking allows researchers to evaluate the quality of the results of different 3D shape retrieval approaches.\n\nTask description:\nThe task is to evaluate the dissimilarity between every two objects in the database mentioned above and then output the dissimilarity matrix.\n\nDataset:\nAll the 3D models in the generic 3D dataset will be based on the combination of models from our previous generic 3D benchmarks. In this generic 3D dataset, there will be 1200 3D models, classified into 60 object categories based mainly on visual similarity. The file format used to represent the 3D models will be the ASCII Object File Format (*.off).\n\nEvaluation Methodology:\nWe will employ the following evaluation measures: Precision-Recall curve (PR), Nearest Neighbor (NN), First-Tier (FT), Second-Tier (ST), E-Measure (E), Discounted Cumulative Gain (DCG) and Average Precision (AP).\n\nPlease cite the paper:\nB. Li, A. Godil, M. Aono, X. Bai, T. Furuya, L. Li, R. Lopez-Sastre, H. Johan, R. Ohbuchi, C. Redondo-Cabrera, A. Tatsuma, T. Yanagimachi, S. Zhang, In: M. Spagnuolo, M. Bronstein, A. Bronstein, and A. Ferreira (eds.): SHREC'12 Track: Generic 3D Shape Retrieval, Eurographics Workshop on 3D Object Retrieval 2012 (3DOR 2012), 2012. http://dx.doi.org/10.2312/3DOR/3DOR12/119-126","language":["en"],"title":"SHREC'12 Track: Generic 3D Shape Retrieval","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2221/SHREC2012_Generic3D.zip.sha256","mediaType":"text/plain","title":"SHA256 File for SHREC'12 Track: Generic 3D Shape Retrieval"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2221/SHREC2012_Generic3D.zip","description":"3D Models, Classification file, Evaluation Code","mediaType":"application/zip","title":"SHREC'12 Track: Generic 3D Shape Retrieval"},{"accessURL":"https://doi.org/10.18434/M32221","title":"DOI Access for SHREC'12 Track: Generic 3D Shape Retrieval"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2012-01-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Data and informatics","Mathematics and Statistics:Image and signal processing"],"issued":"2020-04-22","keyword":["3D Shape Retrieval","3D Models","Evaluation and Measurement Science"]},{"identifier":"ark:/88434/mds2-2222","accessLevel":"public","references":["https://doi.org/10.2312/3DOR/3DOR12/109-118"],"contactPoint":{"hasEmail":"mailto:afzal.godil@nist.gov","fn":"Afzal A. Godil"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2222","description":"The objective of this SHREC'12 track is to evaluate the performance of different sketch-based 3D model retrieval algorithms using both hand-drawn and standard line drawings sketch queries on a watertight 3D model dataset.\n\nSketch-based 3D model retrieval is to retrieve 3D models using a 2D sketch as input. This scheme is intuitive and convenient for users to search for relevant 3D models and also important for several applications including sketch-based modeling and sketch-based shape recognition. However, most existing 3D model retrieval algorithms target the Query-by-Model framework, that is, using existing 3D models as queries. Much less research work has been done regarding the Query-by-Sketch framework. In addition, until now there was no comprehensive evaluation or comparison for available sketch-based retrieval algorithms. Considering of this, we organized this track to foster this challenging research area by providing a common sketch-based retrieval benchmark and soliciting retrieval results from current state-of-the-art retrieval methods for comparison. We also provide corresponding evaluation code for computing a set of performance metrics similar to those used in the Query-by-Model retrieval technique.\n\nDataset: 3D target Models is 400, 2D query set comprises two subsets: (1) Hand-drawn sketches, and (2) Standard line drawings\n\nPlease cite the paper:\n[1] B. Li, T. Schreck, A. Godil, M. Alexa, T. Boubekeur, B. Bustos, J. Chen, M. Eitz, T. Furuya, K. Hildebrand, S. Huang, H. Johan, A. Kuijper, R. Ohbuchi, R. Richter, J. M. Saavedra, M. Scherer, T. Yanagimachi, G. J. Yoon, S. M. Yoon, In: M. Spagnuolo, M. Bronstein, A. Bronstein, and A. Ferreira (eds.), SHREC'12 Track: Sketch-Based 3D Shape Retrieval, Eurographics Workshop on 3D Object Retrieval 2012 (3DOR 2012), 2012.","language":["en"],"title":"SHREC'12 Track: Sketch-Based 3D Shape Retrieval","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2222/SHREC2012_SBR.zip.sha256","mediaType":"text/plain","title":"SHA256 File for  SHREC'12 Track: Sketch-Based 3D Shape Retrieval"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2222/SHREC2012_SBR.zip","description":"3D models, Hand-drawn sketches, Standard line drawings, Classification file, etc.","mediaType":"application/zip","title":"SHREC'12 Track: Sketch-Based 3D Shape Retrieval"},{"accessURL":"https://doi.org/10.18434/M32222","title":"DOI access to SHREC'12 Track: Sketch-Based 3D Shape Retrieval"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2012-01-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Data and informatics","Mathematics and Statistics:Image and signal processing"],"issued":"2020-04-14","keyword":["3D Shape Retrieval","3D models","Hand-drawn Sketch data","Standard line drawings","Evaluation"]},{"identifier":"ark:/88434/mds2-2223","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jonathan.wyrick@nist.gov","fn":"Jonathan Wyrick"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/ABDNavigator","description":"ABDNavigator is software written for scanning probe (e.g. scanning tunneling microscope) control and sample navigation of atom-based devices.  Here atom-based devices refer to devices whose components span from the micron scale range down to sub nanometer and are probed, and typically fabricated by scanning tunneling microscope.","language":["en"],"title":"Atom-Based Device Navigator (ABDNavigator)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2223/license.txt.sha256","mediaType":"text/plain","title":"SHA256 File for NIST Software License"},{"accessURL":"https://doi.org/10.18434/M32223","title":"DOI access to Atom-Based Device Navigator (ABDNavigator)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-04-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1M","theme":["Physics:Atomic, molecular, and quantum","Nanotechnology:Nanofabrication/manufacturing"],"issued":"2020-04-14","keyword":["atom-based fabrication","STM","Scanning tunneling microscope","Hydrogen lithography"]},{"identifier":"ark:/88434/mds2-2224","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2089","https://nvlpubs.nist.gov/nistpubs/jres/125/jres.125.015.pdf"],"contactPoint":{"hasEmail":"mailto:enrico.lucon@nist.gov","fn":"Enrico Lucon"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2224","description":"We investigated the influence of the state of wear of Charpy machine anvils on test results by performing impact tests on NIST specimens of three energy levels with a machine equipped with new anvils (compliant with both ASTM E23 and ISO 148-2) and worn anvils (anvil corner radii and distance outside ASTM tolerances, but within ISO tolerances).","language":["en"],"title":"Influence of machine anvil wear on Charpy test results","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2224/Influence%20anvil%20wear%20-%20Low%20energy.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Data for low-energy specimens"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2224/Influence%20anvil%20wear%20-%20High%20energy.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Data for high-energy specimens"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2224/Influence%20anvil%20wear%20-%20Super-High%20energy.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Test results for super-high-energy specimens"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2224/Influence%20anvil%20wear%20-%20Low%20energy.csv","format":"CSV file","description":"Test results for low-energy specimens, plus statistical analyses for the significance of the differences between new and worn anvils.","mediaType":"text/csv","title":"Data for low-energy specimens"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2224/Influence%20anvil%20wear%20-%20High%20energy.csv","format":"CSV file","description":"Test results for high-energy specimens, plus statistical analyses for the significance of the differences between new and worn anvils.","mediaType":"text/csv","title":"Data for high-energy specimens"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2224/Influence%20anvil%20wear%20-%20Super-High%20energy.csv","format":"CSV file","description":"Test results for super-high-energy specimens, plus statistical analyses for the significance of the differences between new and worn anvils.","mediaType":"text/csv","title":"Test results for super-high-energy specimens"},{"accessURL":"https://doi.org/10.18434/M32224","title":"DOI Access for Influence of machine anvil wear on Charpy test results"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-01-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Infrastructure","Materials:Metals","Materials:Materials characterization"],"issued":"2020-04-28","keyword":["Absorbed energy","ASTM E23","Charpy","ISO 148","machine anvils","span","wear"]},{"identifier":"ark:/88434/mds2-2225","accessLevel":"public","contactPoint":{"hasEmail":"mailto:allan.harvey@nist.gov","fn":"Allan H. Harvey"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Values of second dielectric and refractivity virial coefficients computed, with rigorous accounting for quantum effects, from state-of-the-art pair potentials and interaction polarizabilities.  Values given at 1 K intervals (finer intervals below 10 K) for helium (both mass 3 and 4 isotopes), neon (both mass 20 and 22 isotopes), and argon (mass 40 isotope).  Upper temperature limit is 2000 K in all cases. Lower temperature limit is 0.5 K for helium, 4 K for neon, 50 K for argon.\nThese files are Supplemental information to: G. Garberoglio and A.H. Harvey, \"Path-integral Calculation of the Second Dielectric and Refractivity Virial Coefficients of Helium, Neon, and Argon,\" published in  J. Res. NIST , volume 125, article 125022 (2020) [https://doi.org/10.6028/jres.125.022], which describes how the values were calculated.","language":["en"],"title":"Calculated values of the second dielectric and refractivity virial coefficients of helium, neon, and argon.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/BR_Ar40.dat","format":"ASCII .dat file","description":"Second refractivity virial coefficient and dispersion correction factor for argon-40","mediaType":"application/octet-stream","title":"B_R for argon-40"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/BR_Ar40.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/BR_He3.dat","format":"ASCII .dat file","description":"Second refractivity virial coefficient and dispersion correction factor for helium-3","mediaType":"application/octet-stream","title":"B_R for helium-3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/BR_He3.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/BR_He4.dat","format":"ASCII .dat file","description":"Second refractivity virial coefficient and dispersion correction factor for helium-4","mediaType":"application/octet-stream","title":"B_R for helium-4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/BR_He4.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/BR_Ne20.dat","format":"ASCII .dat file","description":"Second refractivity virial coefficient and dispersion correction factor for neon-20","mediaType":"application/octet-stream","title":"B_R for neon-20"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/BR_Ne20.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/BR_Ne22.dat","format":"ASCII .dat file","description":"Second refractivity virial coefficient and dispersion correction factor for neon-22","mediaType":"application/octet-stream","title":"B_R for neon-22"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/BR_Ne22.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_Ar40.dat","format":"ASCII .dat file","description":"Second dielectric virial coefficient of argon-40, quantum calculation, with its expanded uncertainty.","mediaType":"application/octet-stream","title":"B_eps for argon-40"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_Ar40.dat.sha256","mediaType":"text/plain","title":"SHA256 File for B_eps for argon-40"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_Ar_classical.dat","format":"ASCII .dat file","description":"Second dielectric virial coefficient for argon, calculated classically","mediaType":"application/octet-stream","title":"Classical B_eps for argon"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_Ar_classical.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Classical B_eps for argon"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_He3.dat","format":"ASCII .dat file","description":"Second dielectric virial coefficient for helium-3, calculated fully quantum, with its expanded uncertainty","mediaType":"application/octet-stream","title":"B_eps for helium-3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_He3.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_He4.dat","format":"ASCII .dat file","description":"Second dielectric virial coefficient for helium-4, calculated fully quantum, with its expanded uncertainty.","mediaType":"application/octet-stream","title":"B_eps for helium-4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_He4.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_He_classical.dat","format":"ASCII .dat file","description":"Second dielectric virial coefficient for helium, calculated classically","mediaType":"application/octet-stream","title":"Classical B_eps for helium"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_He_classical.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_Ne20.dat","format":"ASCII .dat file","description":"Second dielectric virial coefficient for neon-20, calculated fully quantum","mediaType":"application/octet-stream","title":"B_eps for neon-20"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_Ne20.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_Ne22.dat","format":"ASCII .dat file","description":"Second dielectric virial coefficient for neon-22, calculated fully quantum","mediaType":"application/octet-stream","title":"B_eps for neon-22"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_Ne22.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_Ne_classical.dat","format":"ASCII .dat file","description":"Second dielectric virial coefficient for neon, calculated classically","mediaType":"application/octet-stream","title":"Classical B_eps for neon"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Beps_Ne_classical.dat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Readme.txt","format":"txt file","description":"Readme file documenting the various data files","mediaType":"text/plain","title":"Readme.txt"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2225/Readme.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Readme"},{"accessURL":"https://doi.org/10.18434/M32225","title":"DOI Access for Calculated values of the second dielectric and refractivity virial coefficients of helium, neon, and argon."}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-04-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Atomic, molecular, and quantum","Metrology:Thermometry metrology","Metrology:Pressure and vacuum metrology","Chemistry:Theoretical chemistry and modeling"],"issued":"2020-05-08","keyword":["argon","dielectric virials","helium","neon","pressure","refractivity virials","thermometry"]},{"identifier":"ark:/88434/mds2-2227","accessLevel":"public","contactPoint":{"hasEmail":"mailto:peter.fontana@nist.gov","fn":"Peter Fontana"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/active-evaluation","description":"This software repository contains a python package Aegis (Active Evaluator Germane Interactive Selector) package that allows us to evaluate machine learning systems's performance (according to a metric such as accuracy) by adaptively sampling trials to label from an unlabeled test set to minimize the number of labels needed. This includes sample (public) data as well as a simulation script that tests different label-selecting strategies on already labelled test sets. This software is configured so that users can add their own data and system outputs to test evaluation.","language":["en"],"title":"Active Evaluation Software for Selection of Ground Truth Labels","distribution":[{"accessURL":"https://doi.org/10.18434/M32227","title":"DOI Access for Active Evaluation Software for Selection of Ground Truth Labels"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-04-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Data and informatics"],"issued":"2020-07-09","keyword":["active evaluation","machine learning","ar"]},{"identifier":"ark:/88434/mds2-2229","accessLevel":"public","references":["https://doi.org/10.6028/jres.124.038"],"contactPoint":{"hasEmail":"mailto:raied.caromi@nist.gov","fn":"Raied Caromi"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2229","description":"This software tool generates simulated radar signals and creates RF datasets. The datasets can be used to develop and test detection algorithms by utilizing machine learning/deep learning techniques for the 3.5 GHz Citizens Broadband Radio Service (CBRS) or similar bands. In these bands, the primary users of the band are federal incumbent radar systems. The software tool generates radar waveforms and randomizes the radar waveform parameters. The pulse modulation types for the radar signals and their parameters are selected based on NTIA testing procedures for ESC certification, available at http://www.its.bldrdoc.gov/publications/3184.aspx. Furthermore, the tool mixes the waveforms with interference and packages them into one RF dataset file. The tool utilizes a graphical user interface (GUI) to simplify the selection of parameters and the mixing process.  A reference RF dataset was generated using this software. The RF dataset is published at https://doi.org/10.18434/M32116.","language":["en"],"title":"Simulated Radar Waveform and RF Dataset Generator for Incumbent Signals in the 3.5 GHz CBRS Band","distribution":[{"accessURL":"https://github.com/usnistgov/SimulatedRadarWaveformGenerator","format":"Matlab code and app designer files.","description":"A software tool that generates simulated radar signals and creates RF datasets for developing and testing machine/deep learning detection algorithms.","title":"Simulated Radar Waveform Generator"},{"accessURL":"https://doi.org/10.18434/M32229","title":"DOI Access for Simulated Radar Waveform and RF Dataset Generator for Incumbent Signals in the 3.5 GHz CBRS Band"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-05-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"issued":"2020-07-23","keyword":["3.5 GHz","CBRS","LTE","ESC","radar","radio frequency signals","spectrum","machine learning","deep learning","detection"]},{"identifier":"ark:/88434/mds2-2230","accessLevel":"public","contactPoint":{"hasEmail":"mailto:robert.mcmichael@nist.gov","fn":"Robert D. McMichael"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2230","description":"Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters.  Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters.  Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties.   A TCP socket interface allows the software to be used from experimental control software written in other programming languages. Code is developed in Python, and shared via GitHub's USNISTGOV organization.","language":["en"],"title":"Optimal Bayesian Experimental Design Version 1.0.1","distribution":[{"accessURL":"https://pages.nist.gov/optbayesexpt/","title":"Documentation for Optimal Bayesian Experimental Design"},{"accessURL":"https://doi.org/10.18434/M32230","title":"DOI Access for Optimal Bayesian Experimental Design Version 1.0.1"},{"downloadURL":"https://github.com/usnistgov/optbayesexpt","format":"Python source code, documentation in Jupyter notebook, markdown and rst formats","description":"Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters.  Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters.  Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties.   A TCP socket interface allows the software to be used from experimental control software written in other programming languages. Code is developed in Python, and shared via GitHub's USNISTGOV organization.","mediaType":"text/plain","title":"Optimal Bayesian Experimental Design v. 1.0.1"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-04-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Magnetics"],"keyword":["GitHub pages template","experimental design","Bayesian","optbayesexpt","python","measurement"]},{"identifier":"ark:/88434/mds2-2231","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2134"],"contactPoint":{"hasEmail":"mailto:piotr.domanski@nist.gov","fn":"Piotr A. Domanski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/services-resources/software/cycled-hx-nist-vapor-compression-cycle-model-accounting-refrigerant-0","description":"The CYCLE_D-HX software package simulates the performance of single-component refrigerants and refrigerant blends in subcritical vapor-compression refrigeration cycles. The basic system simulated by CYCLE_D-HX consists of a compressor, discharge line, condenser, expansion device, evaporator, compressor suction line, and an optional liquid-line/suction-line heat exchanger.  The other cycles may contain a second compressor, one or two economizers, or an intercooler. In contrast to simplified vapor compression cycle model, which require refrigerant saturation temperatures in the evaporator and condenser as input, CYCLE_D-HX establishes saturation temperatures in the heat exchangers using the temperatures profiles of heat source and heat sink and the mean effective temperature differences (?Thx) in the evaporator and condenser, respectively, which are specified as input to the program. This representation of heat exchangers facilitates the inclusion of both thermodynamic and transport properties in cycle simulations and makes CYCLE_D-HX suitable for comparative evaluations of different refrigerants, particularly when applied in systems relying on forced-convection heat transfer of refrigerant in the heat exchangers.  This software package was developed by the National Institute of Standards and Technology (NIST), is not subject to copyright protection, and is in the public domain.","language":["en"],"title":"CYCLE_D-HX: NIST Vapor Compression Cycle Model Accounting for Refrigerant Thermodynamic and Transport Properties Version 2.0","distribution":[{"accessURL":"https://doi.org/10.18434/M32231","title":"DOI Access for CYCLE_D-HX: NIST Vapor Compression Cycle Model Accounting for Refrigerant Thermodynamic and Transport Properties Version 2.0"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-01-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Buildings and Construction:Air conditioning and heating equipment"],"issued":"2021-05-19","keyword":["Air conditioning; Coefficient of performance; Cycle simulation; Refrigerants; Thermodynamic properties; Transport properties; Vapor compression; Volumetric capacity."]},{"identifier":"ark:/88434/mds2-2233","accessLevel":"public","contactPoint":{"hasEmail":"mailto:brandon.lane@nist.gov","fn":"Brandon Lane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2233","description":"This dataset includes files from the experiment titled 'OverhangPartX4' pertaining to a three-dimensional (3D) additive manufacturing (AM) build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung and Brandon Lane on June 28, 2019.  The files include the input command files, materials data, in-situ process monitoring data, and metadata.  This data is one of a set of 'AMMT Process Monitoring Datasets', as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project at the National Institute of Standards and Technology (NIST).  Ex-situ part characterization data, including X-ray computed tomography measurements, will be provided as they are made available.  Readers should refer to the AMMT datasets web page for updates (https://www.nist.gov/el/ammt-temps/datasets).","language":["en"],"title":"Process Monitoring Dataset from the Additive Manufacturing Metrology Testbed (AMMT): Overhang Part X4","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Build%20Metadata.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Build%20Metadata.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Part%20Geometry.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Part%20Geometry.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/XYPT%20Commands.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/XYPT%20Commands.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Melt%20Pool%20Camera%20Metadata.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Melt%20Pool%20Camera%20Metadata.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Melt%20Pool%20Camera%20AVIs%20Lagarith.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Melt%20Pool%20Camera%20AVIs%20Lagarith.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Melt%20Pool%20Camera%20TIFF%20Stacks.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Melt%20Pool%20Camera%20TIFF%20Stacks.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Layer%20Camera%20Metadata.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/Layer%20Camera%20Metadata.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/LayerCamera_PNGs.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/LayerCamera_PNGs.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/LayerCameraAfterSpreading.tif","mediaType":"image/tiff"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/LayerCameraAfterSpreading.tif.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/LayerCameraBurned.tif","mediaType":"image/tiff"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/LayerCameraBurned.tif.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/DAQ.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2233/DAQ.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32233","title":"DOI Access for Process Monitoring Dataset from the Additive Manufacturing Metrology Testbed (AMMT): Overhang Part X4"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-05-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Additive manufacturing","Manufacturing:Process measurement and control"],"issued":"2020-08-04","keyword":["additive manufacturing; laser powder bed fusion; selective laser melting;"]},{"identifier":"ark:/88434/mds2-2235","accessLevel":"public","references":["https://www.nist.gov/tpo/reports-and-publications/annual-reports"],"contactPoint":{"hasEmail":"mailto:tpo@nist.gov","fn":"Courtney Silverthorn"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/tpo/reports-and-publications","description":"Every Federal agency that operates or directs one or more Federal laboratories or that conducts research and development is required to prepare and submit an annual report of its technology transfer activities as described in 15 U.S.C. § 3710(f).","language":["en"],"title":"Federal Lab Technology Transfer Database","distribution":[{"accessURL":"https://doi.org/10.18434/M32235","title":"DOI Access for Federal Lab Technology Transfer Database"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-11-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Infrastructure"],"issued":"2020-05-27","keyword":["technology transfer","patent","invention","license"]},{"identifier":"ark:/88434/mds2-2236","accessLevel":"public","contactPoint":{"hasEmail":"mailto:tpo@nist.gov","fn":"Courtney Silverthorn"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2236","description":"This is a searchable database of NIST patents","language":["en"],"title":"NIST Patents","distribution":[{"accessURL":"https://www.nist.gov/patents","format":"Search portal","description":"This is the portal through which NIST patents can be located.","title":"NIST Patents"},{"accessURL":"https://doi.org/10.18434/M32236","title":"DOI Access for NIST Patents"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-04-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Infrastructure"],"issued":"2020-05-27","keyword":["patent"]},{"identifier":"ark:/88434/mds2-2237","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nvlap@nist.gov","fn":"Dana Leaman"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2237","description":"This is a searchable database of NVLAP-accredited laboratories. NVLAP stands for National Voluntary Laboratory Accreditation Program.","language":["en"],"title":"National Voluntary Laboratory Accreditation Program (NVLAP)-Accredited Laboratories","distribution":[{"accessURL":"https://doi.org/10.18434/M32237","title":"DOI Access for National Voluntary Laboratory Accreditation Program (NVLAP)-Accredited Laboratories"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-08-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/PT1S","theme":["Standards","Metrology","Infrastructure"],"issued":"2020-05-14","keyword":["NVLAP","accreditation"]},{"identifier":"ark:/88434/mds2-2238","accessLevel":"public","contactPoint":{"hasEmail":"mailto:standardsinfo@nist.gov","fn":"Rebecca Bruner"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2238","description":"This is a searchable historical collection of standards referenced in regulations - Voluntary consensus standards, government-unique standards, industry standards, and international standards referenced in the Code of Federal Regulations (CFR).","language":["en"],"title":"Standards Incorporated by Reference (SIBR) Database","distribution":[{"accessURL":"https://sibr.nist.gov/","format":"Search portal","description":"A searchable, historical collection of Standards Incorporated by Reference.  No longer being updated.","title":"Standards Incorporated by Reference (SIBR) Database"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-03-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Standards:Documentary standards"],"issued":"2023-04-21","keyword":["Standards Coordination Office","SCO","Incorporated by Reference","IBR","Federal Register","CFR"]},{"identifier":"ark:/88434/mds2-2240","accessLevel":"public","references":["https://doi.org/10.1155/2020/8198767"],"contactPoint":{"hasEmail":"mailto:wesley.garey@nist.gov","fn":"Wesley Garey"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2240","description":"Summary of  simulation results that are used to generate figures in a paper titled \"Performance Evaluation of Proximity Services and Wi-Fi for PublicSafety Mission Critical Voice Application\".  The paper was submitted to the special issue \"Broadband Wireless Access for Rural and Remote Areas\" of journal \"Wireless Communications and Mobile Computing\" on December 12, 2019.","language":["en"],"title":"Performance Evaluation of Proximity Services and Wi-Fi for PublicSafety Mission Critical Voice Application","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2240/dataset.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Performance Evaluation of Proximity Services and Wi-Fi for Public Safety Mission Critical Voice Application Dataset"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2240/dataset.zip","format":"Compressed file of CSV files","description":"An archive of CSV files that includes the data from the \"Performance Evaluation of Proximity Services and Wi-Fi for Public Safety Mission Critical Voice Application\" analysis.","mediaType":"application/x-zip-compressed","title":"Performance Evaluation of Proximity Services and Wi-Fi for Public Safety Mission Critical Voice Application Dataset"},{"accessURL":"https://doi.org/10.18434/M32240","title":"DOI Access for Performance Evaluation of Proximity Services and Wi-Fi for PublicSafety Mission Critical Voice Application"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-12-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research"],"issued":"2020-07-16","keyword":["Mission Critical Push to Talk (MCPTT)","Mission Critical Voice (MCV)","Access Time","Coverage Range","Proximity Services","Off-Network Mode","Direct Mode","Wi-Fi"]},{"identifier":"ark:/88434/mds2-2242","accessLevel":"public","references":["https://www.nist.gov/publications/graph-database-approach-wireless-iiot-work-cell-performance-evaluation"],"contactPoint":{"hasEmail":"mailto:karl.montgomery@nist.gov","fn":"Karl Montgomery"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2242","description":"The work-cell is an essential industrial environment for testing wireless communication techniques in factory automation processes. A graph database approach to storing and analyzing network performance data from a manufacturing factory work-cell is introduced. A robotic testbed performs a pick-and-place task using two collaborative grade robot arms, machine emulators, and wireless communication devices. A graph database is implemented to capture network data and operational event data among the actors within the testbed. Using a proposed schema, the database is then populated with events from the testbed and the resulting graph is constructed. Query commands are then presented to examine and analyze network performance and relationships within the actors of the network. The resulting data from the experiments conducted are included in this dataset.","language":["en"],"title":"Measurement and Processed Data From A Graph Database Approach to Wireless IIoT Work-cell Performance Evaluation","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2242/CoaxChannel_GDB_Data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Measurement and Processed Data From a Graph Database Approach to Wireless IIoT Work-cell Performance"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2242/CoaxChannel_GDB_Data.zip","format":".csv and .pcap files","description":"The work-cell is an essential industrial environment for testing wireless communication techniques in factory automation processes. A graph database approach to storing and analyzing network performance data from a manufacturing factory work-cell is introduced. A robotic testbed performs a pick-and-place task using two collaborative grade robot arms, machine emulators, and wireless communication devices. A graph database is implemented to capture network data and operational event data among the actors within the testbed. Using a proposed schema, the database is then populated with events from the testbed and the resulting graph is constructed. Query commands are then presented to examine and analyze network performance and relationships within the actors of the network. The resulting data from the experiments conducted are included in this dataset.","mediaType":"application/x-zip-compressed","title":"Measurement and Processed Data From a Graph Database Approach to Wireless IIoT Work-cell Performance"},{"accessURL":"https://doi.org/10.18434/M32242","title":"DOI Access for Measurement and Processed Data From A Graph Database Approach to Wireless IIoT Work-cell Performance Evaluation"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-05-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)","Manufacturing:Factory communications","Manufacturing:Factory operations planning and control"],"issued":"2020-07-26","keyword":["industrial wireless","factory automation","testbed","measurement","instrumentation","graph database"]},{"identifier":"ark:/88434/mds2-2243","accessLevel":"public","references":["https://doi.org/10.1109/LWC.2020.3020014"],"contactPoint":{"hasEmail":"mailto:thao.t.nguyen@nist.gov","fn":"Thao T. Nguyen"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2243","description":"In a shared spectrum environment, as is the case in the 3.5 GHz Citizens Broadband Radio Service (CBRS), the secondary users with lower priority are managed by independent spectrum access systems (SASs) in order to protect the incumbents with higher priority from interference. The interference protection is guaranteed in terms of a percentile of the aggregate interference power. The current practice requires each SAS to obtain a global snapshot of interference and use a common algorithm to manage it. We present a simplified method to permit each SAS to independently manage its users while still meeting overall aggregate interference protection requirements. The data include statistical upper bound on aggregate interference for some known distributions, which is the core idea of the proposed method. The data also include numerical results of using the proposed interference protection criterion in terms of two metrics, the total number of users moved from the channel in order to protect the incumbent (i.e., the size of the move list) and the realized aggregate interference of all co-channel users at the incumbent. The data is associated with the letter, \"Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment,\" M. R. Souryal and T. T. Nguyen, in IEEE Wireless Communication Letters, Jan. 2021.","language":["en"],"title":"Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2243/Figure2_ratio_perctnl_upper_bound.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2243/Figure2_ratio_perctnl_upper_bound.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2243/Figure4_moveList_aggInterf.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Ratio of p_th percentile upper bound"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2243/Figure4_moveList_aggInterf.xlsx","description":"Data used to plot Figure 2 and Figure 4.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Ratio of p_th percentile upper bound, move list size, and aggregate interference"},{"accessURL":"https://doi.org/10.18434/M32243","title":"DOI Access for Independent Calculation of Move Lists for Incumbent Protection in a Multi-SAS Shared Spectrum Environment"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-01-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Statistical analysis","Advanced Communications:Wireless (RF)"],"issued":"2021-01-28","keyword":["Aggregate interference","CBRS","incumbent protection","spectrum access system","spectrum sharing."]},{"identifier":"ark:/88434/mds2-2244","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ian.bell@nist.gov","fn":"Ian Bell"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2244","description":"Initial configurations from equilibration needed to reproduce simulation results","language":["en"],"title":"Supporting Information to accompany: Excess Entropy Scaling in Supercooled Binary Mixtures","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2244/ExcessScalingData.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Zip file of equilibrated configurations"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2244/ExcessScalingData.zip","mediaType":"application/x-zip-compressed","title":"Zip file of equilibrated configurations"},{"accessURL":"https://doi.org/10.18434/M32244","title":"DOI Access for Supporting Information to accompany: Excess Entropy Scaling in Supercooled Binary Mixtures"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-05-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Atomic, molecular, and quantum"],"issued":"2020-07-09","keyword":["entropy scaling; transport properties; molecular dynamics"]},{"identifier":"ark:/88434/mds2-2245","accessLevel":"public","contactPoint":{"hasEmail":"mailto:raymond.plante@nist.gov","fn":"Raymond Plante"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2245","description":"We share an XML Schema for describing data collected from a laboratory experiment in an organized way that attempts to communicate scientific intent and the phased process of data creation. It was developed as part of laboratory information management system (LIMS) prototyping effort by a collaboration at NIST between the NEXUS Microscopy facility (managed within the Materials Science and Engineering Division) and the Material Measurement Lab's Office of Data and Informatics. When a facility user reserves and then operates one of the facility microscopes, the LIMS system automatically gathers metadata from the reservation calendar, the files created by the instrument, and potentially other sources and organizes them into an XML document describing what was done. The system uses the NIST Configurable Data Curation System (CDCS) to display the document as a scientist-oriented summary of the microscopy experiment. The schema is fairly general and free of specifics tied to microscopy (apart perhaps from a reference to samples). This schema is expected to be a useful prototype not only across LIMS efforts within NIST but possibly more broadly across the global microscopy research community. The XML schema is fully documented internally, including definitions of all elements and types.","language":["en"],"title":"Nexus-Experiment: an XML schema for describing data collected from electron microscopes","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2245/nexus-experiment-v1.0.xsd","format":"XML Schema definition document","description":"The schema used to describe experiments from the NIST NEXUS Microscopy facility","mediaType":"application/xml","title":"NEXUS Experiment XML Schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2245/example_NexusLIMS_record.xml.sha256","mediaType":"text/plain","title":"SHA256 File for Example Nexus Experiment description file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2245/example_NexusLIMS_record.xml","format":"XML instance file","description":"This is an example XML instance file describing an experiment that complies with the Nexus Experiment schema.","mediaType":"text/xml","title":"Example Nexus Experiment description file"},{"accessURL":"https://doi.org/10.18434/M32245","title":"DOI Access for Nexus-Experiment: an XML schema for describing data collected from electron microscopes"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2245/nexus-experiment-v1.0.xsd.sha256","mediaType":"text/plain","title":"SHA256 File for NEXUS Experiment XML Schema"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-02-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Data and informatics","Materials","Metrology"],"issued":"2020-08-31","keyword":["XML Schema","data curation","metadata","microscopy","TEM","SEM","laboratory information management system (LIMS)","laboratory notebook"]},{"identifier":"ark:/88434/mds2-2246","accessLevel":"public","references":["https://doi.org/10.6028/NIST.SP.800-53r4","https://doi.org/10.6028/NIST.SP.800-70r4","https://doi.org/10.6028/NIST.SP.800-219"],"contactPoint":{"hasEmail":"mailto:macadmin@nist.gov","fn":"Bob Gendler"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2246","description":"The macOS security compliance project is an open source effort to provide a programmatic approach to generating security guidance. This project can be used to create customized security baselines of technical security controls by leveraging a library of atomic actions which are mapped to compliance requirements in existing security guides or used to develop customized guidance. Through the use of a library of atomic actions that enhance security, and mapping them back to existing guides and policies, a single project can support multiple security guides and regulated industry policies while also allowing for documentation and QA to be uniformly managed through a single effort. This approach simplifies, and radically accelerates, the updating of annual security guidance through a unification and standardization of effort.Rationale for this project:Normalize and accelerate annual adoption of OS/Hardware by having guidance available to meet the needs of new operating systems on releaseReduce worldwide effort in creating annual guidance by unifying and consolidating compliance efforts into a single projectDevelop a methodology to foster collaboration between baseline authors, reducing overhead and redundancyUnify approach in setting controlsProvide MDM/EMM/security/audit vendors and Apple insight into customer hardening needsImportant note: This project is a programmatic approach to security policy and can produce output content to be used IN CONJUNCTION with management and security tools to achieve compliance.","language":["en"],"title":"macOS Security Compliance Project","license":"https://github.com/usnistgov/macos_security/blob/master/LICENSE.md","bureauCode":["006:55"],"modified":"2020-06-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Cybersecurity"],"issued":"2020-06-15","keyword":["macOS","mac","security","compliance","SCAP","baseline","FISMA","STIG","OVAL","XCCDF"]},{"identifier":"ark:/88434/mds2-2249","accessLevel":"public","references":["https://doi.org/10.6028/NIST.AMS.200-5"],"contactPoint":{"hasEmail":"mailto:douglas.thomas@nist.gov","fn":"Douglas Thomas"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/services-resources/software/smart-investment-tool","description":"This tool calculates metrics for investment analysis documented in NIST Advanced manufacturing Series 200-5. It calculates net present value, internal rate of return, and payback period along with executing sensitivity analysis using Monte Carlo techniques. The tool helps to identify the most economical projects/investments. For instance, it could be used to identify the most economical heating and cooling system or it might be used to rank a set of potential investments.","language":["en"],"title":"Smart Investment Tool","distribution":[{"accessURL":"https://doi.org/10.18434/M32249","title":"DOI Access for Smart Investment Tool"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-06-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Statistical analysis","Mathematics and Statistics:Modeling and simulation research","Manufacturing:Factory operations planning and control","Manufacturing:Manufacturing systems design and analysis","Manufacturing:Lean manufacturing","Manufacturing"],"issued":"2020-08-07","keyword":["net present value","internal rate of return","payback","investment","monte carlo","manufacturing","cost effective","economics"]},{"identifier":"ark:/88434/mds2-2251","accessLevel":"public","contactPoint":{"hasEmail":"mailto:srms@nist.gov","fn":"David R. Black"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2251","description":"The data from this instrument consists of sets of measurements of Xray intensity as a function of diffraction angle.  Almost all of it is collected using a position-sensitive detector (PSD), and is stored in CIF format.","language":["en"],"title":"Diffraction Data for SRM 640f","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2251/srm640f_cifs_20200601a.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Diffraction Data for SRM 640f"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2251/srm640f_cifs_20200601a.zip","description":"X-ray diffraction data expressed as intensity as a function of angle","mediaType":"application/x-zip-compressed","title":"Diffraction Data for SRM 640f"},{"accessURL":"https://doi.org/10.18434/M32251","title":"DOI Access for Diffraction Data for SRM 640f"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-06-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization"],"issued":"2020-06-22","keyword":["Xray Powder Diffraction; Diverging Beam Diffractometer; Powder Diffraction SRM"]},{"identifier":"ark:/88434/mds2-2252","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2095"],"contactPoint":{"hasEmail":"mailto:william.dols@nist.gov","fn":"William Stuart Dols"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/services-resources/software/fatima","description":"The web-based tool Fate and Transport of Indoor Microbiological Aerosols (FaTIMA) allows the determination of the fate of indoor microbiological aerosols associated with ventilation, filtration, deposition, and inactivation mechanisms. FaTIMA provides a representation of a single, well-mixed zone that is served by a mechanical ventilation system and incorporates source and removal mechanisms for an aerosol with a single, user-defined representative particle size. The simple mechanical ventilation system model allows specification of supply, return, and outdoor air intake rates to represent either a positive, negative or balanced ventilation system. Aerosol sources are provided to enable any combination of continuous, e.g., breathing-related emissions, or intermittent, e.g., coughing-related emissions. Aerosol removal mechanisms include filters within the ventilation system, room air cleaners, and deposition onto floors, walls, and ceilings. Simulations can be run for a 24-h period, with the results including the time history of the airborne concentration and surface loading, as well as integrated exposure of an occupant. 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The tests were conducted at 11 different temperatures, and at both quasi-static and dynamic loading rates. 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Certified values of thermal conductivity are valid for mean temperatures from 280 K to 360 K and barometric pressures from 60 kPa to sea-level pressure (101.325 kPa).","language":["en"],"title":"SRM 1450e Fibrous Glass Board","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2256/SRM1450e_Data%20.csv.sha256","mediaType":"text/plain","title":"SHA256 File for SRM 1450e Fibrous Glass Board Certification Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2256/SRM1450e_Data%20.csv","format":"comma delimited","description":"Thermal characterization of NIST SRM 1450e Fibrous Glass Board","mediaType":"application/vnd.ms-excel","title":"SRM 1450e Fibrous Glass Board Certification Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2256/SRM1450e_Readme.txt.sha256","mediaType":"text/plain","title":"SHA256 File for SRM1450e_Readme.txt"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2256/SRM1450e_Readme.txt","format":"space delimited","description":"Data dictionary for file: SRM1450e_Data.csv","mediaType":"text/plain","title":"SRM1450e_Readme.txt"},{"accessURL":"https://doi.org/10.18434/M32256","title":"DOI Access for SRM 1450e Fibrous Glass Board"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2019-11-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization","Buildings and Construction:Building materials","Standards:Reference materials"],"issued":"2020-07-01","keyword":["bulk density","certified reference material","fit","guarded hot plate","high density molded fibrous glass board","model","regression analysis","standard reference material","thermal conductivity","thermal insulation"]},{"identifier":"ark:/88434/mds2-2257","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2099"],"contactPoint":{"hasEmail":"mailto:brandon.lane@nist.gov","fn":"Brandon Lane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2257","description":"This dataset includes a Microsoft Excel spreadsheet (*.xlsx file)  provided as supplemental material for the NIST Technical Note (TN-2099) publication titled \"Quasi-Static Position Calibration of the Galvanometer Scanner on the Additive Manufacturing Metrology Testbed\".  The file contains two tabs, titled \"Pre-Compensation\" and \"Post-Compensation\", which provides example measurement data and calculations pertaining to the calibration procedures described in the publication.  The spreadsheets also include multiple plots that are used to calculate fit lines, calibration constants, and calibration errors.","language":["en"],"title":"Calculation Sheet for Quasi-Static Position Calibration of the Galvanometer Scanner on the Additive Manufacturing Metrology Testbed","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2257/GalvoCalibrationCalculationSheet.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Calculation Sheet for Quasi-Static Position Calibration of the Galvanometer Scanner on the Additive Manufacturing Metrology Testbed"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2257/GalvoCalibrationCalculationSheet.xlsx","format":"Microsoft Excel Workbook (*.xlsx)","description":"This dataset includes a Microsoft Excel spreadsheet (*.xlsx file)  provided as supplemental material for the NIST Technical Note (TN-2099) publication titled \"Quasi-Static Position Calibration of the Galvanometer Scanner on the Additive Manufacturing Metrology Testbed\".  The file contains two tabs, titled \"Pre-Compensation\" and \"Post-Compensation\", which provides example measurement data and calculations pertaining to the calibration procedures described in the publication.  The spreadsheets also include multiple plots that are used to calculate fit lines, calibration constants, and calibration errors.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Calculation Sheet for Quasi-Static Position Calibration of the Galvanometer Scanner on the Additive Manufacturing Metrology Testbed"},{"accessURL":"https://doi.org/10.18434/M32257","title":"DOI Access for Calculation Sheet for Quasi-Static Position Calibration of the Galvanometer Scanner on the Additive Manufacturing Metrology Testbed"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-06-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Additive manufacturing"],"issued":"2020-06-29","keyword":["Additive Manufacturing","Calibration","Galvanometer","Laser Powder Bed Fusion"]},{"identifier":"ark:/88434/mds2-2258","accessLevel":"public","references":["https://www.sciencedirect.com/science/article/pii/S0379711221000825","https://link.springer.com/article/10.1007/s10694-020-01022-9"],"contactPoint":{"hasEmail":"mailto:waicheong.tam@nist.gov","fn":"Andy Tam"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2258","description":"This data set provides heat detector temperatures in a single story three-compartment structure. 1000 sets of detector temperatures are generated using CData [1]. The data set are obtained based on simulation runs with various t-squared fires. The peak heat release rate and time to peak range from approximately 50 kW to 2200 kW and from 50 s to 1400 s, respectively. A detailed description of this work can be found in Ref. [2]. [1] Tam, W.C., Fu, E.Y., Peacock, R., Reneke, P., Wang, J., Li, J. and Cleary, T., 2020. Generating synthetic sensor data to facilitate machine learning paradigm for prediction of building fire hazard. Fire Technology, pp.1-22. [2] Wang, J., Tam, W.C., Jia, Y., Peacock, R., Reneke, P., Fu, E.Y. and Cleary, T., 2021. P-Flash - A machine learning-based model for flashover prediction using recovered temperature data. Fire Safety Journal, 122, p.103341.","language":["en"],"title":"Synthetic Temperature Data for P-Flash - A Machine Learning-Based Model for Flashover Prediction Using Recovered Temperature Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2258/P-Flash%20all%20data%202020-6-26.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Heat Detector Temperature Data and Supplemental Material"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2258/P-Flash%20all%20data%202020-6-26.xlsx","format":"Excel spreadsheet with 4 tabs","description":"Schematic of simulation setup, list of simulation runs and conditions, specification of heat detectors, and temperature data of all heat detectors from 1000 simulation runs.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Heat Detector Temperature Data and Supplemental Material"},{"accessURL":"https://doi.org/10.18434/M32258","title":"DOI Access for Synthetic temperature data for development of a machine learning based flashover prediction model"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-03-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Fire:Fire detection","Fire:Fire fighting"],"issued":"2020-09-04","keyword":["Machine learning; Synthetic temperature data; Flashover occurrence prediction; Smart firefighting"]},{"identifier":"ark:/88434/mds2-2259","accessLevel":"public","contactPoint":{"hasEmail":"mailto:david.ross@nist.gov","fn":"David J. Ross"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2259","description":"This data table contains the processed data for the LacI genotype-phenotype landscape, a quantitative measurement of the phenotype for approximately 60,000 variants of the E. coli LacI protein. The table includes, for example, the DNA barcode sequencing counts and the estimated fitness for each variant across 24 different chemical environments. It also includes the estimated dose-response curves for each variant under induction with IPTG.","language":["en"],"title":"LacI protein genotype-phenotype landscape data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2259/LacI%20landscape%20data.hdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2259/Example%20code_Open%20LacI%20Data%20Table.ipynb","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2259/Example%20code_Open%20LacI%20Data%20Table.ipynb.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2259/LacI%20landscape%20data%20columns%20description.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2259/LacI%20landscape%20data%20columns%20description.csv.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32259","title":"DOI Access for LacI protein genotype-phenotype landscape data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2259/LacI%20landscape%20data.hdf","mediaType":"application/x-hdf","title":"Lacl landscape data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-06-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Cell biology","Bioscience:Proteomics","Bioscience:Engineering/synthetic biology"],"issued":"2020-07-10","keyword":["allostery","E. coli","Lac repressor","fitness landscape","genotype-phenotype relationships","DNA barcode sequencing","engineering biology"]},{"identifier":"ark:/88434/mds2-2261","accessLevel":"public","contactPoint":{"hasEmail":"mailto:joseph.conny@nist.gov","fn":"Joseph M. Conny"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2261","description":"This dataset contains measurements of organic carbon, elemental carbon, and total carbon in atmospheric particulate matter with the thermal-optical carbon analyzer. The data are associated with the following publication: C.D. Grimes, J.M. Conny, R.R. Dickerson, 2020, \"Evaluation of Thermal-Optical Analysis Using an Aqueous Binary Mixture,\" Atmospheric Environment, in press. Below is the abstract for the publication.\n\nThermal-Optical Analysis (TOA), a commonly implemented technique used to measure the amount of particulate carbon in the atmosphere or deposited on a filter substrate, distinguishes organic carbon (OC) from elemental carbon (EC) through the monitoring of laser light, heating, and measuring evolved carbon.  Here, we present a method to characterize the TOA transmission method with an aqueous binary mixture containing EC and OC that can easily be deposited onto a filter at low volumes. Known amounts of EC and OC were deposited onto a quartz-fiber filter and analyzed with different temperature protocols.  Results with the NIST-EPA-C temperature protocol agreed with the reference values to better than 2 % for EC, OC, total carbon (TC), and EC/TC. Uncertainty in TC among all temperature protocols was less than 5 % of the reference value while all protocols had EC/TC ratios with an uncertainty less than 10 %.","language":["en"],"title":"Evaluation of Thermal Optical Analysis Using an Aqueous Binary Mixture","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2261/All_runs_TOA.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Measurements of Particulate Organic Carbon, Elemental Carbon, and Total Carbon by Thermal-Optical Transmission Analysis"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2261/All_runs_TOA.xlsx","description":"The Excel file contains carbon concentrations from quartz fiber filters as micrograms of carbon per square centimeter of filter material. EC, OC, and TC are elemental carbon, organic carbon, and total carbon, respectively.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Measurements of Particulate Organic Carbon, Elemental Carbon, and Total Carbon by Thermal-Optical Transmission Analysis"},{"accessURL":"https://doi.org/10.18434/M32261","title":"DOI Access for Evaluation of Thermal Optical Analysis Using an Aqueous Binary Mixture"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-06-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Standards:Reference materials","Environment:Environmental health","Environment:Air / water / soil quality","Chemistry:Analytical chemistry"],"issued":"2020-07-10","keyword":["black carbon","elemental carbon","organic carbon","organic carbon aerosol","thermal optical transmission analysis","TOT"]},{"identifier":"ark:/88434/mds2-2262","accessLevel":"public","contactPoint":{"hasEmail":"mailto:william.bernstein@nist.gov","fn":"William Z. Bernstein"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/services-resources/software/step-x3d-translator","description":"The NIST STEP to X3D Translator is open-source software that translates a STEP (ISO 10303) Part 21 file (.stp or .step) to an X3D (ISO/IEC 19776) file (.x3d) or X3DOM file (.html). Developed at the National Institute of Standards and Technology (NIST), the software is based on the Open CASCADE STEP Processor and written in C++.","language":["en"],"title":"NIST STEP to X3D Translator (STP2X3D)","distribution":[{"accessURL":"https://github.com/usnistgov/STP2X3D","format":"text/html","title":"GitHub site for NIST STEP to X3D Translator (STP2X3D)"},{"accessURL":"https://doi.org/10.18434/M32262","title":"DOI Access for NIST STEP to X3D Translator (STP2X3D)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-07-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Visualization research","Standards","Manufacturing:Systems integration","Manufacturing:Product data","Manufacturing:Interoperability in manufacturing","Manufacturing"],"conformsTo":"https://www.web3d.org/documents/specifications/19775-1/V3.2/Part01/Architecture.html","issued":"2021-04-23","keyword":["X3D","X3DOM","STEP","translation","3D models","visualization","STEP File Analyzer and Viewer","engineering design","smart manufacturing"]},{"identifier":"ark:/88434/mds2-2263","accessLevel":"public","references":["https://doi.org/10.1016/j.jqsrt.2020.107197"],"contactPoint":{"hasEmail":"mailto:joseph.conny@nist.gov","fn":"Joseph M. Conny"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2263","description":"The project that produced these data involved the analysis and modeling of atmospheric Asian dust particles and background marine air particles collected in Hawaii, USA at the Mauna Loa Observatory (MLO) of the National Oceanic and Atmospheric Administration. Individual heterogeneous dust particles were analyzed with focused ion-beam scanning electron microscopy and energy-dispersive X-ray spectroscopy. Three-dimensional spatial and composition models of the particles from focused ion-beam tomography were used in the calculation of optical properties by the discrete dipole approximation method with incident light at 589 nm. Optical properties included the phase function, extent of linear polarization, scattering, backscattering, and extinction cross sections, and the backscatter fraction. In addition, the project involved optical property calculations for particles as geometric shapes with volumes equivalent to those of the MLO particles. Shapes modeled for each particle included spheres, spheroids, ellipsoids, cubes, square prisms, rectangular prisms, tetrahedra, and triangular pyramids. The representation of particles as geometric shapes is an important component of algorithms used in remote sensing (satellite-based and ground-based) for determining how atmospheric dust affects climate. \n\n\nThe dataset consists of Microsoft Excel and Word files, text files, and bitmap image files. The Asian dust particles in the dataset are labeled: CaMg 1D, CaMg 2N, CaMg 3D, CaMg 4N1, Ca-rich 1D, Ca-rich 2N, Ca-rich 3D, Ca-rich 4N1, Ca-rich 4N2. The background marine air particles are labeled: Ca-S 1D, Ca-S 2N, Ca-S 3D, Ca-S 4N.\n\n\nData are contained in seven folders arranged by the following topics:\n\n\n1 -- Particle compositions by FIB-SEM-EDX and volumes of material phases within particles (folder: 1_Particle_Compositions_Volumes);\n2 -- Spatial and optical parameters for optical modeling of particles and geometric shapes (folder: 2_Particles_Shapes_Spatial_Optical_Parameters (and subfolders));\n3 -- Complex refractive indices for particles and shapes based on Maxwell Garnett average dielectric function (folder: 3_Complex_RIs_Maxwell_Garnett (and subfolders));\n4 -- Results from discrete dipole approximation modeling software DDSCAT ver. 7.3 (folder: 4_DDSCAT_Scattering_Output (and subfolders));\n5 -- Mueller scattering matrix elements (folder: 5_Matrix_Elements (and subfolders));\n6 -- Root-mean-square calculations for phase function and degree of linear polarization (folder: 6_PhaseFunction_LinearPolarization_RMS);\n7 -- Calculations for the backscatter fraction (folder: 7_Backscatter_Fraction)","language":["en"],"title":"Optical Modeling of Single Asian Dust and Marine Air Particles: A Comparison with Geometric Particle Shapes for Remote Sensing","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2263/Dataset_Catalog.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2263/Dataset_Catalog.pdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2263/NISTdataset_ModelingSingleAsianDustParticlesAndGeometricShapes.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2263/NISTdataset_ModelingSingleAsianDustParticlesAndGeometricShapes.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32263","title":"DOI Access for Optical Modeling of Single Asian Dust and Marine Air Particles: A Comparison with Geometric Particle Shapes for Remote Sensing"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-05-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Spectroscopy","Physics:Optical physics","Environment:Environmental health","Environment:Air / water / soil quality"],"issued":"2020-07-20","keyword":["atmospheric aerosol","Asian dust","light scattering","light absorption","radiative forcing","climate change","focused ion-beam scanning electron microscopy","FIB-SEM","energy-dispersive X-ray spectroscopy","EDX","focused ion-beam tomography","optical property modeling","discrete dipole approximation method","environment and climate"]},{"identifier":"ark:/88434/mds2-2265","accessLevel":"public","contactPoint":{"hasEmail":"mailto:edward.garboczi@nist.gov","fn":"Edward Garboczi"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2265","description":"Supplementary Data for the paper \"Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders\" by E.J. Garboczi and N. Hrabe. Contains all the data and programs described in this paper to generate the results discussed in the paper. Also contains all the results that are generated by the data and programs, with which the user can compare to check their results.","language":["en"],"title":"Supplementary Data for the paper \"Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders\" by E.J. Garboczi and N. Hrabe","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/OriA-0500.tiff.sha256","mediaType":"text/plain","title":"SHA256 File for Tiff image of k=500 slice of 1st particle microstructure"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/OriA-0500.tiff","format":"8 bit tiff image","description":"Tiff image of k=500 slice of 1st particle microstructure","mediaType":"image/tiff","title":"Tiff image of k=500 slice of 1st particle microstructure"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/part-lwt-listnum-unitvector.f.sha256","mediaType":"text/plain","title":"SHA256 File for MPI Fortran for processing SH anm files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/part-lwt-listnum-unitvector.f","format":"Fortran 90 code, ascii","description":"MPI Fortran for processing SH anm files","mediaType":"application/octet-stream","title":"MPI Fortran for processing SH anm files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/PixA-0500.tiff.sha256","mediaType":"text/plain","title":"SHA256 File for k=500 slice of 1st particle microstructure after segmentation"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/LWT-dist-single-multiple.f.sha256","mediaType":"text/plain","title":"SHA256 File for Fortran 77 file to process and make graph files for 3D particle data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/LWT-dist-single-multiple.f","format":"Fortran 77 program listing, ascii","description":"Fortran 77 file to process and make graph files for 3D particle data. Should be compiled in double precision.","mediaType":"application/octet-stream","title":"Fortran 77 file to process and make graph files for 3D particle data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/nonSH-lwt-un-scalar.f.sha256","mediaType":"text/plain","title":"SHA256 File for Fortran 77 program to compute 2D projections for nonSH particles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/nonSH-lwt-un-scalar.f","format":"Fortran 77 program ascii text","description":"Fortran 77 program to compute 2D projections for nonSH particles","mediaType":"application/octet-stream","title":"Fortran 77 program to compute 2D projections for nonSH particles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/PixA-0500.tiff","format":"8bit tiff image","description":"k=500 slice of 1st particle microstructure after segmentation","mediaType":"image/tiff","title":"k=500 slice of 1st particle microstructure after segmentation"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/porosity-analyze.f.sha256","mediaType":"text/plain","title":"SHA256 File for analyzes porosity files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/porosity-analyze.f","format":"Fortran 77 program, ASCII","description":"analyzes porosity files, for both SH and nonSH particles","mediaType":"application/octet-stream","title":"analyzes porosity files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/pp-Otsu.f.sha256","mediaType":"text/plain","title":"SHA256 File for Main code to analyze particle microstructures and generate particles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/pp-Otsu.f","format":"Fortran 77 code, ASCII","description":"Main code to analyze particle microstructures and generate particles","mediaType":"application/octet-stream","title":"Main code to analyze particle microstructures and generate particles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/proj2D-nonSH-perim-Feret.f.sha256","mediaType":"text/plain","title":"SHA256 File for Generates 2D projections for nonSH particles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/proj2D-nonSH-perim-Feret.f","format":"Fortran 77 code, ASCII","description":"Generates 2D projections for nonSH particles","mediaType":"application/octet-stream","title":"Generates 2D projections for nonSH particles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/proj-mpi-SH-rot.f.sha256","mediaType":"text/plain","title":"SHA256 File for Generates 2D projections for SH particles, MPI code"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/proj-mpi-SH-rot.f","format":"Fortran 90 code, MPI, ASCII text","description":"Generates 2D projections for SH particles, MPI code","mediaType":"application/octet-stream","title":"Generates 2D projections for SH particles, MPI code"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/tiff2array.c.sha256","mediaType":"text/plain","title":"SHA256 File for C program to stack reconstructed  image files into 3D ASCII microstructure"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/tiff2array.c","format":"C code, ASCII text","description":"C program to stack reconstructed  image files into 3D ASCII microstructure. Compile in C.","mediaType":"application/octet-stream","title":"C program to stack reconstructed  image files into 3D ASCII microstructure"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/VRML-select-multi-single.f.sha256","mediaType":"text/plain","title":"SHA256 File for Generates list of 100 SH and nonSH particles for L/T cutoff determination"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/VRML-select-multi-single.f","format":"Fortran 77 code, ASCII","description":"Generates list of 100 SH and nonSH particles for L/T cutoff determination. Generates 10 particles per 0.1 increments for L/T (i.e. 1 < L/T < 1.1, 1.1 < L/T <1.2, up to L/T =2).","mediaType":"application/octet-stream","title":"Generates list of 100 SH and nonSH particles for L/T cutoff determination"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/anm.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/anm.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-H.mic.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-H.mic.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-I.mic.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-I.mic.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/nonSH.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/nonSH.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/VRML-files-for-LT-cutoff-determination.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/VRML-files-for-LT-cutoff-determination.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/wrl.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/wrl.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-A.mic.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-A.mic.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-B.mic.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-B.mic.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-C.mic.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-C.mic.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-D.mic.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-D.mic.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-E.mic.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-E.mic.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-F.mic.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-F.mic.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-G.mic.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2265/ASTM-AMPM2-G.mic.gz.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/M32265","title":"DOI Access for Supplementary Data for the paper \"Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders\" by E.J. Garboczi and N. Hrabe"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM2-LT-cutoff.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for FIle to find L/T cutoff for SH and nonSH particles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM2-LT-cutoff.xlsx","format":"Excel file","description":"The information in this file is used to determine the L/T cutoff values for SH and for nonSH particles, in order to be able classify all particles into SnS and NS particles.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"FIle to find L/T cutoff for SH and nonSH particles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-particles-data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for compressed auxiliary data output file from operation of pp-Otsu.f"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-particles-data.zip","format":"compressed ascii text file","description":"compressed auxiliary data output file from operation of pp-Otsu.f","mediaType":"application/x-zip-compressed","title":"compressed auxiliary data output file from operation of pp-Otsu.f"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-SH-internal-porosity.dat.sha256","mediaType":"text/plain","title":"SHA256 File for list of porosities for SH particles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-SH-internal-porosity.dat","format":"ascii text","description":"list of porosities for SH particles plus other information","mediaType":"application/octet-stream","title":"list of porosities for SH particles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-sysconfig.dat.sha256","mediaType":"text/plain","title":"SHA256 File for input data file for pp-Otsu.f"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-sysconfig.dat","format":"ascii text","description":"input data file for pp-Otsu.f. Contains microstructure file name roots and file size information and information about the run","mediaType":"application/octet-stream","title":"input data file for pp-Otsu.f"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/gauss120.dat.sha256","mediaType":"text/plain","title":"SHA256 File for list of Gaussian weights and points for N=120"},{"format":"ascii numbers","downloadURL":"https://data.nist.gov/od/ds/mds2-2265/gauss120.dat","description":"list of Gaussian weights and points for N=120","mediaType":"application/octet-stream","title":"list of Gaussian weights and points for N=120"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/area-volume-dist.f","format":"Fortran 77 program, to be compiled double precision","description":"Fortran 77 program to process 2D projection files","mediaType":"application/octet-stream","title":"Fortran 77 program to process 2D projection files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/area-volume-dist.f","mediaType":"text/plain","title":"Fortran 77 program to process 2D projection files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-intern-poros-analysis.txt.sha256","mediaType":"text/plain","title":"SHA256 File for particle porosity data output"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-intern-poros-analysis.txt","format":"ASCII text","description":"3D particle porosity data output file, for SnS and NS particles and both combined","mediaType":"text/plain","title":"particle porosity data output"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-intern-poros-list.txt.sha256","mediaType":"text/plain","title":"SHA256 File for list of particle porosities"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-intern-poros-list.txt","format":"ascii text, two columns","description":"All porosities for SnS and NS particles","mediaType":"text/plain","title":"list of particle porosities"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-nonSH-internal-porosity.dat.sha256","mediaType":"text/plain","title":"SHA256 File for list of porosities for nonSH particles"},{"format":"ascii text, columns","downloadURL":"https://data.nist.gov/od/ds/mds2-2265/ASTM-AMPM-2-nonSH-internal-porosity.dat","description":"list of porosities for nonSH particles plus other information","mediaType":"application/octet-stream","title":"list of porosities for nonSH particles"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-06-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Numerical methods and software","Materials:Modeling and computational material science","Materials:Metals","Materials:Materials characterization","Materials:Concrete/cement","Materials:Composites","Materials:Ceramics","Manufacturing:Additive manufacturing"],"issued":"2020-10-13","keyword":["additive manufacturing","metal powder","laser powder bed fusion","particle shape analysis","X-ray computed tomography. spherical harmonics","image analysis","powder analysis","powder particles","titanium alloy","Ti64"]},{"identifier":"ark:/88434/mds2-2269","accessLevel":"public","references":["https://www-s.nist.gov/srmors/view_detail.cfm?srm=1979","https://doi.org/10.6028/jres.125.020"],"contactPoint":{"hasEmail":"mailto:david.black@nist.gov","fn":"David R. Black"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2269","description":"These datasets are the high-resolution data collected at the APS 11-BM synchrotron beamline, and used to certify the microstructural characteristics of the two components (15 nm and 60 nm nominal microscrystallites) of SRM 1979. They are presented as CIF files containing the data as collected and processed by the 11BM software to make uniformly-spaced angle step data files.  Each CIF contains the 'raw' data from 11BM, the same data with an angular scale corrected by refinement of SRM660c data collected in the same run, and finally a computed data set from a Rietveld structural fit to the results. There are 9 CIF files in the ZIP archive.  Each file is names xxxx_yy_nm_zzz.cif where xxxx is the run number at APS under which the data were collected, yy is either 15 or 60 for the nominal crystallite size, and zzz is the bottle number form the random sampling of the SRM used to take this measurement.","language":["en"],"title":"Diffraction data for SRM 1979","distribution":[{"accessURL":"https://doi.org/10.18434/M32269","title":"DOI Access for Diffraction data for SRM 1979"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2269/srm_1979_cifs_20201110a.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Zip file containing CIF datasets for SRM 1979 from synchrotron"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2269/srm_1979_cifs_20201110a.zip","format":"ZIP file","description":"Each CIF file contains the data for a specific sample, presented 3 ways. The first set is the data, as measured and converted to a continuous scan by the 11BM software.  The second set is the measured data, but with a corrected angular scale.  The third is a computed pattern from Topas, on the corrected angular scale, based on a Rietveld fit.","mediaType":"application/zip","title":"Zip file containing CIF datasets for SRM 1979 from synchrotron"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-11-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Materials characterization"],"issued":"2020-07-17","keyword":["Xray Power Diffraction; Diverging Beam Diffractometer; Power Diffraction SRM"]},{"identifier":"ark:/88434/mds2-2270","accessLevel":"public","contactPoint":{"hasEmail":"mailto:sean.lehman@nist.gov","fn":"Sean Lehman"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2270","description":"This is a file containing aggregation data for two proteins that were thermomechanically aggregated. The aggregated proteins were separated by asymmetric flow field flow fractionation and separated protein fractions were detected and quantified by UV spectrophotometry and multi-angle light scattering. The UV spectrophotometry was used to quantify the amount of residual monomer, which is reported herein. The multi-angle light scattering was fitted to a relevant model to calculate the molecular weight of the aggregated protein, also reported herein. The protein aggregation was characterized as a function of time and also the azide (preservative) concentration, which is indicated as being relevant to the aggregation process. The data contained here is plotted in a manuscript submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.","language":["en"],"title":"Aggregation of Purified Protein Reference Materials Characterized by Asymmetric Flow Field Flow Fractionation","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2270/Protein%20Aggregation%20Summary%20Data.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Aggregation of Purified Protein Reference Materials Characterized by Asymmetric Flow Field Flow Fractionation"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2270/Protein%20Aggregation%20Summary%20Data.xlsx","format":".xslx","description":"Data of aggregated NIST BSA (927f) and NISTmAb (8670) protein particles characterized by asymmetric flow field flow fractionation with UV spectrophotometry and mutli-angle static light scattering. Data is the monomer concentration and aggregate molecular weight calculated from this analysis.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Aggregation of Purified Protein Reference Materials Characterized by Asymmetric Flow Field Flow Fractionation"},{"accessURL":"https://doi.org/10.18434/M32270","title":"DOI Access for Aggregation of Purified Protein Reference Materials Characterized by Asymmetric Flow Field Flow Fractionation"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"https://www.nist.gov/topics/data/public-access-nist-research/copyright-fair-use-and-licensing-statements-srd-data-and","modified":"2020-07-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Bioscience:Bioprocessing"],"issued":"2020-08-21","keyword":["electrical sensing zone","flow imaging","light obscuration","particle","protein aggregate","protein particle","subvisible particle","visible particle","Biosciences and Health"]},{"identifier":"ark:/88434/mds2-2271","accessLevel":"public","contactPoint":{"hasEmail":"mailto:allan.harvey@nist.gov","fn":"Allan H. Harvey"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Values are computed for the dilute-gas diffusivity of water isotopologues in N2, O2, and their mixture at a composition representing air, for standard conditions of 101.325 kPa and water mole fraction approaching zero.  Calculations employ state-of-the-art intermolecular potentials and classical trajectory calculations as described in the paper by R. Hellmann and A.H. Harvey, \"First-Principles Diffusivity Ratios for Kinetic Isotope Fractionation of Water in Air\", published in Geophysical Research Letters volume 47, issue 18, article e2020GL089999 (2020), https://doi.org/10.1029/2020GL089999.  Separate files are given for the diffusivities of H2O, HDO (where D is deuterium), and the 17-O and 18-O substitutions of H2O.","language":["en"],"title":"Calculated Diffusivities for Water Isotopologues in Nitrogen, Oxygen, and Air","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2271/Readme.txt.sha256","mediaType":"text/plain","title":"SHA256 File for readme file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2271/Readme.txt","format":"text","description":"Summary of dataset","mediaType":"text/plain","title":"readme file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2271/Diffusivity_H2O.dat.sha256","mediaType":"text/plain","title":"SHA256 File for DIffusivity of H2O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2271/Diffusivity_H2O.dat","format":"text","description":"DIffusivity of the H2O molecule in nitrogen, oxygen, and air.","mediaType":"application/octet-stream","title":"DIffusivity of H2O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2271/Diffusivity_HDO.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Diffusivity of HDO"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2271/Diffusivity_HDO.dat","format":"text","description":"DIffusivity of the HDO molecule in nitrogen, oxygen, and air.","mediaType":"application/octet-stream","title":"Diffusivity of HDO"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2271/Diffusivity_H2-17O.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Diffusivity of H2-17O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2271/Diffusivity_H2-17O.dat","format":"text","description":"DIffusivity of the H2-17O molecule in nitrogen, oxygen, and air.","mediaType":"application/octet-stream","title":"Diffusivity of H2-17O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2271/Diffusivity_H2-18O.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Diffusivity of H2-18O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2271/Diffusivity_H2-18O.dat","format":"text","description":"DIffusivity of the H2-18O molecule in nitrogen, oxygen, and air.","mediaType":"application/octet-stream","title":"Diffusivity of H2-18O"},{"accessURL":"https://doi.org/10.18434/M32271","title":"DOI Access for Calculated Diffusivities for Water Isopopologues in Nitrogen, Oxygen, and Air"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-07-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Theoretical chemistry and modeling","Physics:Atomic, molecular, and quantum"],"issued":"2020-07-26","keyword":["air","diffusion","first-principles calculations","isotopic fractionation","kinetic fractionation","kinetic theory","nitrogen","oxygen","water"]},{"identifier":"ark:/88434/mds2-2272","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.ritchie@nist.gov","fn":"Nicholas Ritchie"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pages.nist.gov/FFAST.jl/","description":"An implementation in Julia of a library to make programmatically available the mass absorption and other data provided in the \"X-Ray Form Factor, Attenuation, and Scattering Tables - NIST Standard Reference Database 66\"","language":["en"],"title":"FFAST Mass Absorption Coefficient Library for the Julia language","distribution":[{"accessURL":"https://github.com/usnistgov/FFAST.jl","format":"Julia package","description":"A Julia library to access the X-ray Form Factor, Attenuation and Scattering Tables - NIST Standard Reference Database 66","title":"FFAST.jl"},{"accessURL":"https://doi.org/10.18434/M32272","title":"DOI Access for FFAST Mass Absorption Coefficient Library for the Julia language"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-07-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1M","theme":["Physics:Atomic, molecular, and quantum","Physics:Spectroscopy","Materials:Materials characterization","Chemistry:Theoretical chemistry and modeling","Chemistry:Analytical chemistry"],"issued":"2021-09-27","keyword":["Mass Absorption Coefficient","X-ray form factor","MAC","X-ray","Julia"]},{"identifier":"ark:/88434/mds2-2273","accessLevel":"public","references":["https://doi.org/10.1103/PhysRevA.77.042701","https://doi.org/10.1016/j.adt.2009.08.001"],"contactPoint":{"hasEmail":"mailto:nicholas.ritchie@nist.gov","fn":"Nicholas Ritchie"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2273","description":"An implementation of the Bote-Salvat ionization cross-section parameterized algorithm for electron image up to 1 GeV.  Based on *   D. Bote and F. Salvat, \"Calculations of inner-shell ionization by electron impact with the distorted-wave and plane-wave Born approximations\", Phys. Rev. A77, 042701 (2008). *   Bote, David, et al. \"Cross sections for ionization of K, L and M shells of atoms by impact of electrons and positrons with energies up to 1 GeV: Analytical formulas.\" Atomic Data and Nuclear Data Tables 95.6 (2009): 871-909.","language":["en"],"title":"Bote-Salvat Ionization Cross Section library for the Julia language","distribution":[{"accessURL":"https://github.com/usnistgov/BoteSalvatICX.jl","format":"Julia package","description":"A Julia package implementing the Bote-Salvat ionization cross section for energetic electrons","title":"BoteSalvatICX.jl"},{"accessURL":"https://doi.org/10.18434/M32273","title":"DOI Access for Bote-Salvat Ionization Cross Section library for the Julia language"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-07-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Analytical chemistry","Chemistry:Theoretical chemistry and modeling","Materials:Materials characterization","Physics:Atomic, molecular, and quantum","Physics:Spectroscopy"],"issued":"2021-09-27","keyword":["Electron","Ionization","Cross section","Inner Shell"]},{"identifier":"ark:/88434/mds2-2274","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.ritchie@nist.gov","fn":"Nicholas Ritchie"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pages.nist.gov/NeXLUncertainties.jl/","description":"NeXLUncertainties.jl is a Julia package implementing algorithms for the propagation of uncertainties in multi-variate measurement models.","language":["en"],"title":"NeXLUncertainties.jl - A Julia library implementing uncertainty propagation for multi-variate measurement models.","distribution":[{"accessURL":"https://github.com/usnistgov/NeXLUncertainties.jl","format":"Julia package","description":"A library for propagating the uncertainty in multi-variate measurement models.","title":"NeXLUncertainties.jl uncertainty propagation library for Julia"},{"accessURL":"https://doi.org/10.18434/M32274","title":"DOI Access for NeXLUncertainties.jl - A Julia library implementing uncertainty propagation for multi-variate measurement models."}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-07-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1M","theme":["Metrology:Weights and measures","Chemistry:Theoretical chemistry and modeling","Materials:Modeling and computational material science","Chemistry:Analytical chemistry"],"issued":"2021-09-27","keyword":["uncertainty propagation","Julia language","multivariate measurement model","BIPM 102"]},{"identifier":"ark:/88434/mds2-2275","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.ritchie@nist.gov","fn":"Nicholas Ritchie"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2275","description":"NeXLCore.jl represents the basic physics related data and algorithms necessary to interpret and understand X-ray emission.","language":["en"],"title":"NeXLCore.jl - A library of basic physics for atomic X-ray processes in the Julia language","distribution":[{"accessURL":"https://github.com/usnistgov/NeXLCore.jl","format":"Julia package","description":"A library providing basic X-ray related data and physics.","title":"NeXLCore.jl library for Julia"},{"accessURL":"https://doi.org/10.18434/M32275","title":"DOI Access for NeXLCore.jl - A library of basic physics for atomic X-ray processes in the Julia language"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-07-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1W","theme":["Physics:Atomic, molecular, and quantum","Materials:Materials characterization","Materials:Modeling and computational material science","Chemistry:Theoretical chemistry and modeling","Chemistry:Analytical chemistry"],"issued":"2021-09-27","keyword":["X-ray","physics","energy","mass absorption","ionization","Bremsstrahlung","characteristic"]},{"identifier":"ark:/88434/mds2-2276","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.ritchie@nist.gov","fn":"Nicholas Ritchie"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pages.nist.gov/NeXLMatrixCorrection.jl/","description":"A library implementing basic matrix correction algorithms, iteration algorithms and other quantitative correction algorithms related to estimating the composition from X-ray intensity data.  Includes implementations of various common matrix correction algorithms for bulk samples and provides a framework for additional algorithms to be added.","language":["en"],"title":"NeXLMatrixCorrection.jl - A library implementing algorithms related to matrix correction of electron excited X-ray spectra","distribution":[{"accessURL":"https://github.com/usnistgov/NeXLMatrixCorrection.jl","format":"Julia package","description":"A library for quantitative matrix correction of electron-excited X-ray intensities including both wavelength and energy dispersive measured X-ray intensities.","title":"NeXLMatrixCorrection.jl: A library for matrix correction of electron-excited X-ray intensities"},{"accessURL":"https://doi.org/10.18434/M32276","title":"DOI Access for NeXLMatrixCorrection.jl - A library implementing algorithms related to matrix correction of electron excited X-ray spectra"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-07-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1W","theme":["Physics:Atomic, molecular, and quantum","Materials:Modeling and computational material science","Materials:Materials characterization","Chemistry:Theoretical chemistry and modeling","Chemistry:Analytical chemistry"],"issued":"2021-09-27","keyword":["matrix correction","ZAF","x-ray microanalysis","EPMA","phi(rho z)"]},{"identifier":"ark:/88434/mds2-2279","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2140"],"contactPoint":{"hasEmail":"mailto:jack.sklar@nist.gov","fn":"Jack Sklar"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2279","description":"This data is provided as a supplement to NIST Technical Note 2140, AWS-3 LTE Impacts on AMT, which presents test results assessing the impact of adjacent-band long term evolution (LTE) emissions on aeronautical mobile telemetry (AMT) systems.  The data provided here is sufficient to reproduce all of the plots and analyses in Chapters 5 and 6, and Appendices B, C, D, and E, which present the data analysis results.  All files are spreadsheets in comma-separated values (CSV) format, with labeled columns for various key performance indicators (KPIs) and test configurations.  See the data description document provided in the zip file for an outline of the contents and the technical note referenced above for details on the data collection.","language":["en"],"title":"AWS-3 LTE Impacts on Aeronautical Mobile Telemetry","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2279/LTE_Impacts_on_AMT_data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for AWS-3 LTE Impacts on AMT"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2279/LTE_Impacts_on_AMT_data.zip","format":"CSV","description":"Experimental Data","mediaType":"application/x-zip-compressed","title":"AWS-3 LTE Impacts on AMT"},{"accessURL":"https://doi.org/10.18434/M32279","title":"DOI Access for AWS-3 LTE Impacts on Aeronautical Mobile Telemetry"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-09-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"issued":"2021-03-23","keyword":["spectrum sharing","receiver testing","aeronautical mobile telemetry","LTE"]},{"identifier":"ark:/88434/mds2-2280","accessLevel":"public","references":["https://pubs.acs.org/doi/abs/10.1021/acs.analchem.0c04002"],"contactPoint":{"hasEmail":"mailto:abneesh.srivastava@nist.gov","fn":"Abneesh Srivastava"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2280","description":"Data for mass concentration analysis and spectral analysis for the paper titled, \"Comparison of primary laser spectroscopy and mass spectrometry methods for measuring mass concentration of gaseous elemental mercury\"","language":["en"],"title":"Comparison of primary laser spectroscopy and mass spectrometry methods for measuring mass concentration of gaseous elemental mercury","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2280/Figure%20Data%202020_NIST_Data_Hg-LAS-MS-Comparison.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Figure Data 2020_NIST_Data_Hg-LAS-MS-Comparison.xlsx"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2280/Figure%20Data%202020_NIST_Data_Hg-LAS-MS-Comparison.xlsx","description":"Figure Data 2020_NIST_Data_Hg-LAS-MS-Comparison.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure Data 2020_NIST_Data_Hg-LAS-MS-Comparison.xlsx"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2280/SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2280/SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx","description":"SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"SI_Spreadsheet_2020_NIST_Hg-LAS-MS-Comparison.xlsx"},{"accessURL":"https://doi.org/10.18434/M32280","title":"DOI Access for Comparison of primary laser spectroscopy and mass spectrometry methods for measuring mass concentration of gaseous elemental mercury"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-07-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Analytical chemistry","Environment:Air / water / soil quality","Environment:Environmental health","Metrology","Physics:Spectroscopy"],"issued":"2021-02-11","keyword":["Mercury","Laser Absorption Spectroscopy","Standard Generator","SI traceability","ID-CV-ICP-MS","primary measurement method","Environment and Climate"]},{"identifier":"ark:/88434/mds2-2281","accessLevel":"public","references":["https://doi.org/10.1016/j.jnoncrysol.2019.119828","https://doi.org/10.1063/1.4769995"],"contactPoint":{"hasEmail":"mailto:yvonne.gerbig@nist.gov","fn":"Yvonne Gerbig"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2281","description":"In-situ Raman spectroscopic measurements were conducted during indentation of fused silica (FS) samples. The experiments were conducted using Raman spectroscopy-enhanced instrumented indentation testing (RS-IT). RS-IT was developed at the National Institute of Standards and Technology (NIST) and employs an in-house built instrumented indentation testing device that is coupled to a custom Raman microscope.[Rev. Sci. Instrum. 83 125106 (2012)]\n\nSpectroscopic data were collected with different microscope objectives installed in the Raman microscope: (a) 80X metallurgical objective (MO) with a numerical aperture (NA) of 0.75, (b) 40X objective with a variable coverslip correction (CO) and an NA of 0.95, and (c) 100X objective with a variable correction for thick glass substrates (TO) and an NA of 0.6. The samples used in the experiments were made of optically polished, Corning 7980 UV Grade FS: The thin FS sample has a thickness of 0.2 mm +/- 0.025 mm and the thick FS sample has a thickness of 1 mm +/- 0.05 mm. In-situ spectroscopic measurements were conducted with two test protocols (z-profile and load-sequence experiments).\nIn the z-profile experiments, the indenter probe was brought in contact with the sample and continuously loaded to a maximum indentation force of 300 mN. Then the indentation force was held constant to perform a series of in-situ Raman measurements in the center of the indentation. The surface spectrum was collected with the microscope objective focus set at the surface of the specimen. Then seven consecutive Raman spectra were measured where the z-axis position of the microscope objective was shifted 1 ?m lower, moving the focal plane into the bulk of the sample away from the indenter for each spectrum along the indentation axis. The z-profile experiments were conducted for the objective/ sample pairings: (a) MO/thin FS, (b) MO/thick FS, (c) CO/thin FS, (d) TO/thick FS.\nIn the load-sequence experiments, the indenter probe was brought in contact with the sample and an indentation force of 50 mN was applied. The indentation force was held constant while a Raman spectrum was collected in-situ from the center of the indentation with the microscope objective being focused on the top surface of the specimen. Following collection of the spectrum, the indentation force was increased by 50 mN. A Raman spectrum was then collected at that load after re-adjusting the focus of the objective onto the top surface of the specimen. This routine was continued until a maximum force of 300 mN was reached. Then the indentation force was reduced in 50 mN steps until the sample was completely unloaded. At each unloading step, a Raman spectrum was collected in-situ. The load sequence experiments were conducted for the CO/thin FS and TO/Thick FS objective/sample pairings.\nThe indenter probe was a three-sided, pyramidal diamond probe with a semi-apical angle of 68.8° and a nominal tip radius of about 150 nm.\n\nThe raw experimental data (Raman spectra, indentation curves and while light images) collected in the two test regimens are compiled in datasets A through H of this data publication. In this context, raw spectral data are defined as being direct from the camera with the exception of conversion of the photon energy to Raman shift (1/cm) and application of the instrument non-uniformity correction. Raw indentation data are defined as being direct from the instrument corrected for machine compliance. The aforementioned datasets built the foundation of and serve as companion to the publication: Y.B. Gerbig and C.A. Michaels, J. Non-Cryst. Solids 530 119828 (2020). More details about data collection and processing than already described in this summary can be found in the publication. The data directly underlying the figures presented in this publication are compiled in datasets I through P of this data publication. The accompanying Readme document contains details about organization, content and format of the data sets.","language":["en"],"title":"In-situ Raman spectra of indented fused 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Materials characterization"],"keyword":["Raman spectroscopy","in-situ","instrumented indentation","fused silica"]},{"identifier":"ark:/88434/mds2-2282","accessLevel":"public","references":["https://doi.org/10.1063/5.0039188"],"contactPoint":{"hasEmail":"mailto:robert.mcmichael@nist.gov","fn":"Robert D. McMichael"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2282","description":"Supplemental data for \"Comparison of measured and simulated spin-wave mode spectra of magnetic nanostructures\" by H. T. Nembach, R.D. McMichael, M.L. Schneider, J.M. Shaw, T.J. Silva.1) Experimental spectra of approximately elliptical, 100 nm or 200 nm elliptical magnetic structures. 2) SEM images of the magnetic structures 3) Scripts and data used in micromagnetic modeling and simulated measurements of the structures.  Experiment:  In this work, we prepared two sets of Ni80Fe20 elliptical nanomagnets with nominal long axes lengths (short axes lengths) of 240 nm (200 nm) and 120 nm (100 nm): Thin-film layers of 3 nm Ta/10 nm Ni80Fe20/5 nm Si3N4 were dc-magnetron sputtered onto a sapphire substrate before a 15-nm diamond-like carbon (DLC) layer was deposited via ion-beam deposition in a separate vacuum chamber. The spin wave mode spectra of the magnetization dynamics were measured with a heterodyne magneto-optical microwave microscope (H-MOMM) Simulations: We carried out micromagnetic simulations using the Object Oriented MicroMagnetic Framework (OommF). To determine the shape for modeled nanomagnets, greyscale SEM images of the nanomagnets were converted into binary images using a thresholding algorithm. The original SEM images were given a Gaussian blur over 1.4 nm (3 pixels), rescaled by 25 % and given a secondary blur over 3.8 nm. A threshold value was determined using Otsu's method.  The simulated spectra were extracted from impulse response calculations made at an array of applied field values in the experimental range. The modeling also provides the spatial profile of the spin wave modes. The bulk of the data is associated with the micromagnetic modeling.  Files include OommF input '.mif' scripts, sample masks, modeling output and python scripts for analysis and plotting, and the resulting figures.","language":["en"],"title":"Supplemental data for \"Comparison of measured and simulated spin-wave mode spectra of magnetic nanostructures\" by H. T. Nembach, R.D. McMichael, M.L. Schneider, J.M. Shaw, T.J. Silva.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2282/spin_wave_modes_b.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Supplemental data for \"Comparison of measured and simulated spin-wave mode spectra of magnetic nanostructures\" by H. T. Nembach, R.D. McMichael, M.L. Schneider, J.M. Shaw, T.J. Silva."},{"accessURL":"https://doi.org/10.18434/M32282","title":"DOI Access for Supplemental data for \"Comparison of measured and simulated spin-wave mode spectra of magnetic nanostructures\" by H. T. Nembach, R.D. McMichael, M.L. Schneider, J.M. Shaw, T.J. Silva."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2282/spin_wave_modes_b.zip","format":"Zip archive containing plain text, python, png, svg, csv, opju and ppt files.","description":"Supplemental data for \"Comparison of measured and simulated spin-wave mode spectra of magnetic nanostructures\" by H. T. Nembach, R.C. McMichael, M.L. Schneider, J.M. Shaw, T.J. Silva. 1) Experimental spectra of approximately elliptical, 100 nm or 200 nm elliptical magnetic structures. 2) SEM images of the magnetic structures 3) Scripts and data used in micromagnetic modeling and simulated measurements of the structures. item [1] Experiment: In this work, we prepared two sets of Ni80Fe20 elliptical nanomagnets with nominal long axes lengths (short axes lengths) of 240 nm (200 nm) and 120 nm (100 nm): Thin-film layers of 3 nm Ta/10 nm Ni80Fe20/5 nm Si3N4 were dc-magnetron sputtered onto a sapphire substrate before a 15-nm diamond-like carbon (DLC) layer was deposited via ion-beam deposition in a separate vacuum chamber. The spin wave mode spectra of the magnetization dynamics were measured with a heterodyne magneto-optical microwave microscope (H-MOMM) item [2]Simulations: We carried out micromagnetic simulations using the Object Oriented MicroMagnetic Framework (OommF). To determine the shape for modeled nanomagnets, greyscale SEM images of the nanomagnets were converted into binary images using a thresholding algorithm. The original SEM images were given a Gaussian blur over 1.4 nm (3 pixels), rescaled by 25 % and given a secondary blur over 3.8 nm. A threshold value was determined using Otsu?s method. The simulated spectra were extracted from impulse response calculations made at an array of applied field values in the experimental range. The modeling also provides the spatial profile of the spin wave modes. item [3] The bulk of the data is associated with the micromagnetic modeling. Files include OommF input '.mif' scripts, sample masks, modeling output and python scripts for analysis and plotting, and the resulting figures.","mediaType":"application/x-zip-compressed","title":"Supplemental data for \"Comparison of measured and simulated spin-wave mode spectra of magnetic nanostructures\" by H. T. Nembach, R.D. McMichael, M.L. Schneider, J.M. Shaw, T.J. Silva."}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-08-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Magnetics","Nanotechnology:Nanomagnetics","Electronics:Magnetoelectronics"],"issued":"2021-01-13","keyword":["nanomagnet","micromagnetic","spin wave","HMOMM","oommf","ferromagnetic resonance"]},{"identifier":"ark:/88434/mds2-2283","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2283","description":"Round 1 Test DatasetThe data being generated and disseminated is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1000 trained, human level, image classification AI models using the following architectures (Inception-v3, DenseNet-121, and ResNet50). The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.Errata: This dataset had a software bug in the trigger embedding code that caused 2 models trained for this dataset to have a ground truth value of 'poisoned' but which did not contain any triggers embedded. These models should not be used.Models Without a Trigger Embedded: id-00000077,  id-00000083","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-jun2020-test","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1YoxK4kIe6vZfURPTjaxWLLcA8EAr8uCs?usp=drive_link","title":"image-classification-jun2020-test"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-01-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Cybersecurity","Information Technology:Software research","Information Technology:Computational science"],"issued":"2020-08-31","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2284","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2284","description":"Round1 Holdout DatasetThe data being generated and disseminated is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1000 trained, human level, image classification AI models using the following architectures (Inception-v3, DenseNet-121, and ResNet50). The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-jun2020-holdout","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1o9GcpZJOA8UJVcUWJ5NStD77WCY7XSof?usp=drive_link","title":"image-classification-jun2020-holdout"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-07-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Cybersecurity","Information Technology:Software research","Information Technology:Computational science"],"issued":"2020-08-04","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2285","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2285","description":"Round 2 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1104 trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-aug2020-train","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1Yj2GapLNOCATrvDp7j5BJQNIMc9SHkCg?usp=drive_link","title":"image-classification-aug2020-train"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-08-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2020-08-05","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2286","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.ritchie@nist.gov","fn":"Nicholas Ritchie"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2286","description":"NeXLSpectrum is a collection of tools for reading, writing and processing X-ray spectra.  It includes methods to read common spectrum and hyperspectrum formats, extract spectral data, plot the spectra with common spectrum markers, and perform filter-fitting.  The fitting algorithm has been optimized for both speed and low memory usage (<1 ms and 160 kB per spectrum for 6 elements on a single thread).","language":["en"],"title":"NeXLSpectrum.jl - Spectrum/HyperSpectrum tools for the Julia Language","distribution":[{"accessURL":"https://github.com/usnistgov/NeXLSpectrum.jl","format":"Julia package","description":"A library for X-ray spectrum analysis in the Julia language","title":"NeXLSpectrum library for Julia"},{"accessURL":"https://doi.org/10.18434/M32286","title":"DOI Access for NeXLSpectrum.jl - Spectrum/HyperSpectrum tools for the Julia Language"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-08-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization","Chemistry:Analytical chemistry"],"issued":"2021-09-27","keyword":["X-ray","Spectrum","Hyper-spectrum","Linear Least Squares Fitting","Spectrum Analysis","EPMA"]},{"identifier":"ark:/88434/mds2-2288","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jessica.staymates@nist.gov","fn":"Jessica Staymates"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/mml/mml-accolades/2020-accolades","description":"The MML Accolades Program has been created to recognize exemplary work by staff, associates, and external collaborators in areas of strategic interest and benefit to MML (The Material Measurement Laboratory, an organization within NIST). With peer nominations from MML employees and associates, Accolades focus on the impact of accomplishments and outcomes of the work of individuals or teams working in or with MML.  The MML Accolades program started in 2015. This dataset includes a website with all the past winners since the program's inception.  There is also an attached spreadsheet for each year that lists the winners and citations for each category. Visit the MML Accolades homepage: https://www.nist.gov/mml/mml-accolades/2020-accolades","language":["en"],"title":"2020 MML Accolades Program","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-07-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Performance Excellence"],"issued":"2020-08-31","keyword":["accolades","awards","MML","recognition"]},{"identifier":"ark:/88434/mds2-2289","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ian.bell@nist.gov","fn":"Ian Bell"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2289","description":"Viscosity data for normal alkanes (methane, ethane, etc.) taken from the literature to accompany the paper \"Entropy Scaling of Viscosity -- II: Predictive Scheme for Normal Alkanes\". The data were obtained from an internal version of the NIST ThermoDataEngine database version 10.4.2. File contents include \n\n## Contents ##\n\n* MIDAS_alkanes.csv: The data file containing all the data considered in this study\n\n* MIDAS_alkanes.bib: The bibliography associated with each datasource in BibTeX format\n\n* allbibs.pdf: A PDF conversion of the bibliography\n\n## Data File format ## \n\n* The data are in a comma-separated text format, with the column headings indicating the contents of the column, along with units, where appropriate\n\n* The phase indicates how the data were measured \"L\" indicates a liquid phase, \"G\" a gas phase, \"G L\" a saturated vapor, and \"L G\" a saturated liquid\n\n* For saturated states (\"G L\" or \"L G\"), the saturation temperature fully specifies the state\n\n* For liquid and gaseous states, either the temperature and pressure or temperature and density are provided, and the unused state variable is empty\n\n* Fluid names match the default names of the compound from NIST REFPROP library\n\n* The column \"TRC_code\" indicates the reference code for the data point, and the same references (with spaces and & replaced with hyphens) are used as keys in the BibTeX file","language":["en"],"title":"Data to accompany the paper \"Entropy Scaling of Viscosity -- II: Predictive Scheme for Normal Alkanes\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2289/MIDAS_alkanes.bib.sha256","mediaType":"text/plain","title":"SHA256 File for Bibliographic information"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2289/MIDAS_alkanes.bib","description":"BibTeX database","mediaType":"text/plain","title":"Bibliographic information"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2289/MIDAS_alkanes.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Viscosity data from TDE"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2289/MIDAS_alkanes.csv","mediaType":"text/csv","title":"Viscosity data from TDE"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2289/README.md.sha256","mediaType":"text/plain","title":"SHA256 File for README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2289/README.md","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2289/allbibs.pdf.sha256","mediaType":"text/plain","title":"SHA256 File for PDF of the bibliography"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2289/allbibs.pdf","mediaType":"application/pdf","title":"PDF of the bibliography"},{"accessURL":"https://doi.org/10.18434/mds2-2289","title":"DOI Access for Data to accompany the paper \"Entropy Scaling of Viscosity -- II: Predictive Scheme for Normal Alkanes\""}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-08-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Energy","Physics:Thermodynamics","Physics:Condensed matter"],"issued":"2020-08-14","keyword":["viscosity data","entropy","ThermoDataEngine"]},{"identifier":"ark:/88434/mds2-2290","accessLevel":"public","references":["https://dx.doi.org/10.1117/12.816569","https://dx.doi.org/10.1117/12.827676","https://dx.doi.org/10.1364/AO.51.006196","https://dx.doi.org/10.1088/1361-6501/aa5586","https://dx.doi.org/10.1117/12.2551504"],"contactPoint":{"hasEmail":"mailto:bryan.barnes@nist.gov","fn":"Bryan Barnes"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2290","description":"This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines.  These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry. Here, the array of features measured and modeled is periodic in one-dimension (i.e., a line grating) with a nominal line width of 100 nm placed at 300 nm intervals, or pitch = 300 nm; the short-hand label for the features is \"L100P300.\" The entirety of the modeled data is included, over two thousand simulations that are indexed using a top, middle, and bottom linewidth as floating parameters.  Two subsets of these data, featuring differing sampling strategies, are also provided.  This data set also contains angle-resolved optical measurements with uncertainties for nine arrays which differ in their dimensions due to lithographic variations using a focus/exposure matrix, as identified in a previous publication (https://doi.org/10.1117/12.777131). We have previously reported line widths determined from these measurements based upon non-linear regression to compare theory to experiment.  Machine learning approaches are to be fostered for solving such inverse problems.  Data are formatted for direct use in \"Model-Based Optical Metrology in R: MoR\" software which is also available from data.nist.gov. (https://doi.org/10.18434/T4/1426859). Note: Certain commercial materials are identified in this dataset in order to specify the experimental procedure adequately.  Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials are necessarily the best available for the purpose.","language":["en"],"title":"Optical scattering measurements and simulation data for one-dimensional (1-D) patterned periodic sub-wavelength features","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Pi_L100P300_sim_All.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Pi_L100P300_sim_All.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Pi_L100P300_sim_All.csv","format":"comma-separated values","description":"2566 x 3 matrix of parametric values used as inputs the scattering code for simulation.  Values are in nanometers.","mediaType":"application/vnd.ms-excel","title":"Pi_L100P300_sim_All.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Phi_L100P300_sim_All.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Phi_L100P300_sim_All.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Phi_L100P300_sim_All.csv","format":"comma-separated values","description":"2566 x 84 matrix of simulated intensities from the scattering off a periodic structure parameterized using a double-trapezoid cross-section.  These intensities are normalized and unit-less.","mediaType":"application/vnd.ms-excel","title":"Phi_L100P300_sim_All.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Imean_L100P300_exp.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Imean_L100P300_exp.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Imean_L100P300_exp.csv","format":"comma-separated values","description":"9 x 84 matrix of measurements determined from repeated measurements of the intensities scattered off the nine measured \"L100P300\" targets, specifically within nine dies on a single wafer.  Each row of intensities corresponds to a single die.  These intensities are normalized and unit-less.","mediaType":"application/vnd.ms-excel","title":"Imean_L100P300_exp.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/labels_L100P300.csv.sha256","mediaType":"text/plain","title":"SHA256 File for labels_L100P300.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/labels_L100P300.csv","format":"comma-separated values","description":"3 x 84 matrix of labels assignable to the 84 columns. Rows correspond to linear polarization state, plane of incidence, and angle of incidence (in degrees).","mediaType":"application/vnd.ms-excel","title":"labels_L100P300.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/y_L100P300_Die1_exp.csv.sha256","mediaType":"text/plain","title":"SHA256 File for y_L100P300_Die1_exp.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/invV_L100P300_Die1_exp.csv.sha256","mediaType":"text/plain","title":"SHA256 File for invV_L100P300_Die1_exp.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/invV_L100P300_Die1_exp.csv","format":"comma-separated values","description":"84 x 84 matrix that is formatted for use with M.o.R.  It is the inverse of the V matrix in the regression.  It corresponds to the same die as that for \"y_L100P300_Die1.csv\".  Unitless.","mediaType":"application/vnd.ms-excel","title":"invV_L100P300_Die1_exp.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Pi_L100P300_sim_467.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Pi_L100P300_sim_467.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Phi_L100P300_sim_467.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Phi_L100P300_sim_467.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Phi_L100P300_sim_467.csv","format":"comma-separated values","description":"467 x 84 matrix of simulated intensities from the scattering off a periodic structure parameterized using a double-trapezoid cross-section.  Normalized and  unit-less.  Formatted for use in M.o.R., this is a subset of the larger dataset.","mediaType":"application/vnd.ms-excel","title":"Phi_L100P300_sim_467.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Pi_L100P300_sim_140.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Pi_L100P300_sim_140.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Pi_L100P300_sim_140.csv","format":"comma-separated values","description":"140 x 3 matrix of parametric values used as inputs the scattering code for simulation.  Values in  nanometers. This is a subset of the larger dataset formatted for use in M.o.R., sized for use in machine learning.","mediaType":"application/vnd.ms-excel","title":"Pi_L100P300_sim_140.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Phi_L100P300_sim_140.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Phi_L100P300_sim_140.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Phi_L100P300_sim_140.csv","format":"comma-separated values","description":"140 x 84 matrix of simulated intensities from the scattering off a periodic structure paramerized using a double-trapezoid cross-section.  Normalized and unitless. This is a subset of the larger dataset formatted for use in M.o.R., sized for use in machine learning.","mediaType":"application/vnd.ms-excel","title":"Phi_L100P300_sim_140.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Pi_L100P300_sim_Comparison.png.sha256","mediaType":"text/plain","title":"SHA256 File for Pi_L100P300_sim_Comparison.png"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Pi_L100P300_sim_Comparison.png","format":"PNG image file","description":"Image illustrating the sampling of the full three-parameter space using the full set of 2566 simulations and the smaller subset of 467 simulations.","mediaType":"image/png","title":"Pi_L100P300_sim_Comparison.png"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Pi_L100P300_sim_20_through_140.png.sha256","mediaType":"text/plain","title":"SHA256 File for Pi_L100P300_sim_20_through_140.png"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Pi_L100P300_sim_20_through_140.png","format":"PNG image file","description":"Image illustrating the 140 x 3 sampling of the full three-parameter space as it developed by multiples of 20 using a Halton sequence.","mediaType":"image/png","title":"Pi_L100P300_sim_20_through_140.png"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/scatter1D_readme.txt","format":"text file","description":"README file describing these data files, both experimental and simulation, and the relationships among them.","mediaType":"text/plain","title":"scatter1D_readme.txt"},{"accessURL":"https://doi.org/10.18434/mds2-2290","title":"DOI Access for Optical scattering measurements and simulation data for one-dimensional (1-D) patterned periodic sub-wavelength features"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/y_L100P300_Die1_exp.csv","format":"comma-separated values","description":"1 x 84 vector formatted for use with our group's published regression software, M.o.R (http://doi.org/10.18434/T4/1502429). It is a subset of \"Imean_L100P300_exp.csv\"","mediaType":"application/vnd.ms-excel","title":"y_L100P300_Die1_exp.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2290/Pi_L100P300_sim_467.csv","format":"comma-separated values","description":"467 x 3 matrix of parametric values used as inputs the scattering code for simulation.  Values in nanometers. Formatted for use in M.o.R., this is a subset of the larger dataset.","mediaType":"application/vnd.ms-excel","title":"Pi_L100P300_sim_467.csv"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Certain commercial materials are identified in this dataset in order to specify the experimental procedure adequately.  Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor i","modified":"2020-08-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Nanotechnology:Nanometrology","Metrology:Dimensional metrology","Manufacturing:Process measurement and control"],"issued":"2021-01-05","keyword":["electromagnetic simulations","simulations","experimental","angle-resolved scattering","scattering","gratings","patterned semiconductors","semiconductors","scatterfield microscopy","bright-field microscopy","microscopy","inverse problems","machine learning"]},{"identifier":"ark:/88434/mds2-2291","accessLevel":"public","contactPoint":{"hasEmail":"mailto:brandon.lane@nist.gov","fn":"Brandon Lane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2291","description":"This dataset includes files generated from post-build X-ray computed tomography (XCT) measurements of the four parts built as part of the \"Overhang Part X4\" in-situ process monitoring dataset (available at https://doi.org/10.18434/M32233).  The \"Overhang Part X4\" dataset was a three-dimensional (3D) additive manufacturing (AM) build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung and Brandon Lane on June 28, 2019.  The files in this dataset include XCT image sequences for each part, and stereolithography files (.STL) of the surface data extracted from XCT, measured by Maxwell Praniewicz at the Precision Machining Research Consortium at Georgia Institute of Technology, Atlanta, GA.","language":["en"],"title":"X-ray computed tomography data of Additive Manufacturing Metrology Testbed (AMMT) parts: Overhang Part X4","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4_Part1_Cropped.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4_Part1_Cropped.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4_Part2_Cropped.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4_Part2_Cropped.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4_Part3%20Cropped.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4_Part3%20Cropped.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4_Part4%20Cropped.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4_Part4%20Cropped.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4%20Part1%20Surface_cleaned.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4%20Part1%20Surface_cleaned.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4%20Part2%20Surface_cleaned.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4%20Part2%20Surface_cleaned.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4%20Part3%20Surface_cleaned.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4%20Part3%20Surface_cleaned.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4%20Part4%20Surface_cleaned.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2291/OverhangPartX4%20Part4%20Surface_cleaned.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2291/OverhangPart_9x5x5mm.STL","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2291/OverhangPart_9x5x5mm.STL.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/mds2-2291","title":"DOI Access for X-ray computed tomography data of Additive Manufacturing Metrology Testbed (AMMT) parts: Overhang Part X4"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-08-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Additive manufacturing"],"issued":"2020-09-16","keyword":["Additive Manufacturing; Laser powder bed fusion; x-ray computed tomography;"]},{"identifier":"ark:/88434/mds2-2293","accessLevel":"public","contactPoint":{"hasEmail":"mailto:james.sims@nist.gov","fn":"James S. 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Then run various tests and compare the results with the output found in the various Output files.","language":["en"],"title":"Software for solving large-scale generalized eigenvalue problems on distributed computers.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2293/MPI_GEVP_Package.tar.gz.sha256","mediaType":"text/plain","title":"SHA256 File for A FORTRAN 90+ program for solving the generalized eigenvalue problem on distributed computers"},{"accessURL":"https://doi.org/10.18434/mds2-2293","title":"DOI Access for Software for solving large-scale generalized eigenvalue problems on distributed computers."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2293/MPI_GEVP_Package.tar.gz","format":"tar.gz","description":"An open-source program, written in quadruple precision Fortran90+, for solving the generalized eigenvalue problem on distributed computers which is suitable for large (80,000 by 80,000 or greater) dense matrices. A test program and sample output is provided as well.","mediaType":"application/gzip","title":"A FORTRAN 90+ program for solving the generalized eigenvalue problem on distributed computers"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-09-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Atomic, molecular, and quantum","Mathematics and Statistics:Numerical methods and software"],"issued":"2020-09-09","keyword":["high performance computing visualization"]},{"identifier":"ark:/88434/mds2-2294","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2092"],"contactPoint":{"hasEmail":"mailto:stanley.gilbert@nist.gov","fn":"Stanley W. 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The tables as built by these scripts will differ from those in the published report in terms of formatting and will be in html, but otherwise will be identical to those in NIST Technical Note 2092.\n\nThe zip file contains five files, including R Data files containing data used in the analysis, an R Markdown script file, and R Data files containing results of the analysis.\n\nThe index.html file in the zip file contains a detailed description of the files in the zip file and detailed instructions on their use.","language":["en"],"title":"Estimating the Effect of 16 CFR Part 1633 on Fire Outcomes: Data Release","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2294/Mattress.zip.sha256","mediaType":"text/plain","title":"SHA256 File for NIST TN 2092"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2294/Mattress.zip","format":"ZIP File","description":"Contains the data and scripts used to generate the tables in NIST Technical Note 2092.","mediaType":"application/zip","title":"NIST TN 2092"},{"accessURL":"https://doi.org/10.18434/mds2-2294","title":"DOI Access for Estimating the Effect of 16 CFR Part 1633 on Fire Outcomes: Data Release"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-08-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Fire risk reduction"],"issued":"2020-09-25","keyword":["bed fires","fires","fire data","fire deaths","fire injuries","fire standard effectiveness","fire standards","home fires","mattress fires","16 CFR Part 1633"]},{"identifier":"ark:/88434/mds2-2295","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.galler@nist.gov","fn":"Michael Galler"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/services-resources/software/hvac-simulation-plus-other-systems-hvacsim","description":"The first version of HVACSIM+, which stands for \"HVAC SIMulation PLUS other systems\", was introduced by National Institute of Standards and Technology (NIST) in 1985 as a computer simulation tool to simulate entire building systems. Since then, the HVACSIM+ computer program package and manuals have been distributed to researchers, students, and consultants in more than 40 countries around the world. Since the first distribution of the program to the public, a number of modifications have been made including the addition of the component models developed for the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 825 project.","language":["en"],"title":"HVAC SIMulation PLUS other systems (HVACSIM+)","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2295","title":"DOI Access for HVAC SIMulation PLUS other systems (HVACSIM+)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-08-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Modeling and simulation research","Information Technology:Cyber-physical systems","Energy:Conventional energy","Energy:Electric power / smart grid","Energy:Energy efficiency","Buildings and Construction:Indoor air quality","Buildings and Construction:Air conditioning and heating equipment","Buildings and Construction:Building control systems"],"issued":"2020-09-21","keyword":["Building control systems","Heating","ventilation and air conditioning equipment","Energy","Energy efficiency","Cyber-physical systems","Modeling and simulation research and Numerical methods and software"]},{"identifier":"ark:/88434/mds2-2296","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.85-3273-37","https://data.nist.gov/od/id/mds2-2619"],"contactPoint":{"hasEmail":"mailto:joshua.kneifel@nist.gov","fn":"Joshua D. 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The inventory is a broad inventory of existing resilience indicators (whether proposed or applied) and key information for each indicator. The indicators span all systems likely to be included in the assessment methodology, including physical systems (e.g., buildings and infrastructure), social and economic systems, and natural systems (e.g., natural environment). The inventory is a foundational component of the Community Resilience Program's project that is aimed at developing a first-generation methodology to assess resilience at the community-scale based on community functions, supported by buildings and infrastructure systems, and the recovery of those functions following a disruptive hazard event. One aspect of this work is to identify the types of indicators that should be used as proxies to represent system attributes, dimensions, and dependencies.","language":["en"],"title":"Inventory of Community Resilience Indicators & Assessment Frameworks","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2297/NIST_Resilience_Indicator_Inventory_v.01_Data_Dictionary.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for NIST Resilience Indicators Inventory Data Dictionary"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2297/NIST_Resilience_Indicator_Inventory_v.01_Data_Dictionary.xlsx","description":"Documentation to support use of the data files","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"NIST Resilience Indicators Inventory Data Dictionary"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2297/NIST_Resilience_Indicator_Inventory_v.01.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for NIST Resilience Indicators Inventory Database, v.01"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2297/NIST_Resilience_Indicator_Inventory_v.01.xlsx","description":"Data files for the NIST Resilience Indicators Inventory, v.01","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"NIST Resilience Indicators Inventory Database, v.01"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2297/NIST_Resilience_Indicatory_Inventory_v.01_References.docx.sha256","mediaType":"text/plain","title":"SHA256 File for NIST Resilience Indicators Inventory References"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2297/NIST_Resilience_Indicatory_Inventory_v.01_References.docx","description":"Bibliography for the sources of frameworks and indicators compiled and assessed in the inventory","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"NIST Resilience Indicators Inventory References"},{"accessURL":"https://doi.org/10.18434/mds2-2297","title":"DOI Access for Inventory of Community Resilience Indicators & Assessment Frameworks"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2297/NIST_Resilience_Indicator_Inventory_v.02.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Inventory of Community Resilience Indicators & Assessment Frameworks V.02"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2297/NIST_Resilience_Indicator_Inventory_v.02.xlsx","format":"Excel spreadsheet","description":"Inventory of Community Resilience Indicators & Assessment Frameworks","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Inventory of Community Resilience Indicators & Assessment Frameworks V.02"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Please credit all framework sources and authors per the references provided in the inventory dataset; Further information about this data is provided in a forthcoming NIST Technical Note","modified":"2021-04-16 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://data.nist.gov/od/id/mds2-2297","accrualPeriodicity":"R/P3Y","theme":["Resilience:Community resilience","Resilience:Disaster resilience","Standards:Frameworks"],"issued":"2021-01-05","keyword":["community resilience","resilience","indicators","measurement","metrics","framework","methodology","physical system","social system","economic system","natural system"]},{"identifier":"ark:/88434/mds2-2298","accessLevel":"public","contactPoint":{"hasEmail":"mailto:peter.linstrom@nist.gov","fn":"Peter Linstrom"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2298","description":"NIST distributed the NIST Properties of Molten Salts Database from 1990 to 2004. As part of the NIST Standard Reference Data Program, the database was designated as Standard Reference Database 27 or SRD 27. Since the database has not been actively maintained since 1991 the SRD designation has been dropped. The database was distributed as a program for DOS compatible operating systems. To enable use of the data on modern systems, the data have been extracted into comma separated variable (CSV) files. The CSV format should be readable by various programs and provides a stable format for long term preservation of the data. The database was designed to provide engineers and scientists rapid access to critically evaluated data for inorganic salts in the molten state. Properties include density, viscosity, electrical conductance, and surface tension, although not all properties are given for all salts. Properties for approximately 320 single salts and 4,000 multi-component systems are included, the latter being primarily binary. Some data for more complex salt mixtures are also given.","language":["en"],"title":"Data from: NIST Properties of Molten Salts Database (formerly SRD 27)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2298/README.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Read-me file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2298/README.txt","format":"ASCII text with CRLF line terminators","description":"Short text file with information about the data","mediaType":"text/plain","title":"Read-me file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2298/molten-salt-data.pdf.sha256","mediaType":"text/plain","title":"SHA256 File for Description of data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2298/molten-salt-data.pdf","format":"PDF/A","description":"Detailed description of the data in PDF/A format.","mediaType":"application/pdf","title":"Description of 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Biological physics","Bioscience:Microbial measurements","Bioscience:Engineering/synthetic biology"],"issued":"2021-02-03","keyword":["single-cell","single-transcript","FISH","HCR","microscopy","flow cytometry"]},{"identifier":"ark:/88434/mds2-2301","accessLevel":"public","contactPoint":{"hasEmail":"mailto:brian.decost@nist.gov","fn":"Brian DeCost"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2301","description":"The open dataset, software, and other files accompanying the manuscript \"An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models,\" submitted for publication to  Integrated Materials and Manufacturing Innovations.Machine learning and autonomy are increasingly prevalent in materials science, but existing models are often trained or tuned using idealized data as absolute ground truths. In actual materials science, \"ground truth\" is often a matter of interpretation and is more readily determined by consensus. Here we present the data, software, and other files for a study using as-obtained diffraction data as a test case for evaluating the performance of machine learning models in the presence of differing expert opinions. We demonstrate that experts with similar backgrounds can disagree greatly even for something as intuitive as using diffraction to identify the start and end of a phase transformation. We then use a logarithmic likelihood method to evaluate the performance of machine learning models in relation to the consensus expert labels and their variance. We further illustrate this method's efficacy in ranking a number of state-of-the-art phase mapping algorithms. We propose a materials data challenge centered around the problem of evaluating models based on consensus with uncertainty. The data, labels, and code used in this study are all available online at data.gov, and the interested reader is encouraged to replicate and improve the existing models or to propose alternative methods for evaluating algorithmic performance.","language":["en"],"title":"Dataset: An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/VO2%20-Nb2O3%20XRD%20Combiview.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/VO2%20-Nb2O3%20XRD%20Combiview.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/VO2%20-%20Nb2O3%20Composition%20and%20temp%20Combiview.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/VO2%20-%20Nb2O3%20Composition%20and%20temp%20Combiview.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Human%20Labels.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Human%20Labels.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Compare%20ML%20Labels.csv","mediaType":"application/vnd.ms-excel"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Compare%20ML%20Labels.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/cluster_assignment_loglik_all.csv","mediaType":"application/vnd.ms-excel"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/cluster_assignment_loglik_all.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Open%20Data%20Challenge%20Notebook%20Human%20Labels%20and%20Plots.py","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Open%20Data%20Challenge%20Notebook%20Human%20Labels%20and%20Plots.py.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Open%20Data%20Challenge%20Notebook%20Machine%20Labels%20and%20Plots.py","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Open%20Data%20Challenge%20Notebook%20Machine%20Labels%20and%20Plots.py.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Condensing%20Write-Ups%20of%20Human%20and%20Machine%20Labeling%20Metrics.docx","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Condensing%20Write-Ups%20of%20Human%20and%20Machine%20Labeling%20Metrics.docx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Readme.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2301/Readme.txt.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/mds2-2301","title":"DOI Access for Dataset: An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-09-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1Y","theme":["Information Technology:Data and informatics","Materials:Modeling and computational material science","Materials:Materials characterization"],"issued":"2020-10-23","keyword":["machine learning models","X-ray diffraction","human labeling","combinatorial methods","V-Nb-O thin films","quantified uncertainyy","open data challenge"]},{"identifier":"ark:/88434/mds2-2302","accessLevel":"public","contactPoint":{"hasEmail":"mailto:matthew.staymates@nist.gov","fn":"Matthew E. 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My Stay-at-Home Lab Shows How Face Coverings Can Slow the Spread of Disease","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2302/Cover_smart._Do_your_part._Slow_the_spread._Video_Download.mp4","format":"MP4 video","description":"Public service announcement video showing the fluid dynamics of human breathing and coughing with and without a face covering","mediaType":"video/mp4","title":"Cover smart, do your part, slow the spread"},{"accessURL":"https://www.nist.gov/blogs/taking-measure/my-stay-home-lab-shows-how-face-coverings-can-slow-spread-disease","format":"HTML webpage","description":"Description and context of the activities surrounding the public service announcement video on human coughing and breathing with and without a face covering.","title":"Blog: My Stay-at-Home Lab Shows How Face Coverings Can Slow the Spread of Disease"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2302/Cover_smart._Do_your_part._Slow_the_spread._Video_Download.mp4.sha256","mediaType":"text/plain","title":"SHA256 File for Cover smart, do your part, slow the spread"},{"accessURL":"https://doi.org/10.18434/mds2-2302","title":"DOI Access for Cover smart, do your part, slow the spread. My Stay-at-Home Lab Shows How Face Coverings Can Slow the Spread of Disease"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-06-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research"],"issued":"2020-09-23","keyword":["schlieren imaging","flow visualization","COVID-19","cough","face covering","face mask"]},{"identifier":"ark:/88434/mds2-2303","accessLevel":"public","contactPoint":{"hasEmail":"mailto:beck.strauss@nist.gov","fn":"Beck Strauss"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2303","description":"Supplemental data for the manuscript entitled \"Constraining the decline of the lunar dynamo field at ? 3.1 Ga through paleomagnetic analysis of Apollo 12 mare basalts\" by Strauss et al. (submitted 2020). This dataset includes: - Ar-Ar data following the reporting guidelines of Renne et al. (2009) - Paleomagnetism data compatible with PaleoMag software - Microprobe measurement data and text","language":["en"],"title":"Supplemental Data for \"Constraining the decline of the lunar dynamo field at ? 3.1 Ga through paleomagnetic analysis of Apollo 12 mare 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Materials characterization","Physics:Magnetics"],"keyword":["geology","geophysics","Apollo","isotope","geochronology","paleomagnetism","rock magnetism","basalt","lunar","microprobe"]},{"identifier":"ark:/88434/mds2-2304","accessLevel":"public","references":["https://doi.org/10.18434/m31933"],"contactPoint":{"hasEmail":"mailto:stacy.schuur@nist.gov","fn":"Stacy Schuur"},"programCode":["006:052"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2304","description":"Database and annotated bibliography of contaminants in tissues from albatross (Family Diomedeidae) species. 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Software to run MRTs and collect intelligibility data. Software consists of a simple graphical interface. Test consists of collecting basic demographic information from a user, playing MRT phrases with different distortions, and recording the user response.","language":["en"],"title":"MRT GUI Software","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research"],"issued":"2020-10-14","keyword":["Modified Rhyme Test (MRT)","Intelligibility"]},{"identifier":"ark:/88434/mds2-2313","accessLevel":"public","contactPoint":{"hasEmail":"mailto:edward.sisco@nist.gov","fn":"Edward Sisco"},"programCode":["006:045"],"@type":"dcat:Dataset","replaces":"ark:/88434/mds2-2313","landingPage":"https://data.nist.gov/od/id/mds2-2313","description":"The NIST DART-MS Forensics Database is an evaluated collection of in-source collisionally-induced dissociation (is-CID) mass spectra of compounds of interest to the forensics community (e.g. seized drugs, cutting agents, etc.). The is-CID mass spectra were collected using Direct Analysis in Real-Time (DART) Mass Spectrometry (MS), either by NIST scientists or by contributing agencies noted per compound. The database is provided as a general-purpose structure data file (.SDF). For users on Windows operating systems,  the .SDF format library can be converted to NIST MS Search format using Lib2NIST and then explored using NIST MS Search v2.4 for general mass spectral analysis. These software tools can be downloaded at https://chemdata.nist.gov. The database is now (09-28-2021) also  provided in R data format (.RDS) for use with the R programming language. This database, also commonly referred to as a library, is one in a series of high-quality mass spectral libraries/databases produced by NIST (see NIST SRD 1a, https://dx.doi.org/10.18434/T4H594).","language":["en"],"title":"NIST DART-MS Forensics Database (is-CID)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/210915_He_Pos_Library_Dragonfly.zip","mediaType":"application/zip","title":"210915_He_Pos_Library_Dragonfly"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/220120_He_Pos_Library_Earthworm.zip","mediaType":"application/zip","title":"220120_He_Pos_Library_Earthworm"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/230104_He_Pos_Library_Grasshopper.zip","mediaType":"application/zip","title":"230104_He_Pos_Library_Grasshopper"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/230919_HePos_Hornet_v1.zip","mediaType":"application/zip","title":"230919_HePos_Hornet_v1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/240319_HePos_Inchworm_v1.zip","mediaType":"application/zip","title":"240319_HePos_Inchworm_v1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/250101_HePos_Joro.zip","mediaType":"application/zip","title":"250101_HePos_Joro"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Analytical chemistry"],"keyword":["Standard Reference Data","Mass Spectra","Ion Fragmentation","Mass Spectrometry","NIST Mass Spectral Libraries","Chemical Identification","Biosciences and Health","Security and Forensics"]},{"identifier":"ark:/88434/mds2-2314","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2077","https://doi.org/10.6028/NIST.TN.2235"],"contactPoint":{"hasEmail":"mailto:matthew.bundy@nist.gov","fn":"Matthew Bundy"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2314","description":"The National Fire Research Laboratory (NFRL) operates large scale open fire calorimeters to measure the heat and combustion products for fires in the range of 50 kW to 20,000 kW. This Fire Calorimetry Database (FCD) contains results, data files and images of fire experiments conducted at the NFRL. The specimens contained in this database include single burning items, fully furnished rooms, controlled burners, well characterized fuels and real composite fuels of unknown composition.","language":["en"],"title":"NIST Fire Calorimetry Database (FCD)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Fire:Materials flammability","Fire:Fire dynamics and science","Fire:Fire risk reduction"],"issued":"2020-11-19","keyword":["fire","heat release rate","calorimetry","combustion","smoke"]},{"identifier":"ark:/88434/mds2-2315","accessLevel":"public","references":["https://doi.org/10.1017/S0885715620000068"],"contactPoint":{"hasEmail":"mailto:david.black@nist.gov","fn":"David R. Black"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"The data from this instrument consists of sets of measurements of Xray intensity as a function of diffraction angle.  Almost all of it is collected using scintillator point detector with a graphite analyzer, and is stored in CIF format.","language":["en"],"title":"Diffraction Data for SRM 660c","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2315/srm_660c_cifs_20201029_081700.zip.sha256","mediaType":"text/plain","title":"SHA256 File for zip archive of CIF datasets from the SRM 660c certification"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2315/srm_660c_cifs_20201029_081700.zip","format":"zip file of IUCr Crystallographic Information File (CIF) datasets","description":"These CIFs have the original data used to certify SRM660c, along with fits to the data indicative of how we processed it.  The fits are not the fits actually used in certification, since the models have been updated since then, but give essentially the same results.","mediaType":"application/zip","title":"zip archive of CIF datasets from the SRM 660c certification"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-29 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization"],"issued":"2022-03-16","keyword":["Xray Powder Diffraction; Diverging Beam Diffractometer; Powder Diffraction SRM"]},{"identifier":"ark:/88434/mds2-2316","accessLevel":"public","references":["https://dx.doi.org/10.1021/acs.analchem.0c01868"],"contactPoint":{"hasEmail":"mailto:adam.fleisher@nist.gov","fn":"Adam Fleisher"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2316","description":"Data set from peer-reviewed publication:  D. M. Bailey et al., Precision spectroscopy of nitrous oxide isotopocules with a cross-dispersed spectrometer and a mid-infrared frequency comb, Analytical Chemistry, 92, 13759-13766 (2020).\n\nAs a potent greenhouse gas and an ozone depleting agent, nitrous oxide (N2O) plays a critical role in the global climate. Effective mitigation relies on understanding global sources and sinks, which can be supported through isotopic analysis. We present a cross-dispersed spectrometer, coupled with a mid-infrared frequency comb, capable of simultaneously monitoring all singly substituted, stable isotopic variants of N2O. Rigorous evaluation of the instrument lineshape function and data treatment using a Doppler-broadened, low-pressure gas sample are discussed. Laboratory characterization of the spectrometer demonstrates sub-GHz spectral resolution and an average precision of 6.7 x 10^{-6} for fractional isotopic abundance retrievals in 1 s.","language":["en"],"title":"Precision spectroscopy of nitrous oxide isotopocules with a cross-dispersed spectrometer and a mid-infrared frequency comb","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig1.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for VIPA Instrument Lineshape Data (Fig. 1D and Fig. 1E)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig1.xlsx","format":"Excel workbook","description":"Data from Fig. 1D and Fig. 1E:  VIPA instrument lineshape analysis and trends.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"VIPA Instrument Lineshape Data (Fig. 1D and Fig. 1E)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig5.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Worked example of modeled transmission spectra (Fig. 5)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig5.xlsx","format":"Excel workbook","description":"Data from Fig. 5:  Construction of model VIPA transmission spectrum using known instrument lineshape function.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Worked example of modeled transmission spectra (Fig. 5)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig6.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Experimental and simulated spectral data for 0.69 kPa of pure N2O (Fig. 6)."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig6.xlsx","format":"Excel workbook","description":"Data from Fig. 6:  full experimental and simulated model spectra for pure N2O transmission spectrum.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Experimental and simulated spectral data for 0.69 kPa of pure N2O (Fig. 6)."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig7.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Instrument lineshape function characterization (Fig. 7)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig7.xlsx","format":"Excel workbook","description":"Data from Fig. 7:  the composite and full optical instrument lineshape characterization in two dimensions.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Instrument lineshape function characterization (Fig. 7)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig8.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Transmission spectrum of 1.35 kPa of pure N2O at 3 ms of integration time (Fig. 8)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig8.xlsx","format":"Excel workbook","description":"Data from Fig. 8:  experimental, simulated model, and fitted residuals of rapid VIPA spectroscopy at 3 ms of integration time.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Transmission spectrum of 1.35 kPa of pure N2O at 3 ms of integration time (Fig. 8)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig9.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Allan deviation analysis of N2O isotopic composition (Fig. 9)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2316/fig9.xlsx","format":"Excel workbook","description":"Data from Fig. 9:  Allan deviation analysis of N2O isotopic composition plotted versus both integration time and laboratory time.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Allan deviation analysis of N2O isotopic composition (Fig. 9)"},{"accessURL":"https://doi.org/10.18434/mds2-2316","title":"DOI Access for Precision spectroscopy of nitrous oxide isotopocules with a cross-dispersed spectrometer and a mid-infrared frequency comb"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-09-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Analytical chemistry","Physics:Spectroscopy","Physics:Optical physics","Environment:Greenhouse gas measurements"],"issued":"2020-10-27","keyword":["greenhouse gases","nitrous oxide","spectroscopy","optical frequency combs","remote sensing","Environment and Climate"]},{"identifier":"ark:/88434/mds2-2320","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2320","description":"Round 3 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1008 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-dec2020-train","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1jKq-BWGZwSa_Zp73aiDqsJxqFaJa6jwJ?usp=drive_link","title":"image-classification-dec2020-train"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2020-10-30","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2321","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2321","description":"Round 2 Test DatasetThe data being generated and disseminated is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 144 trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-aug2020-test","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1p2NVLddjLdrFfaoewe70t1iysC4LChrh?usp=drive_link","title":"image-classification-aug2020-test"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2020-10-30","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2322","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2322","description":"Round 2 Holdout DatasetThe data being generated and disseminated is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 144 trained, human level, image classification AI models using a variety of  architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-aug2020-holdout","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2322","title":"DOI Access for Trojan Detection Software Challenge - Round 2 Holdout Dataset"},{"accessURL":"https://drive.google.com/drive/folders/1Dk03W-TdDVAMQoti2rwBsDo6I143B2gm?usp=drive_link","title":"image-classification-aug2022-holdout"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2020-10-30","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2326","accessLevel":"public","references":["https://doi.org/10.1109/ACCESS.2020.3001852"],"contactPoint":{"hasEmail":"mailto:camillo.gentile@nist.gov","fn":"Camillo Gentile"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2326","description":"Millimeter-wave channel sounders are much more sensitive to phase drift than their microwave counterparts by virtue of shorter wavelength. This matters when coherently combining untethered channel measurements ? scanned over multiple antennas either electronically or mechanically in seconds, minutes, or even hours ? to obtain directional information. To eliminate phase drift, a synchronization cable between the transmitter and receiver is required, limiting deployment range and flexibility indoors, and precluding most outdoor and mobile scenarios. Instead, we propose a blind technique to calibrate for phase drift by post-processing the channel measurements; the technique is referred to as blind because it requires no reference signal and, as such, works even in non-line-of-sight conditions when the (reference) direct path goes undetected. To substantiate the technique, it was tested on real measurements collected with our 60-GHz virtual phased-array channel sounder, as well as through simulation. The technique was demonstrated robust enough to deal with the most severe case of phase drift (uniformly distributed phase) and in non-line-of-sight conditions.","language":["en"],"title":"Blind Calibration of Phase Drift in Millimeter-Wave Channel Sounders","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/BlindCalibration.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/BlindCalibration.pdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig1.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig1.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig2.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig2.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig4a.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig4a.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig4b.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig4b.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5a.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5a.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5b.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5b.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5c.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5c.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5d.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5d.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5e.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5e.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5f.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig5f.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig6a.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig6a.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig6b.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig6b.mat.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig7a.mat","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2326/Fig7a.mat.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/mds2-2326","title":"DOI Access for Blind Calibration of Phase Drift in Millimeter-Wave Channel Sounders"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-06-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"issued":"2021-01-05","keyword":["5G","beamforming","clock drift","mmWave","phased-array antennas","phase coherence"]},{"identifier":"ark:/88434/mds2-2329","accessLevel":"public","contactPoint":{"hasEmail":"mailto:javier.bernal@nist.gov","fn":"Javier Bernal"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2329","description":"A software package for computing the elastic registration of two simple curves in d-dimensional space, d a positive integer, and therefore the elastic shape distance between them. The implementation of the package is in Matlab with the exception of the Dynamic Programming routine which is written in Fortran but is executed as a Matlab mex file. The Dataset includes all the routines in the package and some input data files.","language":["en"],"title":"On computing elastic registration of two simple curves in d-dimensional space and the elastic shape distance between them.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2329/ESD_alldim.zip.sha256","mediaType":"text/plain","title":"SHA256 File for On computing elastic shape distances between curves in d-dimensional space"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2329/ESD_alldim.zip","format":"A zip file","description":"Software for computing the elastic registration of two simple curves in d-dimensional space, d a positive integer, and the elastic shape distance between them.","mediaType":"application/x-zip-compressed","title":"On computing elastic shape distances between curves in d-dimensional space"},{"accessURL":"https://doi.org/10.18434/mds2-2329","title":"DOI Access for On computing elastic registration of two simple curves in d-dimensional space and the elastic shape distance between them."}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-11-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Numerical methods and software","Mathematics and Statistics"],"issued":"2020-11-30","keyword":["dynamic programming","elastic shape distance","FFT","rotation matrix","shape analysis","singular value decomposition","trace"]},{"identifier":"ark:/88434/mds2-2333","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2132"],"contactPoint":{"hasEmail":"mailto:marcia.huber@nist.gov","fn":"Marcia L. Huber"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2333","description":"This spreadsheet and accompanying files are the result of work performed for U.S. Army Ground Vehicle Systems Center by the National Institute of Standards and Technology (NIST), Applied Chemicals and Materials Division on the development of a computer program for fire-suppressant bottle-filling calculations under interagency agreement number 11478007. The work includes the development of an Excel spreadsheet that is to be used with the NIST23 (REFPROP) (that must be obtained separately at https://www.nist.gov/srd/refprop) to provide two bottle-filling calculations (1) given vessel size, mass of agent, mass of pressurizing agent, and filling temperature compute the filling pressure, and the temperature and pressure conditions at which the fluid in the vessel becomes single phase, and (2) given the vessel size, mass of agent, and filling temperature and pressure, compute the mass of pressurizing fluid, and the temperature and pressure conditions at which the fluid in the vessel becomes single phase. The agents include CF3I, R-218, R-125, R-227ea, R 13B1, R-236fa, HFE-7100, Novec 649 (also known as Novec 1230 and FK-5-1-12), R 1233zd(E), R-1336mzz(Z), and R1336-mzz(E). Two pressurizing agents are available, nitrogen and carbon dioxide. There also is an option to include solid sodium bicarbonate powder in the calculations, and the ability to generate tables of conditions in the vessel as a function of temperature summarized with simple graphics.","language":["en"],"title":"PROperties of FIre Suppressant SYstems: \"PROFISSY\" - A Spreadsheet Application for Fire-Suppressant Bottle-Filling Calculations","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2333/PROFISSY.release.zip.sha256","mediaType":"text/plain","title":"SHA256 File for PROFISSY spreadsheet and associated files for public release"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2333/PROFISSY.release.zip","description":"This zip file contains the Excel spreadsheet PROFISSY.version1.0.xlsm and additional files HFE7100.fld, R13B1.fld and HMX.BNC that are necessary for the model implementation. The spreadsheet is documented in NIST Technical Note 2132, available at https://doi.org/10.6028/NIST.TN.2132. It is necessary to have NIST Refprop installed as well, this is a separate program available for purchase at https://www.nist.gov/srd/refprop. The Dll must be greater than or equal to 10.0.0.87.","mediaType":"application/x-zip-compressed","title":"PROFISSY spreadsheet and associated files for public release"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-11-29 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Chemical engineering and processing","Fire:Fire fighting","Environment"],"issued":"2021-01-06","keyword":["Filling pressure","fire suppressant","refrigerant","vapor liquid equilibrium"]},{"identifier":"ark:/88434/mds2-2334","accessLevel":"public","contactPoint":{"hasEmail":"mailto:katherine.sharpless@nist.gov","fn":"Katherine E. 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Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants. 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Subcategories"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2339/README.txt.sha256","mediaType":"text/plain","title":"SHA256 File for README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2339/README.txt","format":"TXT","description":"A README file to explain the dataset","mediaType":"text/plain","title":"README"},{"accessURL":"https://doi.org/10.18434/mds2-2339","title":"DOI Access for Mapping of NIST Cybersecurity Framework Subcategories to Threat Scenarios from the National Electric Sector Cybersecurity Organization Resource (NESCOR) Electric Sector Failure Scenarios and Impact Analyses - Version 3.0"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-01-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Energy:Electric power / smart grid","Information Technology:Cybersecurity"],"issued":"2021-01-08","keyword":["Cybersecurity Framework","Subcategories","voltage regulation","distribution system","smart grid","Advanced Metering Infrastructure","Distributed Energy Resource","Distribution Grid Management"]},{"identifier":"ark:/88434/mds2-2341","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2341","description":"Round 3 Test DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 288 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-dec2020-test","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1BWmi4q5bTwVpEmIhNBodR3aQLOL-FTFh?usp=drive_link","title":"image-classification-dec2020-test"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2020-12-17","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2342","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2342","description":"Round 3 Holdout DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 288 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-dec2020-holdout","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1iTDy4J8Vjn842Frab5a-EWer36Ga68fP?usp=drive_link","title":"image-classification-dec2020-holdout"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2020-12-17","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2343","accessLevel":"public","contactPoint":{"hasEmail":"mailto:anna.karion@nist.gov","fn":"Anna Karion"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2343","description":"Data files in this repository contain a static archive of all the observational and model data required to construct fossil-fuel enhancements at tower sites in Los Angeles (LA_data.csv) and DC-Baltimore (DCBalt_data.csv).  These were used in the analyses detailed and presented in the publication \"The impact of COVID-19 on CO2 emissions in the Los Angeles and Washington DC/Baltimore metropolitan areas\" by Yadav et al.","language":["en"],"title":"Carbon Dioxide Mole Fraction Measurements and Model Output in the Northeast Corridor: Baltimore/Washington and Los Angeles Megacity for Jan-May 2018, Jan-May 2019, and Jan-May 2020.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2343/DCBalt_data.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Northeast Corridor: Washington DC/Baltimore data set"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2343/LA_data.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Los Angeles Megacity data set"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2343/LA_data.csv","mediaType":"text/csv","title":"Los Angeles Megacity data set"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2343/readme.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Brief Description of CSV file contents"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2343/readme.txt","mediaType":"text/plain","title":"Brief Description of CSV file contents"},{"accessURL":"https://doi.org/10.18434/mds2-2343","title":"DOI Access for Carbon Dioxide Mole Fraction Measurements and Model Output in the Northeast Corridor: Baltimore/Washington and Los Angeles Megacity for Jan-May 2018, Jan-May 2019, and Jan-May 2020."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2343/DCBalt_data.csv","description":"Data files in this repository contain a static archive of all the observational and model data required to construct fossil-fuel enhancements at tower sites in Los Angeles (LA_data.csv) and DC-Baltimore (DCBalt_data.csv).  These were used in the analyses detailed and presented in the publication \"The impact of COVID-19 on CO2 emissions in the Los Angeles and Washington DC/Baltimore metropolitan areas\" by Yadav et al.","mediaType":"text/csv","title":"Northeast Corridor: Washington DC/Baltimore data set"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-12-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Environment:Greenhouse gas measurements"],"spatial":"United States","issued":"2021-01-28","keyword":["Greenhouse gas measurements and models","urban greenhouse gas domes","GHG concentration measurements"],"temporal":"2018-01-01/2020-06-01"},{"identifier":"ark:/88434/mds2-2344","accessLevel":"public","references":["https://doi.org/10.1021/acsnano.5b01044"],"contactPoint":{"hasEmail":"mailto:james.liddle@nist.gov","fn":"James Alexander Liddle"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2344","description":"Transmission electron microscope tomographic data of aligned carbon nanotubes in epoxy at volume fractions of 0.44%, 2.6%, 4%, and 6.9%.  Reduced data and analysis are available at https://doi.org/10.1021/acsnano.5b01044 .  This is the raw data used to generate the figures in \"The evolution of carbon nanotube network structure in unidirectional nanocomposites resolved by quantitative electron tomography\", Bharath Natarajan, Noa Lachman, Thomas Lam, Douglas Jacobs, Christian Long, Minhua Zhao, Brian L Wardle, Renu Sharma, J Alexander Liddle, ACS Nano, vol. 9, pp 6050-6058 (2015), and is further analyzed in \"Aligned carbon nanotube morphogenesis predicts physical properties of their polymer nanocomposites\", Bharath Natarajan, Itai Y. Stein, Noa Lachman, Namiko Yamamoto, Douglas S. Jacobs, Renu Sharma, J. Alexander Liddle and Brian L. Wardle, Nanoscale, vol. 11, pp16327-16335 (2019), and \"Modeliing the Electromagnetic Scattering Characteristics of Carbon Nanotube Composites Characterized by 3-D Tomographic Transmission Electron Microscopy\", Ahmed M. Hassan, MD Khadimul Islam, Spencer On, Bharath Natarajan, Itai Y. Stein, Noa Lachman, Estelle Cohen, Brian L. Wardle, Renu Sharma, J. Alexander Liddle, and Edward J. Garboczi, IEEE Open Journal of Antennas and Propagation, vol. 1, pp 142-158 (2020). Carbon nanotube (CNT) reinforced polymers are next-generation, high-performance, multifunctional materials with a wide array of promising applications. Successful introduction of such materials is hampered by the lack of a quantitative understanding of process-structure-property relationships. These relationships are developed through the detailed characterization of nanoscale reinforcement morphology within the embedding medium. We reveal the three-dimensional (3D) nanoscale morphology of high volume fraction (Vf) aligned CNT/epoxy-matrix nanocomposites using energy-filtered electron tomography. We present an automated phase-identification method for fast, accurate, representative rendering of the CNT spatial arrangement in these low-contrast bimaterial systems. The resulting nanometer-scale visualizations provide quantitative information on the evolution of CNT morphology and dispersion state with increasing Vf, including network structure, CNT alignment, bundling and waviness. The CNTs  exhibit a nonlinear increase in bundling and alignment and a decrease in waviness as a function of increasing Vf. Our findings explain previously observed discrepancies between the modeled and measured trends in bulk mechanical, electrical and thermal properties. The techniques we have developed for morphological quantitation are applicable to many low-contrast material systems. We use new, nanoscale quantitative 3D morphological information and stochastic modeling to re-interpret experimental measurements of continuous aligned carbon nanotube (A-CNT) PNC properties as a function of A-CNT packing/volume fraction. The 3D tortuosity calculated from tomographic reconstructions and its evolution with Vf is used to develop a novel definition of waviness that incorporates the stochastic nature of CNT growth. The importance of using randomly wavy CNTs to model these materials is validated by agreement between simulated and previously-measured PNC elastic moduli. Secondary morphological descriptors such as CNT-CNT junction density and inter-junction distances are measured for transport property predictions. The scaling of the junction density with CNT volume fraction is observed to be non-linear, and this non-linearity is identified as the reason behind the previously unexplained scaling of aligned-CNT PNC longitudinal thermal conductivity. The measured electrical conductivity scales linearly with Vf as it is relatively insensitive to junction density beyond percolation.","language":["en"],"title":"Transmission electron microscope tomographic data of aligned carbon nanotubes in epoxy at volume fractions of 0.44%, 2.6%, 4%, and 6.9%.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2344/ReadMe%203D%20TEM%20data%20file%20organization_Final.docx.sha256","mediaType":"text/plain","title":"SHA256 File for ReadMe file for TEM tomography data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2344/ReadMe%203D%20TEM%20data%20file%20organization_Final.docx","format":"The file is in Word format","description":"This file describes the file and folder structure that contains the raw and processed transmission electron microscopy images for a series of vertically-aligned carbon nanotube/epoxy composites","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"ReadMe file for TEM tomography data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2344/NIST_VACNT_3D_TEM.7z.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2344/NIST_VACNT_3D_TEM.7z","format":"This data set has been compressed using Windows 7-Zip","description":"This zip file contains the raw and processed image files from a series of vertically-aligned carbon nanotube/epoxy composites.  The file and folder structure is described in the ReadMe file associated with this record.","mediaType":"application/x-zip-compressed","title":"TEM tomography data files"},{"accessURL":"https://doi.org/10.18434/mds2-2344","title":"DOI Access for Transmission electron microscope tomographic data of aligned carbon nanotubes in epoxy at volume fractions of 0.44%, 2.6%, 4%, and 6.9%."}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-12-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Composites","Materials:Materials characterization","Nanotechnology:Nanomaterials"],"issued":"2021-01-21","keyword":["TEM","tomography","carbon nanotube composite","nanocomposite","CNT"]},{"identifier":"ark:/88434/mds2-2345","accessLevel":"public","references":["https://doi.org/10.18434/mds2-2340"],"contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2345","description":"Round 4 Train DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1008 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-feb2021-train","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1C3oF7f683LLopFjFcI7cQhimQV8mYy9D?usp=drive_link","title":"image-classification-feb2021-train"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-12-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2023-08-17","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2346","accessLevel":"public","contactPoint":{"hasEmail":"mailto:anirudha.sahoo@nist.gov","fn":"Anirudha Sahoo"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2346","description":"The General Authorized Access (GAA) users in the Citizens Broadband Radio Service (CBRS) band are the lowest priority users. They must cooperate with each other to minimize mutual interference and increase spectrum utilization. The Wireless Innovation Forum (WInnForum), a standardization body, has recommended three schemes to address this GAA-GAA coexistence issue.  This dataset contains the performance data obtained from simulation experiments run to evaluate the performance of the WInnForum scheme called \"Approach 3\".  Various performance graphs using this dataset has been published in the publication titled \"GAA-GAA Coexistence in the CBRS Band: Performance Evaluation of Approach 3\" which appeared in the IEEE Conference on Local Computer Networks (LCN) 2020.","language":["en"],"title":"GAA-GAA Coexistence in the CBRS Band: Performance Evaluation of Approach 3","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_2.txt.sha256","mediaType":"text/plain","title":"SHA256 File for coverage ratio vs deployment density"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_2.txt","format":"this file is in ASCII text format","description":"This data shows the variation of coverage ratio in San Diego and Virginia Beach as deployment density changes (for both ITM and Hybrid propagation models)","mediaType":"text/plain","title":"coverage ratio vs deployment 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density changes (for both ITM and Hybrid propagation models) when number of Coexistence Groups = 3","mediaType":"text/plain","title":"Average Maximum Allocable Bandwidth of CBSDs in a CxG vs Deployment Density"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_5.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Average Maximum Allocable Bandwidth of CBSDs in a CxG vs Deployment Density"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_5.txt","format":"ASCII text","description":"This data shows the variation of Average Maximum Allocable Bandwidth of CBSDs in a CxG (AMABCC) in San Diego and Virginia Beach as deployment density changes (for both ITM and Hybrid propagation models) when number of Coexistence Groups = 4","mediaType":"text/plain","title":"Average Maximum Allocable Bandwidth of CBSDs in a CxG vs Deployment Density"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_6.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Ratio of inter-CxG Interfered Area of a CxG (RCIAC) vs Deployment Density"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_6.txt","format":"ASCII text","description":"variation of Ratio of inter-CxG Interfered Area of a CxG (RCIAC) in San Diego and Virginia Beach (for both ITM and Hybrid models) as deployment density changes when the number of CxGs=3","mediaType":"text/plain","title":"Ratio of inter-CxG Interfered Area of a CxG (RCIAC) vs Deployment Density"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_7.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Ratio of inter-CxG Interfered Area of a CxG (RCIAC) vs Deployment Density"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_7.txt","format":"ASCII text","description":"variation of Ratio of inter-CxG Interfered Area of a CxG (RCIAC) in San Diego and Virginia Beach (for both ITM and Hybrid models) as deployment density changes when the number of 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CxG (AAICIGC) vs Deployment Density"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_9.txt","format":"ASCII text","description":"variation of Average Aggregate Interference per inter-CxG Interfered Grid of a CxG (AAICIGC) in San Diego and Virginia Beach (for both ITM and Hybrid models) as deployment density varies when the number of CxGs = 4","mediaType":"text/plain","title":"Average Aggregate Interference per inter-CxG Interfered Grid of a CxG (AAICIGC) vs Deployment Density"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_10.txt.sha256","mediaType":"text/plain","title":"SHA256 File for distribution of Inter-CxG Edge Weights"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2346/figure_10.txt","format":"ASCII text","description":"Distribution of Inter-CxG Edge Weights of all CBSDs in CxG_0 when deployment density = 10 CBSD/square km, propagation model=ITM and number of CxGs=4","mediaType":"text/plain","title":"distribution of Inter-CxG Edge Weights"},{"accessURL":"https://doi.org/10.18434/mds2-2346","title":"DOI Access for GAA-GAA Coexistence in the CBRS Band: Performance Evaluation of Approach 3"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-05-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)","Information Technology:Networking"],"issued":"2021-01-05","keyword":["CBRS","3.5 GHz","GAA","Coexistence","WInnForum","Approach 3"]},{"identifier":"ark:/88434/mds2-2347","accessLevel":"public","contactPoint":{"hasEmail":"mailto:carl.simon@nist.gov","fn":"Carl Simon Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2347","description":"The use of the microscope to assess cell viability is an inherently qualitative and subjective process. This project demonstrates a new absorbance microscopy modality for quantitative single-cell assessment of cell viability via trypan blue staining. This dataset consists of .tif images of live and dead stained and unstained Jurkat cells as well as the numerical data related to the corresponding journal article. The images were collected via brightfield microscopy through a 610nm bandpass filter. The data were collected to demonstrate a new imaging modality that enables the collection of traceable and comparable images of cells over time on the same microscope or on different microscopes. The processing of the brightfield images into absorbance images allows the intracellular uptake of trypan blue measured as moles/cell or mmol/L to be determined for individual cells. In this way, measurements of cell viability can be made in a quantitative, reproducible fashion. Quantitative measurements of cell viability are greatly needed to improve consistency in the biomanufacturing of cells and cell-based therapeutics. Note about Downloading:  Please note that the pathlength for the download location must not exceed 260 characters; this is the pathlength limit in Windows.  If you experience an error message about pathlength, then you can create a shorter pathlength for the download. For example, you could create a folder on C drive named 000 (triple zero) which would sort to the top of the list and would be C:\\000 (6 characters).  REFERENCE 1. Babakhanova G,  Zimmerman SM,  Pierce LT,  Sarkar S,  Schaub NJ, Simon Jr CG (2021) Quantitative, Traceable Determination of Cell Viability Using Absorbance Microscopy. Manuscript in preparation. 2. Babakhanova G, Zimmerman SM, Simon Jr CG (2021), Dataset for AbsorbanceQ App for Generating Absorbance Images from Brightfield Image Captures, National Institute of Standards and Technology, https://doi.org/10.18434/mds2-2423 (accessed June 22, 2021) 3. Zimmerman SM,  Simon Jr CG, Babakhanova G (2021) AbsorbanceQ App for Generating Absorbance Images from Brightfield Image Captures. Manuscript in preparation.","language":["en"],"title":"Dataset for Absorbance Microscopy for Quantitative and Traceable Trypan Blue Cell Viability Measurement","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2347/Raw_data_Figure2.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Figure 2 - raw data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2347/Raw_data_Figure2.zip","description":"Figure 2 - raw data","mediaType":"application/x-zip-compressed","title":"Figure 2 - raw data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2347/Raw_data_Figure3.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Figure 3 - raw data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2347/Raw_data_Figure3.zip","description":"Figure 3 - raw data","mediaType":"application/x-zip-compressed","title":"Figure 3 - raw data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2347/Raw_data_Figure7_Heat_shock_experiment_Microscope1.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Raw data - 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PLoS ONE 17(1): e0262119. https://doi.org/10.1371/journal.pone.0262119"},{"accessURL":"https://doi.org/10.6028/jres.126.039","format":"Web link","description":"Article on AbsorbanceQ app for making absorbance images: Zimmerman SM, Simon Jr CG, Babakhanova G (2021) AbsorbanceQ: An App for Generating Absorbance Images from Brightfield Images. Journal of Research of the National Institute of Standards and Technology 126, 126039. https://doi.org/10.6028/jres.126.039","title":"Zimmerman SM, Simon Jr CG, Babakhanova G (2021) AbsorbanceQ: An App for Generating Absorbance Images from Brightfield Images. Journal of Research of the National Institute of Standards and Technology 126, 126039. https://doi.org/10.6028/jres.126.039"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-01-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience","Bioscience:Cell biology","Bioscience:Biomaterials","Manufacturing:Biomanufacturing","Health:Cell therapies"],"issued":"2021-06-15","keyword":["regenerative medicine","cell therapy","tissue engineering","advanced imaging","cell viability","advanced therapy","biomanufacturing","biotechnology"]},{"identifier":"ark:/88434/mds2-2348","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jeffrey.marron@nist.gov","fn":"Jeffrey Marron"},"programCode":["006:045"],"@type":"dcat:Dataset","replaces":"ark:/88434/mds2-2348","landingPage":"https://data.nist.gov/od/id/mds2-2348","description":"THIS DATASET IS OBSOLETE. PLEASE REFERENCE THE NEWER MAPPING IN OLIR (https://doi.org/10.18434/mds2-3215).The spreadsheets in this data set map National Institute of Standards and Technology (NIST) Cybersecurity Framework Subcategory outcomes to requirements of the North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) Standards.","language":["en"],"title":"Mapping of NIST Cybersecurity Framework v1.1 to NERC CIP Reliability Standards","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/NERC%20CIP%20to%20NIST%20CSF%20v1.1.csv.sha256","mediaType":"text/plain","title":"SHA256 File for NERC CIP to NIST CSF v1.1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/NERC%20CIP%20to%20NIST%20CSF%20v1.1.csv","format":"CSV","description":"A mapping of NIST Cybersecurity Framework Subcategories to each NERC CIP reliability standard","mediaType":"text/csv","title":"NERC CIP to NIST CSF v1.1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/NERC%20CIP%20to%20NIST%20CSF%20v1.1.XLSX.sha256","mediaType":"text/plain","title":"SHA256 File for NERC CIP to NIST CSF v1.1_xlsx"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/NERC%20CIP%20to%20NIST%20CSF%20v1.1.XLSX","format":"XLSX","description":"A mapping of NIST Cybersecurity Framework Subcategories to each NERC CIP reliability standard","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"NERC CIP to NIST CSF v1.1_xlsx"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/NIST%20CSF%20v1.1%20to%20NERC%20CIP.csv.sha256","mediaType":"text/plain","title":"SHA256 File for NIST CSF v1.1 to NERC CIP"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/NIST%20CSF%20v1.1%20to%20NERC%20CIP.csv","format":"CSV","description":"A mapping of NERC CIP reliability standards to each of the NIST Cybersecurity Framework Subcategories","mediaType":"text/csv","title":"NIST CSF v1.1 to NERC CIP"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/NIST%20CSF%20v1.1%20to%20NERC%20CIP.XLSX.sha256","mediaType":"text/plain","title":"SHA256 File for NIST CSF v1.1 to NERC CIP_xlsx"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/NIST%20CSF%20v1.1%20to%20NERC%20CIP.XLSX","format":"XLSX","description":"A mapping of NERC CIP reliability standards to each of the NIST Cybersecurity Framework Subcategories","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"NIST CSF v1.1 to NERC CIP_xlsx"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/NIST%20CSF%20v1.1%20to%20NERC%20CIP%20FINAL.XLSX.sha256","mediaType":"text/plain","title":"SHA256 File for NIST CSF v1.1 to NERC CIP FINAL"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/NIST%20CSF%20v1.1%20to%20NERC%20CIP%20FINAL.XLSX","format":"XLSX","description":"A complete mapping of CSF v1.1 to NERC CIP Reliability Standards including a pivot table","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"NIST CSF v1.1 to NERC CIP FINAL"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/README.txt.sha256","mediaType":"text/plain","title":"SHA256 File for README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2348/README.txt","format":"TXT","description":"A README file explaining the mapping data set and its background/uses","mediaType":"text/plain","title":"README"},{"accessURL":"https://doi.org/10.18434/mds2-2348","title":"DOI Access for Mapping of NIST Cybersecurity Framework v1.1 to NERC CIP Reliability Standards"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-06-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Energy:Electric power / smart grid","Information Technology:Cybersecurity"],"issued":"2021-01-08","keyword":["Cybersecurity","Cybersecurity Framework","Subcategories","NERC","critical infrastructure","CIP","standards","reliability","bulk electric system","risk management"]},{"identifier":"ark:/88434/mds2-2350","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ward.johnson@nist.gov","fn":"Ward L. Johnson"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2350","description":"SolidWorks Part file (CAD file) for a CoCr \"lattice\" structure additively manufactured in a laser bed powder fusion (LPBF) system at NIST.","language":["en"],"title":"Additively manufactured CoCr lattice artifact -- 3D design","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2350/Reference%20Lattice-all%20struts.SLDPRT","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2350/Reference%20Lattice-all%20struts.SLDPRT.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/mds2-2350","title":"DOI Access for Additively manufactured CoCr lattice artifact -- 3D design"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-01-19 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Additive manufacturing"],"keyword":["Additive manufacturing","CoCr","lattice structure","CAD drawing"]},{"identifier":"ark:/88434/mds2-2351","accessLevel":"public","contactPoint":{"hasEmail":"mailto:allan.harvey@nist.gov","fn":"Allan H. Harvey"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Values are computed for the dilute-gas diffusivity of water isotopologues in N2, CO2, and their mixture at a composition representing the atmosphere of Mars, for standard conditions of 101.325 kPa and water mole fraction approaching zero. Values are similarly computed for the diffusivity of methane isotopologues in N2, representing the atmosphere of Titan. Calculations employ state-of-the-art intermolecular potentials and classical trajectory calculations as described in the paper by R. Hellmann and A.H. Harvey, \"First-Principles Diffusivity Ratios for Atmospheric Isotope Fractionation on Mars and Titan\", published in  Journal of Geophysical Research: Planets, 126, e2021JE006857 (2021), https://doi.org/10.1029/2021JE006857. Separate files are given for the diffusivities of H2O, HDO (where D is deuterium), and the 17-O and 18-O substitutions of H2O, and for the diffusivities in N2 of CH4, CH3D, and the 13-C substitution of CH4.","language":["en"],"title":"Calculated Diffusivities for Water Isotopologues in Carbon Dioxide, Nitrogen, and the Atmosphere of Mars, and for Methane Isotopologues in Nitrogen Representing the Atmosphere of Titan","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Readme.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Readme file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Readme.txt","description":"Readme file","mediaType":"text/plain","title":"Readme file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_H2O.dat.sha256","mediaType":"text/plain","title":"SHA256 File for DIffusivity of H2O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_H2O.dat","description":"DIffusivity of the H2O molecule in CO2, N2, and the Martian atmosphere","mediaType":"application/octet-stream","title":"DIffusivity of H2O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_HDO.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Diffusivity of HDO"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_HDO.dat","description":"DIffusivity of the HDO molecule in CO2, N2, and the Martian atmosphere","mediaType":"application/octet-stream","title":"Diffusivity of HDO"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_H2-17O.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Diffusivity of H2-17O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_H2-17O.dat","description":"Diffusivity of the H2-17O molecule in CO2, N2, and the Martian atmosphere","mediaType":"application/octet-stream","title":"Diffusivity of H2-17O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_H2-18O.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Diffusivity of H2-18O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_H2-18O.dat","description":"Diffusivity of the H2-18O molecule in CO2, N2, and the Martian atmosphere","mediaType":"application/octet-stream","title":"Diffusivity of H2-18O"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_CH4.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Diffusivity of CH4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_CH4.dat","description":"Diffusivity of the CH4 molecule in N2 (atmosphere of Titan)","mediaType":"application/octet-stream","title":"Diffusivity of CH4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_CH3D.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Diffusivity of CH3D"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_CH3D.dat","description":"Diffusivity of the CH3D molecule in N2 (atmosphere of Titan)","mediaType":"application/octet-stream","title":"Diffusivity of CH3D"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_13CH4.dat.sha256","mediaType":"text/plain","title":"SHA256 File for Diffusivity of 13-CH4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2351/Diffusivity_13CH4.dat","description":"Diffusivity of the 13-CH4 molecule in N2 (atmosphere of Titan)","mediaType":"application/octet-stream","title":"Diffusivity of 13-CH4"},{"accessURL":"https://doi.org/10.18434/mds2-2351","title":"DOI Access for Calculated Diffusivities for Water Isotopologues in Carbon Dioxide, Nitrogen, and the Atmosphere of Mars, and for Methane Isotopologues in Nitrogen Representing the Atmosphere of Titan"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-04-19 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Atomic, molecular, and quantum","Chemistry:Chemical thermodynamics and chemical properties","Chemistry:Theoretical chemistry and modeling"],"issued":"2021-01-28","keyword":["diffusivity","isotopes","Mars","Titan","water","methane","carbon dioxide","nitrogen","atmospheric science"]},{"identifier":"ark:/88434/mds2-2352","accessLevel":"public","contactPoint":{"hasEmail":"mailto:kurt.benkstein@nist.gov","fn":"Kurt D. Benkstein"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2352","description":"These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number. The datasets are organized in the files by instrument type: PTA (particle tracking analysis), RMM (resonant mass measurement), ESZ (electrical sensing zone), and OTH (other techniques not covered in the three largest groups, including holographic particle characterization, laser diffraction, flow imaging, and flow cytometry). In the OTH group, the specific instrument type for each dataset is noted. Each instrument type (PTA, RMM, ESZ, OTH) has a dedicated file. Included in the data files for each dataset are: (1) the cumulative particle number concentration (PNC, (1/mL)); (2) the concentration distribution density (CDD, (1/mL·nm)) based upon five bins centered at each particle population peak diameter; (3) the CDD in higher resolution, varied-width bins. The lower-diameter bin edge (µm) is given for (2) and (3). Additionally, the PTA, RMM, and ESZ files each contain unweighted mean cumulative particle number concentrations and concentration distribution densities calculated from all datasets reporting values. The associated standard deviations and standard errors of the mean are also given. In the OTH file, the means and standard deviations were calculated using only data from one of the sub-groups (holographic particle characterization) that had n = 3 paired datasets. Where necessary, datasets not using the common bin resolutions are noted (PTA, OTH groups). The data contained here are presented and discussed in a manuscript to be submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.","language":["en"],"title":"Datasets from an interlaboratory comparison to characterize a multi-modal polydisperse sub-micrometer bead dispersion","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2352","title":"DOI Access for Datasets from an interlaboratory comparison to characterize a multi-modal polydisperse sub-micrometer bead dispersion"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2352/PdP-ILC_datasets_ReadMe.txt.sha256","mediaType":"text/plain","title":"SHA256 File for PdP-ILC_ReadMe"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2352/PdP-ILC_datasets_ReadMe.txt","format":"text","description":"A description of the data file layout and abbreviations","mediaType":"text/plain","title":"PdP-ILC_ReadMe"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2352/PdP-ILC_RMMdatasets.csv.sha256","mediaType":"text/plain","title":"SHA256 File for PdP-ILC_RMM-datasets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2352/PdP-ILC_RMMdatasets.csv","format":"csv","description":"Resonant mass measurement datasets from polydisperse particles interlaboratory comparison","mediaType":"application/vnd.ms-excel","title":"PdP-ILC_RMM-datasets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2352/PdP-ILC_ESZdatasets.csv.sha256","mediaType":"text/plain","title":"SHA256 File for PdP-ILC_ESZ-datasets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2352/PdP-ILC_ESZdatasets.csv","format":"csv","description":"Electrical sensing zone datasets from polydisperse particles interlaboratory comparison","mediaType":"application/vnd.ms-excel","title":"PdP-ILC_ESZ-datasets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2352/PdP-ILC_OTHdatasets.csv.sha256","mediaType":"text/plain","title":"SHA256 File for PdP-ILC_OTH-datasets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2352/PdP-ILC_OTHdatasets.csv","format":"csv","description":"Other instrument types datasets from polydisperse particles interlaboratory comparison","mediaType":"application/vnd.ms-excel","title":"PdP-ILC_OTH-datasets"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-07-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Bioprocessing","Health:Pharmaceuticals","Manufacturing:Biomanufacturing"],"issued":"2021-03-26","keyword":["Biosciences and Health","particle","protein particle","sub-micrometer particle","subvisible particle","particle tracking analysis","resonant mass measurement","electrical sensing zone","holographic particle characterization","flow imaging","laser diffraction","flow cytometry"]},{"identifier":"ark:/88434/mds2-2353","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ian.bell@nist.gov","fn":"Ian Bell"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2353","description":"Code to accompany paper used to generate Chebyshev expansions of the phase equilibria for pure fluids from multiparameter EOS","language":["en"],"title":"Supporting information to accompany: Efficient and Precise Representation of Pure Fluid Phase Equilibria with Chebyshev Expansions","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2353/code.7z","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2353/code.7z.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/mds2-2353","title":"DOI Access for Supporting information to accompany: Efficient and Precise Representation of Pure Fluid Phase Equilibria with Chebyshev Expansions"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2353/superancillary.hpp","mediaType":"text/plain","title":"The C++ header implementing the superancillary curve"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-01-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Numerical methods and software"],"issued":"2021-01-28","keyword":["phase equilibrium; equation of state; numerical approximation; Chebyshev expansion"]},{"identifier":"ark:/88434/mds2-2355","accessLevel":"public","contactPoint":{"hasEmail":"mailto:joshua.taillon@nist.gov","fn":"Joshua Taillon"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2355","description":"This code repository contains the \"back-end\" of the Nexus Microscopy Facility Laboratory Information Management System (NexusLIMS), developed by the NIST Office of Data and Informatics. Its primary function is to build XML-formatted research experiment records by combining metadata from many different sources (reservation systems, the collected data files, a session logger, etc.). These records are structured according to the \"Nexus Experiment\" schema, meaning they can be loaded into a repository and used for structured data queries.","language":["en"],"title":"NexusLIMS: a Python Package for EM Experiment Metadata Management","distribution":[{"accessURL":"https://github.com/usnistgov/NexusLIMS/","format":"Git repository","description":"The Github--hosted git repository containing the code for the NexusLIMS backend","title":"NexusLIMS Backend Code repository"},{"accessURL":"https://doi.org/10.18434/mds2-2355","title":"DOI Access for NexusLIMS: a Python Package for EM Experiment Metadata Management"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-02-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Data and informatics","Materials:Materials characterization"],"issued":"2021-03-08","keyword":["laboratory information management","materials microscopy","electron microscopy","data management","scientific data","reproducibility","open science"]},{"identifier":"ark:/88434/mds2-2356","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8328"],"contactPoint":{"hasEmail":"mailto:alison.kahn@nist.gov","fn":"Alison Kahn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2356","description":"These are measurement data associated with https://doi.org/10.6028/NIST.IR.8328. Code to process data is available at https://github.com/usnistgov/accessTime. Includes access delay measurement results for P25 Direct operating in unencrypted and encrypted modes, P25 Trunked Phase 1 and Phase 2 operating in unencrypted and encrypted modes, and an early development of a 3GPP compliant LTE MCPTT application server. Transmit audio and associated cutpoints files are also included.","language":["en"],"title":"Mission Critical Voice Quality of Experience Access Time Measurement Method Addendum Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2356/Access%20Time%20Addendum%20Paper%20Data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Example Access Time Addendum Paper Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2356/Access%20Time%20Addendum%20Paper%20Data.zip","format":"zip","description":"Measurement data associated with https://doi.org/10.6028/NIST.IR.8328. 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NIST?s PSCR division developed a method to measure and quantify the access time of any push to talk (PTT) communication system.","title":"accessTime Repository"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-02-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Public Safety:Public safety communications research"],"issued":"2021-02-17","keyword":["Access delay; Articulation Band Correlation Modified Rhyme Test (ABC-MRT); A-weight; Encryption; Key performance indicator (KPI); Land mobile radio (LMR); Latency; Long Term Evolution (LTE); Mission Critical Push-to-Talk (MCPTT); Modified Rhyme Test (MRT); Mouth-to-ear (M2E); Packetized; Project 25 (P25); Public Safety; Push-to-talk (PTT); Quality of experience (QoE); Receive; Streaming; Transmit; Vocoder"]},{"identifier":"ark:/88434/mds2-2357","accessLevel":"public","contactPoint":{"hasEmail":"mailto:kathryn.keenan@nist.gov","fn":"Katy Keenan"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2357","description":"Data for multi-site, multi-platform comparison of magnetic resonance imaging (MRI) T1 measurement using the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom. 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At 3 T, data is from 3 different vendor systems, 18 total MRI machines.","language":["en"],"title":"Data for Multi-site, multi-platform comparison of magnetic resonance imaging (MRI) T1 measurement using the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2357/README.txt.sha256","mediaType":"text/plain","title":"SHA256 File for README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2357/README.txt","format":"text","description":"README for data files associated with multi-site, multi-platform comparison of MRI T1 measurement using ISMRM/NIST system phantom","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2357/data_sqmr_ir_supp.mat.sha256","mediaType":"text/plain","title":"SHA256 File for Inversion recovery data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2357/data_sqmr_ir_supp.mat","description":"Inversion recovery data for multi-site, multi-platform comparison of MRI T1 measurement using ISMRM/NIST system phantom","mediaType":"application/octet-stream","title":"Inversion recovery data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2357/data_sqmr_vfa2_supp.mat.sha256","mediaType":"text/plain","title":"SHA256 File for Variable flip angle data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2357/data_sqmr_vfa2_supp.mat","format":"MATLAB mat file","description":"Variable flip angle data for multi-site, multi-platform comparison of MRI T1 measurement using ISMRM/NIST system phantom","mediaType":"application/octet-stream","title":"Variable flip angle data"},{"accessURL":"https://doi.org/10.18434/mds2-2357","title":"DOI Access for Data for Multi-site, multi-platform comparison of magnetic resonance imaging (MRI) T1 measurement using the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-02-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Health:Precision medicine","Health:Medical imaging"],"issued":"2021-05-03","keyword":["magnetic resonance imaging","MRI","T1","phantom"]},{"identifier":"ark:/88434/mds2-2358","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2147"],"contactPoint":{"hasEmail":"mailto:aric.sanders@nist.gov","fn":"Aric Sanders"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2358","description":"This data is provided as a supplement to NIST Technical Note 2147, Characterizing LTE User Equipment Emissions Under Closed-Loop Power Control.  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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Resilience:Disaster resilience","Infrastructure","Energy:Electric power / smart grid"],"issued":"2022-04-29","keyword":["Hurricane Irma","Interoperability","Resilience","Smart Grid"]},{"identifier":"ark:/88434/mds2-2360","accessLevel":"public","references":["https://doi.org/10.1364/OL.405299"],"contactPoint":{"hasEmail":"mailto:david.long@nist.gov","fn":"David Long"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2360","description":"Data for D. 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is performing activities representative of several manufacturing operations. The workcell contains two, 6-degree-of-freedom robot manipulators where one robot is performing material handling operations (e.g., transport parts into and out of a specific work space) while the other robot is performing a simulated precision operation (e.g., the robot touching the center of a part with a tool tip that leaves a mark on the part). This precision operation is intended to represent a precise manufacturing operation (e.g., welding, machining). The goal of this data set is to provide robot level and process level measurements of the workcell operating in nominal parameters. There are no known equipment or process degradations in the workcell.  The material handling robot will perform pick and place operations, including moving simulated parts from an input area to in-process work fixtures. Once parts are placed in/on the work fixtures, the second robot will interact with the part in a specified precise manner. In this specific instance, the second robot has a pen mounted to its tool flange and is drawing the NIST logo on a surface of the part. When the precision operation is completed, the material handling robot will then move the completed part to an output. This suite of data includes process data and performance data, including timestamps. Timestamps are recorded at predefined state changes and events on the PLC and robot controllers, respectively. Each robot controller and the PLC have their own internal clocks and, due to hardware limitations, the timestamps recorded on each device are relative to their own internal clocks. All timestamp data collected on the PLC is available for real-time calculations and is recorded. The timestamps collected on the robots are only available as recorded data for post-processing and analysis. The timestamps collected on the PLC correspond to 14 part state changes throughout the processing of a part. Timestamps are recorded when PLC-monitored triggers are activated by internal processing (PLC trigger origin) or after the PLC receives an input from a robot controller (robot trigger origin). Records generated from PLC-originated triggers include parts entering the work cell, assignment of robot tasks, and parts leaving the work cell. PLC-originating triggers are activated by either internal algorithms or sensors which are monitored directly in the PLC Inputs/Outputs (I/O). Records generated from a robot-originated trigger include when a robot begins operating on a part, when the task operation is complete, and when the robot has physically cleared the fixture area and is ready for a new task assignment. Robot-originating triggers are activated by PLC I/O. Process data collected in the workcell are the variable pieces of process information. This includes the input location (single option in the initial configuration presented in this paper), the output location (single option in the initial configuration presented in this paper), the work fixture location, the part number counted from startup, and the part type (task number for drawing robot).  Additional information on the context of the workcell operations and the captured data can be found in the attached files, which includes a README.txt, along with several noted publications. Disclaimer: Certain commercial entities, equipment, or materials may be identified or referenced in this data, or its supporting materials, in order to illustrate a point or concept. Such identification or reference is not intended to imply recommendation or endorsement by NIST; nor does it imply that the entities, materials, equipment or data are necessarily the best available for the purpose. The user assumes any and all risk arising from use of this dataset.","language":["en"],"title":"Process and robot data from a two robot workcell representative performing representative manufacturing operations.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2361/Setup%20and%20Testplan.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Data Collection Setup and Test Plan"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2361/Setup%20and%20Testplan.txt","description":"This file presents the parameters of the setup and test execution of the NIST workcell for the 60 test runs for which data is collected.","mediaType":"text/plain","title":"Data Collection Setup and Test Plan"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2361/PLC%20Data.zip","description":"This file contains three .CSV files - Part Data, UR3Data, and UR5Data. 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Included in this dataset are method parameter files, mass spectra, mass spectral databases, and retention time / retention index data.","language":["en"],"title":"Data Supporting the Development of Targeted GC-MS Methods for Seized Drug Analysis","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2367/Cannabinoid%20Retention%20Times%20%26%20Indices.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Cannabinoid Retention Time & Index List"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2367/Cannabinoid%20Retention%20Times%20%26%20Indices.xlsx","description":"A list of retention times and indices for all compounds added to the synthetic cannabinoid targeted method. The numeric identifiers for the corresponding mass spectra are also provided in this document.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Cannabinoid Retention Time & Index List"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2367/Synthetic%20Cannabinoid%20Method%20Parameters.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Cannabinoid Method Parameters"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2367/Synthetic%20Cannabinoid%20Method%20Parameters.txt","description":"Synthetic Cannabinoids Method Parameters","mediaType":"text/plain","title":"Cannabinoids Method Parameters"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2367/Cannabinoid_Spectra.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Cannabinoid Mass Spectra"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2367/Cannabinoid_Spectra.zip","description":"Replicate mass spectra for the synthetic cannabinoids evaluated. Files are named ##-##, where the first two digits represent the compound (refer to the Cannabinoids Retention Time & Index file for assignments) and the third and fourth digits represent the replicate number.","mediaType":"application/x-zip-compressed","title":"Cannabinoid Mass Spectra"},{"accessURL":"https://doi.org/10.18434/mds2-2367","title":"DOI Access for Data Supporting the Development of Targeted GC-MS Methods for Seized Drug Analysis"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2367/Cathinone%20Data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Data to Support the Cathinone Targeted GC-MS Method"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2367/Cathinone%20Data.zip","description":"Contains GC-MS method parameters, mass spectra, and compound lists.","mediaType":"application/x-zip-compressed","title":"Data to Support the Cathinone Targeted GC-MS Method"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-02-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/PT1S","theme":["Forensics:Drugs and toxicology"],"issued":"2021-02-19","keyword":["Forensic Science","Forensics","GC-MS","Seized Drug","Drugs","Targeted Methods"]},{"identifier":"ark:/88434/mds2-2369","accessLevel":"public","references":["https://doi.org/10.1038/s41567-021-01226-y"],"contactPoint":{"hasEmail":"mailto:adam.fleisher@nist.gov","fn":"Adam Fleisher"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2369","description":"Data set from peer-reviewed publication:  A. J. Fleisher et al., Absolute 13C/12C Isotope Amount Ratio for Vienna Pee Dee Belemnite from Infrared Absorption Spectroscopy, Nature Physics. Measurements of isotope ratios are predominantly made with reference to standard specimens that have been characterized in the past. In the 1950s, the carbon isotope ratio was referenced to a belemnite sample collected by Heinz Lowenstam and Harold Urey in South Carolina?s Pee Dee region. Due to the exhaustion of the sample since then, reference materials that are traceable to the origin artefact are used to define the Vienna Pee Dee Belemnite (VPDB) scale for stable carbon isotope analysis. However, these reference materials have also become exhausted or proven unstable over time, mirroring issues with the international prototype of the kilogram that led to a revised International System of Units. A campaign to elucidate the stable carbon isotope ratio of VPDB is underway, but independent measurement techniques are required to support it. Here we report an accurate value for the stable carbon isotope ratio inferred from infrared absorption spectroscopy, fulfilling the promise of this fundamentally accurate approach. Our results agree with a value recently derived from mass spectrometry, and therefore advance the prospects of SI-traceable isotope analysis. Further, our calibration-free method could improve mass balance calculations and enhance isotopic tracer studies in CO2 source apportionment.","language":["en"],"title":"Absolute 13C/12C Isotope Amount Ratio for Vienna Pee Dee Belemnite from Infrared Absorption Spectroscopy","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/Fleisher_NPHYS_rev2_fig3_A_data.xls","format":"CSV","description":"Scatter plot of all 301 unique AIR-IS measurements of R(13C/12C)_VPDB, along with error bars showing a relative precision of approximately 0.2E-3. Histogram of values fitted to a normal distribution.","mediaType":"application/vnd.ms-excel","title":"AIR-IS value of R(13C/12C)_VPDB."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/Fleisher_NPHYS_rev2_fig1_BC_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for Accurate isotope ratio infrared spectroscopy (AIR-IS)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/Fleisher_NPHYS_rev2_fig1_BC_data.xls","format":"CSV","description":"Representative high-resolution cavity ring-down absorption spectra of 13C16O2 and 12C16O2 for a sample of NIST Standard Reference Material 1720 Northern Continental Air.","mediaType":"application/vnd.ms-excel","title":"Accurate isotope ratio infrared spectroscopy (AIR-IS)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/Fleisher_NPHYS_rev2_fig2_AB_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for AIR-IS measurements of R(13C/12C)_sample versus IRMS assignments of delta-13C."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/Fleisher_NPHYS_rev2_fig2_AB_data.xls","format":"CSV","description":"AIR-IS values of R(13C/12C)_sample were measured for five CO2-in-air samples with delta-13C VPDB value assignments by IRMS.","mediaType":"application/vnd.ms-excel","title":"AIR-IS measurements of R(13C/12C)_sample versus IRMS assignments of delta-13C."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/Fleisher_NPHYS_rev2_fig2_C_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for Representative delta-13C values."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/Fleisher_NPHYS_rev2_fig2_C_data.xls","format":"CSV","description":"Expanded ranged of delta-13C values for some terrestrial and marine carbon sources.","mediaType":"application/vnd.ms-excel","title":"Representative delta-13C values."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/Fleisher_NPHYS_rev2_fig3_A_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for AIR-IS value of R(13C/12C)_VPDB."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/Fleisher_NPHYS_rev2_fig3_C_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for Literature values of R(13C/12C)_VPDB."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/Fleisher_NPHYS_rev2_fig3_C_data.xls","format":"CSV","description":"Comparison of literature values of R(13C/12C)_VPDB, including this work. Error bars show the combined standard uncertainty.","mediaType":"application/vnd.ms-excel","title":"Literature values of R(13C/12C)_VPDB."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/readme.txt.sha256","mediaType":"text/plain","title":"SHA256 File for README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2369/readme.txt","format":"TXT","description":"Information regarding this data set.","mediaType":"text/plain","title":"README"},{"accessURL":"https://doi.org/10.18434/mds2-2369","title":"DOI Access for Absolute 13C/12C Isotope Amount Ratio for Vienna Pee Dee Belemnite from Infrared Absorption Spectroscopy"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-02-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Environment:Greenhouse gas measurements","Environment:Air / water / soil quality","Chemistry:Analytical chemistry","Physics:Spectroscopy","Metrology:Amount of substance"],"issued":"2021-04-09","keyword":["greenhouse gases","carbon dioxide","oceans","ph","marine mammals","remote sensing","seabirds","Environment and Climate"]},{"identifier":"ark:/88434/mds2-2371","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2371","description":"Round 4 Test DatasetThe data being generated and disseminated is the test data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 288 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-feb2021-test","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1xnz1MXdcGXr53ztUZVjpmfr1MwZw5vBT?usp=drive_link","title":"image-classification-feb2021-test"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-12-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2021-03-08","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2372","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2372","description":"Round 4 Holdout DatasetThe data being generated and disseminated is the holdout data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 288 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - image-classification-feb2021-holdout","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1ccyIg2gl1Iw-uWPGnaxGHizPjKl_2jpI?usp=drive_link","title":"image-classification-feb2021-holdout"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-12-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2021-03-03","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2373","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2373","description":"Round 5 Train DatasetThe data being generated and disseminated is the train data used to construct trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1656 adversarially trained, sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from movie and product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present. Errata: The following models were contaminated during dataset packaging. This caused nominally clean models to have a trigger. Please avoid using these models. Due to the similarity between the Round5 and Round6 datasets (both contain similarly trained sentiment classification AI models), the dataset authors suggest ignoring the Round5 data and only using the Round6 dataset. Corrupted Models: [id-00000007, id-00000014, id-00000030, id-00000036, id-00000047, id-00000074, id-00000080, id-00000088, id-00000089, id-00000097, id-00000103, id-00000105, id-00000122, id-00000123, id-00000124, id-00000127, id-00000148, id-00000151, id-00000154, id-00000162, id-00000165, id-00000181, id-00000184, id-00000185, id-00000193, id-00000197, id-00000198, id-00000207, id-00000230, id-00000236, id-00000239, id-00000240, id-00000244, id-00000251, id-00000256, id-00000258, id-00000265, id-00000272, id-00000284, id-00000321, id-00000336, id-00000364, id-00000389, id-00000391, id-00000396, id-00000423, id-00000425, id-00000446, id-00000449, id-00000463, id-00000468, id-00000479, id-00000499, id-00000516, id-00000524, id-00000532, id-00000537, id-00000563, id-00000575, id-00000577, id-00000583, id-00000592, id-00000629, id-00000635, id-00000643, id-00000644, id-00000685, id-00000710, id-00000720, id-00000724, id-00000730, id-00000735, id-00000780, id-00000784, id-00000794, id-00000798, id-00000802, id-00000808, id-00000818, id-00000828, id-00000841, id-00000864, id-00000867, id-00000923, id-00000970, id-00000971, id-00000973, id-00000989, id-00000990, id-00000996, id-00001000, id-00001036, id-00001040, id-00001041, id-00001044, id-00001048, id-00001053, id-00001059, id-00001063, id-00001116, id-00001131, id-00001139, id-00001146, id-00001159, id-00001163, id-00001166, id-00001171, id-00001183, id-00001188, id-00001201, id-00001211, id-00001233, id-00001251, id-00001262, id-00001291, id-00001300, id-00001302, id-00001305, id-00001312, id-00001314, id-00001327, id-00001341, id-00001344, id-00001346, id-00001364, id-00001365, id-00001373, id-00001389, id-00001390, id-00001391, id-00001392, id-00001399, id-00001414, id-00001418, id-00001425, id-00001449, id-00001470, id-00001486, id-00001516, id-00001517, id-00001518, id-00001532, id-00001533, id-00001537, id-00001542, id-00001549, id-00001579, id-00001580, id-00001581, id-00001586, id-00001591, id-00001599, id-00001600, id-00001604, id-00001610, id-00001618, id-00001643, id-00001650]","language":["en"],"title":"Trojan Detection Software Challenge - nlp-sentiment-classification-mar2021-train","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1GPyxRSCbh2PibtuqS7C5kUw5Yz_vVhBC?usp=drive_link","title":"nlp-sentiment-classification-mar2021-train"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-02-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2021-03-08","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2375","accessLevel":"public","references":["https://doi.org/10.1093/mnras/staa485","https://doi.org/10.1088/0067-0049/204/2/16","https://doi.org/10.1093/mnras/stac2792"],"contactPoint":{"hasEmail":"mailto:yuri.ralchenko@nist.gov","fn":"Yuri Ralchenko"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2375","description":"The database contains radiative line-binned opacities for lanthanides (atomic number 57<=Z<=70) and actinides (atomic number 89<=Z<=102) which are of paramount importance for simulation of light curves and spectra produced by kilonovae. The opacities were calculated by the LANL group under the assumption of local thermodynamic equilibrium. The temperature grid cover the range from 0.01 eV to 5.0 eV while the mass density grid covers 16 orders of magnitude. The database provide search and selection tools along with graphical and tabular outputs.","language":["en"],"title":"NIST-LANL Lanthanide/Actinide Opacity Database","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2375","title":"DOI Access for NIST-LANL Lanthanide Opacity Database"},{"accessURL":"https://nlte.nist.gov/OPAC/about.html","description":"Background information and details about the database","title":"About the NIST-LANL Opacity Database"},{"accessURL":"https://nlte.nist.gov/OPAC","format":"NIST-LANL Lanthanide/Actinide Opacity Database","title":"NIST-LANL Lanthanide/Actinide Opacity Database"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-09-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Atomic, molecular, and quantum","Physics:Radiation"],"issued":"2021-09-17","keyword":["kilonova","lanthanides","opacity","radiation","neutron star merger","atomic physics","actinides"]},{"identifier":"ark:/88434/mds2-2376","accessLevel":"public","contactPoint":{"hasEmail":"mailto:samuel.stavis@nist.gov","fn":"Samuel Stavis"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Sample data and code for \"Accurate localization microscopy by intrinsic aberration calibration\", Craig R. Copeland, Craig D. McGray, B. Robert Ilic, Jon Geist, and Samuel M. Stavis, Nature Communications X, Y (2021). For use in MATLAB.","language":["en"],"title":"Sample code and data for \"Accurate localization microscopy by intrinsic aberration calibration\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2376/Sample%20code%20for%20Intrinsic%20Aberrations.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Sample data and code for \"Accurate localization microscopy by intrinsic aberration calibration\""},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2376/Sample%20code%20for%20Intrinsic%20Aberrations.zip","format":"Matlab data and code as .mat and .m files","description":"Sample data and code associated with the publication \"Accurate localization microscopy by intrinsic aberration calibration\", in the journal Nature Communications. For use in Matlab.","mediaType":"application/x-zip-compressed","title":"Sample data and code for \"Accurate localization microscopy by intrinsic aberration calibration\""},{"accessURL":"https://doi.org/10.18434/mds2-2376","title":"DOI Access for Sample code and data for \"Accurate Localization Microscopy by Intrinsic Aberration Calibration\""},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2376/Sample%20code%20and%20data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Sample code and data for \u201cAccurate localization microscopy by intrinsic aberration calibration\u201d"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2376/Sample%20code%20and%20data.zip","format":"MATLAB data files (.mat) and scripts (.m)","description":"Sample code and data for Accurate localization microscopy by intrinsic aberration calibration, Craig R. Copeland, Craig D. McGray, B. Robert Ilic, Jon Geist, and Samuel M. Stavis, Nature Communications X, Y (2021)","mediaType":"application/x-zip-compressed","title":"Sample code and data for \u201cAccurate localization microscopy by intrinsic aberration calibration\u201d"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology:Optical, photometry, and laser metrology","Nanotechnology:Nanomechanics","Mathematics and Statistics:Image and signal processing","Mathematics and Statistics:Uncertainty quantification"],"issued":"2021-03-16","keyword":["localization","microscopy","particle tracking","intrinsic aberration","MEMS"]},{"identifier":"ark:/88434/mds2-2378","accessLevel":"public","references":["https://doi.org/10.1002/fam.3007"],"contactPoint":{"hasEmail":"mailto:mauro.zammarano@nist.gov","fn":"Mauro Zammarano"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2378","description":"The effectiveness and the failure mechanism of fire barriers in residential upholstered furniture (RUF) were investigated by full-scale flaming tests on single upholstered chair mock-ups. Six fire barriers were tested. The fire barriers were screened for (1) the presence of elements that are typically used in fire retardants and, (2) the presence of targeted fire retardants. For each fire barrier, triplicate flammability tests were run on chair mock-ups where polyurethane foam and polyester fiber fill were used as the padding materials, and each chair component was fully wrapped with the fire barrier and a polypropylene cover fabric. The ignition source was an 18 kW square propane burner, impinging on the top surface of the seat cushion for 80 s. Results showed all six fire barriers reduced the peak heat release rate (up to 64 %) and delayed its occurrence (up to 19 min) as compared to the control chair mock-ups. The heat release rate remained at a relatively low plateau level until liquid products (generated by either melting or pyrolysis of the padding material) percolated trough the fire barrier at the bottom of the seat cushion and ignited, while the fire barrier was still intact. The peak heat release rate occurred shortly after. These observations indicated that the failure mechanism of the fire barrier was related to the ignition of the percolating liquid under the chair.","language":["en"],"title":"Data from \"Flaming Tests on Upholstered Chair Mock-Ups\"","distribution":[{"accessURL":"https://www.nist.gov/el/fire-research-division-73300/flammability-reduction-73304/low-heat-release-upholstered-2","description":"The effectiveness and the failure mechanism of fire barriers in residential upholstered furniture (RUF) were investigated by full-scale flaming tests on single upholstered chair mock-ups.","title":"Flaming Tests on Upholstered Chair Mock-Ups"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Fire:Fire risk reduction","Fire:Materials flammability"],"issued":"2023-11-16","keyword":["fire barrier","residential upholstered furniture","flexible polyurethane foam","flammability","full-scale","bench-scale"]},{"identifier":"ark:/88434/mds2-2379","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2129"],"contactPoint":{"hasEmail":"mailto:mauro.zammarano@nist.gov","fn":"Mauro Zammarano"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This study looks at fire growth in a furnished compartment intended to mimic a living room where a couch, either with or without a barrier fabric, served as the primary fuel load. The experiments were designed to quantify the effect of barrier fabrics on the fire hazard (e.g., tenability conditions, heat release rate, time to flashover and smoke generation) of residential upholstered furniture (RUF) ignited by a flaming-ignition source and to validate the failure mechanisms of barrier fabrics previously observed in chair mock-up experiments.Three commercially available couches compliant with California Technical Bulletin TB-117 were procured. The couches were identical except for the type of cover fabric: two couches used a cotton blend cover and one used a thermoplastic polyester cover. One of the two couches with the cotton blend cover fabric was modified by adding a barrier fabric as a liner between the cover fabric and the couch padding material. The barrier contained only intrinsically fire-resistant fibers without additional, chemically active fire retardants. Three fire tests, one per couch type, were conducted. A throw pillow on the arm of the couch, ignited with a propane flame, acted as an ignition source. Measurements included heat release rate, gas species (O2,CO, CO2), video, heat flux and temperature.","language":["en"],"title":"Data from \"Full-Scale Experiments to Demonstrate Flammability Risk of Residential Upholstered Furniture and Mitigation Using Barrier Fabric\"","distribution":[{"accessURL":"https://www.nist.gov/el/fire-research-division-73300/flammability-reduction-73304/low-heat-release-upholstered-1","description":"This study looks at fire growth in a furnished compartment intended to mimic a living room where a couch, either with or without a barrier fabric, served as the primary fuel load. The experiments were designed to quantify the effect of barrier fabrics on the fire hazard (e.g., tenability conditions, heat release rate, time to flashover and smoke generation) of residential upholstered furniture (RUF) ignited by a flaming-ignition source and to validate the failure mechanisms of barrier fabrics previously observed in chair mock-up experiments.","title":"Full-Scale Experiments to Demonstrate Flammability Risk of Residential Upholstered Furniture and Mitigation Using Barrier Fabric"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2379/Room%20tests.zip","format":"excel","description":"This study looks at fire growth in a furnished compartment intended to mimic a living room where a couch, either with or without a barrier fabric, served as the primary fuel load. The experiments were designed to quantify the effect of barrier fabrics on the fire hazard (e.g., tenability conditions, heat release rate, time to flashover and smoke generation) of residential upholstered furniture (RUF) ignited by a flaming-ignition source and to validate the failure mechanisms of barrier fabrics previously observed in chair mock-up experiments.","mediaType":"application/x-zip-compressed","title":"Full-Scale Experiments to Demonstrate Flammability Risk of Residential Upholstered Furniture and Mitigation Using Barrier Fabric"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Fire risk reduction","Fire:Materials flammability"],"issued":"2023-11-16","keyword":["Barrier fabric","Residential upholstered furniture","Compartment fire","Flashover","Flexible polyurethane foam"]},{"identifier":"ark:/88434/mds2-2380","accessLevel":"public","contactPoint":{"hasEmail":"mailto:raied.caromi@nist.gov","fn":"Raied Caromi"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2380","description":"This project aims to create a comprehensive framework for generating radio frequency (RF) datasets, designing deep learning (DL) detectors, and evaluating their detection performance using both simulated and experimental test data. The proposed tools and techniques are developed in the context of dynamic spectrum use for the 3.5 GHz Citizens Broadband Radio Service (CBRS), but they can be utilized and expanded for standardization of machine learned spectrum awareness technologies and methods. This dataset consists of pre-trained DL models for radar detection in the CBRS band using simulated waveforms. The code for creating and using these models is available at https://github.com/usnistgov/BaselineDeepLearningRadarDetectors.","language":["en"],"title":"Baseline Deep Learning Detectors for Radar Detection in the 3.5 GHz CBRS Band","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/Xception-SpectroMaxHold_model_V1.h5","description":"Spectrogram max-hold pretrained model (Xception)","mediaType":"application/octet-stream","title":"Xception-SpectroMaxHold_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/Xception-SpectroMaxHold_model_V1.h5.sha256","mediaType":"text/plain","title":"SHA256 File for Xception-SpectroMaxHold_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/ResNet50-SpectroMaxHold_model_V1.h5.sha256","mediaType":"text/plain","title":"SHA256 File for ResNet50-SpectroMaxHold_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/MobileNetV2-SpectroMaxHold_model_V1.h5.sha256","mediaType":"text/plain","title":"SHA256 File for MobileNetV2-SpectroMaxHold_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/CNN5-Spectro_model_V1.h5.sha256","mediaType":"text/plain","title":"SHA256 File for CNN5-Spectro_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/CNN4-Spectro_model_V1.h5.sha256","mediaType":"text/plain","title":"SHA256 File for CNN4-Spectro_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/CNN3-SpectroMaxHold_model_V1.h5.sha256","mediaType":"text/plain","title":"SHA256 File for CNN3-SpectroMaxHold_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/ResNet50-SpectroMaxHold_model_V1.h5","description":"Spectrogram max-hold pretrained model (ResNet50)","mediaType":"application/octet-stream","title":"ResNet50-SpectroMaxHold_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/MobileNetV2-SpectroMaxHold_model_V1.h5","description":"Spectrogram max-hold pretrained model (MobileNetV2)","mediaType":"application/octet-stream","title":"MobileNetV2-SpectroMaxHold_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/CNN5-Spectro_model_V1.h5","description":"Spectrogram pretrained model (CNN5)","mediaType":"application/octet-stream","title":"CNN5-Spectro_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/CNN4-Spectro_model_V1.h5","description":"Spectrogram pretrained model (CNN4)","mediaType":"application/octet-stream","title":"CNN4-Spectro_model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2380/CNN3-SpectroMaxHold_model_V1.h5","description":"Spectrogram max-hold pretrained model (CNN3)","mediaType":"application/octet-stream","title":"CNN3-SpectroMaxHold_model"},{"accessURL":"https://doi.org/10.18434/mds2-2380","title":"DOI Access for Baseline Deep Learning Detectors for Radar Detection in the 3.5 GHz CBRS Band"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"issued":"2021-04-05","keyword":["3.5 GHz; CBRS; radar; detection; deep learning; radio frequency signals; spectrum; MLSA"]},{"identifier":"ark:/88434/mds2-2382","accessLevel":"public","references":["https://doi.org/10.1029/2021JG006290"],"contactPoint":{"hasEmail":"mailto:julia.marrs@nist.gov","fn":"Julia Marrs"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2382","description":"Monthly zip files from November 2016 to December 2021, with each zip containing hourly .tif files with gridded CO2 fluxes at 0.02 degree resolution in an eastern North American domain from 33° to 47° north and 92° to 67° west.   Annual zip files from November 2016 to December 2021, with each zip containing hourly .tif files with gridded CO2 fluxes at 0.01 degree resolution in an eastern North American domain from 38.4° to 39.6° north and 77.8° to 76.2° west.   For each hour, there is a file with ecosystem respiration fluxes, as well as a file for gross ecosystem exchange (GEE) during daytime hours only. To derive net ecosystem exchange, respiration and GEE fluxes must be summed.  Units are micromoles per m2*s after dividing original data by 1000. Data updated as of 8/2/2024.","language":["en"],"title":"Biospheric CO2 surface flux estimates from the Vegetation Photosynthesis & Respiration Model (VPRM) in the eastern USA and Canada: November 2016 to December 2021","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/README_update_20240802.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/README_update_20240802.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2016.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2016.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2017.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2017.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2018.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2018.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2019.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2019.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2020.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2020.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2021.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p01/VPRM.d03_2021.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202008.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202008.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202101.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202101.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202102.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202102.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202103.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202103.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202104.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202104.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202105.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202105.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202106.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202106.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202107.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202107.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202108.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202108.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202109.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202109.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202110.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202110.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202111.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202111.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202112.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2382/tif_p02/VPRM.new_202112.tar.gz.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2025-03-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Environment:Greenhouse gas measurements"],"spatial":"92/33, 67/47; 77.8/38.4, 76.2/39.6 ","issued":"2025-03-13","keyword":["Greenhouse gas measurements and models","urban greenhouse gas domes","biospheric CO2 fluxes","carbon cycle","biospheric models"],"temporal":"2016-11-01/2021-12-31"},{"identifier":"ark:/88434/mds2-2383","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2144"],"contactPoint":{"hasEmail":"mailto:aziza.benmosbah@nist.gov","fn":"Aziza Ben Mosbah"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2383","description":"User Equipment (UE)-to-Network Relay functionality was introduced in Release 13 of the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) to extend and improve user connectivity.  A UE connected to the network (relay UE) provides network access to another UE (remote UE) by establishing a direct one-to-one connection with it using the sidelink (SL) channels over the PC5 interface. The PC5 signaling is used for the establishment and maintenance of this direct connection, and different procedures and timers are linked to these processes.  We focus on evaluating the PC5 signaling protocol performance through system-level simulations to provide some insights on minimum timer values that may increase the probability of successfully establishing and maintaining the connection between a remote UE and a relay UE. This data is in multiple forms: - source .txt files that contains data based on network simulations in ns-3. - scripts .gnu files for processing the data and generating plots as shown in NIST Technical Note 2144. - readme file explaining the data and associated scripts. Each .txt file (starting with \"DirectSecurityTrace\") represents the cumulative distribution function (CDF) of the roundtrip time (RTT) for the security procedure (one set of message exchange: request/response) over ProSe for a specific sidelink period value (e.g. 40, 60, 80, ..., 320 ms). Each .txt file (starting with \"DirectCommunicationTrace\") represents the cumulative distribution function (CDF) of the roundtrip time (RTT) for direct link setup procedure (one request/response encapsulating another request/response) over ProSe for a specific sidelink period value (e.g. 40, 60, 80, ..., 320 ms). Each .txt file (starting with \"DirectCommunicationTrace\") represents the success probability of establishing a remote/relay connection for a specific sidelink period value (e.g. 40, 60, 80, ..., 320 ms). The rest of the .txt files (minvaluesfile.txt, avgvaluesfile.txt, maxvaluesfile.txt) includes results from the theoretical model in \"Access Time Analysis of MCPTT Off-Network Mode over LTE\" by Yishen Sunby Yishen Sun et al (https://doi.org/10.1155/2019/2729370): providing the minimum, maximum, and average values of the RTT over ProSe direct communication. Running the .gnu scripts generates the .eps figures (Fig. 3/4/5) included in NIST Technical Note 2144: They plot the corresponding RTT in function of time (ms) and sidelink period, and the corresponding success probability in function of the sidelink period.","language":["en"],"title":"Evaluation of Timers Related to ProSe-based UE-to-Network Relays","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2383/plots.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Evaluation of Timers Related to ProSe-based UE-to-Network Relays"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2383/plots.zip","mediaType":"application/x-zip-compressed","title":"Evaluation of Timers Related to ProSe-based UE-to-Network Relays"},{"accessURL":"https://doi.org/10.18434/mds2-2383","title":"DOI Access for Evaluation of Timers Related to ProSe-based UE-to-Network Relays"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-09-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)"],"issued":"2021-04-06","keyword":["public safety communication","device-to-device","D2D","wireless communication"]},{"identifier":"ark:/88434/mds2-2384","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2384","description":"Round 5 Test DatasetThis is the test data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 504 adversarially trained, sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from movie and product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present. Errata: The following models were contaminated during dataset packaging. This caused nominally clean models to have a trigger. Please avoid using these models. Due to the similarity between the Round5 and Round6 datasets (both contain similarly trained sentiment classification AI models), the dataset authors suggest ignoring the Round5 data and only using the Round6 dataset. Corrupted Models: [id-00000000, id-00000003, id-00000004, id-00000005, id-00000011, id-00000022, id-00000074, id-00000076, id-00000084, id-00000091, id-00000094, id-00000147, id-00000149, id-00000156, id-00000159, id-00000162, id-00000166, id-00000168, id-00000171, id-00000176, id-00000178, id-00000216, id-00000217, id-00000220, id-00000222, id-00000223, id-00000227, id-00000233, id-00000238, id-00000239, id-00000246, id-00000290, id-00000293, id-00000301, id-00000314, id-00000323, id-00000367, id-00000368, id-00000369, id-00000372, id-00000379, id-00000388, id-00000433, id-00000438, id-00000441, id-00000447, id-00000451]","language":["en"],"title":"Trojan Detection Software Challenge - nlp-sentiment-classification-mar2021-test","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1kRVDu5WUgWVonrHzekcEcrF6pfRzfEDB?usp=drive_link","title":"nlp-sentiment-classification-mar2021-test"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2021-03-29","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2385","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2385","description":"Round 5 Holdout DatasetThis is the holdout data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 504 adversarially trained, sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from movie and product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present. Errata: The following models were contaminated during dataset packaging. This caused nominally clean models to have a trigger. Please avoid using these models. Due to the similarity between the Round5 and Round6 datasets (both contain similarly trained sentiment classification AI models), the dataset authors suggest ignoring the Round5 data and only using the Round6 dataset. Corrupted Models: [id-00000000, id-00000019, id-00000033, id-00000084, id-00000087, id-00000104, id-00000146, id-00000148, id-00000167, id-00000212, id-00000221, id-00000230, id-00000233, id-00000237, id-00000239, id-00000246, id-00000281, id-00000284, id-00000288, id-00000295, id-00000302, id-00000303, id-00000310, id-00000343, id-00000349, id-00000351, id-00000361, id-00000366, id-00000367, id-00000369, id-00000371, id-00000376, id-00000407, id-00000418, id-00000423, id-00000425, id-00000428, id-00000439]","language":["en"],"title":"Trojan Detection Software Challenge - nlp-sentiment-classification-mar2021-holdout","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1g0mUpE9RHlGINvtCXAvfcfc7qKSYtOoK?usp=drive_link","title":"nlp-sentiment-classification-mar2021-holdout"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2021-03-29","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2386","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2386","description":"Round 6 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 48 adversarially trained, sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from movie and product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-train","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1o1h6rLyu2cZArdh7lHUreQsyG6qfO7Rh?usp=drive_link","title":"nlp-sentiment-classification-apr2021-train"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity","Information Technology:Computational science"],"issued":"2021-03-29","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2387","accessLevel":"public","contactPoint":{"hasEmail":"mailto:benjamin.place@nist.gov","fn":"Benjamin Place"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2387","description":"A list of structures (and other identifiers) of possible per- and polyfluoroalkyl substances (PFAS). The data provided in the PFAS Suspect List has been aggregated from public sources and users, including peer-reviewed literature, patent literature, and public websites. Therefore, the quality and accuracy of the compound names, structures, and other properties have not been validated. The National Institute of Standards & Technology does not endorse or provide any assessment of confidence with the information provided.","language":["en"],"title":"Suspect List of Possible Per- and Polyfluoroalkyl Substances (PFAS)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2387/PFAS%20Suspect%20List.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2387/PFAS%20Suspect%20List.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2387/ReadMe.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2387/ReadMe.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2387/Contributors.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2387/Contributors.txt.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-29 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Analytical chemistry","Environment:Environmental health"],"issued":"2022-12-21","keyword":["PFAS","environmental","high-resolution mass spectrometry","perfluoroalkyl substances","polyfluoroalkyl substances"]},{"identifier":"ark:/88434/mds2-2388","accessLevel":"public","contactPoint":{"hasEmail":"mailto:anna.karion@nist.gov","fn":"Anna Karion"},"programCode":["006:047"],"@type":"dcat:Dataset","replaces":"ark:/88434/mds2-2388","landingPage":"https://data.nist.gov/od/id/mds2-2388","description":"NOTE: please see the latest (April 2025) update at https://doi.org/10.18434/mds2-3814 . Hourly observations of carbon dioxide (CO2) and methane (CH4) mole fractions in dry air from tower- and rooftop-based sites in the Los Angeles Megacity Carbon Project network.  Carbon monoxide (CO) observations exist at several stations in this network but are not included in this data release pending additional calibration verification.  Please contact the authors for higher frequency data, which are available on request.  Data files are comma delimited (CSV).  Measurements of each species may be from two or more different heights above ground.  Site locations, heights, and other information are in a separate ASCII (CSV) file (LAM_sites.csv).  Data are currently reported for the years 2015-2023. An ASCII Readme file (LAM_Readme_2024) is also posted.  CO2 data are reported on the NOAA/WMO X2007 calibration scale. A full revision on the NOAA/WMO X2019 scale for CO2 is forthcoming, and will be linked here.  CH4 data are reported on the NOAA/WMO X2004A calibration scale.  Current update: May 17, 2024.","language":["en"],"title":"In Situ Carbon Dioxide, Methane, and Carbon Monoxide Mole Fractions from the Los Angeles Megacity Carbon 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A README is also included in the dataset with a more comprehensive description of the data format and included files.","language":["en"],"title":"UE-to-Network Relay Model B Discovery in ProSe-Enabled LTE Networks","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2390/Data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for UE-to-Network Relay Model B Discovery in ProSe-Enabled LTE  Networks"},{"accessURL":"https://doi.org/10.18434/mds2-2390","title":"DOI Access for UE-to-Network Relay Model B Discovery in ProSe-Enabled LTE Networks"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2390/Data.zip","description":"This dataset contains the numerical results presented on the NIST technical note 2149 entitled \"UE-to-Network Relay Model B Discovery in ProSe-Enabled LTE Networks\", which is available in https://doi.org/10.6028/NIST.TN.2149. The technical note develops an analytical model for the metric \"average number of discovery periods needed by a Remote UE to discover a given Relay UE\", depending on different system parameters such as the number of Relay UEs in the system (Ny), the number of Remote UEs in the system (Nx), and the discovery pool configuration which includes the value of the discovery message transmission probability (txProbability).  The analytical model is then validated using ns-3 system level simulations, and results using both approaches are presented in the numerical results section of the technical note. A README is also included in the dataset with a more comprehensive description of the data format and included files.","mediaType":"application/x-zip-compressed","title":"UE-to-Network Relay Model B Discovery in ProSe-Enabled LTE  Networks"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-08-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Public Safety:Public safety communications research","Information Technology:Networking","Information Technology:Mobile"],"issued":"2021-04-07","keyword":["public safety communication","device-to-device","D2D","wireless communication","direct discovery","LTE","ProSe","UE-to-Network relay"]},{"identifier":"ark:/88434/mds2-2391","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jonathan.seppala@nist.gov","fn":"Jonathan Seppala"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2391","description":"In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath. This dataset focuses on the effect of Newtonian viscosity and Herschel-Bulkley plateau (pre-yielded) viscosity on the extrusion of single lines. OpenFOAM is an open source computational fluid dynamics solver. This work used a combination of OpenFOAM v1912 and OpenFOAM 8 on desktop computers. OpenFOAM input files were generated using Python 3.7. Output files were analyzed using Paraview 5.8.0 and Python 3.7. Associated code can be found on Github: https://github.com/usnistgov/openfoamEmbedded3DP, doi:10.18434/mds2-2392 This data is described in: Friedrich, L., & Seppala, J.E. (2021) Simulated filament shapes in embedded 3D printing, submitted for publication See the file, mds2-2391-filelisting.csv, for a complete listing of the data files that available as part of this collection with URLs for downloading them.","language":["en"],"title":"Simulated filament shapes in embedded 3D printing","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2391/README.txt.sha256","mediaType":"text/plain","title":"SHA256 File for README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2391/README.txt","description":"Information about this dataset","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2391/y_umag.mp4.sha256","mediaType":"text/plain","title":"SHA256 File for A condensed video of side views of all simulations"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2391/y_umag.mp4","description":"Video of all of the side views of all simulations, where all simulations for a certain fluid type combination (Newtonian+Newtonian, Newtonian ink+Herschel Bulkley support, etc...) are plotted together, in time.","mediaType":"video/mp4","title":"A condensed video of side views of all simulations"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2391/mds2-2391-filelisting.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Collection file listing"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2391/mds2-2391-filelisting.csv","format":"CSV-formatted table","description":"This CSV-formatted table lists for each file in this collection its name (as a file path within the collection), size, type, URL for downloading, and a SHA-256 checksum hash (which can be used to verify an uncorrupted download).","mediaType":"text/csv","title":"Collection file listing"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2391/legend_general.csv","description":"A table of simulation metadata for all simulations in this dataset","mediaType":"text/csv","title":"Simulation legend"},{"accessURL":"https://doi.org/10.18434/mds2-2391","title":"DOI Access for Simulated filament shapes in embedded 3D printing"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2391/legend_general.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Simulation legend"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2391/summaryVideo.mp4.sha256","mediaType":"text/plain","title":"SHA256 File for Summary video"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2391/summaryVideo.mp4","description":"A video showing the filament surface during extrusion for all simulations in this dataset","mediaType":"video/mp4","title":"Summary video"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-04-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Polymers","Materials:Modeling and computational material science","Manufacturing:Additive manufacturing"],"issued":"2021-07-15","keyword":["3D-printing","extrusion","support-bath","Herschel-Bulkley","rheology","surface tension","OpenFOAM"]},{"identifier":"ark:/88434/mds2-2392","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jonathan.seppala@nist.gov","fn":"Jonathan Seppala"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2392","description":"In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath. OpenFOAM is an open source computational fluid dynamics solver. This repository contains the following Python tools: - Tools for generating input files for OpenFOAM v1912 or OpenFOAM v8 tailored to a nozzle extruding a filament into a static support bath. - Tools for monitoring the status of OpenFOAM simulations and aborting them if they are too slow. - Tools for moving output files between storage locations. (For example, it can automatically move all files to a server, but only necessary files to your hard drive) - Tools for generating images and tables from the 3D time series. - Tools for compiling images into videos. - Tools for analyzing, summarizing, and plotting data.","language":["en"],"title":"Python tools for OpenFOAM simulations of filament shapes in embedded 3D printing","distribution":[{"accessURL":"https://github.com/usnistgov/openfoamEmbedded3DP/releases/tag/v1.0.0","format":"github repository","description":"Github link to OpenFOAM Embedded 3D printing Python tools","title":"Github: OpenFOAM Embedded 3D Printing"},{"accessURL":"https://doi.org/10.18434/mds2-2392","title":"DOI Access for Python tools for OpenFOAM simulations of filament shapes in embedded 3D printing"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-04-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing","Materials:Polymers","Materials:Modeling and computational material science"],"issued":"2021-04-28","keyword":["3D-printing","extrusion","support-bath","Herschel-Bulkley","rheology","surface tension","OpenFOAM"]},{"identifier":"ark:/88434/mds2-2393","accessLevel":"public","references":["https://doi.org/10.6028/jres.126.026"],"contactPoint":{"hasEmail":"mailto:steven.mates@nist.gov","fn":"Steven P. Mates"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2393","description":"This dataset contains raw mechanical data (strain, stress and strain rate curves and initial temperatures) for compression experiments performed on annealed 1045 steel using the NIST Kolsky Bar apparatus and a servohydraulic test machine that were used to generate fit coefficients for the plasticity model described in the NIST publication entitled: \"A Dynamic Plasticity Model for Rapidly-Heated 1045 Steel up to 1000 °C\" with authors S. P. Mates and S.-Y. Li.","language":["en"],"title":"A Dynamic Plasticity Model for Rapidly-Heated 1045 Steel up to 1000 °C","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2393/MIDAS_steel_data_and_fits_below_400C.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 1045 steel data and fits below 400 C"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2393/MIDAS_steel_data_and_fits_below_400C.csv","format":"comma separated value","description":"File contains stress-strain data and fits for initial temperatures below 400 C along with corresponding model fit coefficients and selected additional evaluated model factors","mediaType":"application/vnd.ms-excel","title":"1045 steel data and fits below 400 C"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2393/MIDAS_steel_data_and_fits_to_A1.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 1045 Steel data and fits to the A1 temperature"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2393/MIDAS_steel_data_and_fits_to_A1.csv","format":"comma separated value","description":"1045 Steel data and fits to the A1 temperature","mediaType":"application/vnd.ms-excel","title":"1045 Steel data and fits to the A1 temperature"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2393/MIDAS_steel_data_and_fits_above_A1.csv.sha256","mediaType":"text/plain","title":"SHA256 File for 1045 steel data and fits above the A1 temperature"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2393/MIDAS_steel_data_and_fits_above_A1.csv","format":"comma separated value","description":"1045 steel data and fits above the A1 temperature","mediaType":"application/vnd.ms-excel","title":"1045 steel data and fits above the A1 temperature"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2393/2393_README.txt.sha256","mediaType":"text/plain","title":"SHA256 File for README file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2393/2393_README.txt","format":"plain text file","description":"Contains additional information on the dataset","mediaType":"text/plain","title":"README file"},{"accessURL":"https://doi.org/10.18434/mds2-2393","title":"DOI Access for A Dynamic Plasticity Model for Rapidly-Heated 1045 Steel up to 1000 °C"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2393/NIST_Model_1045_Steel_Gen.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2393/Steel_1045_NIST_Model_Gen_documentation.pptx","mediaType":"application/vnd.openxmlformats-officedocument.presentationml.presentation"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-04-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Machining","Materials:Metals"],"issued":"2021-09-08","keyword":["Carbon Steel","Constitutive Model","High Strain Rate","High Temperature","Machining "]},{"identifier":"ark:/88434/mds2-2394","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ian.bell@nist.gov","fn":"Ian Bell"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2394","description":"Supplementary files to accompany the the paper \"Super-Ancillary Equations for Cubic Equations of State\" of Ian Bell and Ulrich Deiters in Ind. Eng. Chem. Res. Abstract: Calculation of thermodynamic phase equilibrium is error-prone and can fail both near the critical point and at very low temperatures due to the limited precision available in double precision arithmetic. Most importantly, these calculations frequently represent a computational bottleneck. In this work we extend the \"super-ancillary\" equation approach developed for reference multiparameter equations of state to classical cubic equations of state (van der Waals, Redlich-Kwong-Soave, Peng-Robinson). Iterative calculations in double precision are replaced by non-iterative evaluation of pre-built Chebyshev expansions constructed with extended precision arithmetic. Exact solutions for the equation of state constants are given. The Chebyshev expansions are shown to reproduce the equation of state values to within nearly double precision (aside from in the very near vicinity of the critical point) and are more than 40 times faster to evaluate than the VLE calculations from the fastest computational library. In this way we further expand the domains in which iterative calculations for pure fluid phase equilibria may be rendered obsolete. A C++ header implementing these expansions (and with no external dependencies) is provided as supplemental information. Contact Ian Bell (ian.bell@nist.gov) for more information about this paper and/or the supporting information","language":["en"],"title":"Supporting information to accompany: Super-Ancillary Equations for Cubic Equations of State","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2394","title":"DOI Access for Supporting information to accompany: Super-Ancillary Equations for Cubic Equations of State"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2394/code.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Supplementary code"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2394/code.zip","mediaType":"application/x-zip-compressed","title":"Supplementary code"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-04-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Chemical engineering and processing","Chemistry:Thermochemical properties","Mathematics and Statistics:Numerical methods and software"],"issued":"2021-04-23","keyword":["Chebyshev approximation","equation of state","numerical approximation"]},{"identifier":"ark:/88434/mds2-2395","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2159"],"contactPoint":{"hasEmail":"mailto:aric.sanders@nist.gov","fn":"Aric Sanders"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2395","description":"This data is provided as a supplement to NIST Technical Note 2159 Laboratory Method for Recording AWS-3 LTE Waveforms available at https://doi.org/10.6028/NIST.TN.2159. In particular, the data provided here are a compressed version of all the IQ recordings discussed in the report, with diagnostic information. The data is structured as a compressed archive, Data.zip, for each experimental configuration and capture repeat, resulting in 112 compressed archives organized by directory structure. The compressed archive, Data.zip, contains six files: IQ.csv, configuration.csv, diagnostic.csv, UE_diagnostic_spectrogram.csv, IQ_spectrogram.csv and IQ_spectrogram.png. - IQ.csv Description: Measured IQ at a sampling rate of 61.44 MS / s Contents: I and Q as signed integers, relative units- configuration.csv Description: The experimental configuration under which the IQ recording was made. Corresponds to a row in Table 4.1: Test Configurations of the technote. Contents: Scheduler_Awareness, Scheduler_Allocation, PowerControl_PUSCH, PowerControl_PUCCH,Scheduler_RBMask, P0_PUSCH, P0_PUCCH, alpha, UTG_NumUEs, UTG_ULRate, UTG_TrafficType, UTG_UERSRP, DUT_UE_ULRate, DUT_UE_TrafficType, UT_UE2_ULRate, DUT_UE2_TrafficType - diagnostic.csv Description: UE Diagnostic information with a time-axis in ms based on system subframes. Contents: Elapsed time (ms), Total Tx Power (dBm), Resource Block Start, Number of Resource Blocks, MCS Index - UE_diagnostic_spectrogram.csv Description: The UE diagnostic report of power and resource block allocation organized into physical resource blocks with a time-axis in ms based on system subframes. Contents: Elapsed time (ms),  0 ? max physical resource blocks in shared channel with a power in mw. - IQ_spectrogram.csv Description: IQ data organized into physical resource blocks with a time axis aligned to the diagnostic information. Contents:  Elapsed time (ms),  0 ? 199 resource blocks with a relative power.  - IQ_spectrogram.png Description: Plot of 140 ms of spectrogram, max relative power versus frequency and mean relative power, corresponds to appendix B of technote.   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Thousands of Fab and Fc fragments are described in the Protein Data Bank. Whole antibodies have been imaged by EM methods and in a few cases, crystallized. The central hinge lacks a unique stable conformation and its dynamic properties are important to antibody function. Monte Carlo and molecular dynamics simulations and small-angle scattering  methods have been used to analyze the wide range of configurations that are accessible to antibodies in solution. In order to support the development of antibody-based medicines, the National Institute of Standards and Technology (NIST) has released an extensively characterized IgG1 mAb, called Reference Material 8671 or NISTmAb1. To facilitate modeling of whole antibodies we now report the construction of an all-atom 3-D model of RM 8671. Atomic model structure of the intact monoclonal antibody Reference Material 8671 (NISTmAb), including PDBv3 formatted file containing coordinates for each atom in x, y, z format and the FASTA formatted file containing the primary sequence.","language":["en"],"title":"Atomic Model Structure of Intact Monoclonal Antibody Reference Material 8671 (NISTmAb)","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2396","title":"DOI Access for Atomic Model Structure of Intact Monoclonal Antibody Reference Material 8671 (NISTmAb)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2396/mAb8671_2021_0629.pdb.sha256","mediaType":"text/plain","title":"SHA256 File for mAb 8671"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2396/mAb8671_2021_0629.pdb","format":"PDBv3","description":"An updated version format.","mediaType":"application/octet-stream","title":"mAb 8671"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2396/mAb8671_2021_0629.mmcif.sha256","mediaType":"text/plain","title":"SHA256 File for mAb 8671 mmcif format"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2396/mAb8671_2021_0629.mmcif","format":"mmCIF/PDBX","description":"A new format in use, compliant with PDBX/mmCIF version 5.344.","mediaType":"application/octet-stream","title":"mAb 8671 mmcif format"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2396/NISTmAb.fasta.sha256","mediaType":"text/plain","title":"SHA256 File for FASTA NISTmAb RM 8671"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2396/NISTmAb.fasta","format":"FASTA","description":"Primary sequence of NISTmAb RM 8671","mediaType":"application/octet-stream","title":"FASTA NISTmAb RM 8671"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-04-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference materials","Chemistry:Theoretical chemistry and modeling"],"issued":"2021-06-09","keyword":["intact antibody","molecular model","NISTmAb","RM8671"]},{"identifier":"ark:/88434/mds2-2397","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ian.bell@nist.gov","fn":"Ian Bell"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2397","description":"This record contains a Python script that was used to generate the phase boundaries with superancillary Chebyshev expansion curves.  Running the script in Python 3.8 will output the figures and results from the paper. This paper was presented at the ORC 2021 conference in Munich, Germany, paper # 141. Requirements: conda/pip: numpy, scipy, matplotlib, pandas. pip: CoolProp, ChebTools","language":["en"],"title":"Supporting information to accompany: Accelerating Iterative Equation of State Calculations With Superancillary Phase Boundaries","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2397/make_hs.py.sha256","mediaType":"text/plain","title":"SHA256 File for Script to generate the figures from the paper"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2397/make_hs.py","mediaType":"text/x-python","title":"Script to generate the figures from the paper"},{"accessURL":"https://doi.org/10.18434/mds2-2397","title":"DOI Access for Supporting information to accompany: Accelerating Iterative Equation of State Calculations With Superancillary Phase Boundaries"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-04-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Chemical engineering and processing","Mathematics and Statistics:Numerical methods and software"],"issued":"2021-04-28","keyword":["Chebyshev","equation of state","numerical approximation"]},{"identifier":"ark:/88434/mds2-2398","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8372"],"contactPoint":{"hasEmail":"mailto:fernando.cintron@nist.gov","fn":"Fernando Cintron"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2398","description":"This dataset contains results from numerical analysis of the sidelink physical layer capacities for LTE and NR.","language":["en"],"title":"Study of 5G New Radio (NR) Support for Direct Mode Communications","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2398/NR_sidelink_capacity.csv.sha256","mediaType":"text/plain","title":"SHA256 File for NR sidelink capacity"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2398/NR_sidelink_capacity.csv","description":"NR sidelink capacity is computed in Mbit/s at varying number of HARQ transmissions (1 = no HARQ). 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The authors provide this data set to enable new users of MAUD a better user experience, and provide a series of training opportunities to ensure users of MAUD understand how the software operates beyond treating it as a black box.","language":["en"],"title":"MAUD-Tutorial Files for \"MAUD Rietveld Refinement Software for Neutron Diffraction Texture Studies of Single and Dual-Phase Materials\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2400/ReadMe.txt.sha256","mediaType":"text/plain","title":"SHA256 File for ReadMe.txt"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2400/ReadMe.txt","format":"Text file","description":"Overview of the dataset","mediaType":"text/plain","title":"ReadMe.txt"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2400/LICENSE.txt.sha256","mediaType":"text/plain","title":"SHA256 File for License.txt"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2400/LICENSE.txt","format":"text","description":"License for the data set","mediaType":"text/plain","title":"License.txt"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2400/hippo_sc_151003_45panels.prm.sha256","mediaType":"text/plain","title":"SHA256 File for hippo_sc_151003_45panels.prm"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2400/hippo_sc_151003_45panels.prm","format":"These files use the Instrument Parameter File format described in the GSAS Technical Manual [4].","description":"Information file specifying the detectors panel used in data acquisition. 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cavity ring-down spectroscopy","advanced spectroscopic line shapes"]},{"identifier":"ark:/88434/mds2-2403","accessLevel":"public","references":["https://doi.org/10.1016/j.jqsrt.2021.107669"],"contactPoint":{"hasEmail":"mailto:erin.adkins@nist.gov","fn":"Erin Adkins"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2403","description":"Dataset for generation of figures in Air-broadening in near-infrared carbon dioxide line shapes: quantifying contributions from O2, N2, and Ar (https://doi.org/10.1016/j.jqsrt.2021.107669)","language":["en"],"title":"Air-broadening in near-infrared carbon dioxide line shapes: quantifying contributions from O2, N2, and Ar","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2403/CO2_Broadening%20Paper%20Figure%20Data.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Air-broadening in near-infrared carbon dioxide line shapes: quantifying contributions from O2, N2, and Ar"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2403/CO2_Broadening%20Paper%20Figure%20Data.xlsx","format":"excel workbook","description":"Data to generate figures in Air-broadening in near-infrared carbon dioxide line shapes: quantifying contributions from O2, N2, and Ar","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Air-broadening in near-infrared carbon dioxide line shapes: quantifying contributions from O2, N2, and Ar"},{"accessURL":"https://doi.org/10.18434/mds2-2403","title":"DOI Access for Air-broadening in near-infrared carbon dioxide line shapes: quantifying contributions from O2, N2, and Ar"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-05-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Spectroscopy"],"issued":"2021-05-14","keyword":["spectroscopy","higher order lineshapes","carbon dioxide"]},{"identifier":"ark:/88434/mds2-2404","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2404","description":"Round 6 Test DatasetThis is the test data used to construct and evaluate trojan detection software solutions. 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Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-test","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1UJdc9BMqFup9EsjBh6FkZUo2Qnf1XO9j?usp=drive_link","title":"nlp-sentiment-classification-apr2021-test"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity"],"issued":"2021-05-14","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2405","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2405","description":"Round 6 Train Dataset part2This is the training data used to construct and evaluate trojan detection software solutions. 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Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-train part2","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2405/round6-train-dataset2.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2405/round6-train-dataset2.tar.gz.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/mds2-2405","title":"DOI Access for Trojan Detection Software Challenge - Round 6 Train Dataset part2"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity"],"issued":"2021-05-14","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2406","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2406","description":"Round 6 Holdout DatasetThis is the holdout data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 480 sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-holdout","distribution":[{"accessURL":"https://drive.google.com/drive/folders/114ilRpdmIf3-tsJwDyliK__pG2-1oBGW?usp=drive_link","title":"nlp-sentiment-classification-apr2021-holdout"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity"],"issued":"2021-05-14","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2407","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2407","description":"Round 7 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform named entity recognition (NER) on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 192 sentiment classification AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - nlp-named-entity-recognition-may2021-train","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1nR1mgbuEzIs2XgkxXBQSkwfZdVZqYTtr?usp=drive_link","title":"nlp-named-entity-recognition-may2021-train"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-04-29 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity"],"issued":"2021-05-14","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2409","accessLevel":"public","references":["https://doi.org/10.1364/OL.427083"],"contactPoint":{"hasEmail":"mailto:adam.fleisher@nist.gov","fn":"Adam Fleisher"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2409","description":"Data from peer-reviewed publication:  G. Zhao et al., Frequency stabilization of a quantum cascade laser by weak resonant feedback from a Fabry-Perot cavity, Optics Letters. Frequency-stabilized mid-infrared lasers are valuable tools for precision molecular spectroscopy. However, their implementation remains limited by complicated stabilization schemes. Here we achieve optical self-locking of a quantum cascade laser to the resonant leak-out field of a highly mode-matched two-mirror cavity. The result is a simple approach to achieving ultra-pure frequencies from high-powered mid-infrared lasers. For short time scales (<0.1 ms), we report a linewidth reduction factor of 3×10^(-6) to a linewidth of 12 Hz. Furthermore, we demonstrate two-photon cavity-enhanced absorption spectroscopy of an N2O overtone transition near a wavelength of 4.53 um.","language":["en"],"title":"Frequency stabilization of a quantum cascade laser by weak resonant feedback from a Fabry-Perot cavity","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig1_data.xls","format":".xls","mediaType":"application/vnd.ms-excel","title":"Fig. 1:  Conceptualization and model for a quantum cascade laser coupled to a Fabry-Perot cavity by weak optical feedback."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig1_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for Fig. 1:  Conceptualization and model for a quantum cascade laser coupled to a Fabry-Perot cavity by weak optical feedback."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/readme.txt.sha256","mediaType":"text/plain","title":"SHA256 File for Read me file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/readme.txt","format":".txt","mediaType":"text/plain","title":"Read me file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig4_data.xls","format":".csv","mediaType":"application/vnd.ms-excel","title":"Fig. 4:  QCL line width analysis - power spectral densities"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig5_data.xls","format":".csv","mediaType":"application/vnd.ms-excel","title":"Fig. 5:  Two-photon absorption spectroscopy of N2O in the mid-infrared"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig4_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for Fig. 4:  QCL line width analysis - power spectral densities"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2409/Fleisher_stableQCL_rev1_fig5_data.xls.sha256","mediaType":"text/plain","title":"SHA256 File for Fig. 5:  Two-photon absorption spectroscopy of N2O in the mid-infrared"},{"accessURL":"https://doi.org/10.18434/mds2-2409","title":"DOI Access for Frequency stabilization of a quantum cascade laser by weak resonant feedback from a Fabry-Perot cavity"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-05-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Analytical chemistry","Environment:Greenhouse gas measurements","Metrology:Optical, photometry, and laser metrology","Physics:Spectroscopy"],"issued":"2021-05-20","keyword":["laser stabilization","diode lasers","quantum cascade lasers","laser metrology","optical resonators","two-photon absorption","greenhouse gases","nitrous oxide","oceans","ph","marine mammals","remote sensing","seabirds","Environment and Climate"]},{"identifier":"ark:/88434/mds2-2410","accessLevel":"public","references":["https://www.nist.gov/publications/open-media-forensics-challenge-openmfc-2021-workshop-presentations","https://doi.org/10.6028/NIST.IR.8396","https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8377.pdf","https://doi.org/10.6028/NIST.IR.8377","https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930801","https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932934"],"contactPoint":{"hasEmail":"mailto:haiying.guan@nist.gov","fn":"Haiying Guan"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2410","description":"The datasets contain the following parts for Open Media Forensics Challenge (OpenMFC) evaluations:1. NC16 Kickoff dataset2. NC17 development and evaluation datasets3. MFC18 development and evaluation datasets4. MFC19 development and evaluation datasets5. MFC20 development and evaluation datasets6. OpenMFC2022 steg datasets7. OpenMFC2022 deepfake datasets","language":["en"],"title":"Open Media Forensics Challenge (OpenMFC) Evaluation Datasets","distribution":[{"accessURL":"https://mfc.nist.gov","title":"Open Media Forensics Challenge (OpenMFC) Evaluation"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2410/NIST.IR.8377_published.pdf","mediaType":"application/pdf","title":"User Guide for NIST Media Forensic Challenge (MFC) Datasets"},{"accessURL":"https://doi.org/10.18434/mds2-2410","title":"DOI Access for Open Media Forensics Challenge (OpenMFC) Evaluation Datasets"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-08-29 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Forensics:Digital and multimedia evidence","Information Technology:Identity management"],"issued":"2022-03-04","keyword":["Open Media Forensics Challenge (OpenMFC); Media Forensics Challenge (MFC); Nimble Challenge (NC); Deepfakes; Steganography Detection Datasets;"]},{"identifier":"ark:/88434/mds2-2411","accessLevel":"public","contactPoint":{"hasEmail":"mailto:alison.kahn@nist.gov","fn":"Alison Kahn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2411","description":"These are measurement data associated with https://doi.org/10.6028/NIST.TN.2166. Code to process data is available at https://github.com/usnistgov/accessTime. Includes access delay measurement results with the start of word correction for Analog Direct, P25 Direct , P25 Trunked Phase 1 and Phase 2 and an early development LTE server. Transmit audio and associated cutpoints files are also included.","language":["en"],"title":"Mission Critical Voice Start-of-Word Correction for Access Delay Measurement System Measurement Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2411/SoWC-Publishable-Data.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Example access delay data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2411/SoWC-Publishable-Data.zip","format":"zip file","description":"Example access delay measurement data that uses the start-of-word correction. Data for a variety of PTT technologies, including analog FM, P25, and LTE.","mediaType":"application/x-zip-compressed","title":"Example access delay data"},{"accessURL":"https://doi.org/10.18434/mds2-2411","title":"DOI Access for Mission Critical Voice Start-of-Word Correction for Access Delay Measurement System Measurement Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-05-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research"],"issued":"2021-08-03","keyword":["Access delay; Articulation Band Correlation Modified Rhyme Test (ABC-MRT); A-weight; Encryption; Key performance indicator (KPI); Land mobile radio (LMR); Latency; Long Term Evolution (LTE); Mission Critical Push-to-Talk (MCPTT); Modified Rhyme Test (MRT); Mouth-to-ear (M2E); Packetized; Project 25 (P25); Public Safety; Push-to-talk (PTT); Quality of experience (QoE); Receive; Streaming; Transmit; Vocoder"]},{"identifier":"ark:/88434/mds2-2412","accessLevel":"public","contactPoint":{"hasEmail":"mailto:benjamin.place@nist.gov","fn":"Benjamin Place"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2412","description":"Supporting data for the results of Interlaboratory 1 of the Method Assessment for Non-Targeted Analyses. The datasets include the chemical compound descriptions, laboratory mean responses, and the tools for the principal components analysis of the datasets. In addition, a Microsoft Excel file, which was given to all participants, allowed for the analysis of the metadata.","language":["en"],"title":"Supporting Data for Method Assessment for Non-Targeted Analyses (MANTA) Program: Interlaboratory Study 1 Results","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/MANTA_analysis.pdf.sha256","mediaType":"text/plain","title":"SHA256 File for MANTA Analysis Markdown"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/MANTA_analysis.pdf","format":"pdf","description":"Markdown PDF of the MANTA Analysis scripts used to analyze the datasets","mediaType":"application/pdf","title":"MANTA Analysis Markdown"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/MANTA_analysis.Rmd.sha256","mediaType":"text/plain","title":"SHA256 File for R Markdown of MANTA Analysis data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/MANTA_analysis.Rmd","format":"R Markdown","description":"R Markdown script of the MANTA Analysis scripts used to analyze the datasets","mediaType":"application/octet-stream","title":"R Markdown of MANTA Analysis data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/MANTA1_PCA%20Report%20final.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for PCA Analysis of MANTA Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/MANTA1_PCA%20Report%20final.xlsx","format":"Microsoft Excel","description":"PCA Analysis in Microsoft Excel format for the analysis of metadata associated with MANTA study.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"PCA Analysis of MANTA Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/allanalytes.csv.sha256","mediaType":"text/plain","title":"SHA256 File for analyte list"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/allanalytes.csv","format":"comma-separated value text file","description":"List of analytes used for study","mediaType":"application/vnd.ms-excel","title":"analyte list"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/MANTA1_retentiontimes.csv.sha256","mediaType":"text/plain","title":"SHA256 File for retention times of compounds by laboratories"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/MANTA1_retentiontimes.csv","format":"comma-separated value text file","description":"A list of all compounds and their mean retention times in the datasets of individual participants.","mediaType":"application/vnd.ms-excel","title":"retention times of compounds by laboratories"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/MANTAmean_PCAdata.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Values used for MANTA PCA"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2412/MANTAmean_PCAdata.csv","format":"comma-separated value text file","description":"Mean values of figures of merit used for the MANTA study principal components analysis","mediaType":"application/vnd.ms-excel","title":"Values used for MANTA PCA"},{"accessURL":"https://doi.org/10.18434/mds2-2412","title":"DOI Access for Supporting Data for Method Assessment for Non-Targeted Analyses (MANTA) Program: Interlaboratory Study 1 Results"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-05-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Environment:Environmental health","Chemistry:Analytical chemistry"],"issued":"2021-07-29","keyword":["non-targeted analysis; environmental analysis; metabolomics; data analysis"]},{"identifier":"ark:/88434/mds2-2413","accessLevel":"public","references":["https://www.its.bldrdoc.gov/publications/details.aspx?pub=3262","https://www.its.bldrdoc.gov/publications/details.aspx?pub=3261","https://doi.org/10.6028/NIST.TN.2140"],"contactPoint":{"hasEmail":"mailto:aric.sanders@nist.gov","fn":"Aric Sanders"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2413","description":"This data contains in-phase and quadrature waveforms of AWS-1 LTE uplink emissions collected at NASA Langley Research Center's Langley Research Antenna System. 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The ThermoML archive is a subset of Thermodynamics Research Center (TRC) data holdings corresponding to cooperation between NIST TRC and five journals: Journal of Chemical Engineering and Data (ISSN: 1520-5134), The Journal of Chemical Thermodynamics (ISSN: 1096-3626), Fluid Phase Equilibria (ISSN: 0378-3812), Thermochimica Acta (ISSN: 0040-6031), and International Journal of Thermophysics (ISSN: 1572-9567). Data from initial cooperation (around 2003) through the 2019 calendar year are included.  The original scope of the archive has been expanded to include JSON files. The JSON files are structured according to the ThermoML.xsd (available below) and rendered from the same experimental thermophysical and thermochemical property data reported in the corresponding articles as the ThermoML files. In fact, the ThermoML files are generated from the JSON files to keep the information in sync. The JSON files may contain additional information not supported by the ThermoML schema. For example, each JSON file contains the md5 checksum on the ThermoML file (THERMOML_MD5_CHECKSUM) that may be used to validate the ThermoML download. This data.nist.gov resource provides a .tgz file download containing the JSON and ThermoML files for each version of the archive.  Data from initial cooperation (around 2003) through the 2019 calendar year are provided below (ThermoML.v2020-09.30.tgz).  The date of the extraction from TRC databases, as specified in the dateCit field of the xml files, are 2020-09-29 and 2020-09-30.  The .tgz file contains a directory tree that maps to the DOI prefix/suffix of the entries; e.g. unzipping the .tgz file creates a directory for each of the prefixes ( 10.1007, 10.1016, and 10.1021) that contains all the .json and .xml files. The data and other information throughout this digital resource (including the website, API, JSON, and ThermoML files) have been carefully extracted from the original articles by NIST/TRC personnel. Neither the Journal publisher, nor its editors, nor NIST/TRC warrant or represent, expressly or implied, the correctness or accuracy of the content of information contained throughout this digital resource, nor its fitness for any use or for any purpose, nor can they, or will they, accept any liability or responsibility whatever for the consequences of its use or misuse by anyone. In any individual case of application, the respective user must check the correctness by consulting other relevant sources of information.","language":["en"],"title":"ThermoML/Data Archive","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2422","title":"DOI Access for ThermoML/Data Archive"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2422/ThermoML.v2020-09-30.tgz.sha256","mediaType":"text/plain","title":"SHA256 File for ThermoML/Data Archive json and xml files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2422/ThermoML.v2020-09-30.tgz","description":"corresponds to the state of the archive as of 2020-09-30, which contains ThermoML entries published through the 2019 calendar year. The date of the extraction from TRC databases, as specified in the dateCit field of the xml files, are 2020-09-29 and 2020-09-30.  The .tgz file contains a directory tree that maps to the DOI prefix/suffix of the entries; e.g. unzipping the .tgz file creates a directory for each of the prefixes ( 10.1007, 10.1016, and 10.1021) that contains all the .json and .xml files.","mediaType":"application/gzip","title":"ThermoML/Data Archive json and xml files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2422/ThermoML.xsd.sha256","mediaType":"text/plain","title":"SHA256 File for ThermoML XSD"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2422/ThermoML.xsd","description":"ThermoML XML schema definition","mediaType":"application/octet-stream","title":"ThermoML XSD"},{"accessURL":"https://onlinelibrary.wiley.com/share/author/WKPMRWMYRCFW79RXEQPW?target=10.1002/jcc.26842","description":"The associated software note contains more detailed information about this data resource and associated web application. Please cite this publication and the data.nist.gov entry (doi:10.18434/mds2-2422) if this resource is used in published work.","title":"Associated publication: J. Comput. Chem. 2022, 43( 12), 879. (https://doi.org/10.1002/jcc.26842)"},{"accessURL":"https://trc.nist.gov/ThermoML","description":"This associated web application can be used to explore this data resource.","title":"https://trc.nist.gov/ThermoML"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-03-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Thermodynamics","Chemistry:Thermochemical properties","Chemistry:Chemical thermodynamics and chemical properties","Standards:Reference data"],"issued":"2021-08-05","keyword":["Thermophysical Properties","Thermochemical Properties","ThermoML","JSON"]},{"identifier":"ark:/88434/mds2-2423","accessLevel":"public","contactPoint":{"hasEmail":"mailto:carl.simon@nist.gov","fn":"Carl Simon Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2423","description":"This page hosts the AbsorbanceQ app which generates absorbance images from brightfield image captures. Absorbance microscopy can be used to make trypan blue cell viability measurements more quantitative and traceable by measuring the moles of trypan blue in each dead cell. AbsorbanceQ was written in MATLAB and can be downloaded as a .zip file.  When unzipped, the program will be installed and can run as a standalone .exe file with no dependencies beyond the operating system. This website includes images that can be downloaded to validate that AbsorbanceQ is functioning properly. References: [1] Babakhanova G,  Zimmerman SM,  Pierce LT,  Sarkar S,  Schaub NJ, Simon Jr CG (2021) Quantitative, Traceable Determination of Cell Viability Using Absorbance Microscopy, in press. [2] Babakhanova G, Zimmerman SM, Pierce LT, Sarkar S, Schaub NJ, Simon Jr CG (2021), Dataset for Absorbance Microscopy for Quantitative and Traceable Trypan Blue Cell Viability Measurement, National Institute of Standards and Technology, https://doi.org/10.18434/mds2-2347. [3] Zimmerman SM,  Simon Jr CG, Babakhanova G (2021) AbsorbanceQ: An App for Generating Absorbance Images from Brightfield Images. J Res Natl Inst Stan 126:126039, https://doi.org/10.6028/jres.126.039.","language":["en"],"title":"AbsorbanceQ  App for Generating Absorbance Images from Brightfield Image Captures","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Key_Code.docx.sha256","mediaType":"text/plain","title":"SHA256 File for Key_Code.docx"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/ReadMe.docx.sha256","mediaType":"text/plain","title":"SHA256 File for ReadMe file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Source_Code.docx","description":"This is the Matlab source code for the AbsorbanceQ app.","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"Source code"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/MyAppInstaller_mcr.zip.sha256","mediaType":"text/plain","title":"SHA256 File for MyAppInstaller_mcr.zip"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Cell_Images_16bit.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Cell_Images_16bit.zip"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/MyAppInstaller_mcr.zip","description":"This is a zip file that contains a file called \"MyAppInstaller_mcr.exe\" that will install the AbsorbanceQ app on your computer.","mediaType":"application/x-zip-compressed","title":"MyAppInstaller_mcr.zip"},{"accessURL":"https://doi.org/10.18434/mds2-2423","title":"DOI Access for AbsorbanceQ  App for Generating Absorbance Images from Brightfield Image Captures"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Key_Code.docx","description":"These are the key lines of functional code from the app that have been decoupled from the user interface.","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"Key_Code.docx"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Cell_Images_16bit.zip","description":"This is a zip file containing brightfield images of dead cells that can be used as input into the app in order to generate absorbance images.  The images are [16 bit monochrome tif files].  There are 10 cell images I, one Imax image and one Imin image.","mediaType":"application/x-zip-compressed"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Source_Code.docx.sha256","mediaType":"text/plain","title":"SHA256 File for Source code"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Synthetic_Images_32bit.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Synthetic_Images_32bit"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Synthetic_Images_32bit.zip","description":"This is a .zip file containing synthetic images that can be used to validate that the app works properly using [32 bit floating point monochrome .tif files] as input images. There are five synthetic neutral density filter images (I) (0.1, 0.5, 1.0, 1.5 and a striped image with four values), one synthetic Imax image and one synthetic Imin image.","mediaType":"application/x-zip-compressed","title":"Synthetic_Images_32bit"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Synthetic_Images_16bit.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Synthetic_Images_16bit"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Synthetic_Images_16bit.zip","description":"This is a .zip file containing synthetic images that can be used to validate that the app works properly using [16 bit monochrome .tif files] as input images.  There are six synthetic neutral density filter images (I) (-0.1, 0.1, 0.5, 1.0, 1.5 and a striped image with four values), one synthetic Imax image and one synthetic Imin image.","mediaType":"application/x-zip-compressed","title":"Synthetic_Images_16bit"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Synthetic_Images_8bit.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Synthetic_Images_8bit"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/Synthetic_Images_8bit.zip","description":"This is a .zip file containing synthetic images that can be used to validate that the app works properly using [8 bit monochrome .tif files] as input images.  There is a synthetic neutral density filter image (I) (0.5), one synthetic Imax image and one synthetic Imin image.","mediaType":"application/x-zip-compressed","title":"Synthetic_Images_8bit"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/ReadMe.docx","description":"ReadMe file that explains the contents of the website.","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"ReadMe file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2423/License.txt","format":".txt file","description":"License","mediaType":"text/plain","title":"License"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-06-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Process measurement and control","Manufacturing:Biomanufacturing","Manufacturing","Health:Cell therapies","Bioscience:Cell biology","Bioscience:Bioprocessing","Bioscience:Biomaterials"],"issued":"2021-06-23","keyword":["AbsorbanceQ","absorbance imaging","cell viability","regenerative medicine","trypan blue"]},{"identifier":"ark:/88434/mds2-2424","accessLevel":"public","contactPoint":{"hasEmail":"mailto:edward.sisco@nist.gov","fn":"Edward Sisco"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2424","description":"This dataset includes templates to assist with laboratory implementation of direct analysis in real time mass spectrometry (DART-MS) for seized drug analysis. These templates should be modified as necessary to meet the needs of the particular laboratory. Certain commercial equipment, instruments, or materials are identified in this paper in order to specify the experimental procedure adequately.  Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose. These opinions, recommendations, findings, and conclusions do not necessarily reflect the views or policies of NIST or the United States Government.","language":["en"],"title":"Templates for the Implementation of DART-MS for Seized Drug Analysis","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20Maintenance_Unformatted.docx","description":"Example maintenance manual for DART-MS.","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"DART-MS Maintenance Manual"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20Validation%20Run%20Sheets.docx","description":"Run sheets to accompany the DART-MS validation plan.","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"DART-MS Run Sheets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20Validation%20Data%20Workup_Blank.xlsx","description":"Excel sheet to assist in working up DART-MS validation data.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"DART-MS Validation Data Workup"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20Validation%20Plan_Unformatted.docx","description":"Example validation plan for DART-MS analysis of seized drugs.","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"DART-MS Validation Plan"},{"accessURL":"https://doi.org/10.18434/mds2-2424","title":"DOI Access for Templates for the Implementation of DART-MS for Seized Drug Analysis"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20SOP_Unformatted.docx","description":"Example SOP for operation and analysis of data.","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"Example SOP"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20Maintenance_Unformatted.docx.sha256","mediaType":"text/plain","title":"SHA256 File for DART-MS Maintenance Manual"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20Validation%20Run%20Sheets.docx.sha256","mediaType":"text/plain","title":"SHA256 File for DART-MS Run Sheets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20Validation%20Data%20Workup_Blank.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for DART-MS Validation Data Workup"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20Validation%20Plan_Unformatted.docx.sha256","mediaType":"text/plain","title":"SHA256 File for DART-MS Validation Plan"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20SOP_Unformatted.docx.sha256","mediaType":"text/plain","title":"SHA256 File for Example SOP"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/210301_Seized%20Drug%20Search%20List.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Search List"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20Validation%20Data%20Workup_Blank_v2.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Validation Plan Data Workup"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/210301_Seized%20Drug%20Search%20List.xlsx","description":"Example search list for use with MassMountaineer. Search list last updated March 1, 2021.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Search List"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2424/DART%20Validation%20Data%20Workup_Blank_v2.xlsx","description":"Excel document to assist with data analysis using the validation plan.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Validation Plan Data Workup"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-06-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Forensics:Drugs and toxicology"],"issued":"2021-06-15","keyword":["DART-MS","Forensics","Seized Drug","Implementation","Validation"]},{"identifier":"ark:/88434/mds2-2428","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jack.sklar@nist.gov","fn":"Jack Sklar"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2428","description":"This software and data have been superseded. Please visit https://doi.org/10.18434/mds2-2532","language":["en"],"title":"**SUPERSEDED** Software and Data for Modeling OFDM Communication Signals with Generative Adversarial Networks","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2428","title":"DOI Access for Software and Data for Modeling OFDM Communication Signals with Generative Adversarial Networks"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-06-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)","Mathematics and Statistics:Image and signal processing"],"issued":"2021-06-29","keyword":["generative adversarial network","machine learning","wireless communications"]},{"identifier":"ark:/88434/mds2-2429","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ann.virts@nist.gov","fn":"Ann Virts"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://www.nist.gov/el/intelligent-systems-division-73500/exoskeletons-and-exosuits-research-and-standard-test-methods-1","description":"The National Institute of Standards and Technology, Intelligent Systems Division has collected data measuring human subjects, while performing common, simulated industrial manufacturing tasks with and without wearing an exoskeleton. Five tests were completed as part of a research study to develop measurement science towards standard test methods. For simulated industrial manufacturing tasks were performed using a novel, now standardized apparatus, called the Position and Load Test Apparatus for Exoskeletons (PoLoTAE). In addition, a set of novel optical tracking marker artifacts were worn by the subject for synchronous tracking of exoskeleton and human leg position and orientation. The standard test artifacts were intended to address the challenges of measurement uncertainty variation between different marker clusters and marker movement on soft tissue and marker occlusion when using traditional bio-mechanical marker models while wearing an exoskeleton. The PoLoTAE tests simulated generic industrial tasks (load positioning, load alignment, peg-in-hole, applied force). The knee bend tests were performed to synchronously track the exoskeleton and human lower limb position and orientation for analysis such as comparing the exoskeleton fit to the subjectâ\u0080\u0099s leg.Overall, the tests included 116 subjects of which 68 subjects (59% of total subjects) consented to publication of their raw test data described in this paper.  While some subjects performed more than one test, at least 30 subjects performed each of the five tests totaling 158 tests performed. To date, aggregate data for the load positioning and knee bend tests have been analyzed and are referenced in this paper. Sensor data was collected from each subject, which included: repetition number, heart rate, videos, skeletal joint pose estimation, and survey data.","language":["en"],"title":"Exoskeleton Performance Data","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-06-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Robotics in manufacturing:Manufacturing"],"keyword":["Exoskeleton","Exosuits Research","Standard Test Methods","Robotics","Manufacturing"]},{"identifier":"ark:/88434/mds2-2431","accessLevel":"public","references":["https://doi.org/10.6028/jres.126.028"],"contactPoint":{"hasEmail":"mailto:stacy.schuur@nist.gov","fn":"Stacy Schuur"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2431","description":"Here we provide curated analytical chemistry data for eggs collected from 1999 to 2010 on a subset of species and analytes that were measured regularly and reasonably systematically. Included in this publication are 487 samples analyzed for 174 ubiquitous environmental contaminants such as (poly)brominated diphenyl ethers (BDEs), mercury, organochlorine pesticides (OCPs), and polychlorinated biphenyls (PCBs). Data were collated to form a dataset useful in chemometric and related analyses of the marine ecosystem in the north Pacific Ocean.","language":["en"],"title":"Data Supporting \"Seabird Tissue Archival and Monitoring Project (STAMP) Data from 1999-2010\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/stamp_chemistry.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Analytical Chemistry Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/stamp_chemistry.csv","format":"comma-separated values","description":"Analytical chemistry values for this data set including aliquot ID, analyte name, detection limit, measured value, detection status, units, analyte category, and a statistical value. Column name definitions are available in \"reported_fields.csv\"","mediaType":"application/vnd.ms-excel","title":"Analytical Chemistry Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/appendix.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for Appendix A"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/appendix.xlsx","format":"excel workbook","description":"Expanded information about analytical chemistry results including batch-specific processing information and quality assurance comparisons.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Appendix A"},{"accessURL":"https://doi.org/10.18434/mds2-2431","title":"DOI Access for Data Supporting \"Seabird Tissue Archival and Monitoring Project (STAMP) Data from 1999-2010\""},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/reported_fields.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Data Dictionary"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/reported_fields.csv","format":"comma-separated-value","description":"A list of the column names present in files \"stamp_chemistry.csv\" and \"stamp_samples.csv\" with human meaningful definitions.","mediaType":"application/vnd.ms-excel","title":"Data Dictionary"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/stamp_samples.csv.sha256","mediaType":"text/plain","title":"SHA256 File for Sample Properties"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2431/stamp_samples.csv","format":"comma-separated values","description":"Properties relevant to samples in this data set including aliquot and sample IDs, colony name, geographic region, species and common name, latitude and longitude, collection year, and the type of analysis performed on the aliquot. Column name definitions are available in \"reported_fields.csv\"","mediaType":"application/vnd.ms-excel","title":"Sample Properties"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-07-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://data.nist.gov/od/ds/mds2-2431/reported_fields.csv","theme":["Information Technology:Data and informatics","Environment:Marine science","Environment:Environmental health","Environment:Air / water / soil quality","Chemistry:Analytical chemistry"],"spatial":"North Pacific Ocean","issued":"2021-11-09","keyword":["NIST Biorepository","eggs","tissues","BDEs","PBDEs","PCBs","pesticides","mercury","trace elements","heavy metals","organic","inorganic","chemistry","stable isotopes","genetics","Environment and Climate","chemometric","machine learning","ML","seabird","bird","Pacific Ocean"],"temporal":"1999-05-01/2010-12-31"},{"identifier":"ark:/88434/mds2-2432","accessLevel":"public","contactPoint":{"hasEmail":"mailto:alison.kahn@nist.gov","fn":"Alison Kahn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2432","description":"Data collected includes results for a transmit volume optimization (TVO) method utilized in audio device characterization. Optimal transmit volume of Land Mobile Radios (LMR) including direct mode operation (P25 and analog) and trunked mode operation (P25 trunked Phase 2).  Final Data is included as .csv and raw .mat files, as well as transmit audio files to be used for testing.","language":["en"],"title":"Optimal Transmit Volume Conditions for MCV QoE Measurement Systems Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2432/Pub_Data/Transmit_Vol_Opt_Data.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2432/Pub_Data/Transmit_Vol_Opt_Data.zip.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/mds2-2432","title":"DOI Access for Optimal Transmit Volume Conditions for MCV QoE Measurement Systems Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-07-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research","Advanced Communications:Wireless (RF)"],"issued":"2021-09-03","keyword":["Analog","Audio","A-weight","Communications","Direct  mode","Distortion","Frequency","Frequency slope fit (FSF)","Key performance indicator (KPI)","Land mobile radio (LMR)","Mission Critical Push-to-Talk (MCPTT)","Mission critical voice (MCV)","Mouth-to-ear (M2E)","Optimal volume plateau identification algorithm (OVPIA)","Project 25 (P25)","Plateau","Public  safety","Push-to-talk  (PTT)","Quality  of  experience  (QoE)","System  under  test  (SUT)","Transmit volume optimization (TVO)","Volume."]},{"identifier":"ark:/88434/mds2-2434","accessLevel":"public","contactPoint":{"hasEmail":"mailto:philip.kent@nist.gov","fn":"Philip Kent"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2434","description":"The NIST scan framework is a framework that greatly simplifies the process of writing and maintaining scans of experimental parameters using the ARTIQ control system and language.  The framework adopts the philosophy of convention over configuration where datasets are stored for analysis and plotting in a standard directory structure. The framework provides a number of useful features such as automatic calculation of statistics, fitting, validation of fits, and plotting that do not need to be performed by the user.  This reduces the size and complexity of scan experiments to make them fast to implement, easy to read, and easy to maintain.","language":["en"],"title":"The NIST Scan Framework for ARTIQ","distribution":[{"accessURL":"https://pages.nist.gov/artiq_scan_framework/","description":"Current location of all documentation for the NIST scan framework.","title":"Documentation for the NIST scan framework."},{"accessURL":"https://github.com/usnistgov/artiq_scan_framework","description":"Current Github repository which will contain all source files for the NIST scan framework.  Please contact Philip D. Kent (philip.kent@nist.gov).","title":"Source code repository for the NIST scan framework"},{"accessURL":"https://doi.org/10.18434/mds2-2434","title":"DOI Access for The NIST Scan Framework for ARTIQ"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-07-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Quantum information science","Physics:Atomic, molecular, and quantum"],"conformsTo":"https://semver.org/spec/v2.0.0.html","issued":"2022-02-28","keyword":["ARTIQ","Python","Scans"]},{"identifier":"ark:/88434/mds2-2436","accessLevel":"public","references":["https://dx.doi.org/10.5220/0010513200300041"],"contactPoint":{"hasEmail":"mailto:wesley.garey@nist.gov","fn":"Wesley Garey"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2436","description":"This dataset contains the raw data for the paper: Garey, W.; Henderson, T.; Sun, Y.; Rouil, R. and Gamboa, S. (2021). Modeling MCPTT and User Behavior in ns-3. In Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH, ISBN 978-989-758-528-9, pages 30-41. DOI: 10.5220/0010513200300041. This includes: Figure 4 - The talk spurt and talk session duration CDF. Figure 5 -  Orchestrator pusher model example. Figure 7 - MCPTT Access Time for immediately granted requests with queuing disabled. Figure 8 - MCPTT Access Time for queued or immediately granted requests with queuing enabled. Figure 9 - Mcptt Mouth-to-Ear Latency","language":["en"],"title":"Modeling MCPTT and User Behavior in ns-3","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2436/plots.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Modeling MCPTT and User Behavior in ns-3"},{"accessURL":"https://doi.org/10.18434/mds2-2436","title":"DOI Access for Modeling MCPTT and User Behavior in ns-3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2436/plots.zip","description":"The data included in this package contains the raw data for the paper: Garey, W.; Henderson, T.; Sun, Y.; Rouil, R. and Gamboa, S. (2021). Modeling MCPTT and User Behavior in ns-3. In Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH, ISBN 978-989-758-528-9, pages 30-41. DOI: 10.5220/0010513200300041. To support the advancement of public safety communications technology, the Third Generation Partnership Project (3GPP) has created several standards to define Mission Critical Push-To-Talk (MCPTT) over LongTerm Evolution (LTE) networks. As this is a new service that can be used in dire situations, it is imperative that the behavior and performance meet the expectations of first responders. This paper introduces an extension to the network simulator, ns-3, that models MCPTT and user Push-To-Talk (PTT) activity, so that researchers can gain insights and evaluate the performance of this service. In this paper we will describe MCPTT based on 3GPP definitions, the implementation of the MCPTT model in ns-3, and some results, including Key Performance Indicators (KPIs), that can be extracted from this model. Figure 4 was generated using proprietary  data taken from a public safety LMR communication system. Figure 4 is comprised of two Cumulative Distribution Function (CDF) to graphically represent the length of talk spurts and talk sessions. Figure 5 was generated using the Mission Critical Push-To-Talk (MCPTT) model described in the paper. Figure 5 uses a step graph to show the transition of states for pushers and sessions during an arbitrary simulation for two users in the same group. Data is generated using 'mcptt-on-network-rebase' branch of https://github.com/tomhenderson/pscr-net-sim at commit b72658ddf (Jan 26, 2021). Figure 7 uses a CDF to graphically show the recorded MCPTT access time from the case study that is described in the paper when queuing is disabled. Figure 9 uses a CDF to graphically display the mouth-to-ear latency recorded from the case study that is described in the paper when queuing is disabled. Figure 7 and Figure 9 data is generated from this command: ./waf --run 'mcptt-operational-modes-static --callDuration=5000s --showProgress=1' Figure 8 uses a CDF to graphically show the recorded MCPTT access time from the case study that is described in the paper when queuing is enabled. Figure 8 data is generated from this command: ./waf --run 'mcptt-operational-modes-static --callDuration=5000s --showProgress=1 --queueing=1'","mediaType":"application/x-zip-compressed","title":"Modeling MCPTT and User Behavior in ns-3"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-07-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Networking","Advanced Communications:Wireless (RF)","Public Safety:Public safety communications research","Mathematics and Statistics:Modeling and simulation research"],"issued":"2021-08-09","keyword":["public safety communication","mission critical voice","MCV","push-to-talk","wireless communication","ns-3","MCPTT"]},{"identifier":"ark:/88434/mds2-2437","accessLevel":"public","contactPoint":{"hasEmail":"mailto:david.ross@nist.gov","fn":"David J. Ross"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2437","description":"Flow cytometry measurements of FISH-labeled RNA at timepoints following rifampicin addition.  These data folders contain raw data (.fcs files), analysis files, and summary statistics (\"summary.csv\") for each experiment. The \"v1\" data represent biological replicates. The \"v2\" data represent technical replicates of the original sample preparations for each experiment.  Both \"v2\" datasets were collected on July 16, 2021.","language":["en"],"title":"Results of flow-FISH for RNA 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Conditions for MCV QoE Measurement Systems https://doi.org/10.6028/NIST.TN.NUMBER. Code to process data or run a test is available at https:https://github.com/usnistgov/MCV-QOE-TVO. The data folder contains two main folders. These files contain measurement results for P25 Direct, P25 trunked Phase 2, and analog direct operating in unencrypted modes. Transmit audio and associated cutpoints files are also included.","title":"Optimal Transmit Volume Conditions for MCV QoE Measurement Systems Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-08-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research"],"issued":"2021-09-27","keyword":["Analog","Audio","A-weight","Communications","Direct  mode","Distortion","Frequency","Frequency slope fit (FSF)","Key performance indicator (KPI)","Land mobile radio (LMR)","Mission Critical Push-to-Talk (MCPTT)","Mission critical voice (MCV)","Mouth-to-ear (M2E)","Optimal volume plateau identification algorithm (OVPIA)","Project 25 (P25)","Plateau","Public  safety","Push-to-talk  (PTT)","Quality  of  experience  (QoE)","System  under  test  (SUT)","Transmit volume optimization (TVO)","Volume."]},{"identifier":"ark:/88434/mds2-2448","accessLevel":"public","contactPoint":{"hasEmail":"mailto:edward.sisco@nist.gov","fn":"Edward Sisco"},"programCode":["006:045"],"@type":"dcat:Dataset","replaces":"ark:/88434/mds2-2448","landingPage":"https://data.nist.gov/od/id/mds2-2448","description":"Direct Analysis in Real Time Mass Spectrometry (DART-MS) is an analytical chemistry technology that is being increasingly employed in forensic applications. This form of mass spectrometry rapidly yields rich structural information about an analyte with minimal sample preparation. The challenge with DART-MS data, much like other data generated with high throughput technologies, lies in the data interpretation. This is especially true when the analyzed samples are multi-component mixtures like seized drug evidence. The NIST/NIJ DART-MS Data Interpretation Tool (DIT) is a freely available and open-source software tool developed to support the interpretation of in-source collision induced dissociation (is-CID) DART-MS data. The NIST/NIJ DART-MS DIT can be used to view reference mass spectra from DART-MS spectral libraries, search query DART-MS mass spectra of mixtures against reference libraries, using the Inverted Library Search Algorithm, and generate printable reports from search results. Several of the features, including the formatting of generated reports, were iteratively designed with input from local, state, and federal forensic practitioners, ensuring that the program is intuitive and usable for the expected users.","language":["en"],"title":"NIST/NIJ DART-MS Data Interpretation Tool","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2448/DITv3.22_Joro.zip","mediaType":"application/zip","title":"DITv3.22_Joro"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2448/DITv3.zip.sha256","description":"An updated version of the DART-MS Data Interpretation Tool. Updated to enable the ability to display database spectra of deuterated compounds and to be able to search deuterated compounds. The database has also been updated to the latest version (Joro).","mediaType":"text/plain","title":"DITv3.22"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2448/DITv3.zip","mediaType":"application/zip"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-09-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Analytical chemistry","Forensics:Drugs and toxicology"],"keyword":["source code","DART-MS","Forensic Chemistry","Seized Drug Analysis","ILSA"]},{"identifier":"ark:/88434/mds2-2449","accessLevel":"public","contactPoint":{"hasEmail":"mailto:austin.mcdannald@nist.gov","fn":"Austin McDannald"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2449","description":"ANDiE the Autonomous Neutron Diffraction Explorer is a tool for autonomously discovering the magnetic transition temperature and transition dynamics of a material from neutron diffraction experiments. The Jupyter notebooks used to implement ANDiE can be found here: https://github.com/usnistgov/ANDiE-v1_0 The Jupyter notebooks contained therein are of ANDiE as implemented at the WAND2 instrument at the HB-2C beamline at the High-Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). The outputs of the cells are consistent with the autonomous experiment run as performed on 7/24/2021 and subsequent post-processing analysis for a powder sample Fe1.09Te on WAND2. The powder diffraction data for the autonomous experiments with Fe1.09Te and MnO powder samples, as well as ad hoc collected data for Fe1.09Te are available in the \"ANDiE data.zip\" folder. The data is in .xye format with 'x' being the diffraction angle (2?) in degrees, 'y' being the neutron intensity, and 'e' being the error in that intensity. The \"List of Experiments.csv\" file provides the metadata (including the sample temperature) about each diffraction measurement in \"ANDiE data.zip\"  \"Thermal_Outputs_run1_2021-08-02.p\", \"Isothermal_Outputs_run1_2021-08-02.p\", and \"Isothermal_1peak_Outputs_run1_2021-08-02.p\" are example inference output files as generated by ANDiE for the autonomously collected data of the Fe1.09Te sample. These files are in the form of Pickled (Python 3.9) Pandas DataFrames. They are used as inputs to for the post-processing hypothesis testing step of ANDiE.","language":["en"],"title":"ANDiE: the Autonomous Neutron Diffraction Explorer.","distribution":[{"accessURL":"https://github.com/usnistgov/ANDiE-v1_0","format":"GitHub repository for Jupyter notebooks to implement ANDiE","title":"Jupyter notebooks used to implement ANDiE: the Autonomous Neutron Diffraction Explorer"},{"accessURL":"https://doi.org/10.18434/mds2-2449","title":"DOI Access for ANDiE: the Autonomous Neutron Diffraction Explorer."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2449/ANDiE%20data.zip","mediaType":"application/zip","title":"ANDiE data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-08-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Neutron Research:Materials"],"keyword":["Autonomous Experiments","Neutron Diffraction","Machine Learning","Active Learning","Artificial Intelligence"]},{"identifier":"ark:/88434/mds2-2450","accessLevel":"public","references":["https://doi.org/10.1063/1.2764372","https://doi.org/10.1088/0957-0233/17/10/041","https://doi.org/10.1088/0957-4484/23/37/375702"],"contactPoint":{"hasEmail":"mailto:william.osborn@nist.gov","fn":"William Alexander Osborn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2450","description":"SRM 3461 is an AFM sized chip with an array of seven cantilevers on each chip.  The uniformity of the chips offered for sale from wafer 2 is excellent and the SRM certificate reports values and uncertainties for the entire lot of chips for sale; however, this dataset provides the raw data used to certify the SRM.  Since each chip is serialized, the raw data for a specific SRM chip can be accessed from this dataset.  In addition to the laser Doppler vibrometry data and optical micrograph, this dataset contains the Mathematica code used to process the data.  This code reads the binary file formats from the Polytec.pvd files and calculates vibration spectra and fitted peak values that are then exported into the Excel files that are being made available for each chip.","language":["en"],"title":"Calibration Data for Wafer 2 of SRM 3461 - MEMS Cantilever 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The output pulse is calculated for seven values of the finesse of the comb.","language":["en"],"title":"Simulating Photon Echoes for Quantum Memory","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2454/2454_README.txt.sha256","mediaType":"text/plain","title":"SHA256 File for README file for project"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2454/PhotonEchoDemonstration07.nb","mediaType":"application/mathematica"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2454/PhotonEchoDemonstration07.nb.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2454/PhotonEchoDemonstration07.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2454/PhotonEchoDemonstration07.pdf.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/mds2-2454","title":"DOI Access for Simulating Photon Echoes for Quantum Memory"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-09-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Optical physics"],"issued":"2021-09-21","keyword":["Quantum Optics ; Quantum Memory ; Theory ; Simulation ; Photon Echoes ; Semiclassical Model"]},{"identifier":"ark:/88434/mds2-2455","accessLevel":"public","contactPoint":{"hasEmail":"mailto:katherine.sharpless@nist.gov","fn":"Katherine E. 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NISTIR 8084, which summarizes NIST's plan for providing public access is accessible via  http://dx.doi.org/10.6028/NIST.IR.8084.","language":["en"],"title":"Semiannual Metrics Reported to the Office of Science and Technology Policy to Demonstrate Implementation of NIST's Public Access Plan","distribution":[{"accessURL":"https://doi.org/10.18434/T4/1503048","format":"text/html","description":"DOI Access to Semiannual Metrics Reported to the Office of Science and Technology Policy to Demonstrate Implementation of NIST's Public Access Plan","title":"DOI Access to Semiannual Metrics Reported to OSTP"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2455/2021%20midyear%20metrics.csv","format":"The file is saved as a csv.","description":"These are numbers reported to OSTP in July 2021 for their report to Congress. 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This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform named entity recognition (NER) on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 384 named entity recognition AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - nlp-named-entity-recognition-may2021-holdout","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1pJcBEKzpNQA6LFp86p1TkA3S-XmmtVEP?usp=drive_link","title":"nlp-named-entity-recognition-may2021-holdout"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-05-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity"],"issued":"2021-09-16","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2460","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2460","description":"Round 8 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering (QA on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 120 QA AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - nlp-question-answering-sep2021-train","distribution":[{"accessURL":"https://drive.google.com/drive/folders/14HRUuFyw7S7JZsJroUEy1sqeEPaJyiGh?usp=drive_link","title":"nlp-question-answering-sep2021-train"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-08-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity"],"issued":"2021-09-16","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2462","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jessica.staymates@nist.gov","fn":"Jessica Staymates"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2462","description":"The MML Accolades Program has been created to recognize exemplary work by staff, associates, and external collaborators in areas of strategic interest and benefit to MML (The Material Measurement Laboratory, an organization within NIST). 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Nguyen"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2465","description":"The 3.5 GHz citizens broadband radio service (CBRS) band in the U.S. is a key portion of mid-band spectrum shared between commercial operators and existing federal and non-federal incumbents. To protect the federal incumbents from harmful interference, a spectrum access system (SAS) is required to use a common, standardized algorithm, called the move list algorithm, to suspend transmissions of some CBRS devices (CBSDs) on channels in which the incumbent becomes active. However, the current reference move list implementation used for SAS testing is non-deterministic in that it uses a Monte Carlo estimate of the 95th percentile of the aggregate interference from CBSDs to the incumbent. This leads to uncertainty in move list results and in the aggregate interference check of the test. We propose to use upper and lower bounds on the aggregate interference distribution to compute deterministic move lists. These include the reference move list used by the testing system and an operational move list used by the SAS itself. We evaluate the performance of the proposed deterministic move lists using reference implementations of the standards and simulated CBSD deployments in the vicinity of federal incumbent dynamic protection areas (DPAs).  The data include numerical results of the proposed deterministic move lists for a single protection point Pensacola DPA and forty offshore DPAs along the U.S. coasts. The data is associated with the article, \"Deterministic Move Lists for Federal Incumbent Protection in the CBRS Band,\" T. T. Nguyen and M. R. Souryal, in IEEE Transactions on Cognitive Communications and Networking, Vol. 7, No. 3, September 2021.","language":["en"],"title":"Deterministic Move Lists for Federal Incumbent Protection in the CBRS Band","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2465/Figure8_Pensacola_dpa_movelist_keeplist.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2465/Figure8_Pensacola_dpa_movelist_keeplist.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2465/Figure9_Pensacola_dpa_move_list_diff.kml","mediaType":"application/vnd.google-earth.kml+xml"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2465/Figure9_Pensacola_dpa_move_list_diff.kml.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2465/Figure10_Pensacola_dpa_agg_interf.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2465/Figure10_Pensacola_dpa_agg_interf.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2465/Figure11_coastal_dpa_results.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2465/Figure11_coastal_dpa_results.xlsx.sha256","mediaType":"text/plain"},{"accessURL":"https://doi.org/10.18434/mds2-2465","title":"DOI Access for Deterministic Move Lists for Federal Incumbent Protection in the CBRS Band"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-09-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Statistical analysis","Advanced Communications:Wireless (RF)"],"issued":"2021-09-16","keyword":["3.5 GHz","aggregate interference","citizens broadband radio service","incumbent protection","radar","spectrum sharing","uncertainty","upper and lower bounds"]},{"identifier":"ark:/88434/mds2-2466","accessLevel":"public","contactPoint":{"hasEmail":"mailto:daniel.siderius@nist.gov","fn":"Daniel Siderius"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2466","description":"The dissertation \"The Thermodynamics and Hysteresis of Adsorption\" by A. J. Brown, contains unique measurements of adsorption/desorption isotherms of Xenon, Krypton, and Carbon Dioxide in Vycor Glass. A particular hallmark of this set of isotherms is an extensive set of scanning isotherms of Xenon at 151 K, examining numerous trajectories inside the adsorption-desorption hysteresis loop. The isotherms were provided in tabular form in appendices of the dissertation, allowing for conversion to digital files without loss of fidelity. The digital files contain the dissertation's tables verbatim, with no modification or editorial adjustments. When there are suspected data errors (primarily typographic errors, but some possible measurement errors), the digital files contain annotations that point out the possible errors and proposed editorial corrections.The isotherms are stored in self-descriptive CSV files, which can be opened and manipulated with any spreadsheet software. For ease of use, several scripts for handling and processing the isotherms are available in a GitHub repository:  https://github.com/nist-isodb/AJBrownThesisIsotherms.  The repository also includes example Jupyter Notebook sheets that demonstrate how to use the supporting software scripts.","language":["en"],"title":"Digitized boundary and scanning isotherms from \"The Thermodynamics and Hysteresis of Adsorption\" by A. J. 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Access for Challenging Medically-Relevant Genes Benchmark Set"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-09-29 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Bioscience:Genomics"],"keyword":["Human genomics","DNA sequencing","Reference materials","Medical genomics","Bioinformatics","Bioinformatics"]},{"identifier":"ark:/88434/mds2-2476","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.ritchie@nist.gov","fn":"Nicholas Ritchie"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2476","description":"Automated particle analysis (SEM/EDS) data from samples known to have been exposed to gunshot residue and from samples occasionally mistaken for gunshot residue - like brake dust and fireworks. The dataset consists of analyses of 30 discrete samples: 12 from sampling automobiles (\"brake dust\"), 10 from sampling fireworks (\"sparklers\" and \"spinners\" and \"roman candles\"), 8 from shooter's left or right hands. The analysis configuration meta-data for each analysis are contained in the \"configuration.txt\" and \"script.py\" files.  The raw data from each analysis is in the file pair \"data.pxz\" and \"data.hdz\".  The HDZ-file details the contents of the PXZ-file.  In addition, the \"mag0\" directory contains TIFF images with embedded X-ray spectra for each particle in the dataset. Additional HDZ/PXZ files contain the results of reprocessing the \"data.hdz/.pxz\" in light of the \"mag0\" spectra and the standard spectra in \"25 keV.zip\" The samples came from Amy Reynolds (amy.reynolds@pd.boston.gov) at the Boston Police Department.  The \"Shooter\" samples were taken from a volunteer who fired a gun at a local firing range and was then sampled immediately after.  They are part of a time series that was used to study GSR retention. The TIFF Image/Spectrum files can be read using NIST DTSA-II (https://www.nist.gov/services-resources/software/nist-dtsa-ii) or NeXLSpectrum.jl (https://doi.org/10.18434/M32286).  The HDZ/PXZ files can be read using NIST Graf (available on request) or NeXLParticle.jl (https://github.com/usnistgov/NeXLParticle.jl).","language":["en"],"title":"Automated particle analysis (SEM/EDS) data from samples known to have been exposed to gunshot residue and from samples occasionally mistaken for gunshot residue - like brake dust and 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Access for Automated particle analysis (SEM/EDS) data from samples known to have been exposed to gunshot residue and from samples occasionally mistaken for gunshot residue - like brake dust and fireworks."},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2476/Roman%20Candles%20-%20Post-handling%2C%20pre-ignition.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2476/Roman%20Candles%20-%20Post-handling%2C%20pre-ignition.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2476/Roman%20Candles%20-%20Debris%20from%20JWC.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2476/Roman%20Candles%20-%20Debris%20from%20JWC.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2476/Roman%20Candles%20-%20Post%20cleanup.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2476/Roman%20Candles%20-%20Post%20cleanup.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2476/Shooter%20%233%20-%20Zero%20time%20R.zip","mediaType":"application/zip"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-09-29 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The data set are obtained based on simulation runs with various fire conditions and door opening conditions. The fire is described based on t-squared law. The peak heat release rate and time to peak range from approximately 1667 kW to 4620 kW and from 210 s to 1540 s, respectively.  A detailed description of this work can be found in Ref. [2].[1] Reneke, P.A., Peacock, R.D., Gilbert, S.W. and Cleary, T.G., 2021. CFAST Consolidated Fire and Smoke Transport (Version 7) Volume 5: CFAST Fire Data Generator (CData). NIST Technical Note 1889v5. Gaithersburg, MD.[2] Fu, E.Y., Tam, W.C., Wang, J., Peacock, R., Reneke, P., Ngai, G., Leong, H.V. and Cleary, T., 2021, May. Predicting Flashover Occurrence using Surrogate Temperature Data. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 14785-14794).","language":["en"],"title":"Synthetic Temperature Data for Predicting Flashover Occurrence Using Surrogate Temperature Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2479/RangeHouseData8001-10000.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for RangeHouseData8001-10000"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2479/RangeHouseData8001-10000.xlsx","description":"Data for Experiment 8001-10000","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"RangeHouseData8001-10000"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2479/RangeHouseData10001-12000.xlsx.sha256","mediaType":"text/plain","title":"SHA256 File for RangeHouseData10001-12000"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2479/RangeHouseData10001-12000.xlsx","description":"Data for Experiment 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No hand-written derivatives are required, and the computational speed is competitive with, or better than, the results from hand-written derivatives in low-level programming languages.","language":["en"],"title":"teqp: Templated EQuation of state Package","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2483","title":"DOI Access for teqp: Templated EQuation of state Package"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-10-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Chemical thermodynamics and chemical properties","Mathematics and Statistics:Numerical methods and software"],"issued":"2021-10-01","keyword":["equation of state","derivatives","differentiation","C++","mixture properties"]},{"identifier":"ark:/88434/mds2-2484","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2165","https://doi.org/10.14264/e6af102"],"contactPoint":{"hasEmail":"mailto:selvarajah.ramesh@nist.gov","fn":"Selvarajah Ramesh"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2484","description":"The compartment fire test was conducted on the 9.1 m x 6.1 m steel-concrete composite floor, located in the south-central bay of the two-story steel gravity frame two bays by three bays in plan. The test floor assembly was designed and constructed to achieve a 2-hour fire-resistance rating per American Society for Testing and Materials (ASTM) standard E119. The fire-exposed composite slab was reinforced with the steel wire mesh mat (60 mm2/m) equivalent to the minimum code-prescribed steel reinforcement for shrinkage and temperature crack control under normal conditions. The fire exposure condition was created using natural gas burners to mimic a standard temperature-time relationship in the upper layer of the test compartment. During fire exposure, the heated floor assembly was subjected to a mechanical load of 2.7 kPa, determined from the gravity load combination for extraordinary events as permitted by the applicable building code. This is the dataset from the first (Test #1) of four compartment fire tests conducted as part of the research project \"Measurement of Structural Performance in Fire: Steel-Concrete Composite Floor Systems Subject to Fire Phase 2\" under the National Institute of Standards and Technology (NIST) Engineering Laboratory's Fire Risk Reduction in Buildings Program.","language":["en"],"title":"Data from Fire Resilience of a Steel-Concrete Composite Floor System: Full-Scale Experimental Evaluation for Influence of Slab Reinforcement (Test #1)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2484/NFRL_CompositeFloorTest_No1.zip.sha256","mediaType":"text/plain","title":"SHA256 File for NFRL Composite Floor Test Phase 2 Test #1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2484/NFRL_CompositeFloorTest_No1.zip","description":"This is the dataset from the first (Test #1) of four compartment fire tests conducted as part of the project ?Measurement of Structural Performance in Fire: Steel-Concrete Composite Floor Systems Subject to Fire ? Phase 2? under the National Institute of Standards and Technology (NIST) Engineering Laboratory?s Fire Risk Reduction in Buildings Program.","mediaType":"application/x-zip-compressed","title":"NFRL Composite Floor Test Phase 2 Test #1"},{"accessURL":"https://doi.org/10.18434/mds2-2484","title":"DOI Access for Fire Resilience of a Steel-Concrete Composite Floor System: Full-Scale Experimental Evaluation for U.S. Prescriptive Approach with a 2-Hour Fire-Resistance Rating (Test #1)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-10-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Structural fire resistance"],"issued":"2021-10-26","keyword":["fire","composite floor","steel beam","shear connection","steel building","fire resistance","passive fire protection"]},{"identifier":"ark:/88434/mds2-2486","accessLevel":"public","contactPoint":{"hasEmail":"mailto:kurt.benkstein@nist.gov","fn":"Kurt D. Benkstein"},"programCode":["006:045"],"@type":"dcat:Dataset","replaces":"ark:/88434/mds2-2352","landingPage":"https://data.nist.gov/od/id/mds2-2486","description":"These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number. The datasets are organized in the files by instrument type: PTA (particle tracking analysis), RMM (resonant mass measurement), ESZ (electrical sensing zone), and OTH (other techniques not covered in the three largest groups, including holographic particle characterization, laser diffraction, flow imaging, and flow cytometry). In the OTH group, the specific instrument type for each dataset is noted. Each instrument type (PTA, RMM, ESZ, OTH) has a dedicated file. Included in the data files for each dataset are: (1) the cumulative particle number concentration (PNC, (1/mL)); (2) the concentration distribution density (CDD, (1/mL·nm)) based upon five bins centered at each particle population peak diameter; (3) the CDD in higher resolution, varied-width bins. The lower-diameter bin edge (µm) is given for (2) and (3). Additionally, the PTA, RMM, and ESZ files each contain unweighted mean cumulative particle number concentrations and concentration distribution densities calculated from all datasets reporting values. The associated standard deviations and standard errors of the mean are also given. In the OTH file, the means and standard deviations were calculated using only data from one of the sub-groups (holographic particle characterization) that had n = 3 paired datasets. Where necessary, datasets not using the common bin resolutions are noted (PTA, OTH groups). The data contained here are presented and discussed in a manuscript to be submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.","language":["en"],"title":"Datasets from an interlaboratory comparison to characterize a multi-modal polydisperse sub-micrometer bead dispersion","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2486/PdP-ILC_RMMdatasets.csv","format":"csv","description":"Resonant mass measurement datasets from polydisperse particles interlaboratory comparison","mediaType":"application/vnd.ms-excel","title":"PdP-ILC_RMM-datasets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2486/PdP-ILC_ESZdatasets.csv","format":"csv","description":"Electrical sensing zone datasets from polydisperse particles interlaboratory comparison","mediaType":"application/vnd.ms-excel","title":"PdP-ILC_ESZ-datasets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2486/PdP-ILC_OTHdatasets.csv","format":"csv","description":"Other instrument types datasets from polydisperse particles interlaboratory comparison","mediaType":"application/vnd.ms-excel","title":"PdP-ILC_OTH-datasets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2486/PdP-ILC_PTAdatasets.csv","format":"csv","description":"Particle tracking analysis datasets from polydisperse particles interlaboratory comparison","mediaType":"application/vnd.ms-excel","title":"PdP-ILC_PTA-datasets"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2486/PdP-ILC_datasets_ReadMe.txt","format":".txt","description":"A description of the data file layout and abbreviations","mediaType":"text/plain","title":"PdP-ILC_ReadMe"},{"accessURL":"https://doi.org/10.18434/mds2-2486","title":"DOI Access for Datasets from an interlaboratory comparison to characterize a multi-modal polydisperse sub-micrometer bead dispersion"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-10-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Bioprocessing","Health:Pharmaceuticals","Manufacturing:Biomanufacturing"],"issued":"2021-11-03","keyword":["Biosciences and Health","particle","protein particle","sub-micrometer particle","subvisible particle","particle tracking analysis","resonant mass measurement","electrical sensing zone","holographic particle characterization","flow imaging","laser diffraction","flow cytometry"]},{"identifier":"ark:/88434/mds2-2487","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ian.bell@nist.gov","fn":"Ian Bell"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2487","description":"The files in this repository can be used to generate the complete set of figures in the paper \"An algorithm to identify vapor-liquid-liquid equilibria from vapor-liquid equilibria\". The zip file, when expanded, includes a conda environment to populate the dependencies, and a set of python scripts.  Running make_figures.py will regenerate all the figures, demonstrating how to use the algorithm.","language":["en"],"title":"Python scripts used to generate the figures in \"An algorithm to identify vapor-liquid-liquid equilibria of binary mixtures from vapor-liquid equilibria\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2487/figs.zip.sha256","mediaType":"text/plain","title":"SHA256 File for Zip file of the files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2487/figs.zip","mediaType":"application/x-zip-compressed","title":"Zip file of the files"},{"accessURL":"https://doi.org/10.18434/mds2-2487","title":"DOI Access for Python scripts used to generate the figures in \"An algorithm to identify vapor-liquid-liquid equilibria from vapor-liquid equilibria\""}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-10-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Numerical methods and software","Chemistry"],"issued":"2021-10-08","keyword":["phase equilibrium; thermodynamics; algorithms"]},{"identifier":"ark:/88434/mds2-2488","accessLevel":"public","contactPoint":{"hasEmail":"mailto:amanda.forster@nist.gov","fn":"Amanda L. Forster"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2488","description":"This dataset contains mechanical testing data at two different loading rates for two different aging conditions (wet and dry at 70 °C) for five different extraction time points for  ultra high molar mass polyethylene (UHMMPE) unidirectional (UD) laminate. In each folder there is a spreadsheet (.csv) with the test data collected during loading, and a (.avi) video file for each of the tests done under those conditions.","language":["en"],"title":"Aged and unaged ultra high molar mass polyethylene flexible unidirectional composite laminate tensile testing for soft body armor applications","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_02_wet.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_02_wet.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_02_dry.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_02_dry.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_02_wet.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_02_wet.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_10_dry.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_10_dry.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_10_wet.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_10_wet.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_02_dry.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_02_dry.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_21_dry.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_21_dry.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_21_wet.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_21_wet.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_33_dry.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_33_dry.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_33_wet.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_33_wet.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_48_dry.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_48_dry.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_48_wet.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_250_48_wet.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_cyclic_tests.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_cyclic_tests.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/PE%20laminate%20specimens.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/PE%20laminate%20specimens.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UD_UHMMPE_readme.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UD_UHMMPE_readme.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_00.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_00.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_10_dry.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_10_dry.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_10_wet.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_10_wet.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_21_dry.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_21_dry.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2488/UDUHMMPE_250x70_002_21_wet.zip","mediaType":"applicatio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Access for Aged and unaged ultra high molar mass polyethylene flexible unidirectional composite laminate tensile testing for soft body armor applications"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-08-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Law enforcement:Materials:Composites"],"keyword":["UHMMPE","UHMWPE","laminate","aging","body armor"]},{"identifier":"ark:/88434/mds2-2490","accessLevel":"public","references":["https://doi.org/10.4242/BalisageVol26.Piez01"],"contactPoint":{"hasEmail":"mailto:michaela.iorga@nist.gov","fn":"Michaela Iorga"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2490","description":"Project documentation and demonstrations processing OSCAL (the Open Security Controls Assessment Language).","language":["en"],"title":"OSCAL Client-side XSLT (CSX) Demonstrations","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2490","title":"DOI Access for OSCAL Client-side XSLT (CSX) Demonstrations"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-10-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://pages.nist.gov/OSCAL/reference/","theme":["Standards:Documentary standards","Standards:Conformity assessment","Information Technology:Cybersecurity"],"issued":"2022-01-05","keyword":["XML","XSLT","validation"]},{"identifier":"ark:/88434/mds2-2491","accessLevel":"public","contactPoint":{"hasEmail":"mailto:anna.karion@nist.gov","fn":"Anna Karion"},"programCode":["006:047"],"@type":"dcat:Dataset","replaces":"ark:/88434/mds2-2126","landingPage":"https://data.nist.gov/od/id/mds2-2491","description":"NOTE: please see the latest (March 2025) update at https://doi.org/10.18434/mds2-3765 . This archive is from November 2021.We have become aware of errors in the inlet heights at multiple sites and have corrected these in the newest version. Hourly observations of CO2 and CH4 from tower-based sites in the NIST Northeast Corridor network; CO observations from one site. Data files are comma delimited (CSV). Measurements of each species may be from two or more different heights above ground. Site locations, heights, and other information is in a separate ascii (CSV) file (NEC_sites.csv). Data in this archive is reported for the years 2015-2020. An ASCII Readme file (NEC_Readme_11012021) is also posted, along with an Updates_11012021.txt file that includes information on updates.  In this update, we added a new directory with Obspack-formatted text and netcdf files for users familiar with those data formats (https://gml.noaa.gov/ccgg/obspack/), and have added Kenneth Schuldt (NOAA-GML and CIRES), who generated this ObsPack, to the data citation. These files include all years 2015-2020. Note about calibrations: CO2 data are reported on the NOAA/WMO X2007 calibration scale. CH4 data are reported on the NOAA/WMO X2004A calibration scale; CO data, where available, are reported on the NOAA/WMO X2014 scale. This archive, with CO2 data on the X2007 scale, will no longer be updated. A full revision of all the data on the NOAA/WMO X2019 scale for CO2 is forthcoming, and will be linked here; that archive will maintain its own record and DOI, include CH4 and CO, and will be continually updated. This data is being freely distributed for research, academic and related non-commercial purposes consistent with NIST's mandate to further the science and the promulgation of appropriate standards.Current update: Nov 1, 2021.","language":["en"],"title":"Observations of CO2, CH4, and CO mole fractions from the NIST Northeast Corridor urban 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\"Structure-dilute property relationships of comb-like macromolecules in a good solvent\" by Robert J. S. Ivancic (OrcID: https://orcid.org/0000-0001-9969-2534, National Institute of Standards and Technology, Material Measurement Laboratory, Division 642, Group 1), Sara V. Orski (OrcID: https://orcid.org/0000-0002-3455-0866, National Institute of Standards and Technology, Material Measurement Laboratory, Division 642, Group 1), and Debra J. Audus (OrcID: https://orcid.org/0000-0002-5937-7721, National Institute of Standards and Technology, Material Measurement Laboratory, Division 642, Group 1) and (2) \"The importance of branch placement on the dilute  solution properties of comb-like macromolecules\" by Robert J. S. Ivancic, Chase B. Thompson (OrcID: https://orcid.org/0000-0002-8534-486X, Leidos), Devin A. Golla, Bintou Koroma, Jack F. Douglas (OrcID: https://orcid.org/0000-0001-7290-2300, National Institute of Standards and Technology, Material Measurement Laboratory, Division 642, Group 1), Sara V. Orski, and Debra J. Audus. The README.md file describes the dataset.Abstract from publication (1) : The structural characterization of branched polymers still poses experimental challenges despite their technological potential. This lack of clarity is egregious in linear low-density polyethylene (LLDPE), a common industrial plastic. Here, we design a coarse-grain, implicit solvent molecular dynamics model for LLDPE in 1,2,4-trichlorobenzene, a canonical good solvent, thatreplicates all-atom simulations and experiments. We employ this model to test the relationship between the contraction factors, the ratios of branched to linear dilute solution properties. In particular, we relate the contraction factor of the radius of gyration to that of the intrinsic viscosity and the hydrodynamic radius. The contraction exponents are constant as we vary branchlength and spacing in contrast to theoretical expectations. We use this observation to develop a general theory for the dilute solution properties of linear polymers with linear side-chain branches, comb-like macromolecules, in a good solvent and validate the theory by generating master curves for LLDPE.Abstract from publication (2) : Branch density and length substantially impact the properties of comb-like polymers. Scientists often use the dilute solution properties of these materials to quantify their architecture. As branch spacing decreases and branch length increases at a fixed molecular mass, dilute solution properties such as the radius of gyration, intrinsic viscosity, and hydrodynamic radius typically decrease because the length of the backbone decreases. However, this decrease is only partially driven by this change in backbone length, even for relatively short branches. While many models focus on predicting the dilute solution properties of these materials with fixed branch spacing, most comb-like polymers exhibit statistical branch spacing which leads to non-trivial changes in excluded volume effects. Using molecular dynamics simulations, we show how changing the distribution of branches from fixed to statistical and then to diblock affects the dilute solution properties of a coarse-grained linear low-density polyethylene (LLDPE), a canonical comb-like polymer, in 1,2,4-trichlorobenzene, a standard good solvent. This approach explicitly accounts for excluded volume interactions that were not included in prior theories. We extend our previous theoretical work to account for statistical branch spacing and test prior renormalization group estimates of diblocks in good solvent to show that it is consistent with our numerical results. Ourapproach provides a framework for a more quantitative understanding of chain architecture from dilute solution properties, yielding better structure-property relationships.","language":["en"],"title":"Dilute solution properties and force field model parameters of LLDPE in good solvent from \"Structure-dilute property relationships of comb-like macromolecules in a good solvent\" and \"The importance of branch placement on the dilute solution properties of comb-like macromolecules\".","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2494/input.lammps_force_field","description":"input file containing pair, bond, angle, and dihedral styles,  coefficients and masses for coarse-grain force field. This force-field may be used to simulate other polyolefins in good solvent.","mediaType":"application/octet-stream","title":"Force field"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2494/dilute_solution_properties_data.csv","description":"This file is a comma-separated-values (csv) file of the mean and bootstrap standard error for each dilute solution property measured (radius of gyration [Rg], intrinsic viscosity [eta], and radius of hydration [Rh]) at each branch spacing [S], branch length [L], and mass [M] of LLDPE studied in the paper. (S,L) = (\\infty, 0) corresponds to high-density polyethylene. dilute_solution_properties_data.csv : This file is a comma-separated-values (csv) file of the mean and bootstrap standard error for each dilute solution property measured (radius of gyration [Rg], intrinsic viscosity [eta], and radius of hydration [Rh]) at each branch spacing [S], branch length [L], and mass [M] of LLDPE studied in the paper. (S,L) = (\\infty, 0) corresponds to high-density polyethylene. ZENO (https://zeno.nist.gov/) is used to compute dilute solution properties from ensemble of configurations. Monomers with 2 and 4 carbons use a ZENO radius of 2.1 and 2.8 nanometers, respectively. The intrinsic viscosity values are scaled down by a factor of C = 0.68.","mediaType":"text/csv","title":"Dilute solution properties data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2494/publication2_constant_dilute_solution_properties_data.csv","description":"This file contains data for LLDPE with constant branch spacing from publication (2).  This file is a csv with the same properties as measured in `dilute_solution_properties_data.csv`.","mediaType":"text/csv","title":"Constant branch spacing dilute solution property data from publication 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2494/publication2_diblock_dilute_solution_properties_data.csv","description":"This file contains data for LLDPE with diblock spacing from publication (2). Here, one of the blocks is polyethylene and the other is LLDPE with a constant branch spacing of 8 carbons. This file is a csv with the with the same properties as measured in `dilute_solution_properties_data.csv` except the property branch spacing is replaced with branch fraction [f], which is the fraction of the chain backbone that is contant spacing LLDPE.","mediaType":"text/csv","title":"Diblock branch spacing dilute solution properties for publication (2)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2494/publication2_random_dilute_solution_properties_data.csv","description":"This file contains data for LLDPE with random branch spacing from publication (2). Here, branches are placed along the backbone of a polyethylene with a minimum branch spacing of 8 carbons. This file has the same properties as `publication2_dilute_solution_properties_data_diblock.csv`, where a branch fraction of 1 indicates LLDPE with a constant branch spacing of 8 carbons.","mediaType":"text/csv","title":"Random branch spacing dilute solution properties for publication (2)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2494/README.md","description":"README describing data in repository","mediaType":"text/markdown","title":"README for data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-10-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Modeling and simulation research","Chemistry:Molecular characterization","Materials:Polymers"],"keyword":["124-trichlorobenzene","TCB","linear low-density polyethylene","LLDPE","good solvent","radius of gyration","intrinsic viscosity","radius of hydration","molecular dynamics","coarse-grain"]},{"identifier":"ark:/88434/mds2-2495","accessLevel":"public","contactPoint":{"hasEmail":"mailto:niksa.blonder@nist.gov","fn":"Niksa Blonder"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2495","description":"The Nuclear Magnetic Resonance Spectral Measurement Database (NMR-SMDB) was developed for the purpose of organizing and searching NMR spectral data of protein therapeutics, linking spectra to corresponding sample information and enabling quick access to full datasets and entire studies. In addition to supporting internal NIST research, the system could facilitate data access to stakeholders outside of NIST, and future versions of the database software itself could be installed by others for their own data storage and retrieval.","language":["en"],"title":"The NMR Spectral Measurement Database: A System for Organizing and Accessing NMR Spectra of Protein Therapeutics","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2495/ubuntu_nmrdb.ova","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2495/ubuntu_nmrdb.ova.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2495/NMR%20Spectral%20Measurement%20Database%20Documentation.pdf","description":"The NMR Spectral Measurement Database user manual that describes how the software described in the NIST JRES article \"The NMR Spectral Measurement Database: a System for Organizing and Accessing NMR Spectra of Protein Therapeutics\" can be used","mediaType":"application/pdf","title":"The NMR Spectral Measurement Database Doccumentation"},{"accessURL":"https://doi.org/10.18434/mds2-2495","title":"DOI Access for The NMR Spectral Measurement Database: A System for Organizing and Accessing NMR Spectra of Protein Therapeutics"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-10-19 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Bioscience:Bioprocessing"],"issued":"2021-11-09","keyword":["NMR Spectral Measurement Database","Nuclear Magnetic Resonance","NMR","Database","NMR-SMDB"]},{"identifier":"ark:/88434/mds2-2496","accessLevel":"public","contactPoint":{"hasEmail":"mailto:william.ratcliff@nist.gov","fn":"William D. Ratcliff"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2496","description":"This is a repository for using semi-supervised learning to classify diffraction patterns","language":["en"],"title":"Semi-Supervised-Learning-for-Diffraction","distribution":[{"accessURL":"https://github.com/usnistgov/semi-supervised-neutron","description":"Semi-supervised Learning for neutron diffraction","title":"Semi-supervised Learning for neutron diffraction"},{"accessURL":"https://doi.org/10.18434/mds2-2496","title":"DOI Access for Semi-Supervised-Learning-for-Diffraction"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-10-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Neutron Research"],"issued":"2021-11-09","keyword":["neutron","crystallography","artificial intelligence"]},{"identifier":"ark:/88434/mds2-2497","accessLevel":"public","references":["https://doi.org/10.1021/jasms.0c00415","https://doi.org/10.1021/jasms.2c00129"],"contactPoint":{"hasEmail":"mailto:trina.mouchahoir@nist.gov","fn":"Trina Mouchahoir"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2497","description":"These LC-MS and LC-MS/MS raw data were collected for purposes of an interlaboratory study evaluating the multi-attribute method (MAM).  Tryptic digests of native NISTmAb (the \"Reference\" and the \"Unknown\" samples), degraded NISTmAb (the \"pH Stress\" sample) and NISTmAb spiked with 15 heavy-labeled synthetic peptides (the \"Spike\" sample) were sent to each participating laboratory.  One injection of each digest was acquired in MS-only mode, while a second injection was acquired in MS/MS mode.  Three injections of a mixture of 15 heavy-labeled synthetic peptides (\"Calibration\" sample) were also analyzed as a means of evaluating instrument performance.  Although all data were collected using the same C18 column and LC method, the instrumentation used by each laboratory differed.  Additional details regarding the samples, their preparation, the LC method used, and an evaluation of the results pertaining to the new peak detection aspect of MAM can be found in \"New Peak Detection Performance Metrics from the MAM Consortium Interlaboratory Study\" (https://pubs.acs.org/doi/10.1021/jasms.0c00415).  Evaluation of the results pertaining to the attribute analytics aspect of MAM can be found in \"Interlaboratory Attribute Analytics Metrics from the MAM Consortium Round Robin Study\" (link to be provided). Raw data that was optionally submitted by sixteen participating laboratories are provided here.  To preserve the integrity of the data, the files are provided in their original vendor format.Please note that Linux and Mac users may require the use of 7Zip (https://www.7-zip.org/) to extract zipped folders, rather than the extraction tool provided by their operating system.For any difficulty with downloading or extracting data files, please e-mail the contact listed above.","language":["en"],"title":"MAM Consortium Interlaboratory Study Raw 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the Global Alliance for Genomic Health (GA4GH) Benchmarking Team, the Genome in a Bottle Consortium and the Telomere-to-Telomere Consortium are intended as a standard resource of BED files for use in stratifying true positive, false positive, and false negative variant calls in challenging and targeted regions of the the genome.  v3.0 stratifications contain new and revised stratification files and replace v2.0 stratifications.","language":["en"],"title":"Genome In A Bottle - v3.0 Genome 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The experiment included 55 separate laser scanned 'pads' created on a bare-metal plate.  Each pad corresponds to a different laser processing setting, described in the paper Yeung et al. 2020 (https://doi.org/10.1016/j.mfglet.2020.07.005). Files include the input command files, in-situ process monitoring data and metadata, and ex-situ microscope photographic images of the pad surfaces.  This data is one of a set of 'AMMT Process Monitoring Datasets', as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project at the National Institute of Standards and Technology (https://www.nist.gov/el/ammt-temps/datasets).","language":["en"],"title":"Process Monitoring Dataset from the Additive Manufacturing Metrology Testbed (AMMT): RHF 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Additive manufacturing"],"issued":"2022-02-28","keyword":["Additive manufacturing; laser powder bed fusion; selective laser melting;"]},{"identifier":"ark:/88434/mds2-2510","accessLevel":"public","contactPoint":{"hasEmail":"mailto:stephen.zimmerman@nist.gov","fn":"Stephen Zimmerman"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2510","description":"The Virus Particle Exposure in Residences (ViPER) Webtool is a single zone indoor air quality and ventilation analysis tool developed by the National Institute of Standards and Technology (NIST) for evaluating an occupant's relative exposure to viral particles exhaled by a temporary visitor inside the home.","language":["en"],"title":"Virus Particle Exposure in Residences (ViPER) 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The \"Overhang Part X16\" dataset was a three-dimensional (3D) additive manufacturing (AM) build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung and Brandon Lane on July 3, 2019.  The files in this dataset include XCT image sequences for each part, and stereolithography files (.STL) of the surface data extracted from XCT, measured by Maxwell Praniewicz at the Precision Machining Research Consortium at Georgia Institute of Technology, Atlanta, GA.  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Access for X-ray Computed Tomography Data of Additive Manufacturing Metrology Testbed (AMMT) Parts: Overhang Part X16"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2514/DataDescription_OverhangPartX16_XCT.pdf","format":"PDF Document","description":"Document providing details on the measurement methods, processing, and structure and filetypes of this dataset. Document last updated 2/4/2022","mediaType":"application/pdf","title":"Data Description Document"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2514/Surface_STLs/OverhangPartX16_Part2_3.stl","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2514/Surface_STLs/OverhangPartX16_Part2_4.stl","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2514/Surface_STLs/OverhangPartX16_Part3_1.stl","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2514/Surface_STLs/OverhangPartX16_Part3_2.stl","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-12-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing"],"issued":"2022-02-28","keyword":["Additive Manufacturing; Laser Powder Bed Fusion; X-ray Computed Tomography;"]},{"identifier":"ark:/88434/mds2-2515","accessLevel":"public","contactPoint":{"hasEmail":"mailto:gary.howarth@nist.gov","fn":"Gary Howarth II"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2515","description":"SDNist (v1.3) is a set of benchmark data and metrics for the evaluation of synthetic data generators on structured tabular data. This version (1.3) reproduces the challenge environment from Sprints 2 and 3 of the Temporal Map Challenge. These benchmarks are distributed as a simple open-source python package to allow standardized and reproducible comparison of synthetic generator models on real world data and use cases. These data and metrics were developed for and vetted through the NIST PSCR Differential Privacy Temporal Map Challenge, where the evaluation tools, k-marginal and Higher Order Conjunction, proved effective in distinguishing competing models in the competition environment.SDNist is available via `pip` install: `pip install sdnist==1.2.8` for Python >=3.6 or on the [USNIST/Github](https://github.com/usnistgov/Differential-Privacy-Temporal-Map-Challenge-assets/). The sdnist Python module will download data from NIST as necessary, and users are not required to download data manually.","language":["en"],"title":"SDNist v1.3: Temporal Map Challenge Environment","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/GA_NC_SC_10Y_PUMS.parquet","mediaType":"application/octet-stream","title":"Census GA_NC_SC data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/GA_NC_SC_10Y_PUMS.json","mediaType":"application/json","title":"Census GA_NC_SC schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/IL_OH_10Y_PUMS.parquet","mediaType":"application/octet-stream","title":"Census IL-OH data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/IL_OH_10Y_PUMS.json","mediaType":"application/json","title":"Census IL_OH schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/NY_PA_10Y_PUMS.parquet","mediaType":"application/octet-stream","title":"Census NY-PA data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/NY_PA_10Y_PUMS.json","mediaType":"application/json","title":"Census NY_PA schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi2016.parquet","mediaType":"application/octet-stream","title":"Taxi 2016"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi2016.json","mediaType":"application/json","title":"Taxi 2016 schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi2020.parquet","mediaType":"application/octet-stream","title":"Taxi 2020 data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi2020.json","mediaType":"application/json","title":"Taxi 2020 schema"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi.parquet","mediaType":"application/octet-stream","title":"Taxi data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi.json","mediaType":"application/json","title":"Taxi schema"},{"accessURL":"https://doi.org/10.18434/mds2-2515","title":"DOI Access for SDNist: Benchmark data and evaluation tools for data synthesizers."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/report2.jinja2","description":"A jinja2 report template to help humans read the k-marginal data","mediaType":"application/octet-stream","title":"K-marginal report template"},{"accessURL":"https://github.com/usnistgov/SDNist/","format":"Python 3.8 module","description":"SDNist: Benchmark data and evaluation tools for synthetic data generators","title":"SDNist software respository at Github"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/census-datasets-CSVs.zip","format":"CSV","description":"Three compressed CSV files to run the 'Census'-related functions in SDNist.","mediaType":"application/zip","title":"Datasets for 'Census' evaluation in CSV format"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2515/taxi-datasets-CSVs.zip","format":"CSV","description":"Three compressed CSV files to run the 'Taxi'-related functions in SDNist.","mediaType":"application/zip","title":"Taxi datasets in CSV format"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-12-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Public Safety:Public safety communications research","Information Technology:Privacy","Information Technology:Artificial Intelligence"],"issued":"2021-12-28","keyword":["private information sharing","differential privacy","privacy","benchmarks","synthetic data"]},{"identifier":"ark:/88434/mds2-2516","accessLevel":"public","references":["https://arxiv.org/abs/2111.12778"],"contactPoint":{"hasEmail":"mailto:ari.feldman@nist.gov","fn":"Ari Feldman"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2516","description":"Included here are data used to generate figures from the paper \"Digital control of a superconducting qubit using a Josephson pulse generator at 3 K\".Abstract: Scaling of quantum computers to fault-tolerant levels relies critically on the integration of energy-efficient, stable, and reproducible qubit control and readout electronics. In comparison to traditional semiconductor control electronics (TSCE) located at room temperature, the signals generated by Josephson junction (JJ) based rf sources benefit from small device sizes, low power dissipation, intrinsic calibration, superior reproducibility, and insensitivity to ambient fluctuations. Previous experiments to co-locate qubits and JJ-based control electronics resulted in quasiparticle poisoning of the qubit; degrading the qubit's coherence and lifetime. In this paper, we digitally control a 0.01~K transmon qubit with pulses from a Josephson pulse generator (JPG) located at the 3~K stage of a dilution refrigerator. We directly compare the qubit lifetime $T_1$, coherence time $T_2^*$, and thermal occupation $P_{th}$ when the qubit is controlled by the JPG circuit versus the TSCE setup. We find agreement to within the daily fluctuations on $\\pm 0.5~\\mu$s and $\\pm 2~\\mu$s for $T_1$ and $T_2^*$, respectively, and agreement to within the 1\\% error for $P_{th}$. Additionally, we perform randomized benchmarking to measure an average JPG gate error of $2.1 \times 10^{-2}$. In combination with a small device size ($<25$~mm$^2$) and low on-chip power dissipation ($\\ll 100~\\mu$W), these results are an important step towards demonstrating the viability of using JJ-based control electronics located at temperature stages higher than the mixing chamber stage in highly-scaled superconducting quantum information systems","language":["en"],"title":"Digital control of a superconducting qubit using a Josephson pulse generator at 3 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After generating the test vectors, the program also validates the correctness of the responses from the user.","language":["en"],"title":"Automated Cryptographic Validation Test System Generators and Validators","distribution":[{"accessURL":"https://doi.org/10.18434/mds2-2518","title":"DOI Access for Automated Cryptographic Validation Test System Generators and Validators"},{"accessURL":"https://github.com/usnistgov/ACVP-Server","format":"Available via .zip, .tar.gz, or git repository","description":"The ACVP-Server GitHub is a software version control resource used to track releases of the ACVTS project. Issues and release notes can be found here.","title":"GitHub Repository ACVP-Server"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-12-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Cybersecurity"],"issued":"2022-01-05","keyword":["Cryptography Algorithm Testing Validation Automated Module"]},{"identifier":"ark:/88434/mds2-2522","accessLevel":"public","references":["https://doi.org/10.1002/acp.4003"],"contactPoint":{"hasEmail":"mailto:carina.hahn@nist.gov","fn":"Carina Hahn"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2522","description":"This is the corresponding analysis script for the preprint paper, Forensic facial examiners vs. super-recognizers: Evaluating behavior beyond accuracy available from https://doi.org/10.1002/acp.4003.","language":["en"],"title":"Forensic facial examiners vs. super-recognizers: Evaluating behavior beyond accuracy","distribution":[{"accessURL":"https://github.com/usnistgov/face-recognition-humans-machines/tree/main/Examiners%20vs.%20Super-recognizers","format":"GitHub Repository","description":"This is the GitHub repository containing the code for Forensic facial examiners vs. super-recognizers: Evaluating behavior beyond accuracy.","title":"Forensic facial examiners vs. super-recognizers: Evaluating behavior beyond accuracy"},{"accessURL":"https://doi.org/10.18434/mds2-2522","title":"DOI Access for Forensic facial examiners vs. super-recognizers: Evaluating behavior beyond accuracy"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Biometrics","Forensics"],"issued":"2021-12-21","keyword":["biometrics","face recognition","forensics"]},{"identifier":"ark:/88434/mds2-2524","accessLevel":"public","contactPoint":{"hasEmail":"mailto:laura.pierce@nist.gov","fn":"Laura Pierce"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2524","description":"This dataset is associated with the manuscript \"Evaluating fit-for-purpose cell viability assays that are sensitive to proliferative capacity\".  This dataset consists of 12 individual studies containing Jurkat cell proliferation data and viability assay data. A README file describes the data sets.","language":["en"],"title":"Data set for \"Evaluating fit-for-purpose cell viability assays that are sensitive to proliferative capacity\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2524/Data%20for%20Supplement%208.zip","description":"Re-generated download URL","mediaType":"application/zip","title":"Data for Supplement 8"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2524/Data%20for%20Figure%204.zip","description":"Re-generated download URL","mediaType":"application/zip","title":"Data for Figure 4"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2524/Data%20for%20Figure%205a.zip","description":"Re-generated download URL","mediaType":"application/zip","title":"Data for Figure 5a"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2524/Data%20for%20Figure%207.zip","description":"Re-generated download URL","mediaType":"application/zip","title":"Data for Figure 7"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2524/Data%20for%20Figure%201.zip","description":"Re-generated download URL","mediaType":"application/zip","title":"Data for Figure 1"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2524/2524_README.txt","description":"Re-generated download URL","mediaType":"text/plain","title":"2524_README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-12-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Health","Bioscience:Cell biology","Health:Cell therapies"],"issued":"2023-10-02","keyword":["cell viability","cell counting","cell proliferation","cellular therapeutic product","acridine orange","DAPI","ATP","Annexin","LDH","cell therapy","Gompertz model","nonlinear regression","classification"]},{"identifier":"ark:/88434/mds2-2525","accessLevel":"public","contactPoint":{"hasEmail":"mailto:brian.simonds@nist.gov","fn":"Brian Simonds"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2525","description":"The absolute laser absorption was measured simultaneously with X-ray imaging during laser melting of Ti-6Al-4V solid metal. The data included here are the time-resolved absolute absorbed power and the Xray images acquired at the same time, along with timing data for synchronization. Also included is information about the experimental configuration including applied laser power, laser beam spatial profile, and the experimental setup. A text document is included that describes all files. There are datasets from two experimental configurations: 1) A stationary, 2.0 ms laser pulse and 2) a 700 mm/s scanned laser.","language":["en"],"title":"Asynchronous AM Bench 2022 Challenge Data: Real-time, simultaneous absorptance and high-speed Xray imaging","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/2525_README_v200.txt","description":"A text file containing references to data, experimental information, and descriptions of all files available for download.","mediaType":"text/plain","title":"2525_README_v200.txt"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/Al_Scan_AA_MWD_ASR_Results.csv","description":"Results from the AM Bench challenge problems for average absorption, melt pool width, melt pool depth, and average solidification rate for a scanned laser on aluminium.","mediaType":"text/csv","title":"Al_Scan_AA_MWD_ASR_Results.csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/Al_Scan_AA_MWD_ASR_Results.csv.sha256","description":"Results from the AM Bench challenge problems for average absorption, melt pool width, melt pool depth, and average solidification rate for a scanned laser on aluminium.","mediaType":"text/plain","title":"Al_Scan_AA_MWD_ASR_Results.csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/2525_README_v200.txt.sha256","description":"A text file containing references to data, experimental information, and descriptions of all files available for download.","mediaType":"text/plain","title":"2525_README_v200.txt"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/Al_Scan_TDA_v2_Results.csv","description":"Results from the AM Bench challenge problems for time dependent absorption during scanned laser on aluminium.","mediaType":"text/csv","title":"Al_Scan_TDA_v2_Results.csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/Al_Scan_TDA_v2_Results.csv.sha256","description":"Results from the AM Bench challenge problems for time dependent absorption during scanned laser on aluminium.","mediaType":"text/plain","title":"Al_Scan_TDA_v2_Results.csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/Al_Spot_AA_ASR_Results.csv","description":"Results for AM Bench challenge problems for average absorption and average solidification rate of a stationary laser spot on aluminum.","mediaType":"text/csv","title":"Al_Spot_AA_ASR_Results.csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/Al_Spot_AA_ASR_Results.csv.sha256","description":"Results for AM Bench challenge problems for average absorption and average solidification rate of a stationary laser spot on aluminum.","mediaType":"text/plain","title":"Al_Spot_AA_ASR_Results.csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/Al_Spot_TDA_Results.csv","description":"Results for AM Bench challenge problems for time-dependent absorption during a stationary laser spot on aluminum.","mediaType":"text/csv","title":"Al_Spot_TDA_Results.csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/Al_Spot_TDA_Results.csv.sha256","description":"Results for AM Bench challenge problems for time-dependent absorption during a stationary laser spot on aluminum.","mediaType":"text/plain","title":"Al_Spot_TDA_Results.csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/Al_Spot_TDW_Results.csv","description":"Results for AM Bench challenge problems for time-dependent width during  a stationary laser exposure on aluminum.","mediaType":"text/csv","title":"Al_Spot_TDW_Results.csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2525/Al_Spot_TDW_Results.csv.sha256","description":"Results for AM Bench challenge problems for time-dependent width during  a stationary laser exposure on aluminum.","mediaType":"text/plain","title":"Al_Spot_TDW_Results.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Beam%20Profile_5p5micron%20pixels_Normalized%20Integral.csv","description":"Beam profile data whose integral is normalized to 1. Therefore, multiplying these data by the laser power gives the correct integrated laser power. An example Python script is given as ?Beam Profile Imaging.ipynb? This data was obtained at focal plane of laser. The data is in units of pixels and the pixel pitch is 5.5 micrometers square.","mediaType":"application/vnd.ms-excel","title":"Beam Profile_5p5micron pixels_Normalized integral"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/NIST%20SRM%20654b%20Ti64%20data%20sheet.pdf","description":"Composition datasheet for NIST standard reference material (SRM) 654b, a Ti-6Al-4V alloy. The samples used in this study were cut from this feedstock. Additional information found at https://www-s.nist.gov/srmors/view_detail.cfm?srm=654B","mediaType":"application/pdf","title":"NIST SRM 654b Ti64 data sheet"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Spot%20on%20Bare%20Metal_Xray%20with%20Absorption.avi","description":"An example movie made from the frames in ?Spot on Bare Metal_XrayImages_processed_wAbsorption?.","mediaType":"video/example","title":"Spot on Bare Metal_Xray with Absorption"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Spot%20on%20Bare%20Metal_XrayImages_processed_wAbsorption.zip","description":"Frames containing processed Xray images along with a time series plot of the measured laser absorption up to the time that the Xray image as captured.","mediaType":"application/x-zip-compressed","title":"Spot on Bare Metal_XrayImages_processed_wAbsorption"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Scan%20on%20Bare%20Metal_Calibrated%20Absorption%20Data.csv","description":"File containing time-dependent absorption and camera timing data. Nine columns.  1: Numerical index. 2: ?Time? ? in seconds. 3: ?InputLaser? ? Temporal profile of laser pulse. Sharp peak for first few hundred nanoseconds is an artifact and is to be ignored. 4: ?AbsoluteAbsorption? ? The calculated absolute absorbed power in units of Watts. See [1] and [4] for discussion of how this is calculated. 5: ?AbsAbsorptionUncertainty? ? Absolute expanded uncertainty in the absorbed power in units of Watts. See additional documentation for how this is calculated. 6: ?RelativeAbsorption? ? Percent absorption. 7: ?CameraTrigger? ? TTL pulse when camera was triggered to start capturing images. 8: ?FrameTrigger? ? Camera output pulse where leading edge is start of frame exposure. 9: ?FrameNumber? ? The frame number is given at the start of each frame.","mediaType":"application/vnd.ms-excel","title":"Scan on Bare Metal_Calibrated Absorption Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Scan%20on%20Bare%20Metal_XrayImages_processed_captioned.zip","description":"Processed Xray images prepared using the methods described in [3]. Time stamps are also overlayed on the images.","mediaType":"application/x-zip-compressed","title":"Scan on Bare Metal_XrayImages_processed_captioned"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Scan%20on%20Bare%20Metal_XrayImages_Raw.zip","description":"Unprocessed (raw) Xray images.","mediaType":"application/x-zip-compressed","title":"Scan on Bare Metal_XrayImages_Raw"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Sample%20Diagram_V110.pdf","description":"This file contains diagrams of the experimental setup and sample configuration.","mediaType":"application/pdf","title":"Sample Diagram"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Beam%20Profile%20Imaging.ipynb","description":"This is a script for visualizing the beam profile data given in the above file. The user should adjust the \"LaserPower\" parameter to be equal to the value specified for the absorption data. The user also needs to input an appropriate value for the data file location.","mediaType":"application/octet-stream","title":"Laser Beam Profile Script"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Data%20Synchronization%20Example.ipynb","description":"This is a sample script for comparing raw and processed Xray images along with the absorption at a particular time. The time is selected by the user by a choice of image frame to display.","mediaType":"application/octet-stream","title":"Data Synchronization Example"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Spot%20on%20Bare%20Metal_Calibrated%20Absorption%20Data.csv","description":"File containing time-dependent absorption and camera timing data. Nine columns.  1: Numerical index. 2: \"Time\" in seconds. 3: \"InputLaser\" Temporal profile of laser pulse. Sharp peak for first few hundred nanoseconds is an artifact and is to be ignored. 4: \"AbsoluteAbsorption\" The calculated absolute absorbed power in units of Watts. See [1] and [4] for discussion of how this is calculated. 5: \"AbsAbsorptionUncertainty\" Absolute expanded uncertainty in the absorbed power in units of Watts. See additional documentation for how this is calculated. 6: \"RelativeAbsorption\" Percent absorption. 7: \"CameraTrigger\" TTL pulse when camera was triggered to start capturing images. 8: \"FrameTrigger\" Camera output pulse where leading edge is start of frame exposure. 9: \"FrameNumber\" The frame number is given at the start of each frame.","mediaType":"application/vnd.ms-excel","title":"Spot on Bare Metal_Calibrated Absorption Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Spot%20on%20Bare%20Metal_XrayImages_processed_captioned.zip","description":"Spot on Bare Metal_XrayImages_processed_captioned","mediaType":"application/x-zip-compressed","title":"Spot on Bare Metal_XrayImages_processed_captioned"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Spot%20on%20Bare%20Metal_XrayImages_Raw.zip","description":"Unprocessed (raw) Xray images.","mediaType":"application/x-zip-compressed","title":"Spot on Bare Metal_XrayImages_Raw"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Scan%20on%20Bare%20Metal_Xray%20with%20Absorption.avi","description":"An example movie made from the frames in \"Scan on Bare Metal_XrayImages_processed_wAbsorption\".","mediaType":"video/example","title":"Scan on Bare Metal_Xray with Absorption"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Scan%20on%20Bare%20Metal_XrayImages_processed_wAbsorption.zip","description":"Frames containing processed Xray images along with a time series plot of the measured laser absorption up to the time that the Xray image as captured.","mediaType":"application/x-zip-compressed","title":"Scan on Bare Metal_XrayImages_processed_wAbsorption"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2525/Absorption_Uncertainty_Analysis.pdf","description":"This document contains the uncertainty analysis calculations for the absorptivity data given in this dataset. The relevant measurement equations are given along with uncertainty equations for the reflected power, absorbed power, and laser coupling efficiency. An example calculation is also presented using the \"Spot on Bare Metal\" data set.","mediaType":"application/pdf","title":"Absorption Uncertainty Analysis"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-01-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing"],"keyword":["Additive Manufacturing","Laser Welding"]},{"identifier":"ark:/88434/mds2-2526","accessLevel":"public","contactPoint":{"hasEmail":"mailto:oleg.aulov@nist.gov","fn":"Oleg Aulov"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2526","description":"The KAIROS Evaluation Software Suite was developed by NIST in support of evaluation of DARPA's Program on Knowledge Directed Artificial Intelligence Reasoning Over Schemas (KAIROS). Some of the capabilities of this software include:* calculating a variety of metrics and scores indicative of performance of individual KAIROS systems* processing and format conversion of KAIROS system output, data annotations, and human assessment results* analyzing metrics, scores, and assessment results* generating statistics and charts summarizing these results","language":["en"],"title":"KAIROS Evaluation Software","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-12-19 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://github.com/usnistgov/KAIROS","accrualPeriodicity":"irregular","theme":["Information Technology:Data and informatics"],"issued":"2022-10-17","keyword":["Evaluation of AI systems","Artificial Intelligence (AI) Explainability","Human Language Technology (HLT)","Multimodal and Multilingual Information Retrieval (IR)"]},{"identifier":"ark:/88434/mds2-2529","accessLevel":"public","contactPoint":{"hasEmail":"mailto:barbara.guttman@nist.gov","fn":"Barbara Guttman"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2529","description":"The Software & Algorithms Catalog serves as a searchable database of forensically relevant algorithms and software across various forensic disciplines. The catalog is a tool for guiding future research in this important topic area and will provide a deeper understanding of the state of practice in algorithmic forensic science, including identification of areas without algorithms and software.","language":["en"],"title":"Forensic Software and Algorithms Catalog","distribution":[{"accessURL":"https://forensicsoftware.nist.gov/index.php","description":"The Software & Algorithms Catalog serves as a searchable database of forensically relevant algorithms and software across various forensic disciplines. The catalog is a tool for guiding future research in this important topic area and will provide a deeper understanding of the state of practice in algorithmic forensic science, including identification of areas without algorithms and software.","title":"Forensics Software and Algorithms Catalog"},{"accessURL":"https://doi.org/10.18434/mds2-2529","title":"DOI Access for Forensic Software and Algorithms Catalog"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-12-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/PT1S","theme":["Forensics:Trace evidence","Forensics:Pattern evidence","Forensics:Drugs and toxicology","Forensics:Digital and multimedia evidence","Forensics:DNA and biological evidence","Forensics:Ballistics"],"issued":"2022-01-26","keyword":["forensic software","forensic algorithms"]},{"identifier":"ark:/88434/mds2-2530","accessLevel":"public","contactPoint":{"hasEmail":"mailto:mehdi.dadfarnia@nist.gov","fn":"Mehdi Dadfarnia"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2530","description":"Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems. The object classes are also designed to be extensible so that they can be used to model more complex processes.In addition to modeling the behavior of existing systems, Simantha is also intended for use with simulation-based optimization and planning applications. For instance, users may be interested in evaluating alternative maintenance policies for a particular system. Estimating the expected system performance under each candidate policy will require a large number of simulation replications when the system is subject to a high degree of stochasticity. Simantha therefore supports parallel simulation replications to make this procedure more efficient.Github repository: https://github.com/usnistgov/simantha","language":["en"],"title":"Simantha: Simulation for Manufacturing","distribution":[{"accessURL":"https://github.com/usnistgov/simantha","description":"Github repository","title":"Simantha"},{"accessURL":"https://doi.org/10.18434/mds2-2530","title":"DOI Access for Simantha: Simulation for Manufacturing"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-01-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Manufacturing systems design and analysis","Manufacturing:Factory operations planning and control"],"issued":"2022-02-07","keyword":["discrete-event simulation","manufacturing","production","maintenance","python"]},{"identifier":"ark:/88434/mds2-2531","accessLevel":"public","contactPoint":{"hasEmail":"mailto:ian.soboroff@nist.gov","fn":"Ian Soboroff"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2531","description":"This dataset is called the \"IIT CDIP collection\".  \"CDIP\" stands for \"Complex Document Information Processing\" and \"IIT\" stands for \"Illinois Institute of Technology\" who originally built the dataset.  The dataset consists of documents from the states' lawsuit against the tobacco industry in the 1990s. As a result of the settlement of that lawsuit (the \"Master Settlement Agreement\"), the companies had to make all the documents public in an archive, which currently resides at UCSF, the University of California, San Francisco.IIT used this data to build a dataset of \"messy\" documents that were challenging for existing systems to process.  There is handwriting on the documents, stains, etc.  TREC used an automatic text conversion of this dataset in the TREC Legal Track, and we also have the original TIFF scans of the documents.  The dataset consists of around 7 million documents, preprocessed with 90s-era OCR, and also the original page scans in TIFF format. See contact information in this record for access to this dataset.","language":["en"],"title":"Complex Document Information Processing (CDIP) 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Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - nlp-summary-jan2022-train","distribution":[{"accessURL":"https://drive.google.com/drive/folders/13OAOIabpF-iHdIC9G5LxGOl7IL0UBcIL?usp=drive_link","title":"nlp-summary-jan2022-train"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-01-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Cybersecurity","Information Technology:Software research"],"issued":"2022-01-26","keyword":["Trojan Detection","Artificial Intelligence","AI","Machine Learning","Adversarial Machine Learning"]},{"identifier":"ark:/88434/mds2-2540","accessLevel":"public","references":["https://doi.org/10.1038/s41467-022-28823-6"],"contactPoint":{"hasEmail":"mailto:darwin.reyes@nist.gov","fn":"Darwin Reyes-Hernandez"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2540","description":"This data set contains the data/information used to plot the graphs published in the 2022 Nat. 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Over time, mechanical wear can cause reduced preload levels and eventually backlash development, which leads to non-conforming behavior and a loss of positional accuracy in the ball screw. This dataset can be used to develop methods to track the loss of preload and the emergence of backlash.","language":["en"],"title":"Linear Axis Testbed at IMS Center - Run-to-Failure Experiment 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The data is organized as described in the header (first row) of the csv file.","mediaType":"text/csv","title":"Figure 7: Data Reconstructions"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2550/Figure_8a_data_Reconstructions_Theta1_0_Theta2_piOver2_vs_lambda_c.csv","description":"This file contains the data for Figure 8a, which shows near field signal to reconstruction noise ratio versus the RGSF cutoff parameter lambda_c as calculated according RGSF CS method and using measurements limited in the polar angle to [0, pi/2] (radians). The data is organized as described in the header (first row) of the csv file.","mediaType":"text/csv","title":"Figure 8a: Data Reconstructions"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2550/Figure_8b_data_CoefficientError_Theta1_0_Theta2_piOver2_vs_lambda_c.csv","description":"This file contains the data for Figure 8b, which shows the spherical wave coefficient error versus the RGSF cutoff parameter lambda_c as calculated according RGSF CS method and using measurements limited in the polar angle to [0, pi/2] (radians). The data is organized as described in the header (first row) of the csv file.","mediaType":"text/csv","title":"Figure 8b: Data CoefficientError"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2550/README.txt","description":"This is a \"read me\" file that contains and overview of the dataset.","mediaType":"text/plain","title":"Read me file"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-02-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"issued":"2022-03-16","keyword":["compressive sensing; far-field pattern; near-field pattern; antenna characterization; Wigner d-functions; SVD"]},{"identifier":"ark:/88434/mds2-2551","accessLevel":"public","contactPoint":{"hasEmail":"mailto:edward.sisco@nist.gov","fn":"Edward Sisco"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2551","description":"Source code associated with the Inverted Library Search Algorithm (ILSA) for identifying mixture components using is-CID mass spectra. This source code is frozen to accompany the manuscript titled \"Updates to the Inverted Library Search Algorithm for mixture analysis\" by Moorthy et. al. https://pubs.acs.org/doi/10.1021/jasms.2c00090The primary function, \"asm_ILSA.R\" in the \"source/Functions\" directory, receives as input up to three is-CID query mass spectra, a pure compound reference is-CID mass spectral library, and a set of search parameters. To help a user organize their input to the asm_ILSA.R function, we have provided in the root directory \"asm_ILSA_CLA.R\"--a prototype command line application. A user can use this prototype application as a guide for using the ILSA in their own research, adjusting input files, search libraries and search parameters. At present, search libraries must be formatted as .RDS files as produced by NIST. Query spectra can be in several standard formats for is-CID mass spectra: \".txt\", \".csv\", \".jsp\".","language":["en"],"title":"Supplemental Data and Source Code for ILSA Research","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Forensics:Drugs and toxicology","Chemistry:Analytical chemistry"],"issued":"2022-03-21","keyword":["Mass Spectrometry","Mixture Analysis","Search Algorithms","Seized Drug Analysis"]},{"identifier":"ark:/88434/mds2-2553","accessLevel":"public","contactPoint":{"hasEmail":"mailto:daniel.kuester@nist.gov","fn":"Dan Kuester"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2553","description":"During the COVID-19 pandemic, National Institute of Standards and Technology (NIST) performed a targeted campaign of measurements of channel power and occupancy rate in the radio spectrum. This is the dataset that resulted from that effort.Measurements were performed in 13 residential sites after a rapid engineering development effort, followed by spot measurements at a local hospital. The study targeted radio frequency allocations that support key wireless data infrastructure. These included the cellular network bands below 1 GHz, and unlicensed bands that support wireless local-area network (WLAN) and wireless personal area network (WPAN) traffic in 2.4 GHz and 5 GHz bands. The principal test locations were the home telework sites of NIST research staff, postdoctoral researchers, graduate and undergraduate students, and summer high school interns. Measurements continued where possible from late spring in 2020 until the autumn of 2021.The resulting data are released here in the following forms:1) Time series of band power, integrated on 4-MHz bandwidth in 0.5 ms bins;2) Time series of band power, denoised to remove average additive noise produced by the receiver;3) Occupancy durations computed from the denoised band power time series;4) Histograms of band power readings computed from denoised band power time series; and5) Diagnostic network profiling data that indicates PHY characteristics of cellular and WLAN signals.","language":["en"],"title":"Distributed Sensing of Radio Spectrum Occupancy Amid COVID-19","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/time%20series%20denoised/01.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/time%20series%20denoised/08.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/time%20series%20denoised/08.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/time%20series%20denoised/06.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/time%20series%20denoised/06.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/time%20series%20denoised/hospital.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/time%20series%20denoised/hospital.tar.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/occupancy%20durations/hospital.csv.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/occupancy%20durations/hospital.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/source-code.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/source-code.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/739.0%20MHz%20cellular%20downlink%20profiling.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2007/731.5%20MHz%20cellular%20downlink%20profiling.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2007/782.0%20MHz%20cellular%20uplink%20profiling.csv.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2007/782.0%20MHz%20cellular%20uplink%20profiling.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2007/751.0%20MHz%20cellular%20downlink%20profiling.csv.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2007/751.0%20MHz%20cellular%20downlink%20profiling.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/709.0%20MHz%20cellular%20uplink%20profiling.csv.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/709.0%20MHz%20cellular%20uplink%20profiling.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/739.0%20MHz%20cellular%20downlink%20profiling.csv.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/821.3%20MHz%20cellular%20uplink%20profiling.csv.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/821.3%20MHz%20cellular%20uplink%20profiling.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/866.3%20MHz%20cellular%20downlink%20profiling.csv.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/866.3%20MHz%20cellular%20downlink%20profiling.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/701.5%20MHz%20cellular%20uplink%20profiling.csv.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/701.5%20MHz%20cellular%20uplink%20profiling.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/731.5%20MHz%20cellular%20downlink%20profiling.csv.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/731.5%20MHz%20cellular%20downlink%20profiling.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/782.0%20MHz%20cellular%20uplink%20profiling.csv.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/782.0%20MHz%20cellular%20uplink%20profiling.csv.gz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2553/network%20profiling/site%2011/75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Speed of sound was measured in the liquid phase using a dual-path pulse-echo instrument at temperatures between 230 and 345 K, with pressures from 2.1 to 70 MPa. Vapor-phase density was measured using a dual-sinker densimeter instrument at temperatures from 240 to 340 K and pressures from 0.1 to 1.61 MPa.","language":["en"],"title":"Speed of Sound and Density of Difluoromethane","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2554/README.docx","description":"A readme file to accompany the data","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2554/Difluoromethane_Density_Data-updated.txt","mediaType":"text/plain","title":"Difluoromethane Density Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2554/Difluoromethane_SOS_Data-updated.txt","mediaType":"text/plain","title":"Difluoromethane Speed of Sound (SoS) Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2554/Propane_SOS_Data-updated.txt","mediaType":"text/plain","title":"Propane Calibration Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-02-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Materials characterization","Standards:Reference data","Physics:Thermodynamics"],"issued":"2023-01-04","keyword":["Speed of Sound","Density","DFM","R-32","HFC-32","low-GWP","Material property","Reference data"]},{"identifier":"ark:/88434/mds2-2555","accessLevel":"public","contactPoint":{"hasEmail":"mailto:aaron.rowane@nist.gov","fn":"Aaron Rowane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2555","description":"Speed of sound data measured in the search for a Low-GWP Alternative Refrigerant Blends for HFC-134a. Speed of sound data are being used to improve current REFPROP mixture models for binary mixtures of R-134a, R-1234yf, and R-1234ze(E).","language":["en"],"title":"Speed of Sound Measurements of Binary Mixtures of 1,1,1,2-Tetrafluoroethane (R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2555/R1234yf_134a_1234ze%28E%29_SOSData.csv","mediaType":"application/vnd.ms-excel"},{"accessURL":"https://doi.org/10.18434/mds2-2555","title":"DOI Access for Speed of Sound Measurements of Binary Mixtures of 1,1,1,2-Tetrafluoroethane (R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-02-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Chemical thermodynamics and chemical properties"],"issued":"2022-02-25","keyword":["Refrigerant Blends","Speed of Sound","R-134a","R-1234ze(E)","R-1234yf"]},{"identifier":"ark:/88434/mds2-2556","accessLevel":"public","contactPoint":{"hasEmail":"mailto:benjamin.neely@nist.gov","fn":"Ben Neely"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2556","description":"Bats can harbor many pathogens without showing disease. However, the mechanisms by which bats resolve these infections or limit pathology remain unclear. To illuminate the bat immune response to coronaviruses, viruses with high public health significance, we will use serum proteomics to assess broad differences in immune proteins of uninfected and infected vampire bats (Desmodus rotundus). In contrast to global profiling techniques of blood such as transcriptomics, proteomics provides a unique perspective into immunology, as the serum proteome includes proteins from not only blood but also those secreted from proximal tissues. Here, we expand our recent work on the serum proteome of wild vampire bats (Desmodus rotundus) to better understand CoV pathogenesis. Across 19 bats sampled in 2019 in northern Belize with available sera, we detected CoVs in oral or rectal swabs from four individuals. We used data independent acquisition-based mass spectrometry to profile and compare the undepleted serum proteome of these 19 bats. These results will provide much needed insight into changes in the bat serum proteome in response to coronavirus infection.","language":["en"],"title":"Serum proteomics of coronavirus shedding in vampire bats (Desmodus rotundus)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_7.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_7H.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_7H.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_7NH.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_7NH.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_8.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_8.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_9.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_9.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_10.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_10.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_12.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_12.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_13.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_13.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_14.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_14.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_15.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_15.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_16.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_16.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_16H.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_16H.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_16NH.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_16NH.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_17.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_17.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_18.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_18.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_19.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_19.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_20.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_20.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_20H.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_20H.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_20NH.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_20NH.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_21.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2556/2021-7-13_BatCov_21.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Proteomics"],"issued":"2022-12-02","keyword":["proteome","Alphacoronavirus","Desmodus rotundus","ecoimmunology","biomarker"]},{"identifier":"ark:/88434/mds2-2558","accessLevel":"public","references":["https://doi.org/10.1145/3372278.3390742"],"contactPoint":{"hasEmail":"mailto:george.awad@nist.gov","fn":"George Awad"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2558","description":"The DVU dataset consists of 10 creative common movies for training and 4 creative common movies for testing used in the Deep Video Understanding grand challenges in 2020 and 2021. Total duration is about 17 hr.","language":["en"],"title":"Deep Video Understanding (DVU) Dataset","distribution":[{"accessURL":"https://www-nlpir.nist.gov/projects/trecvid/dvu/training/","format":"mp4, xml, json, png","description":"Training dataset applied to DVU Grand Challenge at ACM Multimedia Grand Challenges of 2020, 2021 and ACM Multimedia Asia Grand Challenge of 2021.","title":"DVU - Training Dataset"},{"accessURL":"https://www-nlpir.nist.gov/projects/trecvid/dvu/testing/","format":"mp4, xml, json","description":"This is the testing dataset used in 2021 for the ACM MM Grand Challenge and ACM MM Asia Grand Challenge.","title":"DVU - Testing Dataset"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-07-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1Y","theme":["Information Technology:Data and informatics"],"issued":"2022-03-31","keyword":["Multimedia understanding","multimodal integration","artificial intelligence"]},{"identifier":"ark:/88434/mds2-2561","accessLevel":"public","references":["https://doi.org/10.1021/acs.jctc.8b00593","https://doi.org/10.1021/acs.jpca.7b03195"],"contactPoint":{"hasEmail":"mailto:andrei.kazakov@nist.gov","fn":"Andrei F. Kazakov"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This data set contains enthalpies of formation, entropies, heat capacities, and thermal enthalpies for about 400 compounds available in the NIST TRC SOURCE database. The enthalpies of formation are computed using the ab initio-based protocol reported in the references below. The other properties are calculated by statistical thermodynamics using the \"rigid rotor - harmonic oscillator\" approximation. The conformational contributions are found by the Gibbs energy averaging of the properties of the conformers.","language":["en"],"title":"Ideal-Gas Thermodynamic Properties for Organic Compounds Containing Up to 7 C, O, or N Atoms","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2561/ComputedFormationEnthalpies_2022Feb.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2561/ComputedFormationEnthalpies_2022Feb.csv.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-02-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Thermodynamics","Chemistry:Theoretical chemistry and modeling","Chemistry:Thermochemical properties"],"issued":"2023-03-18","keyword":["Theoretical Thermochemistry","ab initio computations","ideal gas properties","enthalpy of formation","uncertainty"]},{"identifier":"ark:/88434/mds2-2562","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2213"],"contactPoint":{"hasEmail":"mailto:andrew.persily@nist.gov","fn":"Andrew K. Persily"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pages.nist.gov/CONTAM-apps/webapps/CO2Tool/#/","description":"Indoor carbon dioxide (CO2) concentrations have been used for decades to evaluate indoor air quality (IAQ) and ventilation, and more recently in discussions of the risk of airborne infectious disease transmission. However, many applications of indoor CO2 measurement reflect a lack of understanding of the connection between indoor CO2 levels, ventilation and IAQ. In many cases, an indoor concentration of 1000 ppmv has been used as a metric of IAQ and ventilation without an understanding of its basis or significance. After many years trying to dissuade practitioners and researchers from using this or some other single concentration as an overall metric of ventilation and IAQ, an approach has been developed to determine a space-specific CO2 level that can be used as an indicator of the outdoor ventilation rate. The concept is to estimate the CO2 concentration that would be expected in a specific space given its intended or expected ventilation rate per person, the number of occupants and the rate at which they generate CO2. A calculation method is described for estimating the CO2 concentration in a given space at selected times after occupancy starts, which provides a more meaningful metric than a single value for all spaces. An online tool (QICO2, Quick Indoor CO2) has been developed to perform these calculations, and this report also contains a User Guide for that tool.","language":["en"],"title":"QICO2 Indoor Carbon Dioxide Metric Analysis Tool","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Buildings and Construction:Indoor air quality"],"issued":"2022-04-01","keyword":["carbon dioxide; indoor air quality; metric; standards; ventilation"]},{"identifier":"ark:/88434/mds2-2563","accessLevel":"public","contactPoint":{"hasEmail":"mailto:paritosh.manurkar@nist.gov","fn":"Paritosh Manurkar"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"The experiment here was to demonstrate that we can reliably measure the Reference Waveforms designed in the IEEE P1765 proposed standard and calculate EVM along with the associated uncertainties. The measurements were performed using NIST's calibrated sampling oscilloscope and were traceable to the primary standards.We have uploaded the following two datasets. (1) Table 3 contains the EVM values (in %) for the Reference Waveforms 1--7 after performing the uncertainty analyses. The Monte Carlo means are also compared with the ideal values from the calculations in the IEEE P1765 standard.(2) Figure 3 shows the complete EVM distribution upon performing uncertainty analysis for Reference Waveform 3 as an example. Each of the entries in Table 3 is associated with an EVM distribution similar to that shown in Fig. 3.","language":["en"],"title":"Reference Measurements of Error Vector Magnitude","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2563/Figure3_ReferenceWaveform3_histogram.txt","description":"X axis has the EVM values in %Y axis has the number of occurrences for a specific histogram bin after running 1000 Monte Carlo simulations","mediaType":"text/plain","title":"EVM distribution for Reference Waveform #3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2563/Figure3_ReferenceWaveform3_MonteCarlo.txt","description":"This file contains the nominal EVM value, Monte Carlo mean and 95% confidence intervals after running 1000 Monte Carlo simulations.","mediaType":"text/plain","title":"Monte Carlo estimates"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2563/Table3_evm_values.xlsx","description":"Since EVM distributions (see Figure 3 for example) for all 7 waveforms cannot be included in the paper (length limited to 3 pages), we have included the nominal EVM, Monte Carlo means, and 95% confidence intervals for all the waveforms here after performing uncertainty analysis. The Monte Carlo means can also be compared to the expected EVM values in the Table.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Summary of EVM values for Reference Waveforms 1--7"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-02-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"issued":"2022-03-16","keyword":["Wireless communication","digitally modulated signals","quadrature amplitude modulation","error vector magnitude","measurement uncertainty","uncertainty analysis"]},{"identifier":"ark:/88434/mds2-2564","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jonathan.seppala@nist.gov","fn":"Jonathan Seppala"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2564","description":"In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. This repository includes Python code for analyzing and managing images and videos of the printing process during extrusion of single filaments. The zip file contains the state of the code when the associated paper was submitted. The link to the GitHub page goes to version 1.0.0, which is the same as the code attached here. From there, you can also access the current state of the code.Associated with: L. Friedrich, R. Gunther, J. Seppala, Suppression of filament defects in embedded 3D printing, 2022, submitted for publication","language":["en"],"title":"Python tools for measuring filament defects in embedded 3D printing","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2564/embedded3DPvids-1.0.0.zip","format":"zip file","description":"Public version 1.0.0 of the code","mediaType":"application/x-zip-compressed","title":"Embedded3DPvids-1.0.0"},{"accessURL":"https://github.com/usnistgov/embedded3DPvids/releases/tag/v1.0.1","description":"GitHub page for version 1.0.1","title":"GitHub page for version 1.0.1"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Image and signal processing","Manufacturing:Process measurement and control","Manufacturing:Biomanufacturing","Manufacturing:Additive manufacturing","Materials:Polymers"],"issued":"2022-04-25","keyword":["python","digital image analysis","computer vision","3D printing","additive manufacturing","openCV"]},{"identifier":"ark:/88434/mds2-2565","accessLevel":"public","references":["https://www.nist.gov/publications/open-media-forensics-challenge-openmfc-2021-workshop-presentations","https://doi.org/10.6028/NIST.IR.8396","https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8377.pdf","https://doi.org/10.6028/NIST.IR.8377","https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930801","https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932934"],"contactPoint":{"hasEmail":"mailto:haiying.guan@nist.gov","fn":"Haiying Guan"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/MediScore","description":"MediScore (https://github.com/usnistgov/MediScore) is a NIST developed Medifor scoring and evaluation toolkit for Open Media Forensic Evaluation (OpenMFC). 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file","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"The assemblies provided by Human Pangenome Reference Consortium (HPRC) for evaluation were generated by the HPRC, and its data use protocol is at https://humanpangenome.org/data-use-protocol/","modified":"2021-12-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Genomic measurements"],"keyword":["Human genomics; DNA sequencing; Reference materials;  genome assembly; variant calling; benchmarking; HG002; HPRC"]},{"identifier":"ark:/88434/mds2-2581","accessLevel":"public","contactPoint":{"hasEmail":"mailto:73_EL_Data@nist.gov","fn":"Anthony D. Putorti Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2581","description":"This dataset includes high definition video, thermal imaging video, and still images captured from the video.  The video was recorded during full-scale High Energy Arcing Fault experiments in medium voltage electrical enclosures.","language":["en"],"title":"High Energy Arcing Fault (HEAF) Experiments - Medium Voltage Electrical Enclosures - Video","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Buildings and Construction:Building codes and standards","Energy:Alternative energy","Energy:Conventional energy","Energy:Electric power / smart grid","Fire","Infrastructure","Resilience"],"issued":"2022-08-23","keyword":["High Energy Arcing Fault","arc flash","electrical enclosure","electric arc","Fire Probabilistic Risk Assessment"]},{"identifier":"ark:/88434/mds2-2585","accessLevel":"public","references":["https://arxiv.org/abs/2202.08344","https://doi.org/10.6028/jres.126.002","https://pages.nist.gov/optbayesexpt"],"contactPoint":{"hasEmail":"mailto:robert.mcmichael@nist.gov","fn":"Robert D. McMichael"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2585","description":"Examples of adaptive measurement protocols using optimal Bayesian experiment design.  This dataset supports \"Simplified algorithms for adaptive experiment design in parameter estimation\", arXiv 2202.08344 and submitted to Physical Review Applied.  The calculations use python package optbayesexpt, which is available from https://github.com/usnistgov/optbayesexpt.  The software applies to measurements of parameters in nonlinear parametric models.  In the adaptive protocol, Incoming data influences parameter distributions via Bayesian inference and the parameter distribution influences predictions of the impact of future measurements.","language":["en"],"title":"Scripts, data and plotting for \"Simplified algorithms for adaptive experiment design in parameter estimation\" v.2","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2585/2585_README.txt","format":"English text","description":"A guide to the data in utility algorithms.zip","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2585/utility%20algorithms.zip","format":"Folders corresponding to figures in the paper","description":"Data files and python code","mediaType":"application/x-zip-compressed","title":"utility algorithms"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2585/arXiv2202.08344.pdf","format":"pdf formatted manuscript","description":"The associated paper","mediaType":"application/pdf","title":"Simplified algorithms for adaptive experiment design in parameter estimation"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-08 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Experiment design","Mathematics and Statistics:Numerical methods and software"],"issued":"2022-07-06","keyword":["Bayesian","experiment design","experimental design","optimal design","adaptive protocol","adaptive measurement","parametric model","Ramsey","Lorentzian","particle filter","sequential Monte Carlo","utility function"]},{"identifier":"ark:/88434/mds2-2586","accessLevel":"public","contactPoint":{"hasEmail":"mailto:isaac.leventon@nist.gov","fn":"Isaac Leventon"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2586","description":"The MaCFP Condensed Phase Subgroup has been designed to enable the fire research community to make significant progress towards establishing a common framework for the selection of experiments and the methodologies used to analyze these experiments when developing pyrolysis models. Experimental measurements prepared for the MaCFP Condensed Phase Working Group are submitted electronically by participating institutions and are organized and made publicly available in the MaCFP repository, which is hosted on GitHub [https://github.com/MaCFP/matl-db]. This database is version controlled, with each addition to (or edit of) measurement data saved with a unique identifier (i.e., commit tag). The repository was created and is managed by members of the MaCFP Organizing Committee.As of October, 2021, the MaCFP Condensed Phase Material Database contains measurement data from more than 200 unique experiments (conducted under 35 different test conditions on the same exact poly(methyl methacrylate), PMMA). All measurement data submitted by each institution is organized in a single folder with the institution's name. A consistent file naming convention is used for all test data (i.e., across all folders). File names indicate the institution name, experimental apparatus, and basic test conditions (e.g., gaseous environment and incident heat flux or heating rate). Measurement data from repeated experiments is saved in separate, ASCII comma-delimited (.csv) files, each numbered sequentially. Written description of sample preparation, test setup, and test procedure (which define the conditions associated with the experiments conducted) are included in each folder as a README.md file; this file is automatically interpreted by GitHub as Markdown (.md) text and provides a brief description of an institution's data.-------------How to cite this data-------------You may cite the use of this data as follows:Batiot, B., Bruns, M., Hostikka, S., Leventon, I., Nakamura, Y., Reszka, P., Rogaume, T., Stoliarov, S., Measurement and Computation of Fire Phenomena (MaCFP) Condensed Phase Material Database, https://github.com/MaCFP/matl-db, Commit Tag: [give commit; e.g., 7f89fd8], https://doi.org/10.18434/mds2-2586 (Accessed: [give download date])This data is publicly available according to the NIST statements of copyright, fair use and licensing; see:https://www.nist.gov/director/copyright-fair-use-and-licensing-statements-srd-data-and-software---------------Version History---------------The MaCFP repository, which is hosted on GitHub [https://github.com/MaCFP/matl-db], is version controlled,  with each addition (or edit) saved with a unique identifier (i.e., commit tag). When citing this database, you must include the commit tag that identifies the version of the repository you are working with.-------------------Experiments Conducted------------------------ 1. Milligram-Scale Tests: -----1.1 Thermogravimetric Analysis (TGA)1.2 Differential Scanning Calorimetry (DSC)1.3 Microscale Combustion Calorimetry (MCC)----- 2. Gram-Scale Tests -----2.1 Cone Calorimeter2.2 Anaerobic Gasification2.3 Thermal Conductivity and Diffusivity (Hot Disk and Laser Flash)--------------How to interpret and use data in this repository for pyrolysis model calibration and validation--------------Further information regarding the use and interpretation of the data in this repository is available online: https://github.com/MaCFP/matl-db/tree/master/Non-charring/PMMAThis information includes: Key factors influencing material response during testsOutlier Criteria: Identification of clearly incorrect behavior in measurement data--------------------------Methodological Information--------------------------A preliminary summary of the measurement data contained in this repository is available online: https://github.com/MaCFP/matl-db/releases","language":["en"],"title":"Measurement and Computation of Fire Phenomena (MaCFP) Condensed Phase Material Database","distribution":[{"accessURL":"https://github.com/MaCFP/matl-db","format":"A version controlled, Github repository with each addition to (or edit of) measurement data saved with a unique identifier (i.e., commit tag).  Measurement data from repeated experiments is saved in separate, ASCII comma-delimited (.csv) files, each numbered sequentially.","description":"The MaCFP Condensed Phase Subgroup has been designed to enable the fire research community to make significant progress towards establishing a common framework for the selection of experiments and the methodologies used to analyze these experiments when developing pyrolysis models. As of October, 2021, the MaCFP Condensed Phase Material Database contains measurement data from more than 200 unique experiments (conducted under 35 different test conditions on the same exact poly(methyl methacrylate), PMMA). All measurement data submitted by each institution is organized in a single folder with the institution's name. A consistent file naming convention is used for all test data (i.e., across all folders). File names indicate the institution name, experimental apparatus, and basic test conditions (e.g., gaseous environment and incident heat flux or heating rate). Measurement data from repeated experiments is saved in separate, ASCII comma-delimited (.csv) files, each numbered sequentially. Written description of sample preparation, test setup, and test procedure (which define the conditions associated with the experiments conducted) are included in each folder as a README.md file; this file is automatically interpreted by GitHub as Markdown (.md) text and provides a brief description of an institution's data.","title":"The MaCFP Condensed Phase Material Database"},{"accessURL":"https://github.com/MaCFP/matl-db/releases","format":".pdf documents","description":"This page contains untracked files that provide summaries and/or written descriptions of the measurement data contained in the Material Flammability database and presented at MaCFP Workshops.","title":"README: Summary documents and Presentations"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-04-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Fire dynamics and science","Fire:Fire modeling","Fire:Materials flammability"],"issued":"2022-09-02","keyword":["Fire Modeling","Material Flammability","Material Properties","Pyrolysis"]},{"identifier":"ark:/88434/mds2-2587","accessLevel":"public","references":["https://doi.org/10.1007/s40192-023-00307-5"],"contactPoint":{"hasEmail":"mailto:nik.hrabe@nist.gov","fn":"Nik Hrabe"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2587","description":"One additively manufactured (AM) laser powder bed fusion (PBF-L) Inconel 625 mesoscale tensile specimen (gauge dimensions approximately 0.2mm x 0.2 mm x 1mm) was extracted from build AMB2022-CBM-B1 specimen TH1 and tested at room temperature using a quasistatic strain rate of 0.001/s to failure.  Microstructure was measured using x-ray computed tomography (XRCT) and scanning electron microscopy (SEM) techniques on the specimen gauge section or adjacent material.  Large-area electron backscatter diffraction was used to measure crystallographic texture and grain size/morphology of the entire gauge section and two orthogonal planes.  Backscatter electron imaging was used to characterize the subgrain structure and assess recast layer thickness from electric discharge machining.  Electron channeling contrast imaging was used to estimate dislocation density.  XRCT was used to analyze the pore population as well as uncertainty in cross-sectional area for stress calculations.  Literature sources were used to estimate phase fraction, residual stress, and the single crystal C-tensor.  All processing details, specimen preparation details, tensile test method details, and microstructure measurements are provided. Predictions are requested for the subcontinuum stress strain behavior and fracture pathway of one as-built IN625 meso-scale specimen.","language":["en"],"title":"AM Bench 2022 challenge problem Subcontinuum Mesoscale Tensile Test (CHAL-AMB2022-04-MeTT)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2587/ANSWERS%20Subcontinuum_CHAL-AMB2022-04-MeTT.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2587/prediction%20template_CHAL-AMB2022-04-MeTT.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2587/prediction%20template_CHAL-AMB2022-04-MeTT.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2587/Subcontinuum_CHAL-AMB2022-04-MeTT.zip","format":"ZIP","description":"AM Bench 2022 challenge problem Subcontinuum Mesoscale Tensile Test (CHAL-AMB2022-04-MeTT)","mediaType":"application/zip","title":"AM Bench 2022 challenge problem Subcontinuum Mesoscale Tensile Test (CHAL-AMB2022-04-MeTT)"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2587/read%20me_CHAL-AMB2022-04-MeTT.pdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2587/read%20me_CHAL-AMB2022-04-MeTT.pdf","format":"PDF","mediaType":"application/pdf","title":"README File for AM Bench 2202 challenge problem"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2587/ANSWERS%20Subcontinuum_CHAL-AMB2022-04-MeTT.zip","mediaType":"application/zip"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Additive manufacturing","Materials:Metals","Materials:Materials characterization","Materials:Modeling and computational material science","Standards:Reference data"],"issued":"2022-04-08","keyword":["AM Bench","benchmark","additive manufacturing","metal","mechanical characterization","microstructure characterization"]},{"identifier":"ark:/88434/mds2-2588","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nik.hrabe@nist.gov","fn":"Nik Hrabe"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2588","description":"Additively manufactured (AM) laser powder bed fusion (PBF-L) Inconel 625 blocks were built with two different scan strategies: XY and X-only.  96 tensile specimens were extracted from blocks at different tensile axis orientations with respect to the build direction to yield the following conditions: XY scan strategy (0, 30, 45, 60, and 90 degree orientation w.r.t. build direction) and X-only scan strategy (0, 60, 90 degree orientation w.r.t. build direction).  Tensile testing was performed at room temperature using a quasistatic strain rate of 0.001/s to failure.  Microstructure was measured using x-ray computed tomography (XRCT) and scanning electron microscopy (SEM) techniques on representative specimens of each scan strategy.  Large-area electron backscatter diffraction was used to measure crystallographic texture and grain size/morphology for three orthogonal planes.  Backscatter electron imaging was used to characterize the subgrain structure and assess recast layer thickness from electric discharge machining.  Electron channeling contrast imaging was used to estimate dislocation density.  XRCT was used to analyze the pore population.  Literature sources were used to estimate phase fraction, residual stress, and the single crystal C-tensor.  All processing details, specimen preparation details, tensile test method details, and microstructure measurements are provided for both XY and X-only scan strategies. Additionally, true stress strain curves for all XY-scan strategy, 0 degree orientation specimens are provided.  Predictions are requested for the bulk/continuum stress strain behavior of as-built Inconel 625 tensile specimens at different orientations (XY-scan strategy 30, 45, 60, 90 degree orientation w.r.t. build direction) and scan strategy (X-only scan strategy 0, 60, and 90 degree orientation w.r.t. build direction).","language":["en"],"title":"AM Bench 2022 challenge Macroscale Tensile Tests at Different Orientations (CHAL-AMB2022-04-MaTTO)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2588/Macroscale_CHAL-AMB2022-04-MaTTO.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2588/Macroscale_CHAL-AMB2022-04-MaTTO.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2588/prediction%20template_CHAL-AMB2022-04-MaTTO.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2588/prediction%20template_CHAL-AMB2022-04-MaTTO.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2588/read%20me_CHAL-AMB2022-04-MaTTO.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2588/read%20me_CHAL-AMB2022-04-MaTTO.pdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2588/ANSWERS%20Macroscale_CHAL-AMB2022-04-MaTTO.zip","description":"answers for AM Bench 2022 challenge CHAL-AMB2022-04-MaTTO","mediaType":"application/x-zip-compressed","title":"ANSWERS Macroscale_CHAL-AMB2022-04-MaTTO"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing","Materials:Metals","Materials:Materials characterization","Materials:Modeling and computational material science","Standards:Reference data"],"issued":"2022-04-11","keyword":["AM Bench","benchmark","additive manufacturing","metal","mechanical characterization","microstructure characterization"]},{"identifier":"ark:/88434/mds2-2589","accessLevel":"public","contactPoint":{"hasEmail":"mailto:george.awad@nist.gov","fn":"George Awad"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"The testing dataset used at TRECVID for the DSDI task in 2020-2022.The dataset includes public videos, ground truth and features of the DSDI task. As the task is continuing, the dataset will be continually updated.There are 32 features across 5 main categories (Environment, Vehicles, Water, Infrastructure, Damage). All videos are airborne low altitude from natural disaster events.","language":["en"],"title":"Disaster Scene Description and Indexing (DSDI) Dataset","distribution":[{"accessURL":"https://www-nlpir.nist.gov/projects/tv2020/pastdata/disaster.scene.indexing/","description":"Includes master shot boundary reference, whole videos and segmented shots.Also, includes ground truth data","title":"The 2020 DSDI Testing dataset"},{"accessURL":"https://www-nlpir.nist.gov/projects/tv2021/pastdata/disaster.scene.indexing/","description":"Includes the master shot reference, whole videos, segmented shots and ground truth.","title":"The 2021 DSDI Testing dataset"},{"downloadURL":"https://www-nlpir.nist.gov/projects/tv2022/pastdata/disaster.scene.indexing/","format":"video files and ground truth","description":"testing videos used at the DSDI task at TRECVID. It is 2157 short videos collected from FEMA and  the Defense Visual Information Distribution Service (DVIDS): https://www.dvidshub.net/","mediaType":"application/octet-stream","title":"The 2022 DSDI Testing dataset"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1Y","theme":["Information Technology:Data and informatics"],"issued":"2022-03-31","keyword":["airborne videosvideo retrievalTRECVIDnatural disasterCAP"]},{"identifier":"ark:/88434/mds2-2595","accessLevel":"public","contactPoint":{"hasEmail":"mailto:mohamed.hany@nist.gov","fn":"Mohamed Hany"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2595","description":"Calculated beam pattern in Fourier space of a unitary input given two sparsely sampled synthetic aperture arrays: 1. a regularly spaced array sampled at 2*lambda, where lambda is the wavelength of the 40 GHz signal, and 2. the regularly spaced array with random perturbations (of order ~<lambda) to the (x,y) spatial location of each sample point. This dataset is published in \"An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures\" by Vouras, et al. in IEEE Selected Topics in Signal Processing Recent Advances in Wideband Signal Processing for Classical and Quantum Synthetic Apertures.","language":["en"],"title":"Grating Lobes and Spatial Aliasing in Sparse Array Beampatterns","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2595/compute_FFT.m","format":"Matlab function","description":"Matlab function to compute the two-dimensional spatial Fast Fourier Transform (FFT) of an input spatially sampled array along x and y (input variables \"array_x\" and \"array_y\") given a wavelength (\"lambda\"), Fourier space sampling \"U\" and \"V\", and signal magnitude at each spatial location (\"temp\").","mediaType":"application/octet-stream","title":"Matlab function to compute 2D FFT"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2595/Code_to_recreate_Figure_20.m","format":"Matlab script","description":"Matlab script will generate the exact data provided in \"Fig20_GratingLobesDueToSparseSamplingGrid.csv\" and will plot the output. This script requires \"compute_FFT.m\" to be located in the same workspace or directory. To generate the output data given in \"Fig22_OptimizedSparseArrayBeamPattern.csv\", one can replace \"sparse_array_x\" (line 20) and \"sparse_array_y\" (line 21) with the first two columns of \"Fig21_SparseArrayBeforeAndAfterOptimization.csv\" (\"Xposition_m\" and \"Yposition_m_After\").","mediaType":"application/octet-stream","title":"Matlab code to recreate Fig. 20"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2595/readme.txt","format":"plain text","description":"\"Readme\" file provided additional information relating to \"Grating Lobes and Spatial Aliasing in Sparse Array Beampatterns\" simulation dataset.","mediaType":"text/plain","title":"Dataset use, references, and contact information"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2595/Fig20_GratingLobesDueToSparseSamplingGrid.csv","format":"Organized with x data given in the first column and, for line plots, y data given in subsequent columns or, for surface plots, y data given in the second column and z or color data given in the third column. Column headers define each axis with units reported as \"_units\".","description":"Sparsely sampled lattices on a regular grid introduce grating lobes in the beampattern as demonstrated by this simulated dataset of the beam pattern observed in Fourier space (u=sin(theta)cos(phi), v=sin(theta)sin(phi), where theta is the elevation angle and phi is the azimuth angle) when a regularly sampled sparse array is used to acquire the signal.","mediaType":"text/csv","title":"Data to generate Fig. 20: Grating Lobes Due to Sparse Sampling Grid"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2595/Fig21_SparseArrayBeforeAndAfterOptimization.csv","format":"Organized with x data given in the first column and, for line plots, y data given in subsequent columns or, for surface plots, y data given in the second column and z or color data given in the third column. Column headers define each axis with units reported as \"_units\".","description":"A simple approach for mitigating grating lobes in a sparse lattice is to perturb the regularity of the grid spacing by applying a random offset to each spatial sample. These data compare the spatial locations of a regularly sampled sparse array labeled as \"Before\" to the randomly perturbed sparse array sample locations labeled as \"After\".","mediaType":"text/csv","title":"Data to generate Fig. 21: Sparse Array Before and After Optimization"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2595/Fig22_OptimizedSparseArrayBeamPattern.csv","format":"Organized with x data given in the first column and, for line plots, y data given in subsequent columns or, for surface plots, y data given in the second column and z or color data given in the third column. Column headers define each axis with units reported as \"_units\".","description":"Periodicity in the beam pattern, here calculated and reported in Fourier space (u=sin(theta)cos(phi), v=sin(theta)sin(phi), where theta is the elevation angle and phi is the azimuth angle), is eliminated by the use of the randomly perturbed spatial sample locations of the sparse array. These simulated data show the outcome of using the sparse array labeled \"After\" in dataset \"Fig21_SparseArrayBeforeAndAfterOptimization.csv\" and provides a comparison to this randomized perturbation to the regular sampling output given in \"Fig20_GratingLobesDueToSparseSamplingGrid.csv\".","mediaType":"text/csv","title":"Data to generate Fig. 22: Optimized Sparse Array Beam Pattern"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)"],"keyword":["sparse array","synthetic aperture","millimeter-wave"]},{"identifier":"ark:/88434/mds2-2596","accessLevel":"public","contactPoint":{"hasEmail":"mailto:greta.babakhanova@nist.gov","fn":"Greta Babakhanova"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2596","description":"In the field of tissue engineering, 3D scaffolds and cells are often combined to yield constructs that are used as therapeutics to repair or restore tissue function in patients. Viable cells are often required to achieve the intended mechanism of action for the therapy, where the live cells may build new tissue or may release factors that induce tissue regeneration. Thus, there is a need to reliably measure cell viability in 3D scaffolds as a quality attribute of a tissue-engineered medical product. Here, we developed a noninvasive, label-free, 3D optical coherence tomography (OCT) method to rapidly (2.5 min) image large sample volumes (1 mm) to assess cell viability and distribution within scaffolds. OCT imaging was assessed using a model scaffold-cell system consisting of a polysaccharide-based hydrogel seeded with human Jurkat cells. Four test systems were used: hydrogel seeded with live cells, hydrogel seeded with heat-shocked or fixed dead cells and hydrogel without any cells. Time series OCT images demonstrated changes in the time-dependent speckle patterns due to refractive index (RI) variations within live cells that were not observed for pure hydrogel samples or hydrogels with dead cells. The changes in speckle patterns were used to generate live-cell contrast by image subtraction. In this way, objects with large changes in RI were binned as live cells. Using this approach, on average, OCT imaging measurements counted 326 ± 52 live cells per 0.288 mm for hydrogels that were seeded with 288 live cells (as determined by the acridine orange-propidium iodide cell counting method prior to seeding cells in gels). Considering the substantial uncertainties in fabricating the scaffold-cell constructs, such as the error from pipetting and counting cells, a 13% difference in the live-cell count is reasonable. Additionally, the 3D distribution of live cells was mapped within a hydrogel scaffold to assess the uniformity of their distribution across the volume. Our results demonstrate a real-time, noninvasive method to rapidly assess the spatial distribution of live cells within a 3D scaffold that could be useful for assessing tissue-engineered medical products.","language":["en"],"title":"Dataset for optical coherence tomography for label-free cell viability measurements in 3D tissue engineering scaffolds","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2596/Figure1.zip","description":"Figure 1. Human Jurkat cells. Brightfield images of live, dead and dead (heat-shocked) Jurkat cells. Cell viability was determined via acridine orange and propidium iodide (AOPI) staining.","mediaType":"application/x-zip-compressed","title":"Figure 1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2596/Figure2.tif","description":"Figure 2. Experimental setup for OCT imaging. (A) Diagram of assay system where Jurkat cells were encapsulated in polysaccharide hydrogels (PSH) for measurements of cell viability. The diagram is drawn roughly to scale. (B) OCT imaging of samples inside an 8-well strip; (C) OCT brightfield camera image of the Live-Gel sample, where the red square encloses the observation area; (D) PSH samples in an 8-well strip, the dashed black line overlays the concave meniscus; (E) 3D volumetric OCT image (1.00 mm × 1.00 mm × 1.05 mm) of the Live-Gel sample outlined in red in panel (C).","mediaType":"image/tiff","title":"Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2596/Figure3.xlsx","description":"Figure 3. ATP concentration assesses cell viability for each treatment. ATP cell viability assay showed that the ATP concentration does not drop when the samples are held at ambient conditions (80 min outside the incubator at ambient conditions, 23 ºC).","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2596/Figure4.zip","description":"Figure 4. Dynamic speckle. OCT xz cross-sectional images of Gel-Only, Dead-Gel-Fixed, Dead-Gel-Heat and Live-Gel samples. The total intensity of four-pixel regions (2 pixels x 2 pixels) in the center of the objects was plotted over time. The images were captured every 5 sec for 3 min. Cropped regions of two Live-Gel OCT images at ti = 0 and tf = 2.5 min later resulted in the difference image that shows that the speckle pattern differed in ti and tf images.","mediaType":"application/x-zip-compressed","title":"Figure 4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2596/Figure5.tif","description":"Figure 5. Raw OCT image volumes are processed for 3D object counting in Fiji. The representative frame of Live-Gel image volume is shown here. Raw volume (A) is cropped at the top and bottom to eliminate visibly out-of-focus regions. The gaussian blurring of cropped volume (B) creates a ?background? image of the hydrogel (C). Subtracting the background from the cropped volume results in (D), with the diminished intensity of the nebulous structures relative to the cells. The morphological opening operation (see Table 1, line b) preferentially highlights small objects in (E), which is then thresholded to set all but the highest intensity pixels to zero in (F). Scale bars, 100 µm.","mediaType":"image/tiff","title":"Figure 5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2596/Figure6.xlsx","description":"Figure 6. Live-cell object counts in four different treatments. The total number of live cells counted in Gel-Only, Dead-Gel-Fixed, Dead-Gel-Heat and Live-Gel samples in V=0.288 mm3 volume.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure 6"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2596/Figure7.xlsx","description":"Figure 7. Three-dimensional spatial distribution of the counted objects. Distribution of live-cell object centroids found in Gel-Only, Dead-Gel-Fixed, Dead-Gel-Heat and Live-Gel samples (V = 0.288 mm3) in experiment 5. The top of the hydrogel was at the depth of 0?mm and depths greater than 0 mm are within the hydrogel.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure 7"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2596/Figure8.xlsx","description":"Figure 8. Object density as a function of depth (z) in a hydrogel for four different treatments. Live-cell object count and position within a hydrogel in Gel-Only, Dead-Gel-Fixed, Dead-Gel-Heat, and Live-Gel samples (experiment 5). The mean counts were calculated for corresponding four replicates for four different treatments.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure 8"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Health:Cell therapies"],"issued":"2023-03-21","keyword":["cell viability","tissue engineered scaffolds","3D imaging","optical coherence tomography","non-invasive imaging","label-free imaging"]},{"identifier":"ark:/88434/mds2-2598","accessLevel":"public","references":["https://doi.org/10.1016/j.buildenv.2021.107783","https://doi.org/10.6028/NIST.TN.2221-upd1"],"contactPoint":{"hasEmail":"mailto:lisa.ng@nist.gov","fn":"Lisa Ng"},"programCode":["006:045"],"@type":"dcat:Dataset","replaces":"ark:/88434/mds2-2598","landingPage":"https://data.nist.gov/od/id/mds2-3046","description":"This workbook accompanies two publications [1] Evaluating Potential Benefits of Air Barriers in Commercial Buildings using NIST Infiltration Correlations in EnergyPlus DOI: 10.1016/j.buildenv.2021.107783 and [2] Implementing NIST Correlations: https://doi.org/10.6028/NIST.TN.2221-upd1","language":["en"],"title":"NIST Infiltration Correlations for DOE Prototype Commercial Buildings","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2598/Data-InfiltrationInputs(FINAL).xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Data-InfiltrationInputs(FINAL)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Buildings and Construction:Air conditioning and heating equipment","Buildings and Construction:Indoor air quality","Buildings and Construction:Building materials","Environment:Environmental health","Energy:Energy efficiency"],"keyword":["CONTAM","EnergyPlus","airflow modeling","building envelope airtightness","commercial buildings","energy modeling","infiltration"]},{"identifier":"ark:/88434/mds2-2599","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nakia.grayson@nist.gov","fn":"Nakia R. Grayson"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2599","description":"A repository of code and scripts that were developed by the NCCoE Supply Chain Assurance project team to support the defined security characteristics.","language":["en"],"title":"Validating the Integrity of Computing Devices","distribution":[{"accessURL":"https://www.github.com/usnistgov/nccoe-sca","description":"The National Cybersecurity Center of Excellence's Validating the Integrity of Computing Devices project.","title":"NCCoE Supply Chain Assurance GitHub"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Cybersecurity"],"keyword":["anti-counterfeiting; antitampering; asset management system; cryptography; cyber supply chainrisk management; hardware assurance; hardware roots of trust; integrity; provenance"]},{"identifier":"ark:/88434/mds2-2601","accessLevel":"public","references":["https://doi.org/10.1016/j.forc.2022.100435"],"contactPoint":{"hasEmail":"mailto:briana.capistran@nist.gov","fn":"Briana Capistran"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2601","description":"This data set contains raw GC-MS data files of neat ignitable liquids, substrates, and simulated fire debris samples. All samples were analyzed by conventional GC-MS and rapid GC-MS, a fast chromatographic screening technique. The raw data files contain chromatographic data (counts vs. retention time) for all samples analyzed. Gasoline and diesel fuel were chosen as ignitable liquids. Substrates (both unburned and burned without ignitable liquids) included carpet, wood, and primed wood. The simulated fire debris samples were generated by pouring aliquots of each ignitable liquid onto each substrate and subsequently igniting. The debris samples were prepared by passive-headspace extraction and analyzed by rapid GC-MS and traditional GC-MS. Major compounds in both gasoline and diesel fuel were identified following analysis by both techniques.Certain commercial equipment, instruments, or materials are identified in this dataset in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.","language":["en"],"title":"Data to support \"Rapid GC-MS as a Screening Tool for Forensic Fire Debris Analysis\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2601/Raw%20Data.zip","format":"Zipped file containing raw GC-MS data files.","description":"See README.xlsx for list of file names and sample types.","mediaType":"application/x-zip-compressed","title":"Raw GC-MS files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2601/README.xlsx","format":"Excel sheet","description":"List of file names and sample types.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Forensics:Trace evidence"],"keyword":["Gas chromatography-mass spectrometry","Fire debris analysis","Ignitable liquid residue","Screening methods"]},{"identifier":"ark:/88434/mds2-2602","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jonathan.seppala@nist.gov","fn":"Jonathan Seppala"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2602","description":"In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath. OpenFOAM is an open source computational fluid dynamics solver. This repository contains the following Python tools: - Tools for generating input files for OpenFOAM v1912 or OpenFOAM v8 tailored to a conical or cylindrical nozzle extruding a filament into a static support bath. - Tools for monitoring the status of OpenFOAM simulations and aborting them if they are too slow. - Tools for moving output files between storage locations. (For example, it can automatically move all files to a server, but only necessary files to your hard drive) - Tools for generating images and tables from the 3D time series. - Tools for compiling images into videos. - Tools for analyzing, summarizing, and plotting data.This version is associated with the paper:Friedrich, L.M., Gunther, R.T. & Seppala, J.E. 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Simulated stress mitigation strategies in embedded 3D bioprinting, submitted for publication.","language":["en"],"title":"OpenFOAM simulations of stress mitigation strategies in embedded 3D 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing","Manufacturing:Biomanufacturing","Materials:Polymers","Materials:Modeling and computational material science","Mathematics and Statistics:Modeling and simulation research","Mathematics and Statistics:Numerical methods and software"],"keyword":["3D-printing","extrusion","support-bath","Herschel-Bulkley","rheology","surface tension","OpenFOAM"]},{"identifier":"ark:/88434/mds2-2605","accessLevel":"public","references":["https://doi.org/10.1007/s11340-022-00885-z"],"contactPoint":{"hasEmail":"mailto:alexander.landauer@nist.gov","fn":"Alexander Landauer"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2605","description":"To verify and validate the HR-VPTM technique, both synthetic images and gels with embedded particles undergoingcontrolled deformations were used to compared known and reconstructed deformations at assorted strain-ratesor frame-rates. We simulated the light-field representation of particles undergoing motion with ray tracing andinvestigated the sensitivity of the measurement technique to synthetic noise floor and various motion fields. Inexperiments, a custom-built device deformed a hydrogel specimen in nominally simple shear at applied strain ratesapproximately 2 1/s, while light-field images were collected at approximately 500 frames per second frames per second. Files and formats include .tif images (raw data, input), .mat (reconstructed images, tracking results),.txt, .csv, and .yaml (all metadata).See also the data on MINDS@UW (https://minds.wisconsin.edu/handle/1793/83031), the accompanying paper in Experimental Mechanics (https://doi.org/10.1007/s11340-022-00885-z), and the complete code package released by collaborators at UW-Madison (https://github.com/francklab/HR-VPTM).","language":["en"],"title":"High-Rate Volumetric Particle Tracking Microscopy (HR-VPTM) validation 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Image and signal processing","Metrology:Flow metrology and rheology","Materials:Materials characterization"],"issued":"2023-02-14","keyword":["Light field microscopy","particle tracking","motion tracking","volumetric deformation","soft material","3D imaging","high speed imaging"]},{"identifier":"ark:/88434/mds2-2606","accessLevel":"public","contactPoint":{"hasEmail":"mailto:alexander.landauer@nist.gov","fn":"Alexander Landauer"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2606","description":"The database includes mechanical data for structure-properties relationships and mechanical modeling of elastic impact protection foams from a variety of imaging (micro-computed tomography, digital image correlation) and force-sensing instruments (dynamic mechanical analysis, universal test system) under a wide range of experimental conditions and modes. The data repository includes directories for: dynamic mechanical analysis raw data, results, and analysis tools; intermediate rate (servo-hydraulic UTS based) raw data including 2D digital image correlation (DIC) images, results, and analysis tools; quasi-static rate (electro-mechanical UTS based) raw data including 2D digital image correlation (DIC), results, and analysis tools; micro-computed tomography data including raw volume images, filtered images, binarized images, other results, and analysis tools; and, instrumented drop tower data including backface force, high speed video, and results and analyzed data, Fourier Transform Infrared (FTIR) spectra, and differential scanning calorimetry (DSC) data.For more information see the readme and data documentation in each respective directory. A paper describing data collection, analysis, and database documentation is available here: https://doi.org/10.1038/s41597-023-02092-4. A repository containing example usage code is available at: https://github.com/materials-data-facility/foam_db. File formats for data include .txt, .xls, .tri, .tprc, .rcp, .py, .m, .csv, .mat, .vtk, .spa, .exp, .stl, and .tif.","language":["en"],"title":"A Materials Properties Dataset for Elastomeric Foam Impact Mitigating 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Modeling and computational material science","Materials:Materials characterization","Materials:Polymers","Mathematics and Statistics:Image and signal processing"],"keyword":["Foam","impact mitigating materials","impact protection","digital image correlation","dynamic mechanical analysis","micro computed tomography","material data","elastomer","vinyl nitrile","materials data","elastomers","structure-property","strain-fields","materials databases","materials datasets","constitutive data","material modeling","time temperature superposition","universal test machine","force-displacement data","stress-strain data","volumetric image analysis"]},{"identifier":"ark:/88434/mds2-2607","accessLevel":"public","contactPoint":{"hasEmail":"mailto:brandon.lane@nist.gov","fn":"Brandon 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(STL) geometry file for individual bridge structure part."},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/CAD_Geometry/AMB2022-01-AMMT-PlateLayoutAssy.STL","mediaType":"application/octet-stream","title":"Stereolithography (STL) geometry file of the full 3D built parts, including substrate"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/Thermocouples/AMB2022-01-AMMT-B8-Thermocouple.csv","mediaType":"text/csv","title":"Thermocouple timeseries data for AMMT Build 8"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/Thermocouples/AMB2022-01-AMMT-B7-Thermocouple.csv","mediaType":"text/csv","title":"Timeseries thermocouple data for Build 7"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/Thermocouples/AMB2022-01-AMMT-B6-Thermocouple.csv","mediaType":"text/csv","title":"Timeseries thermocouple data for Build 6"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/Scan_Strategy/AMB2022-01-XYPT-ExampleMatlabPlots.m","mediaType":"text/x-objcsrc","title":"Example Matlab XYPT Plotting Scripts"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/Scan_Strategy/AMB2022-01-AMMT-XYPT_v1.h5","mediaType":"application/x-hdf","title":"AMB2022-01 Commanded Scan Strategy"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/Materials/AMB2022-01_PowderMillSpecSheet.pdf","mediaType":"application/pdf","title":"Vendor-provided Powder Spec Sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/CAD_Geometry/AMB2022-01-AMMT-PartCAD.STEP","mediaType":"application/octet-stream","title":"STEP geometry file for individual bridge structure part."},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/CAD_Geometry/AMB2022-01-AMMT-PlateLayoutAssy.STEP","mediaType":"application/octet-stream","title":"STEP geometry file for full 3D build plate"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/CAD_Geometry/AMB2022-01-AMMT-RecoaterGuideCAD.STL","mediaType":"application/octet-stream","title":"Stereolithography (STL) geometry file for the two recoater guide parts"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2607/CAD_Geometry/AMB2022-01-AMMT-RecoaterGuideCAD.STEP","mediaType":"application/octet-stream","title":"STEP geometry file for the two recoater guide parts"},{"format":"ASCII text file","downloadURL":"https://data.nist.gov/od/ds/mds2-2607/2607_README.txt","description":"ASCII text file providing background, file formats, and file structures provided within the \"AM Bench 2022 3D Build Modeling Challenge Description Data (AMB2022-01)\" dataset.","mediaType":"text/plain","title":"Data description 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Additive manufacturing","Manufacturing:Process measurement and control"],"issued":"2022-04-22","keyword":["Laser powder bed fusion; scan strategy; AM-Bench;"]},{"identifier":"ark:/88434/mds2-2608","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.ritchie@nist.gov","fn":"Nicholas Ritchie"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2608","description":"The EDS spectra discussed and used to create figures in the MRS journal article \"Simulating Electron-Excited Energy Dispersive X-Ray Spectra with the NIST DTSA-II Open-Source Software Platform\"","language":["en"],"title":"Data accompanying \"Simulating Electron-Excited Energy Dispersive X-Ray Spectra with the NIST DTSA-II Open-Source Software Platform\" for the MRS 2022 Spring meeting","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2608/EDS_Spectrum_Data.zip","format":"ISO/DIS 22029 - ISO/EMSA Spectrum File Format embedded in a PKZIP file","description":"Individual X-ray spectra (*.msa) and DTSA-II detector definition files (*.xdet) enclosed in a PKZIP-style compressed archive","mediaType":"application/x-zip-compressed","title":"EDS X-Ray Spectrum Data"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2608/2608_README.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2608/2608_README.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2608/EDS_Spectrum_Data.zip","description":"Individual X-ray spectra (*.msa) and DTSA-II detector definition files (*.xdet) enclosed in a PKZIP-style compressed archive","mediaType":"application/x-zip-compressed","title":"EDS X-Ray Spectrum Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Metals","Materials:Materials characterization","Chemistry:Analytical chemistry"],"conformsTo":"https://www.iso.org/obp/ui/#iso:std:iso:22029:dis:ed-3:v1:en","issued":"2022-10-17","keyword":["EDS","X-ray Spectrum","MRS","Monte Carlo simulation"]},{"identifier":"ark:/88434/mds2-2612","accessLevel":"public","contactPoint":{"hasEmail":"mailto:amanda.forster@nist.gov","fn":"Amanda L. Forster"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2612","description":"A compilation of single fiber tensile tests for 6 different materials at 8 different gauge lengths. Fibers included are 3 different aramids, a liquid crystal polyester, regenerated cellulose, and nylon 6,6.","language":["en"],"title":"Single fiber tensile testing data for multiple fibers at multiple gauge lengths","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2612/single%20fiber%20tensile%20measurements.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2612/single%20fiber%20tensile%20measurements.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2612/2612_README%20v2.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2612/2612_README%20v2.txt.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-07-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Polymers"],"issued":"2022-12-01","keyword":["tensile testing","single fiber","mechanical testing","material properties"]},{"identifier":"ark:/88434/mds2-2613","accessLevel":"public","contactPoint":{"hasEmail":"mailto:harrison.skye@nist.gov","fn":"Harrison M. Skye"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2613","description":"This data set includes (1) Experimental measurements, and (2) Simulation data. The experimental data set shows raw measurements and important calculated parameters. The simulation data are for the NIST vapor-compression system model, CYCLE_D-HX, and show the input data and output results. The simulation inputs are based on the experimental measurements.CYCLE_D-HX is a semi-theoretical model that simulates performance of a vapor-compression cycle with forced-convection heat exchangers for specified temperature profiles of the heat source and heat sink. In this study, we validated CYCLE_D-HX using experimental measurements from a small (< 4 kW capacity) heat pump test apparatus operated in cooling mode. We also applied the model to simulate performance of selected refrigerants in a system with optimized refrigerant circuitries in the evaporator and condenser. The tested refrigerants included the medium-pressure refrigerant R-134a and candidate replacements with a lower global-warming potential (GWP): R-513A, R-450A, R-134a/1234yf/1234ze(E) (49.2/33.8/17.0 mass %), R-515B, and R-1234yf. We also tested high-pressure refrigerant R-410A and candidate replacements with lower-GWP: R-32, R-452B, and R-454B. The model generally agreed with experimental results, with COP and Qvol overpredicted by (0 to 3) % for the basic cycle, and by (0 to 5) % for the cycle with the liquid-line/suction-line heat exchanger (LLSL-HX). Simulations with equal compressor efficiency and optimized tube circuitry showed the COP spread among medium-pressure refrigerants could be reduced to 3 % with proper design, compared to (12 to 33) % from the experiments. In optimized systems, the high-pressure refrigerants? COP was (1 to 6) % higher than the COP of the medium-pressure refrigerants. The LLSL-HX improved performance of refrigerants with high molar heat capacity (here, the medium-pressure refrigerants) by (1.0 to 1.5) %.","language":["en"],"title":"Data for NIST Technical Note: Validation and Optimization with a Vapor Compression Cycle Model Accounting for Refrigerant Thermodynamic and Transport Properties","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2613/Experiment%20-%20All%20-%20Rev_1_5.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2613/Experiment%20-%20All%20-%20Rev_1_5.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2613/Model%20data%20-%20Rev_1_1.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2613/Model%20data%20-%20Rev_1_1.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2613/MBHP-CYCLE_D-HX-Verification-Data-Dictionary_Rev_1p0.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2613/MBHP-CYCLE_D-HX-Verification-Data-Dictionary_Rev_1p0.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2613/Reference%20Cases.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2613/Reference%20Cases.zip.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Environment:Environmental health","Energy:Sustainability","Energy:Energy efficiency"],"issued":"2022-09-26","keyword":["model","experimental measurement","Low GWP","refrigerants","air conditioning","heat pump"]},{"identifier":"ark:/88434/mds2-2615","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2217"],"contactPoint":{"hasEmail":"mailto:cuong.nguyen@nist.gov","fn":"Cuong Nguyen"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"An icd (IED (Intelligent Electronic Device) Capability Description) file containing a model for an IEC 61850 profile for distributed energy resources supporting IEEE 1547.","language":["en"],"title":"IEC 61850 Profile for Distributed Energy Resources Supporting IEEE 1547","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2615/Profile200220v1r13.icd","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2615/2615_README.txt","description":"README","mediaType":"text/plain","title":"README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Energy:Electric power / smart grid"],"issued":"2022-04-29","keyword":["Data Modeling","DER","Functional Decomposition","Grid Automation","IEC 61850","IEEE 1547","Interoperability","Profile","Renewable Integration","Smart Grid","Standards","Substation Automation"]},{"identifier":"ark:/88434/mds2-2617","accessLevel":"public","references":["https://doi.org/10.26153/tsw/17660","https://www.nist.gov/publications/laser-path-planning-and-power-control-strategies-powder-bed-fusion-systems","https://doi.org/10.1016/j.promfg.2018.07.112"],"contactPoint":{"hasEmail":"mailto:brandon.lane@nist.gov","fn":"Brandon Lane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2617","description":"The following data files are provided in support of the 2022 Additive Manufacturing Benchmark test series (AM-Bench 2022) modeling challenges associated with laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN718 test objects using variety of custom scan strategies.  These AM builds were performed on the NIST Additive Manufacturing Metrology Testbed (AMMT, https://www.nist.gov/el/ammt-temps).  Note that these 3D builds are an extension of those for the AMB2022-01 challenges, and part geometry, materials data, and 'nominal' 3D build data are available in the corresponding data repository (https://doi.org/10.18434/mds2-2607)Description of the associated 3D builds and measurements are provided on the AMB2022-02 challenge description webpage (https://www.nist.gov/ambench). Note that this dataset may be periodically updated.  Refer to the Version number below, and updates described in this Description and the README file.","language":["en"],"title":"AM Bench 2022 3D Build with Custom Laser Scan Strategies Modeling Challenge (AMB2022-02)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V6-SCR%20submission%20template.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V6-SCR%20submission%20template.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V6-TAM%20submission%20template.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V6-TAM%20submission%20template.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V7-SCR%20submission%20template.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V7-SCR%20submission%20template.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V7-TAM%20submission%20template.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V8-TAM%20submission%20template.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V7-TAM%20submission%20template.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V8-SCR%20submission%20template.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V8-SCR%20submission%20template.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/ChallengeSubmissionTemplates/CHAL-AMB2022-02-V8-TAM%20submission%20template.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/Scan_Strategy/AMB2022-02-AMMT-XYPT-V6.h5","mediaType":"application/x-hdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/Scan_Strategy/AMB2022-02-AMMT-XYPT-V6.h5.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/Scan_Strategy/AMB2022-02-AMMT-XYPT-V7.h5","mediaType":"application/x-hdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/Scan_Strategy/AMB2022-02-AMMT-XYPT-V7.h5.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/Scan_Strategy/AMB2022-02-AMMT-XYPT-V8.h5","mediaType":"application/x-hdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2617/Scan_Strategy/AMB2022-02-AMMT-XYPT-V8.h5.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2617/2617_README.txt","description":"README file providing contents and context for the dataset","mediaType":"text/plain","title":"README File"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing"],"issued":"2022-04-26","keyword":["laser powder bed fusion; scan strategy; AM-Bench"]},{"identifier":"ark:/88434/mds2-2618","accessLevel":"public","references":["https://doi.org/10.1007/s40192-020-00172-6","https://doi.org/10.1007/s40192-020-00170-8","https://doi.org/10.1007/978-3-319-48762-5_30","https://doi.org/10.1007/s40192-021-00212-9"],"contactPoint":{"hasEmail":"mailto:brandon.lane@nist.gov","fn":"Brandon Lane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2618","description":"The following data files are provided in support of the AM Bench 2022 modeling challenges associated with Microstructure measurement extension to AMB2018-01: laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN625 test objects (AMB2022-05).  Description of the associated measurements are provided in the AMB2022-05 challenge description webpage (https://www.nist.gov/ambench).Some of the work was supported by the Exascale Additive Manufacturing Application Development Project, part of the Exascale Computing Project,  (17-SC-20-SC), a collaborative effort of U.S. DOE Office of Science and  NNSA. Lawrence Livermore National Security, LLC.","language":["en"],"title":"AM Bench 2022 IN625 3D Build Microstructure Modeling Challenge Description Data 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OpenFAD is an activity classification and detection evaluation to measure how well systems can automatically classify or temporally detect fine-grained activities collected from the Consented Activities of People (CAP) dataset using handheld devices. Covers Activity Classification- (AC) and Temporal Activity Detection-Task (TAD).","language":["en"],"title":"Open Fine-Grained Activity Detection Scorer (OpenFAD Scorer)","distribution":[{"accessURL":"https://github.com/usnistgov/FadScorer","format":"text/html","description":"Open Fine-Grained Activity Detection Scorer (OpenFAD Scorer) - Git Repo","title":"Open Fine-Grained Activity Detection Scorer (OpenFAD Scorer) - Git Repo"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Biometrics","Mathematics and Statistics:Image and signal processing","Metrology:Metric"],"keyword":["Activity Classification","Activity Detection","Temporal Activity Detection","Video Analytics","Video","Evaluation","Challenge","Software","Scorer","ML","AI","Computer Vision"]},{"identifier":"ark:/88434/mds2-2621","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.ritchie@nist.gov","fn":"Nicholas Ritchie"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2621","description":"Automated Particle Analysis data sets from a TESCAN MIRA3 with 3 Pulsetor EDS detectors automated using NIST DTSA-II with the SEMantics instrument control extension.The data is in the Zeppelin format designed by RJ Lee Group.  Tools for reading the Zeppelin format data are available for the Julia language at https://github.com/usnistgov/NeXLParticle.jl.The data may be processed according to the algorithm described in \"Registering Particle Data Sets Using a Rotation and Translation Invariant Nearest-Neighbor Algorithm\" using the Julia code at https://github.com/usnistgov/EGOS.jl","language":["en"],"title":"Data associated with \"Registering Particle Data Sets Using a Rotation and Translation Invariant Nearest-Neighbor Algorithm\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2621/SW%2348%2060%20s%20EGOS%202-10-2022%20r2.tar.gz","format":"tar.gz","description":"EGOS particle data set r2","mediaType":"multipart/mixed","title":"EGOS particle data set r2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2621/SW%2348%2060%20s%20EGOS%202-10-2022.tar.gz","format":"tar.gz","description":"EGOS particle data set r1","mediaType":"multipart/mixed","title":"EGOS particle data set r1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2621/SW%2348%2060%20s%20EGOS%202-10-2022%20r3.tar.gz","format":"tar.gz","description":"EGOS particle data set r3","mediaType":"multipart/mixed","title":"EGOS particle data set r3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2621/SW%2348%2060%20s%20EGOS%202-10-2022%20r4.tar.gz","format":"tar.gz","description":"EGOS particle data set r4","mediaType":"multipart/mixed","title":"EGOS particle data set r4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2621/SW%2348%2060%20s%20EGOS%202-10-2022%20r5.tar.gz","format":"tar.gz","description":"EGOS particle data set r5","mediaType":"multipart/mixed","title":"EGOS particle data set r5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2621/SW%2348%2060%20s%20EGOS%202-10-2022%20r6.tar.gz","format":"tar.gz","description":"EGOS particle data set r6","mediaType":"multipart/mixed","title":"EGOS particle data set r6"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2621/SW%2348%2060%20s%20EGOS%202-10-2022%20r7.tar.gz","format":"tar.gz","description":"EGOS particle data set r7","mediaType":"multipart/mixed","title":"EGOS particle data set r7"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2621/2621_README.txt","description":"A \"read me\" file associated with the particle data sets","mediaType":"text/plain","title":"A \"read me\" file associated with the particle data sets"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Materials characterization","Information Technology:Computational science"],"keyword":["automated particle analysis","registration","overlay","alignment","EGOS","europium-dope gadolinium oxysulfide"]},{"identifier":"ark:/88434/mds2-2623","accessLevel":"public","contactPoint":{"hasEmail":"mailto:eric.cockayne@nist.gov","fn":"Eric J. Cockayne"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2623","description":"This dataset supports the manuscript \" Interface formation and Schottky barrier height for Y, Nb, Au, and Pt on Ge as determined by hard x-ray photoelectron spectroscopy\", and consists of two parts: (1) hard X-ray photoelectron spectroscopy (HAXPES) results from Ag, Pt, Y, Nb reference samples, and the heterojunction of these metals on Ge. (2)  Input and results of density functional theory (DFT) calculations om the total and angular-momentum-projected density of states of Ge.","language":["en"],"title":"Data for \"Interface formation and Schottky barrier height for Y, Nb, Au, and Pt on Ge as determined by hard x-ray photoelectron spectroscopy\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2623/README.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2623/Ge_metal_data_expt.zip","mediaType":"application/x-zip-compressed","title":"Experimental hard x-ray photoelectron spectroscopy spectra of Ge and Ge-metal heterojunctions"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2623/Ge_metal_data_dft.zip","mediaType":"application/x-zip-compressed","title":"DFT calculations of the electronic density of states of germanium"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials","Physics:Spectroscopy"],"issued":"2022-09-12","keyword":["Germanium","Germanium-metal heterojunctions","Shottky barrier heights","Hard x-ray photoelectron spectroscopy","Density functional theory calculated; Density of state calculations"]},{"identifier":"ark:/88434/mds2-2628","accessLevel":"public","references":["https://www.nist.gov/publications/measurements-ip3-and-p1db-spectrum-monitoring-software-defined-radios"],"contactPoint":{"hasEmail":"mailto:daniel.kuester@nist.gov","fn":"Dan Kuester"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2628","description":"These data were acquired in the process of collecting data for [1]. They comprise IQ waveform outputs of two different software-defined radios (SDRs) that were collected for various combinations of 1- and 2-tone excitation conditions. For details about how the data were collected, see that publication. [1] McNulty, M., Gu, D., Kuester, D. and Nayeri, P. (2022), \"Measurements of IP3 and P1dB for Spectrum Monitoring with Software Defined Radios,\" Proceedings of the 16th European Conference on Antennas and Propagation, Madrid, ES, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934065.","language":["en"],"title":"Measurements of IP3 and P1dB for Spectrum Monitoring with Software Defined Radios (Public Release of Data)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2628/frequency%20sweep%20sdr1.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2628/frequency%20sweep%20sdr1.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2628/imd3%20sdr0.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2628/imd3%20sdr0.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2628/imd3%20sdr1.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2628/README.md.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2628/imd3%20sdr1.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2628/frequency%20sweep%20sdr0.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2628/frequency%20sweep%20sdr0.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2628/README.md","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)","Electronics:Sensors","Metrology:Electrical/electromagnetic metrology"],"keyword":["Spectrum sensing","software defined radios","ip3","p1db","linearity"]},{"identifier":"ark:/88434/mds2-2630","accessLevel":"public","references":["https://doi.org/10.1109/tasc.2022.3167660"],"contactPoint":{"hasEmail":"mailto:paul.dresselhaus@nist.gov","fn":"Paul Dresselhaus"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Data for all measurements published in the manuscript \"Bipolar Waveform Synthesis with an Optically Driven Josephson Arbitrary Waveform Synthesizer,\"  which will be published in the journal \"IEEE Transactions on Applied Superconductivity\".https://doi.org/10.1109/tasc.2022.3167660All files saved as Python pickle files.  General pickle file comments: 1) Pickle file dictionary keys provide detail about the measurement information saved in the file.2) Load pickle files in python using the following commands:   import pickle as pkl   dat=pkl.load(open('005JAWSspectrum.pkl','rb'))005JAWSspectrum.pkl        Pickle file containing the data used to produce Figure 3b and \"1 kHz sine\" in Figure 3a.  The 'Voltage' key contains the recorded output voltage of the synthesized waveform that was  used in the spectral plot.  The 'scope_samples_per_sec' key contains the information for determining the frequency bins in the case of 3a, or the time steps in 3b.RFoff008JAWSspectrum.pkl        Pickle file containing the data used to produce \"RF bias off\" in Figure 3a. The 'Voltage' key contains the recorded output voltage of the synthesized waveform that was  used in the spectral plot. The 'scope_samples_per_sec' key contains the information for determining the frequency bins.000JAWSmargins.pkl         Pickle file containing the data used to produce the quantum locking range plots with respect to dc bias shown in Figure 4. The 'Voltage' key contains the recorded output  voltage of the synthesized waveform that was used in the spectral plot. The bias offset in mA is given by dat['settings']['AWG_sweep_amp_mA'].  The sweep frequency for the  triangular waveform is given by dat['settings']['AWG_sweep_freq_Hz']fundTo6thHarm_m8195ampSweep_47mAlaser_695mAamp_1001pt_Nave2.pkl       Pickle file containing the data used to produce the quantum locking range plot with respect to RF modulation amplitude / RF-AWG output amplitude shown in Figure 5a.  The harmonics of the acquisition were saved in the 'harmonics' key in units of dBm.  The RF-AWG output amplitude was saved in the 'm8195ampSweep' key.fundTo6thHarm_3.5Vto3.7V_mzmSweep_47mAlaser_695mA_1001pt_Nave4.pkl     Pickle file containing the data used to produce the quantum locking range plot with respect to electro-optic modulator / Mach-Zender modulator (MZM) dc bias point shown in Figure 5b.  The harmonics of the acquisition were saved in the 'signal' key in units of dBm. The dc bias point was saved in the 'mzm bias' key.fundTo6thHarm_625mAto750mA_ydfaSweep_47mAlaser_1001pt_Nave4.pkl     Pickle file containing the data used to produce the quantum locking range plot with respect to optical amplifier / ytterbium-doped fiber amplifier (YDFA) output power shown in Figure 5c.  The harmonics of the acquisition were saved in the 'signal' key in units of dBm. The average photocurrent was saved in the 'pd current' key.","language":["en"],"title":"Bipolar Waveform Synthesis with an Optically Driven Josephson Arbitrary Waveform Synthesizer","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2630/fundTo6thHarm_m8195ampSweep_47mAlaser_695mAamp_1001pt_Nave2.pkl","format":"Python pickle file","description":"Pickle file containing the data used to produce the quantum locking range plot with respect to RF modulation amplitude / RF-AWG output amplitude shown in Figure 5a. The harmonics of the acquisition were saved in the 'harmonics' key in units of dBm.  The RF-AWG output amplitude was saved in the 'm8195ampSweep' key.","mediaType":"application/octet-stream","title":"RF-AWG Sweep Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2630/RFoff008JAWSspectrum.pkl","format":"Python pickle file","description":"Pickle file containing the data used to produce \"RF bias off\" in Figure 3a. The 'Voltage' key contains the recorded output voltage timestreams. The 'scope_samples_per_sec' key contains the information for determining the frequency bins.","mediaType":"application/octet-stream","title":"Timestreams for RF Bias Off Spectrum"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2630/000JAWSmargins.pkl","format":"Python pickle file","description":"Pickle file containing the data used to produce the quantum locking range plots with respect to dc bias shown in Figure 4. The 'Voltage' key contains the recorded output voltage of the synthesized waveform that was used in the spectral plot. The bias offset in mA is given by dat['settings']['AWG_sweep_amp_mA'].  The sweep frequency for the triangular waveform is given by dat['settings']['AWG_sweep_freq_Hz']","mediaType":"application/octet-stream","title":"Quantum Locking Range Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2630/fundTo6thHarm_625mAto750mA_ydfaSweep_47mAlaser_1001pt_Nave4.pkl","format":"Python pickle file","description":"Pickle file containing the data used to produce the quantum locking range plot with respect to optical amplifier / ytterbium-doped fiber amplifier (YDFA) output power shown in Figure 5c. The harmonics of the acquisition were saved in the 'signal' key in units of dBm. The average photocurrent was saved in the 'pd current' key.","mediaType":"application/octet-stream","title":"YDFA Sweep Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2630/fundTo6thHarm_3.5Vto3.7V_mzmSweep_47mAlaser_695mA_1001pt_Nave4.pkl","format":"Python pickle file","description":"Pickle file containing the data used to produce the quantum locking range plot with respect to electro-optic modulator / Mach-Zender modulator (MZM) dc bias point shown in Figure 5b. The harmonics of the acquisition were saved in the 'signal' key in units of dBm. The dc bias point was saved in the 'mzm bias' key.","mediaType":"application/octet-stream","title":"MZM Sweep Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2630/README.txt","format":"TXT","description":"READ ME file containing the dataset description.","mediaType":"text/plain","title":"README.TXT"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2630/005JAWSspectrum.pkl","format":"Python pickle file","description":"Pickle file containing the data used to produce Figure 3b and \"1 kHz sine\" in Figure 3a.  The 'Voltage' key contains the recorded output voltage of the synthesized waveform that was used in the spectral plot.  The 'scope_samples_per_sec' key contains the information for determining the frequency bins in the case of 3a, or the time steps in 3b.","mediaType":"application/octet-stream","title":"Timestreams for Synthesized Spectrum"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology:Electrical/electromagnetic metrology","Electronics:Optoelectronics"],"issued":"2022-04-29","keyword":["optical","photodiodes","Josephson junctions","Josephson arbitrary waveform synthesizer"]},{"identifier":"ark:/88434/mds2-2635","accessLevel":"public","contactPoint":{"hasEmail":"mailto:richard.ayers@nist.gov","fn":"Richard Ayers"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2635","description":"This portal is a gateway to documented digital forensic image datasets. These datasets can assist in a variety of tasks including tool testing, developing familiarity with tool behavior for given tasks, general practitioner training and other unforeseen uses that the user of the datasets can devise. Most datasets have a description of the type and locations of significant artifacts present in the dataset. There are descriptions and finding aides to help you locate datasets by the year produced, by author, or by attributes of the dataset.","language":["en"],"title":"Computer Forensic Reference Data Set Portal","distribution":[{"accessURL":"https://www.cfreds.nist.gov","description":"This portal is your gateway to documented digital forensic image datasets. These datasets can assist in a variety of tasks including tool testing, developing familiarity with tool behavior for given tasks, general practitioner training and other unforeseen uses that the user of the datasets can devise. Most datasets have a description of the type and locations of significant artifacts present in the dataset. There are descriptions and finding aides to help you locate datasets by the year produced, by author, or by attributes of the dataset.","title":"Computer Forensic Reference Dataset Portal"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-05-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Forensics:Digital and multimedia evidence"],"issued":"2022-09-07","keyword":["Digital Forensics; Digital and multimedia evidence"]},{"identifier":"ark:/88434/mds2-2637","accessLevel":"public","references":["https://dx.doi.org/10.1021/acsmacrolett.2c00369"],"contactPoint":{"hasEmail":"mailto:debra.audus@nist.gov","fn":"Debra Audus"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2637","description":"A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. Jupyter Notebooks for quickly testing concepts and reproducing figures, as well as source code that computes the mean squared error as a function of dataset size for various machine learning models are included.For additional details on the data, please refer to the README.md associated with the data. For additional details on the code, please refer to the README.md provided with the code repository (GitHub Repo for Theory aware Machine Learning). For additional details on the methodology, see Debra J. Audus, Austin McDannald, and Brian DeCost, \"Leveraging Theory for Enhanced Machine Learning\"  *ACS Macro Letters* **2022** *11* (9), 1117-1122 DOI: [10.1021/acsmacrolett.2c00369](https://doi.org/10.1021/acsmacrolett.2c00369).","language":["en"],"title":"Theory aware Machine Learning 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Polymers","Information Technology:Data and informatics","Materials:Modeling and computational material science","Mathematics and Statistics:Uncertainty quantification"],"keyword":["polymers","machine learning","transfer learning","theory"]},{"identifier":"ark:/88434/mds2-2638","accessLevel":"public","references":["https://doi.org/10.6028/NIST.SP.1900-101","https://doi.org/10.1109/DESTION50928.2020.00008"],"contactPoint":{"hasEmail":"mailto:thomas.roth@nist.gov","fn":"Thomas Roth"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Internet of Things (IoT) is comprised of interacting networks of physical, computational, and human components that coordinate to fulfill time-sensitive functions in their environment. The development of these systems spans all industrial sectors and demands collaborative effort between research and development teams from multiple institutions. Realizing the full potential of IoT requires interoperability between heterogeneous systems and development processes supported by robust platforms for experimentation and testing across domains. Meanwhile, current design and management approaches for these systems are domain-specific and would benefit from a more universally applicable approach.The National Institute of Standards and Technology (NIST) and its partner, the Institute for Software Integrated Systems at Vanderbilt University, have developed a collaborative experiment development environment that integrates best-of-breed tools including programming languages, network simulators, simulation platforms, hardware in the loop, and others. This environment integrates these tools into a standardized communications protocol, IEEE Standard 1516 High Level Architecture (HLA), and provides a graphical modeling language where simulators can easily be configured into different experimental configurations. Its code generation capabilities transform these simple models into executable simulations and code pre-configured to communicate using the standardized HLA services. This environment is called the Universal CPS Environment for Federation (UCEF). UCEF is distributed as a portable, self-contained Ubuntu Virtual Machine which allows it to run on any computational platform.","language":["en"],"title":"Universal Cyber-Physical Systems Environment for Federation (UCEF)","distribution":[{"accessURL":"https://github.com/usnistgov/ucef","format":"plain text files stored on a public GitHub repository","description":"A GitHub repository that contains the latest UCEF source code including documentation on how to compile the source code into a working virtual machine.","title":"UCEF Source Code"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-02-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1Y","theme":["Information Technology:Internet of Things","Information Technology:Cyber-physical systems"],"keyword":["co-simulation","cyber-physical systems","high level architecture","internet of things","modeling and simulation","tool integration"]},{"identifier":"ark:/88434/mds2-2641","accessLevel":"public","contactPoint":{"hasEmail":"mailto:justyna.zwolak@nist.gov","fn":"Justyna Zwolak"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://github.com/usnistgov/SolDet","description":"SolDet is an object-oriented package for solitonic feature detection in absorption images of Bose-Einstein condensate. with wider use for cold atom image analysis. 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Technical details are explained in https://arxiv.org/abs/2111.04881.","language":["en"],"title":"SolDet: Solitonic feature detection package","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-05-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Optical physics","Physics:Atomic, molecular, and quantum"],"keyword":["soliton","machine learning","python package"]},{"identifier":"ark:/88434/mds2-2658","accessLevel":"public","references":["https://doi.org/10.1039/C7SM00950J"],"contactPoint":{"hasEmail":"mailto:jonathan.seppala@nist.gov","fn":"Jonathan Seppala"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2658","description":"Temperature profiles of acrylonitrile butadiene styrene (ABS) filament during material extrusion additive manufacturing. Printing temperature and velocities cover the full \"printable\" range of the ABS filament and are corrected for reflected infrared photons. The profile of the active printing layer and two sub-layers are included. See the associated publication for the full experimental details.","language":["en"],"title":"Temperature profiles of acrylonitrile butadiene styrene (ABS) during bench-scale material extrusion additive manufacturing","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2658/ABS_210_3_336.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2658/ABS_210_3_336.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2658/ABS_210_3_345.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2658/ABS_210_3_345.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2658/ABS_210_3_350.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2658/ABS_210_3_350.txt.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2017-04-19 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Metrology:Thermometry metrology","Materials:Polymers","Manufacturing:Additive manufacturing"],"issued":"2022-08-11","keyword":["Acrylonitrile butadiene styrene","ABS","FDM","Thermography","3D printing","Temperature profiles","material extrusion","additive manufacturing"]},{"identifier":"ark:/88434/mds2-2659","accessLevel":"public","references":["https://doi.org/10.6028/NIST.AMS.200-11"],"contactPoint":{"hasEmail":"mailto:douglas.thomas@nist.gov","fn":"Douglas Thomas"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2659","description":"This tool uses techniques from ASTM E3200 for evaluating manufacturing investments from the perspective of environmentally sustainable manufacturing by pairing economic methods of investment analysis with environmental aspect of manufacturing. The economic techniques used include net present value, internal rate of return, payback period, and hurdle rate. These four techniques are deterministic, meaning that they deal with known values that are certain. The tool also conducts a sensitivity analysis using Monte Carlo techniques. The tool answers questions such as:  Is a new heating, cooling, and ventilation (HVAC) system cost effective and environmentally sustainable? I have 5 projects with varying costs and levels of sustainability. I can only afford 2 of them. Which ones do I choose?","language":["en"],"title":"Cost Assessment Tool for Sustainable Manufacturing (CATS)","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-06-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Statistical analysis","Mathematics and Statistics:Uncertainty quantification","Manufacturing:Sustainable manufacturing"],"conformsTo":"https://www.astm.org/e3200-21.html","issued":"2022-08-22","keyword":["investment analysis","manufacturing","net present value","internal rate of return","environmental impact","sustainability"]},{"identifier":"ark:/88434/mds2-2669","accessLevel":"public","contactPoint":{"hasEmail":"mailto:matthew.simons@nist.gov","fn":"Matt Simons"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2669","description":"On-resonance Rydberg atom-based radio-frequency (RF) electric field sensing methods remain limited by the narrow frequency signal detection bands available by resonant transitions. The use ofan additional RF tuner field to dress or shift a target Rydberg state can be used to return a detuned signal field to resonance and thus dramatically extend the frequency range available for resonantsensing. Here we compare three distinct tuning schemes based on adjacent Rydberg transitions, which are shown to have distinct tuning characteristics and can be tuned with mechanisms based onthe tuning field frequency or field strength. We further show that a two-photon Raman feature can be used as an effective tuning mechanism separate from conventional Autler-Townes splitting. Wecompare our tuning schemes to AC Stark effect-based broadband RF field sensing and show that although the sensitivity is diminished as we tune away from a resonant state, it nevertheless can beused in configurations where there is a low density of Rydberg states, which would result in a weak AC Stark effect.","language":["en"],"title":"Rydberg state engineering: A comparison of tuning schemes for continuous frequency sensing","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig3d_f.zip","format":"zip file containing .dat files with single columns of values","description":"Figure 3 (d) and (f) - A set of false-color plots of sweeps of ?t and ?sig showing the experimental EIT (d) and the experimental mixer data (f) as a function of increasing tuner field strength. The data in panels d and f is also obtained by sweeping the signal RF output frequency. The file names contain the tuner signal generator power used to determine the rabi rate for each figure element (NOTE: there is a naming error, the first number is the power in dBm, not MHz frequency, while the second number is in MHz, not dBm) and the tuner detuning frequency. Each file corresponds to a horizontal cut of the false color plots. The x-axis of the raw traces is in ?pixels? that are linearly distributed in frequency space. This axis needs to be set/generated for the false color plots using the trigger traces (one per set of measurements is sufficient). The trigger traces have a sawtooth characteristic, where each sawtooth represents one frequency sweep across the set range noted in the readme for each figure. The trigger traces are used to identify and crop the figure to this swept range. The x-axis vector is then generated by linearly interpolating between the beginning and end frequency.","mediaType":"application/x-zip-compressed","title":"Figures 3 d and f"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig1d.zip","format":"zip file containing .dat files with single columns of values","description":"Y axis is LO frequencies in the trace filenames.X-axis is constructed from the ref_cell traces in the data for Figure 1 f. Color scale is the values in the trace files.Plot of the shift of the 56D EIT peak due to the AC Stark shift induced by the LO field of varying frequency. Contains experimental EIT traces for the LO frequency (y-axis) indicated in the filename. Each of these files corresponds to the LO frequencies indicated. The data in the files are in volts on the oscilloscope. The x-axis is the same as the x-axis generated for Figure 1(f), using the ?ref_cell? traces in that dataset. The 'beatsig' traces are data from a lock-in amplifier [not shown].","mediaType":"application/x-zip-compressed","title":"Figure 1 d"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig1e.zip","format":"zip file containing .dat files with single columns of values","description":"Y-axis is the electric field determined by the separation of the two peaks in each trace data file, in V/m.X-axis in the coupling laser detuning in MHz, determined by the separation of the two peaks in the ref_cell data files, which is equal to -396 MHz.The color scale is the values in trace data files, averaged across the columns, normalized to the maximum value of the whole data set.Plot of AT splitting of the 56D EIT peak with an RF field applied to the 56D-53F tuning transition at 24.7 GHz. This data is also used to calculate the field strength corresponding to a given signal generator power, which in turn is used to determine the Rabi rates indicated on y-axes throughout the manuscript. The x-axis of the raw traces is in ?pixels? that are linearly distributed in frequency space. This axis needs to be set/generated for the false color plots using the ref_cell traces (one per set of measurements is sufficient). These ref_cell traces show two distinct spectral peaks: one large one and smaller one to the left. The large one is the main EIT peak of interest and the smaller left one is the fine structure peak located at -396 MHz (when coupled to 56D). Finding the difference between these peaks in the number of oscilloscope ?pixels? can then be used to convert between pixels and frequency to generate the x-axis","mediaType":"application/x-zip-compressed","title":"Figure 1 e"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig2c.zip","format":"zip file containing .mat file for MATLAB","description":"Each panel is a different value of DeltaS (matlab variable array) in Hz. X-axis is DeltaC (matlab variable array) in Hz. Y-axis is OmegaT (matlab variable array) in Hz. The color scale is the transmittanceD (matlab variable array) in units relative to the maximum value of the whole set.Plots of the modeled EIT showing the location of the tuning peaks obtained EIT as a function of the tuner Rabi frequency and the coupler laser detuning, for different values of signal frequency detuning. The data for panel c is modeled EIT data. The data is a matlab structure array with each field labeled as follows: transmittanceD is the transmittance through the cell field with the corresponding values of the signal and tuner Rabi rates given by OmegaT and OmegaS and the detunings of the frequencies by DeltaT and DeltaS.","mediaType":"application/x-zip-compressed","title":"Figure 2 c"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig4c.zip","format":"zip file containing .mat file for MATLAB","description":"Each panel is a different value of DeltaS (matlab variable array) in Hz. X-axis is DeltaC (matlab variable array) in Hz. Y-axis is OmegaT (matlab variable array) in Hz. Color scale is the transmittanceD (matlab variable array)  in units relative to the maximum value of the whole set.False color plots of power tuning using the inverted scheme with the modeled results for the experiment in Figure 4(b).  The model data has the identical structure to figure 2c.","mediaType":"application/x-zip-compressed","title":"Figure 4 c"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig3e.zip","format":"zip file containing .mat file for MATLAB","description":"Each panel is a different value of OmegaT (matlab variable array) in Hz. X-axis is DeltaS (matlab variable array) in Hz. Y-axis is DeltaT (matlab variable array) in Hz. Color scale is the transmittanceD (matlab variable array) in units relative to the maximum value of the whole set.A set of false-color plots of sweeps of ?t and ?sig showing the modeled EIT as a function of increasing tuner field strength. Panel e data is model EIT output with the same format as figure 2c.","mediaType":"application/x-zip-compressed","title":"Figure 3 e"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig5b.zip","format":"zip file containing .mat file for MATLAB","description":"Each panel is a different value of OmegaT (matlab variable array) in Hz. X-axis is DeltaS (matlab variable array) in Hz.Y-axis is DeltaT(matlab variable array) in Hz. Color scale is the transmittanceD (matlab variable array) in units relative to the maximum value of the whole set.Figure 5(b) - False color plots of the modeled EIT with frequency tuning using an inverted sequence. The data format of panel b is identical to figure 3e.","mediaType":"application/x-zip-compressed","title":"Figure 5 b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig4b_d.zip","format":"zip file containing .dat files with single columns of values","description":"Each panel is a different value of the RF Signal Detuning in MHz in the filenames of the trace (b) and beatsig (d) files. X-axis is the coupling laser detuning in MHz, calibrated using the ref_cell traces, as described below. Y-axis is the Rabi frequency of the tuning field, determined from the Signal Generator Powers in dBm in the filenames and figure 1(e). False color plots of power tuning using the inverted scheme with the experimental raw EIT signal (b) and the modeled EIT signal (d), showing features due to the 56D-54F transition and the 57D-55F transition  The data for panels b and d has an identical data structure to figure 2b/d. The x-axis of the raw traces is in ?pixels? that are linearly distributed in frequency space. This axis needs to be set/generated for the false color plots using the ref_cell traces (one per set of measurements is sufficient). These ref_cell traces show two distinct spectral peaks: one large one and smaller one to the left. The large one is the main EIT peak of interest and the smaller left one is the fine structure peak located at -374 MHz (57D). Finding the difference between these peaks in the number of oscilloscope ?pixels? can then be used to convert between pixels and frequency to generate the x-axis.","mediaType":"application/x-zip-compressed","title":"Figure 4 b and d"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig5a_c.zip","format":"zip file containing .dat files with single columns of values","description":"Each panel is data from the files with the corresponding Tuning Field Rabi Frequency in MHz in the filenames of the trace (a) and beatsig (c) files. The Rabi frequency is determined by the number in front of the MHz in the filename (which is the signal generator power in dBm) and combining with figure 1(e). X-axis is the RF Signal Detuning in MHz, which is a linearly spaced array with the number of points as in the files, from -800 to +800 MHz.Y-axis is the Tuning Field detuning in MHz as found in the filenames in front of the dBm.Colorscale is the values in the trace (a) and beatsig (c) files. False color plots of frequency tuning using an inverted sequence with the experimental EIT (a) and mixer signal (c). The data format of panels a and c is identical to figure 3d/f. This includes the naming typo that mixes up the MHz and dBm labels. The x-axis of the raw traces is in ?pixels? that are linearly distributed in frequency space. This axis needs to be set/generated for the false color plots using the trigger traces (one per set of measurements is sufficient). The trigger traces have a sawtooth characteristic, where each sawtooth represents one frequency sweep across the set range noted in the readme for each figure. The trigger traces are used to identify and crop the figure to this swept range. The x-axis vector is then generated by linearly interpolating between the beginning and end frequency.","mediaType":"application/x-zip-compressed","title":"Figures 5 a and c"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/figs2b_d.zip","format":"zip file containing .dat files with single columns of values","description":"Each panel is data from the files with the corresponding Frequency in MHz in the filename.Y-axis is the Rabi Frequency in MHz determined using the Power in dBm in the filenames and the data in figure 1(e). X-axis is the coupling laser detuning determined from the ref_cell data files. Color scale is the values in the datafiles (trace for fig b, beatsig for fig d), averaged across columns and relative to the maximum value.False color plots of the experimental EIT and Rydberg mixer as a function of the tuner Rabi frequency and the coupler laser detuning, with different values of signal frequency detuning for each plot indicated. The data for panels b and d with the scope traces of ?trace? and ?beatsig?. The filenames give the anritsu output power, which is converted into Rabi rates using the experimental Autler-Townes splitting of figure 1(e). The filenames also give the signal detuning by which the figure panels are organized.","mediaType":"application/x-zip-compressed","title":"Figures 2 b and d"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig3b_c.zip","format":"zip file containing .dat files with single columns of values","description":"X-axis is the RF Signal field detuning in MHz, linearly spaced from -800 to +800 MHz, for the number of data points in both the trace (b) and beatsig (c) files. Y-axis is the Rabi frequency in MHz of the tuning field determined by the signal generator power in the filename, converted to Rabi frequency using figure 1(e).The colorscale is the values in each trace and beatsig file. Experimental EIT (b) and mixer (c) plots of power tuning. Here, the signal field is set to the resonant 56D-54F transition and the tuning field is applied to the 54F-57D transition with increasing strength, inducing the observed AT-splitting in the 54F state. The data in panels b and c is obtained by sweeping the signal RF output frequency, set to sweep from -800 to +800 MHz detuning from the 56D-54F transition (centered at 18.3 GHz). The trigger signal is needed here to align and window the traces for proper alignment. The x-axis of the raw traces is in ?pixels? that are linearly distributed in frequency space. This axis needs to be set/generated for the false color plots using the trigger traces (one per set of measurements is sufficient). The trigger traces have a sawtooth characteristic, where each sawtooth represents one frequency sweep across the set range noted in the readme for each figure. The trigger traces are used to identify and crop the figure to this swept range. The x-axis vector is then generated by linearly interpolating between the beginning and end frequency.","mediaType":"application/x-zip-compressed","title":"Figures 3 b and c"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig1f.zip","format":"zip file containing .dat files with single columns of values","description":"X-axis in the coupling laser detuning in MHz, determined by the separation of the two peaks in the ref_cell data files, which is equal to 396 MHz. Y-axis is the electric field determined by the separation of the two peaks in each trace data file, in V/m.The color scale is the values in trace data files, averaged across the columns, normalized to the maximum value of the whole data set.Plot of the AT splitting of the 56D EIT peak with an RF field applied to the 56D-54F signal/LO transition at 18.3 GHz. The x-axis of the raw traces is in ?pixels? that are linearly distributed in frequency space. This axis needs to be set/generated for the false color plots using the ref_cell traces (one per set of measurements is sufficient). These ref_cell traces show two distinct spectral peaks: one large one and smaller one to the left. The large one is the main EIT peak of interest and the smaller left one is the fine structure peak located at -396 MHz (when coupled to 56D). Finding the difference between these peaks in the number of oscilloscope ?pixels? can then be used to convert between pixels and frequency to generate the x-axis.","mediaType":"application/x-zip-compressed","title":"Figure 1 f"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/fig1g.zip","format":"zip file containing .dat files with single columns of values","description":"Y-axis is the electric field determined by the separation of the two peaks in each trace data file, in V/m. X-axis in the coupling laser detuning in MHz, determined by the separation of the two peaks in the ref_cell data files, which is equal to 374 MHz. The color scale is the values in trace data files, averaged across the columns, normalized to the maximum value of the whole data set. Plot of the coupling frequency set to the 57D state and a constant RF field of strength omega/2? = 180 MHz applied to the 57D-54F transition (f = 23.4 GHz), we see the emergence of a two-RF photon Raman peak at Delta_c = 0. Each trace is the voltage on the oscilloscope with the RF power indicated in the filename. The Electric field is determined by the power using The x-axis of the raw traces is in ?pixels? that are linearly distributed in frequency space. This axis needs to be set/generated for the false color plots using the ref_cell traces (one per set of measurements is sufficient). These ref_cell traces show two distinct spectral peaks: one large one and smaller one to the left. The large one is the main EIT peak of interest and the smaller left one is the fine structure peak located at -374 MHz (when coupled to 57D). Finding the difference between these peaks in the number of oscilloscope ?pixels? can then be used to convert between pixels and frequency to generate the x-axis.","mediaType":"application/x-zip-compressed","title":"Figure 1 g"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/figure6.zip","format":"a zip file containing .dat files with 5 columns","description":"X-axis the Sensitivity in V/m per root Hz as determined from the signal generator power in the filenames in dBm.Y-axis is the Signal amplitude given by the values in the data files, averaged down the columns, then the average and standard deviation was calculated from those averages. Comparison of the baseline sensitivity of on-resonance detection on the 56D-54F transition, the Raman EIT peak, and the AC stark shift at 21.5 GHz. These are a series of scope traces acquired at different signal field strengths (files named by dBm output power) acquired with a lock-in time constant of 1 second. The plots are generated by averaging over the traces acquired.","mediaType":"application/x-zip-compressed","title":"Figure 6"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2669/2669_README%20%284%29.txt","format":".txt","description":"Readme file for data","mediaType":"text/plain","title":"README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-06-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Electronics:Electromagnetics","Physics:Atomic, molecular, and quantum"],"issued":"2022-08-29","keyword":["Rydberg atoms","atomic physics","receivers","fields strength","electric field","volts/meter"]},{"identifier":"ark:/88434/mds2-2678","accessLevel":"public","contactPoint":{"hasEmail":"mailto:andrew.rotunno@nist.gov","fn":"Drew Rotunno"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2678","description":"Simulated transmission curves illustrating an efficient new calculation method. Data was produced for a publication, and is indexed by figure.","language":["en"],"title":"Data for the paper, \"On Efficient Spectroscopy Calculations for Thermal Distributions of Atoms\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Figure1data.xlsx","description":"Includes a Gaussian distribution, and an inverse-error function sampling","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Data for Figure 1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Figure2data.xlsx","description":"This data contains detuning-scanned transmission values. The 'surface' data has velocity as another scanned parameter.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Data for Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Figure3data.xlsx","description":"Includes a comparison of total transmission curves for a few values of the number of velocities.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Data for Figure 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Figure4data.xlsx","description":"This data set measures computation time, a well as convergence parameters across a scan of the number of velocities.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Data for Figure 4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Figure5data.xlsx","description":"This figure demonstrates the effect on total transmission when only some velocity samples are included.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Data for Figure 5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Fig4d_RMS_per_Time.csv","description":"Comparison of the RMS of residuals to computation time. Col 1: Pop_timeCol 2: Pop_RMS_residualsCol 3: Vel_TimeCol 4: Vel_RMS_residuals","mediaType":"text/csv","title":"Fig4d"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Fig5_vary_velocity_bounds.csv","description":"Comparison of transmission curves, varying the velocity 'bound', xi. Col 1: Detuning_DeltaC_over_2piMHzCol 2: xi1percentCol 3: xi3percentCol 4: xi5percentCol 5: xi10percentCol 6: xi25percentCol 7: xi50percentCol 8: xi100percent","mediaType":"text/csv","title":"Fig5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Fig1a_Velocity_MB_Sampling.csv","description":"col 1: velocity_valuecol2: Gaussian_Probability","mediaType":"text/csv","title":"Fig1a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Fig1b_InvErfSampling.csv","description":"Col 1: Velocity_Sample","mediaType":"text/csv","title":"Fig1b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Fig1c_Error_function.csv","description":"Col 1:Velocitycol 2: Error_function","mediaType":"text/csv","title":"Fig1c"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Fig1d_InvErf_sampling.csv","description":"Col 1: population_sample_eta_vectorcol 2: InvErf_velocity_of_population_sample","mediaType":"text/csv","title":"Fig1d"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Fig2_Pop_trans_surface.csv","description":"An array with rows corresponding to eta_vector and columns corresponding to Delta_C, both in Fig2 Parameters","mediaType":"text/csv","title":"Fig2 Population Surface"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Fig2_Vel_trans_surface.csv","description":"An array with rows corresponding to Sigma_vec and columns corresponding to Delta_C, both in Fig2 Parameters","mediaType":"text/csv","title":"Fig2 Velocity Sampling Surface"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Fig2_parameters.csv","description":"These act as axes for the surfaces in Fig 2. col 1:Delta_C_over_2_Pi_MHzcol 2: Eta_veccol 3: Sigma_vec","mediaType":"text/csv","title":"Fig2 parameters"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2678/Fig2_total_trans_both.csv","description":"Total transmission values for the population and velocity sampling methods, over delta_C. Col 1: Delta_C_over_2_Pi_MHzCol 2: Pop_total_transmissionCol 3: Vel_total_transmission","mediaType":"text/csv","title":"Fig2 Total Transmission"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-06-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Atomic, molecular, and quantum"],"issued":"2023-05-19","keyword":["Rydberg atoms","field sensing","spectroscopy","computational physics"]},{"identifier":"ark:/88434/mds2-2679","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jeanne.quimby@nist.gov","fn":"Jeanne Quimby"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2679","description":"Dataset includes stabilograms associated to a cellular communications anomaly detection experiment detailed in the publication Frey M, Gregg M, Rezac JD, Coder JB, Kord A, Otterstetter A, Quimby J, Weiss A (2022) Stabilograms: Testbed stability in a pilot experiment for cellular communications anomaly detection (National Institute of Standards and Technology, Boulder, CO), (Under Review at time of data submission).","language":["en"],"title":"Stabilograms for a Cellular Communication Anomaly Detection Experiment","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2679/pilot.csv","description":"Dataset contains counts, quantiles, and metadata about each separate measurement during the experiment.","mediaType":"text/csv","title":"Pilot Data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2679/stabilotable.csv","description":"Components of stabilograms for each measurand considered in experiment.","mediaType":"text/csv","title":"List of stabilograms for each measurand"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2679/stabilogram_readme.txt","description":"An in-depth description of dataset files.","mediaType":"text/plain","title":"Readme"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-06-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)","Mathematics and Statistics:Statistical analysis"],"issued":"2022-07-07","keyword":["4G Technology","5G Technology","Base stations","LTE Technology","Telecommunication","Verification","and Wireless Communication System"]},{"identifier":"ark:/88434/mds2-2681","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nik.hrabe@nist.gov","fn":"Nik Hrabe"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2681","description":"This challenge is to predict the macroscopic stress-strain response of compression samples across a range of temperatures taken from the base leg of the IN625 AMB2018-01 build in both the build direction (Z-axis) and a transverse-build direction (Y-axis). The specific temperatures of interest are 298 K, 523 K, and 773 K. The calibration data provided in this dataset corresponds to the build direction compression tests done at 298 K and 773 K.","language":["en"],"title":"Macroscale Compression at Different Temperatures and Orientations (CHAL-AMB2022-04-MaCTO)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2681/README%20for%20AMB2022-04-MaCTO_calibration_data.docx","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2681/AMB2022-04-MaCTO_calibration_data.xlsx","description":"calibration data for AMB2022-04-MaCTO challenge","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"AMB2022-04-MaCTO_calibration_data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2681/CHAL-AMB2022-04-MaCTO%20submission%20ANSWERS.xlsx","format":"Excel Spreadsheet","description":"Answers to CHAL-AMB2022-04-MaCTO Submission","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Answers to CHAL-AMB2022-04-MaCTO Submission"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-06-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing","Materials:Metals","Materials:Materials characterization","Materials:Modeling and computational material science","Standards:Reference data"],"keyword":["AM Bench","benchmark","additive manufacturing","metal","mechanical characterization"]},{"identifier":"ark:/88434/mds2-2682","accessLevel":"public","contactPoint":{"hasEmail":"mailto:alain.rufenacht@nist.gov","fn":"Alain Rufenacht"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This is the dataset for 4 publishable figures in 2 page abstracted titled \"Josephson arbitrary waveform synthesizer for ac voltage calibrations\" submitted to the Conference on Precision Electromagnetic Measurements, CPEM 2022. QLR=Quantum Locking Range.Fig. 1. QLR (2 V rms waveforms) as a function of the dc bias current offset in all 4 arrays of Bias 1.Fig. 2. QLR (2 V rms waveforms) as a function of the compensation current amplitude in all 4 arrays of Bias 1.Fig. 3. QLR (2 V rms waveform) as a function of the pulse amplitude on Bias 1 or Bias 2 high-speed current pulse channel.Fig. 4. QLR (2 V rms waveforms) as a function of the compensation current phase in all 4 arrays of Bias 1.","language":["en"],"title":"Josephson arbitrary waveform synthesizer for ac voltage calibration, CPEM 2022","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2682/Fig2%20Compensation%20Amplitude%20Dither.csv","format":"CSV","description":"Fig. 2.\tQLR (2 V rms waveforms) as a function of the compensation current amplitude in all 4 arrays of Bias 1.","mediaType":"text/csv","title":"Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2682/Fig1%20Bias%20Current%20DC%20Offset.csv","format":"CSV","description":"Fig. 1.\tQLR (2 V rms waveforms) as a function of the dc bias current offset in all 4 arrays of Bias 1.","mediaType":"text/csv","title":"Figure 1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2682/Fig3%20Pulse%20Amplitude%20Dither.csv","format":"CSV","description":"Fig. 3.\tQLR (2 V rms waveform) as a function of the pulse amplitude on Bias 1 or Bias 2 high-speed current pulse channel.","mediaType":"text/csv","title":"Figure 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2682/Fig4%20Compensation%20Phase%20Dither.csv","format":"CSV","description":"Fig. 4.\tQLR (2 V rms waveforms) as a function of the compensation current phase in all 4 arrays of Bias 1.","mediaType":"text/csv","title":"Figure 4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2682/2682_README.txt","mediaType":"text/plain","title":"Readme"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-06-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Standards:Reference instruments","Metrology:Electrical/electromagnetic metrology"],"issued":"2022-07-20","keyword":["Josephson arrays","quantization","signal synthesis","standards","superconducting integrated circuits","voltage measurement","digital-analog conversion"]},{"identifier":"ark:/88434/mds2-2684","accessLevel":"public","references":["https://doi.org/10.1364/OL.465823"],"contactPoint":{"hasEmail":"mailto:david.plusquellic@nist.gov","fn":"David F. Plusquellic"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2684","description":"Data associated with figures in Optics Letters 2022 publication \"A dual chirped-pulse electro-optical frequency comb method for simultaneous molecular spectroscopy and dynamics studies: Formic acid in the THz region\".","language":["en"],"title":"A dual chirped-pulse electro-optical frequency comb method for simultaneous molecular spectroscopy and dynamics studies: Formic acid in the THz 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Spectroscopy"],"issued":"2022-07-01","keyword":["electro-optic combs","dual comb","dual chirped pulse","rapid passage","THz region"]},{"identifier":"ark:/88434/mds2-2686","accessLevel":"public","references":["https://doi.org/10.1016/j.fuproc.2022.107341"],"contactPoint":{"hasEmail":"mailto:tara.fortin@nist.gov","fn":"Tara Fortin"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2686","description":"The data are averaged specific heat capacity measurement results and associated expanded uncertainties for six conventional aviation fuels (JP-10, JP-7, JP-4, JP-TS, TS-1, and Avgas), which are presented in Tables S7-S12 of the Supporting Information file accompanying the manuscript entitled \"Heat capacity measurements of conventional aviation fuels\".","language":["en"],"title":"Data to accompany the paper \"Heat capacity measurements of conventional aviation fuels\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2686/README_ConvFuelcp.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2686/ConvFuelcpData.zip","format":".zip and .csv","description":"Zip file containing individual data files for each fuel","mediaType":"application/zip","title":"ConvFuelcpData"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-09-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Thermochemical properties"],"keyword":["aviation fuels","differential scanning calorimetry","isobaric specific heat capacity","jet fuels"]},{"identifier":"ark:/88434/mds2-2689","accessLevel":"public","references":["https://doi.org/10.1109/TASC.2022.3223853"],"contactPoint":{"hasEmail":"mailto:miranda.thompson@nist.gov","fn":"Miranda Thompson"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2689","description":"Data is for \"Effects of Non-Sinusoidal Current Phase Relationships on Single Flux Quantum Circuits\" and publication in IEEE Transactions on Applied Superconductivity. Data is from WRspice simulations, as well as MALT margin analysis. Further data analysis was done in python. One set of data is the pulse propagation delay of a chain of JTL's with different current phase relationships. The other set of data is the operating margins of T flip-flop circuits that have been optimized for different current-phase relationships. ","language":["en"],"title":"Effects of Non-Sinusoidal Current Phase Relationships on Single Flux Quantum Circuits","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2689/sns_JTLdelay.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2689/wl_JTLdelay.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2689/README.txt","description":"read me file","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2689/2ndharm_JTLdelay.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2689/marginsTFFv2_2ndHarm_opt.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2689/marginsTFFv2_12Harm_opt.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2689/marginsTFFv2_12Harm_tilt_opt.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2689/marginsTFFv2sine_opt.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2689/sine_JTLdelay.txt","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-06-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Electronics:Superconducting electronics"],"issued":"2022-11-29","keyword":["Current Phase Relationships  Single Flux Quantum Circuits"]},{"identifier":"ark:/88434/mds2-2691","accessLevel":"public","references":["https://doi.org/10.1007/s10765-022-03100-2"],"contactPoint":{"hasEmail":"mailto:tara.fortin@nist.gov","fn":"Tara Fortin"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"The data are averaged specific heat capacity measurement results and associated expanded uncertainties for nine conventional and alternative aviation fuels (Jet A, Jet A-1, JP-8, JP-5, S-8, S-5, IPK, HRJ Camelina, and HRJ Tallow), which are presented in Tables S10-S18 of the Supplemental Information file accompanying the manuscript entitled \"Comparison of Heat Capacity Measurements of Alternative and Conventional Aviation Fuels\".","language":["en"],"title":"Data to accompany the paper \"Comparison of Heat Capacity Measurements of Alternative and Conventional Aviation Fuels\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2691/AltFuelcpData.zip","description":"Compressed folder containing individual data files for the nine fuels and the accompanying README file.","mediaType":"application/zip","title":"AltFuelcpData"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-09-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Thermochemical properties"],"issued":"2022-11-09","keyword":["aviation fuels","differential scanning calorimetry","isobaric specific heat capacity","renewable fuels","synthetic fuels","temperature dependence"]},{"identifier":"ark:/88434/mds2-2692","accessLevel":"public","contactPoint":{"hasEmail":"mailto:lyle.levine@nist.gov","fn":"Lyle E. Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2692","description":"The following data files include microstructure measurement results associated with the 2022 Additive Manufacturing Benchmark test series (AM-Bench 2022) AMB2022-01 benchmark on laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN718 test objects. The AM builds were performed on the NIST Additive Manufacturing Metrology Testbed (AMMT) and the microstructure measurements were conducted using scanning electron microscopy (SEM), transmission electron microscopy (TEM), ultra-small-angle X-ray scattering (USAXS), small-angle X-ray scattering (SAXS), wide-angle X-ray scattering (WAXS), and automated serial sectioning. Detailed descriptions of the build process parameters, scan pattern, heat treatment, and descriptions of all of the AMB2022-01 measurements are provided on the AMB2022-01 challenge description webpage (https://www.nist.gov/ambench/amb2022-01-benchmark-measurements-and-challenge-problems).Due to the time-sensitive nature of the AM Bench challenge problems, those measurements and analyses were prioritized. The challenges that this data publication address are:Microstructure (CHAL-AMB2022-01-MS): Histograms of direction-specific grain sizes from specified regions within as-built and heat-treated samples.Phase Evolution (CHAL-AMB2022-01-PE): Formation and evolution of phases and phase fractions, including major precipitates, as a function of time for heat treatments of IN718 from a 2.5 mm leg.The data provided for CHAL-AMB2022-01-PE are preliminary since an additional phase in the as-build material has not yet been positively identified. These data will be updated shortly. Also, additional datasets that are not required for the challenges will be added soon. For updates, please check back here or at www.nist.gov/ambench.","language":["en"],"title":"AM Bench 2022 Microstructure Measurements for IN718 3D 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing"],"keyword":["laser powder bed fusion","AM-Bench"]},{"identifier":"ark:/88434/mds2-2694","accessLevel":"public","references":["https://doi.org/10.1117/1.JBO.27.12.126003"],"contactPoint":{"hasEmail":"mailto:thomas.germer@nist.gov","fn":"Thomas A. 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The script requires Python 3.6 and Matplotlib 3.4.A MATLAB script, analyze.m, is also provided.","language":["en"],"title":"Data associated with manuscript \"Spatial Frequency domain Mueller matrix imaging\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2694/SFD%20MM%20Paper%20Data.zip","mediaType":"application/x-zip-compressed","title":"SFD MM Paper Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Biological physics"],"issued":"2022-09-16","keyword":["biomedical imaging","Mueller matrix","polarization","spatial frequency domain"]},{"identifier":"ark:/88434/mds2-2695","accessLevel":"public","contactPoint":{"hasEmail":"mailto:adam.wunderlich@nist.gov","fn":"Adam Wunderlich"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2695","description":"This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.","language":["en"],"title":"Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models","distribution":[{"accessURL":"https://github.com/usnistgov/NoiseGAN","format":"python source code","description":"GitHub repository","title":"GitHub repository"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Image and signal processing","Mathematics and Statistics:Modeling and simulation research"],"issued":"2022-07-08","keyword":["time series","machine learning","band-limited noise","power law noise","shot noise","impulsive noise","colored noise","fractional Gaussian noise","fractional Brownian motion"]},{"identifier":"ark:/88434/mds2-2696","accessLevel":"public","contactPoint":{"hasEmail":"mailto:david.long@nist.gov","fn":"David Long"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2696","description":"\"High dynamic range electro-optic dual-comb interrogation of optomechanical sensors\" to be published in Optics Letters.","language":["en"],"title":"\"High dynamic range electro-optic dual-comb interrogation of optomechanical sensors\" to be published in Optics Letters.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2696/Fig3_Bottom.csv","mediaType":"text/csv","title":"Fig3_Bottom"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2696/Fig3_Top.csv","mediaType":"text/csv","title":"Fig3_Top"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2696/Fig4.csv","mediaType":"text/csv","title":"Fig4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2696/Fig5.csv","mediaType":"text/csv","title":"Fig5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2696/Fig6.csv","mediaType":"text/csv","title":"Fig6"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2696/Fig7.csv","mediaType":"text/csv","title":"Fig7"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2696/Fig8.csv","mediaType":"text/csv","title":"Fig8"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2696/Fig9.csv","mediaType":"text/csv","title":"Fig9"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2696/2696_README.txt","mediaType":"text/plain","title":"README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Optical physics","Metrology:Acoustic/vibration metrology"],"issued":"2022-08-30","keyword":["Sensors","optical frequency combs","accelerometry"]},{"identifier":"ark:/88434/mds2-2697","accessLevel":"public","contactPoint":{"hasEmail":"mailto:patrick.egan@nist.gov","fn":"Patrick Egan"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2697","description":"Supplemental material to the article \"Expansivity of fused quartz glass measured within 6 x 10^-10 /K\" by P F Egan in International Journal of Thermophysics 2024.  The dataset and proceeding were first presented at the NCSLI Workshop & Symposium 2022 in Grapevine TX.Three datasets are included, corresponding to three separate runs (cycles of expansivity measurement).Run 1:  cycled specimen FP_333 and FP_152 side-by-sideRun 2:  FP_152 was potted with slip-fit tubes at its ends, and the run cycled specimen FP_333 and FP_152Potted side-by-sideRun 3:  cycled specimen FP_333 and FP_154 side-by-sideThe Python file produces Fig. 2 from the article, and includes some literature/historical data on the thermal expansion of fused quartz glass","language":["en"],"title":"Expansivity of fused quartz glass measured within 6 x 10^-10 /K","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2697/supp-ijt-cte.zip","description":"CTE data for three separate runs of fused quartz glass specimens","mediaType":"application/x-zip-compressed","title":"Supplemental material to IJT article"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology:Dimensional metrology","Physics:Thermodynamics","Materials:Materials characterization"],"issued":"2022-07-20","keyword":["thermal expansion coefficient","fused quartz glass","reference material"]},{"identifier":"ark:/88434/mds2-2698","accessLevel":"public","contactPoint":{"hasEmail":"mailto:patrick.egan@nist.gov","fn":"Patrick Egan"},"programCode":["006:045"],"@type":"dcat:Dataset","replaces":"ark:/88434/mds2-2698","landingPage":"https://data.nist.gov/od/id/mds2-2698","description":"Dataset and scripts supporting the article \"D-to-A_eff converter\", a proceeding presented at the NCSLI Workshop & Symposium 2022, Grapevine TX.The dataset consists of two main parts:  dimensional measurements and conversion scripts.  The dataset and scripts produce an estimate for the effective area and distortion coefficient of the piston-cylinder assembly PCA2062 based on the 2020 dimensional characterization.The dimensional measurements of straightness, roundness, and two-point diameter for the piston and cylinder are in the text files:  \"straightnessPis.txt\", \"straightnessCyl.txt\", \"roundnessPis.txt\", \"roundnessCyl.txt\", \"radiusPis.txt\", \"radiusCyl.txt\"The main conversion script is:  \"CalcAeff2062.py\"This calls two other scripts to find the generatrixes of the piston and cylinder:  \"findGcyl2062.py\" and \"findGpis2062.py\"The conversion also relies on finite-element calculation.  These scripts are in the directory /fea.  All scripts and data from the /fea directory are controlled by the main conversion script CalcAeff2062.py.  To run the finite-element software will require a software license.","language":["en"],"title":"Conversion of a piston-cylinder dimensional dataset to the effective area of a mechanical pressure generator","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2698/README.txt","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2698/licensing.txt","mediaType":"text/plain","title":"Licensing"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2698/pca206x-summary.pdf","mediaType":"application/pdf","title":"Pca 206x summary"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2698/2062.zip","mediaType":"application/x-zip-compressed","title":"Data 2062"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2698/2065.zip","mediaType":"application/x-zip-compressed","title":"Data 2065"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2698/2066.zip","mediaType":"application/x-zip-compressed","title":"Data 2066"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Metrology:Dimensional metrology"],"keyword":["piston gage","generatrix","effective area","diameter"]},{"identifier":"ark:/88434/mds2-2700","accessLevel":"public","contactPoint":{"hasEmail":"mailto:andrew.rotunno@nist.gov","fn":"Drew Rotunno"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This set corresponds to a (pending) publication, where we attempt to model spectral features appearing in the lab by calculating many segments of an inhomogeneous field. Every data set here is a transmission value, either normalized to 1 in modeled data, or an arbitrary-scaled voltage reading from a photodiode onto an oscilloscope. These are given in scans over coupling photon detuning, delta_C, which is divided by 2 pi, and given in MHz. Arrays are scans over delta_C, and position/fieldstrength, for figure 3. Data for figure 3 is given in a slightly unorthodox form, where the [electric field over position plot] and the [total transmission over detuning plot] are given along either of the large array's corresponding axes, matching the arrangement in Fig 3. All data is in CSV comma separated value form, with return characters between rows.","language":["en"],"title":"Modeling Line Broadening and Distortion Due to Inhomogeneous Fields for Rydberg Electrometry","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2700/fig3f.csv","format":"array, columns, and rows","description":"Transmission data in an array over cell position (rows) and detuning (columns). Column and row scaling headers each have corresponding data of the total transmission and the field value over position.","mediaType":"text/csv","title":"Figure 3f -Waveguide simulation"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2700/fig3a.csv","format":"array, columns, and rows","description":"Transmission data in an array over cell position (rows) and detuning (columns). Column and row scaling headers each have corresponding data of the total transmission and the field value over position.","mediaType":"text/csv","title":"Figure 3a - constant"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2700/fig3c.csv","format":"array, columns, and rows","description":"Transmission data in an array over cell position (rows) and detuning (columns). Column and row scaling headers each have corresponding data of the total transmission and the field value over position.","mediaType":"text/csv","title":"Figure 3c - linear"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2700/fig3d.csv","format":"array, columns, and rows","description":"Transmission data in an array over cell position (rows) and detuning (columns). Column and row scaling headers each have corresponding data of the total transmission and the field value over position.","mediaType":"text/csv","title":"Figure 3d - 1/r"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2700/fig2.csv","format":"array, scales for axes","description":"Transmission data in an array over cell position (rows) and detuning (columns).","mediaType":"text/csv","title":"Figure 2 - linear change in E"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2700/fig1.csv","format":"columns","description":"transmission curves over coupling detuning. We compare data and theory for an 'optimized' waveguide with a near constant field, and an 'unoptimized' standing wave inside of a waveguide.","mediaType":"text/csv","title":"Figure 1 - tuned waveguide data vs. model"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2700/fig4.csv","format":"columns","description":"Comparison of model and data for a transverse path through a waveguide.","mediaType":"text/csv","title":"Figure 4 - Transverse through waveguide"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2700/fig5.csv","format":"columns","description":"Comparison of model and data for a transverse path through a waveguide. This set uses three different powers labelled, 'low' 'med' and 'high'","mediaType":"text/csv","title":"Figure 5 - Longitudinal Through Waveguide"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2700/2700_README.txt","format":"english","description":"a description of the data.","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2700/fig3e.csv","format":"array, columns, and rows","description":"Transmission data in an array over cell position (rows) and detuning (columns). Column and row scaling headers each have corresponding data of the total transmission and the field value over position.","mediaType":"text/csv","title":"Figure 3e - standing wave"},{"format":"array, columns, and rows","downloadURL":"https://data.nist.gov/od/ds/mds2-2700/fig3b.csv","description":"Transmission data in an array over cell position (rows) and detuning (columns). Column and row scaling headers each have corresponding data of the total transmission and the field value over position.","mediaType":"text/csv","title":"Figure 3b - step-wise"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Atomic, molecular, and quantum"],"issued":"2022-08-08","keyword":["Rydberg atoms","atomic physics","receivers","fields strength","electric field","volts/meter"]},{"identifier":"ark:/88434/mds2-2702","accessLevel":"public","contactPoint":{"hasEmail":"mailto:sean.lehman@nist.gov","fn":"Sean Lehman"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2702","description":"This contains the raw time-series data for a study of liposome stability in aqueous fluid solution over the course of a year. The study was carried out using orthogonal nanoparticle tracking analysis (NTA) and asymmetric flow field flow fractionation (AF4) with multi-angle light scattering techniques to independently measure particle diameter and particle number concentration throughout the course of the study.","language":["en"],"title":"Liposome Stability Study Time-Series Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2702/Liposome%20Stability%20Manuscript%20Data%20Summary.xlsx","format":"Excel Spreadsheet","description":"This file contains the raw time-series data for the associated manuscript concerning liposome measurements of particle diameter and particle number concentration made over the course of a year.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Liposome Stability Manuscript Summary Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-06-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Nanotechnology:Nanometrology","Bioscience:Lipidomics","Nanotechnology:Nanomaterials"],"issued":"2022-07-26","keyword":["Liposomes","MALS","NTA","Particle Metrology","Nanoparticles","Longitudinal Study"]},{"identifier":"ark:/88434/mds2-2703","accessLevel":"public","references":["https://arxiv.org/abs/2205.01147"],"contactPoint":{"hasEmail":"mailto:nathan.newbury@nist.gov","fn":"Nathan R. Newbury"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2703","description":"Data for the figures in the manuscript entitled \"Time Programmable Frequency Comb: Generation and Application to Quantum-Limited Dual-Comb Ranging\"","language":["en"],"title":"Data for manuscript entitled \"Time Programmable Frequency Comb: Generation and Application to Quantum-Limited Dual-Comb Ranging\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2703/Fig1c.mat","format":"matlab figure file","mediaType":"application/octet-stream","title":"Figure 1c data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2703/Fig2a.mat","format":"matlab fig file","mediaType":"application/octet-stream","title":"Figure 2a data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2703/Fig2b.mat","format":"matlab fig file","mediaType":"application/octet-stream","title":"Fig. 2a data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2703/Fig2c.mat","format":"matlab fig file","mediaType":"application/octet-stream","title":"Fig. 2c data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2703/Fig3b.mat","format":"matlab fig file","mediaType":"application/octet-stream","title":"Fig. 3b data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2703/Fig3c.mat","format":"matlab fig file","mediaType":"application/octet-stream","title":"Fig. 3c data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2703/Fig3d.mat","format":"matlab fig file","mediaType":"application/octet-stream","title":"Fig. 3d data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2703/Fig4.mat","format":"matlab fig file","mediaType":"application/octet-stream","title":"Fig. 4 data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2703/README.txt","format":"text file","description":"Description of data files","mediaType":"text/plain","title":"Readme.txt"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Optical physics"],"issued":"2022-07-25","keyword":["frequency comb; ranging;"]},{"identifier":"ark:/88434/mds2-2710","accessLevel":"public","contactPoint":{"hasEmail":"mailto:abneesh.srivastava@nist.gov","fn":"Abneesh Srivastava"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2710","description":"Data for \"Primary measurement of gaseous elemental mercury concentration with a dynamic range of six decades\" article for submission","language":["en"],"title":"Primary measurement of gaseous elemental mercury concentration with a dynamic range of six decades","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2710/Data.Srivastava_Hodges.Primary_measurement_of_gaseous_elemental%20mercury_concentration.xlsx","description":"Excel Dataset","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Excel Dataset (Data.Srivastava_Hodges.Primary_measurement_of_gaseous_elemental mercury_concentration.xlsx)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2710/2710_Data.Srivastava_Hodges.Primary_measurement_of_gaseous_elemental%20mercury_concentration_README.txt","description":"README FILE","mediaType":"text/plain","title":"README (2710_Data.Srivastava_Hodges.Primary_measurement_of_gaseous_elemental mercury_concentration_README.txt)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Environment:Air / water / soil quality","Environment:Environmental health","Chemistry:Analytical chemistry","Physics:Spectroscopy","Physics:Optical physics","Physics:Atomic, molecular, and quantum","Metrology:Environmental metrology"],"issued":"2022-10-07","keyword":["Primary method; SI Traceability;  Absolute Concentration; Reference-material free; Gaseous Elemental Mercury; Atmosphere; ambient; emission; Line profile; LDL; Dynamic Range; Multipass Cell; Primary Standard; FHG laser; uv","tunable frequency laser; Allan deviation"]},{"identifier":"ark:/88434/mds2-2711","accessLevel":"public","contactPoint":{"hasEmail":"mailto:lyle.levine@nist.gov","fn":"Lyle E. Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2711","description":"The following data files include residual elastic strain, residual stress, and part deflection results associated with the 2022 Additive Manufacturing Benchmark test series (AM Bench 2022) AMB2022-01 benchmark on laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN718 test objects. The AM builds were performed on the NIST Additive Manufacturing Metrology Testbed (AMMT).  The residual elastic strains were measured using synchrotron X-ray diffraction at the Cornell High Energy Synchrotron Source (CHESS) and neutron diffraction at the Oak Ridge National Laboratory (ORNL) High Flux Isotope Reactor (HFIR).  Residual stresses were characterized using the contour method by UC Davis and Hill Engineering.  Part deflection after partial cutting of the build part off the build plate was measured at NIST.  Detailed descriptions of the build process parameters, scan pattern, heat treatment, and descriptions of all of the AMB2022-01  measurements are provided on the AMB2022-01 challenge description webpage (https://www.nist.gov/ambench/amb2022-01-benchmark-measurements-and-challenge-problems). Due to the time-sensitive nature of the AM Bench challenge problems, those measurements and analyses were prioritized.  The challenges that this data publication address are:Residual elastic strain (CHAL-AMB2022-01-RS): Residual elastic strain components at select locations internal to the bridge structure, corresponding to synchrotron X-ray diffraction measurements. Part deflection (CHAL-AMB2022-01-PD): Deflection of the as-built (no heat treatment) bridge structure after it is partially separated from the build plate.Additional datasets that are not required for the challenges will be added soon.   For updates, please check back here or at www.nist.gov/ambench.","language":["en"],"title":"AM Bench 2022 Residual Elastic Strain, Residual Stress, and Part Deflection Measurements for IN718 3D Builds","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_results_V2.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_results_V2.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_results_V2.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_results_V2.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_XXstrain.eps","mediaType":"application/postscript"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_XXstrain.eps.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_XXstrain.tif","mediaType":"image/tiff"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_XXstrain.tif.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_ZZstrain.eps","mediaType":"application/postscript"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_ZZstrain.eps.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_ZZstrain.tif","mediaType":"image/tiff"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB22_EDD_ZZstrain.tif.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_ZZstrain.tif","mediaType":"image/tiff"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_ZZstrain.tif.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_results_V2.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_results_V2.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_results_V2.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_results_V2.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_XXstrain.eps","mediaType":"application/postscript"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_XXstrain.eps.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_XXstrain.tif","mediaType":"image/tiff"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_XXstrain.tif.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_ZZstrain.eps","mediaType":"application/postscript"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2711/Solution%20data%20for%20CHAL-AMB2022-01-RS_v1.0/AMB2022_EDD_ZZstrain.eps.sha256","description":"Readme file for AM Bench 2022 Residual Elastic Strain, Residual Stress, and Part deflection Measurements for IN718 test artifacts.","mediaType":"text/plain","title":"Readme File"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2711/AMB2022-01-RS-PD%20measurement%20results_v1.1.docx","description":"General description of AM Bench 2022 Residual Elastic Strain, Residual Stress, and Part Deflection Measurements for IN718 AM Bench 2022 test artifacts.","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"General description of Residual Elastic Stress/Strain and Part Deflection measurements for AM Bench 2022"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2711/AMB2022-01-RS-PD%20measurement%20results_v1.1.pdf","description":"Residual elastic strain and part deflection measurements for IN718 AM Bench 2022 test artifacts. These data are part of the AMB2022-01 set of benchmarks.","mediaType":"application/pdf","title":"Residual Strain and Part Deflection Measurements for IN718 AM Bench 2022 Test Artifacts"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2711/2711_README.txt","description":"README file describing the contents of this data publication","mediaType":"text/plain","title":"2711_README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-08-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Metals","Manufacturing:Additive manufacturing"],"issued":"2023-05-01","keyword":["AM-Bench","laser powder bed fusion","elastic strain","stress","contour method","diffraction","part deflection"]},{"identifier":"ark:/88434/mds2-2713","accessLevel":"public","references":["https://doi.org/10.1021/acs.iecr.2c02916"],"contactPoint":{"hasEmail":"mailto:ian.bell@nist.gov","fn":"Ian Bell"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2713","description":"This dataset includes the code needed to reproduce all the results from \"Superancillary equations for PC-SAFT\" by Ian Bell (NIST) and Ulrich Deiters (Cologne).It includes C++ code for the development of the formulation, and the scripts and other files needed to build the python package and build all the plots from the publicationSee the README.md file for more detailed information of the contents","language":["en"],"title":"Reproducible Code for Fitting Ancillary curves for the PC-SAFT equation of state","distribution":[{"accessURL":"https://github.com/usnistgov/SAFTsuperanc","description":"The working repository from which this record is snapshotted","title":"Working code for PCSAFTsuperanc"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2713/MIDAS.zip","format":"Zip Folder","description":"Zip file containing C++ and Python Scripts","mediaType":"application/gzip","title":"Zip file containing C++ and Python Scripts"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2713/MIDAS.zip","mediaType":"application/zip","title":"Zip of the scripts and data files"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Numerical methods and software"],"issued":"2022-07-26","keyword":["algorithms","equation of state","arbitrary precision","Chebyshev expansion","superancillary"]},{"identifier":"ark:/88434/mds2-2714","accessLevel":"public","contactPoint":{"hasEmail":"mailto:megan.cleveland@nist.gov","fn":"Megan Cleveland"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2714","description":"NIST developed a reference material consisting of synthetic fragments of the SARS-CoV-2 virus RNA, which is the target of molecular diagnostic tests. These RNA fragments can assist in the development and validation of RT-qPCR assays for the detection SARS-CoV-2. The RNA fragments are characterized for concentration using digital PCR methods, may be used to assess limits of detection for SARS-CoV-2 assays, and may calibrate other in-house or commercial SARS-CoV-2 controls. This dataset includes data used for RTGM 10169 SARs-Cov-2 Research Grade Test Material validation. The Illumina and ONT sequence data were used to verify the construct sequence.","language":["en"],"title":"RGTM 10169","distribution":[{"accessURL":"https://github.com/usnistgov/RGTM10169","description":"information related to Research Grade Test Material (RGTM) 10169","title":"Research Grade Test Material (RGTM) 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files include in-situ thermographic measurement results, scan strategy, and various additional data associated with laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN718 test objects for the 2022 Additive Manufacturing Benchmark (AM-Bench) test series. These data are associated with the AMB2022-01 series of modeling challenges described here: https://www.nist.gov/ambench/amb2022-01-benchmark-measurements-and-challenge-problems. However, these data may also be used in future AM-Bench challenges. These AM builds and thermographic measurements were performed on the NIST Additive Manufacturing Metrology Testbed (AMMT, https://www.nist.gov/el/ammt-temps).Information on the directory structure and file formats are provided in the 2715_README.txt file. Note that this dataset will be periodically updated, and additional data will be added as it is made available. Future publications will also provide more in-depth description of the data in this dataset, as will links to available analysis code and scripts. Refer to the Version number below, and updates described in this Description and the 2715_README.txt file.","language":["en"],"title":"AM Bench 2022 Measurement Results Data: 3D Builds In-situ Thermography and Data Processing Scripts 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In718;"]},{"identifier":"ark:/88434/mds2-2718","accessLevel":"public","contactPoint":{"hasEmail":"mailto:brandon.lane@nist.gov","fn":"Brandon Lane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2718","description":"The following data files are provided in support of the AM Bench 2022 modeling challenges associated with bare plate single track and pad laser scans performed on the NIST Additive Manufacturing Metrology Testbed (https://www.nist.gov/el /ammt-temps). These measurements were used in the AMB2022-03 set of  challenges associated with track melt pool geometry (CHAL-AMB2022-03-TMPG) and pad melt pool geometry (CHAL-AMB2022-03-PMPG).  Description of the associated measurements and modeling challenges are provided on the AMB2022-03 challenge description webpage (https://www.nist.gov/document/amb2022-03-measurement-and-challenge-descriptions-version-101).","language":["en"],"title":"AM Bench 2022 Measurement Results Data: Optical Microscopy of Laser-scanned Single Tracks and Pads 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Text","description":"Description and metadata information for the image files in this dataset.","mediaType":"text/plain","title":"README file"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing"],"issued":"2022-11-09","keyword":["laser powder bed fusion; additive manufacturing; AM-Bench; microscopy; melt pool geometry; cross-section;"]},{"identifier":"ark:/88434/mds2-2719","accessLevel":"public","references":["https://doi.org/10.1021/acs.analchem.2c03913"],"contactPoint":{"hasEmail":"mailto:thomas.forbes@nist.gov","fn":"Thomas P. Forbes"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2719","description":"This data publication contains the code and demonstration data from a study using non-negative matrix factorization to learn, characterize, and chemically map crystal polymorphs at the single particle scale from high spatial resolution time-of-flight secondary ion mass spectrometry (ToF-SIMS) images. The data from this study includes the ToF-SIMS chemical imaging of three inkjet printed arrays of acetaminophen deposits, corresponding THz Raman spectra, and ToF-SIMS chemical images of a pure acetaminophen powder and a migraine medicine. Also included are the data analysis code (MATLAB 2022a*) used for non-negative matrix factorization and other processes. The code is used to learn the dataset's latent dimensionality and decompose the data into constituent phases representative of acetaminophen polymorphs. The process is also demonstrated by unmixing a multi-component particle migraine medicine sample.Associated publication: https://doi.org/10.1021/acs.analchem.2c03913*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.","language":["en"],"title":"Pharmaceutical polymorph identification and multicomponent particle mapping with non-negative matrix factorization","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2719/ToF-SIMS-NMFk%20NIST%20Data%20Publication.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2719/ToF-SIMS-NMFk%20NIST%20Data%20Publication.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2719/readme.txt","description":"Updated 20220727","mediaType":"text/plain","title":"readme"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Modeling and computational material science","Materials:Materials characterization","Health:Pharmaceuticals","Chemistry:Molecular characterization","Chemistry:Analytical chemistry"],"issued":"2022-08-12","keyword":["Unsupervised machine learning; Non-negative matrix factorization; ToF-SIMS; THz Raman Spectroscopy; Polymorph; Pharmaceuticals; Chemical mapping"]},{"identifier":"ark:/88434/mds2-2720","accessLevel":"public","contactPoint":{"hasEmail":"mailto:samuel.benz@nist.gov","fn":"Samuel P. Benz"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2720","description":"Dataset for multiple publishable figures in the paper entitled \"Quasi-continuous voltage standard using sinusoidal and pulse-driven Josephson junction arrays\" submitted to Meas. Sci. and Tech. journal. Figure 3.  (a) Pattern of pulses used to generate a dc voltage of 0.2 V, (b) difference from the nominal of the voltage measured using the K3458A versus dc bias current for different RF power biases.Figure 4.  PD JJA voltage difference from nominal, measured with a K3458A, versus dc bias current for different pulse patterns and repetition frequencies.Figure 5.  (a) Current-voltage characteristic of the CWD JJA and (b) the RF power-voltage characteristic of the PD JJA. The error bars show the Type-A uncertainty (k=1) of the measurement of five measurements.Figure 6. Current-voltage characteristic of the CWD JJAs. The error bars show the Type-A uncertainty (k=1) of the measurement.Figure 7. Allan deviation for 0 V. See text for details.Figure 8. Current-voltage characteristic of the CWD JJAs. The error bars show the Type-A uncertainty (k=1) of the measurement.Figure 9. Allan deviation at 1 V.","language":["en"],"title":"Quasi-continuous voltage standard using sinusoidal and pulse-driven Josephson junction arrays","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig%206.csv","format":"CSV","description":"Current-voltage characteristic of the CW-JJAs. The error bars show the Type-A uncertainty (k=1) of the measurement.csv","mediaType":"text/csv","title":"Fig 6 Current-voltage characteristic of the CW-JJAs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig%207.csv","format":"CSV","description":"Fig. 7. Allan deviation for 0 V. See text for details. The straight lines show the weighted least squares fit to a straight line for the data points up to the knee of the curves","mediaType":"text/csv","title":"Fig 7 Allan deviation for 0 V. See text for details"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig%208.csv","format":"csv","description":"Fig 8 Current voltage characteristic of the CW-JJAs. The errors bars show the Type A uncertainty (k=1) of the measurement.csv","mediaType":"text/csv","title":"Fig 8 Current voltage characteristic of the CW-JJAs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig%209.csv","format":"csv","description":"Fig. 9 Allan deviation at 1V","mediaType":"text/csv","title":"Fig. 9 Allan deviation at 1V"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig3a.jpg","format":"jpg","description":"Pattern of pulses used to generate a dc voltage of 0.2 V","mediaType":"image/jpeg","title":"Fig 3a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig3b.jpg","format":"jpg","description":"difference from the nominal of the voltage measured using the K3458A versus dc bias current for different RF power biases. The error bars show the Type-A uncertainty (k=1) of the measurement.","mediaType":"image/jpeg","title":"Fig 3b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig4.jpg","format":"jpg","description":"PD JJA voltage difference from nominal, measured with a K3458A, versus dc bias current for different pulse patterns and repetition frequencies.","mediaType":"image/jpeg","title":"Figure 4."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig5a.jpg","format":"jpg","description":"Current-voltage characteristic of the CWD JJA and","mediaType":"image/jpeg","title":"Figure 5  (a)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig5b.jpg","format":"jpg","description":"the RF power-voltage characteristic of the PD JJA. The error bars show the Type-A uncertainty (k=1) of the measurement of five measurements.","mediaType":"image/jpeg","title":"Frig 5b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig6.jpg","format":"jpg","description":"Figure 6. Current-voltage characteristic of the CWD JJAs. The error bars show the Type-A uncertainty (k=1) of the measurement.","mediaType":"image/jpeg","title":"Fig 6"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig7.jpg","format":"jpg","description":"Figure 7. Allan deviation for 0 V. See text for details.","mediaType":"image/jpeg","title":"Fig 7"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig8.jpg","format":"jpg","description":"Figure 8. Current-voltage characteristic of the CWD JJAs. The error bars show the Type-A uncertainty (k=1) of the measurement.","mediaType":"image/jpeg","title":"Fig 8"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig9.jpg","format":"jpg","description":"Allan deviation at 1 V.","mediaType":"image/jpeg","title":"Figure 9"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/2720_README.txt","format":"txt","description":"This file describes the associated files and their data contained within the csv and jpg plots for the associated paper.","mediaType":"text/plain","title":"Readme file describing the data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig%203b.csv","format":"CSV","description":"Fig. 3b. Difference of the voltage measured using the K3458A from the nominal versus dc bias current for difference RF power bias. The error bars show the Type-A uncertainty (k=1) of the measurement.","mediaType":"text/csv","title":"Fig. 3b. Difference of the voltage measured using the K3458A from the nominal versus dc bias current for difference RF power bias"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig%203a.csv","format":"CSV","description":"Pattern of pulses used to generate a dc voltage of 0.2 V.cvs","mediaType":"text/csv","title":"Fig 3a Pattern of pulses used to generate a dc voltage of 0.2 V.cvs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig%204.csv","format":"CSV","description":"PD-JJA voltage difference from nominal, measured with a K3458A, versus dc bias current for different pulse patterns and repetition frequencies","mediaType":"text/csv","title":"Fig. 4.  PD-JJA voltage difference from nominal"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig%205a.csv","format":"CSV","description":"Current-voltage characteristic of the CW-JJA","mediaType":"text/csv","title":"Fig 5a Current-voltage characteristic of the CW-JJA"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2720/Fig%205b.csv","format":"CSV","description":"#Fig. 5.  (b) the RF power-voltage characteristic of the PD-JJA. The errors bars show the Type-A uncertainty (k=1) of the measurement of five measurements","mediaType":"text/csv","title":"#Fig 5b the RF power-voltage characteristic of the PD-JJA"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Standards:Reference instruments","Electronics:Superconducting electronics"],"issued":"2022-08-17","keyword":["Josephson arbitrary waveform synthesizer","Josephson junction","Josephson standards","programmable Josephson voltage standard","quantum electrical standards","voltage generation."]},{"identifier":"ark:/88434/mds2-2721","accessLevel":"public","contactPoint":{"hasEmail":"mailto:allan.harvey@nist.gov","fn":"Allan H. Harvey"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2721","description":"During work on the new reference equation of state for carbon dioxide [E.W. Lemmon, A.H. Harvey, and R. Hellmann, NIST Internal Report, to be published in 2025], we obtained unpublished data from several sources.  These represent numerical values for data only presented graphically in a publication, or in some cases data not present in the publication at all. With the permission of the authors, we document and deposit these data here so they will be available for future workers.  These include data for the melting curve at high pressures, the isochoric heat capacity (including near the critical point and in the two-phase region), the sound speed and density at high pressures, and the vapor pressure of the liquid.  It also includes values of the second, third, and fourth virial coefficients calculated by R. Hellmann from state-of-the-art pair and three-body potentials.For ease of electronic processing, the data elements are formatted as comma-separated values (.csv).More details about the file contents are in the README.txt file and in the headings of individual files.The literature papers connected with these data are as follows:-- E.H. Abramson, \"Three-Phase Melting Curves in the Binary System of Carbon Dioxide and Water,\" J. Phys.: Conf. Ser. 950, 042019 (2017), https://doi.org/10.1088/1742-6596/950/4/042019-- P.C. Albright, T.J. Edwards, Z.Y. Chen, and J.V. Sengers, \"A scaled fundamental equation for the thermodynamic properties of carbon dioxide in the critical region,\" J. Chem. Phys. 87, 1717 (1987), https://doi.org/10.1063/1.453238-- F. Datchi et al., \"Structure of liquid carbon dioxide at pressures up to 10 GPa,\" Phys. Rev. B 94, 014201 (2016), https://doi.org/10.1103/PhysRevB.94.014201-- V.M. Giordano, F. Datchi, and A. Dewaele, \"Melting curve and fluid equation of state of carbon dioxide at high pressure and high temperature,\" J. Chem. Phys. 125, 054504 (2006), https://doi.org/10.1063/1.2215609-- R. Hellmann, \"Ab initio potential energy surface for the carbon dioxide molecule pair and thermophysical properties of dilute carbon dioxide gas,\" Chem. Phys. Lett. 613, 133-138 (2014), https://doi.org/10.1016/j.cplett.2014.08.057-- R. Hellmann, \"Nonadditive three-body potential and third to eighth virial coefficients of carbon dioxide,\" J. Chem. Phys. 146, 054302 (2017), https://doi.org/10.1063/1.4974995-- A. Kartal Dogan, G. Bonnier, A. Uytun, I Kocas, and Y. Durgut, \"Toward Carbon Dioxide Vapor-Pressure Thermometer,\" Int. J. Thermophys. 32, 2230 (2011), https://doi.org/10.1007/s10765-011-1091-y-- J.W. Magee and J.F. Ely, \"Specific Heats (Cv) of Saturated and Compressed Liquid and Vapor Carbon Dioxide,\" Int. J. Thermophys. 7, 1163 (1986), https://doi.org/10.1007/BF00503973","language":["en"],"title":"Thermodynamic Data from Unpublished Sources to Support the New Reference Equation of State for Carbon Dioxide","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/Abramson_melting-2004.csv","format":"Comma-separated values","description":"Unpublished data of Abramson (2004) for melting curve of CO2 at high pressures.","mediaType":"text/csv","title":"Melting curve of CO2 (Abramson, 2004)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/Abramson_melting-2017.csv","format":"Comma-separated values","description":"Melting-curve data for CO2 at high pressures shown in Fig. 2 of E.H. Abramson, \"Three-Phase Melting Curves in the Binary System of Carbon Dioxide and Water,\" J. Phys.: Conf. Ser. 950, 042019 (2017)","mediaType":"text/csv","title":"Melting curve of CO2 at high pressures (Abramson, 2017)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/Giordano_melting.csv","format":"Comma-separated values","description":"Data for CO2 melting curve shown graphically (Fig. 2) in V.M. Giordano, F. Datchi, and A. Dewaele, \"Melting curve and fluid equation of state of carbon dioxide at high pressure and high temperature,\" J. Chem. Phys. 125, 054504 (2006).","mediaType":"text/csv","title":"Melting-curve data at high pressures from Giordano et al. (2006)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/Hellmann_B2_extended.csv","format":"Comma-separated values","description":"Extension of the second virial coefficient calculations presented in R. Hellmann, Chem. Phys. Lett. 613, 133-138 (2014) to temperatures below 150 K.Furthermore, new values for the second virial coefficient have been calculated for 41 temperatures from 1000 K to 3000 K on a Chebyshev grid.","mediaType":"text/csv","title":"Extended values of 2nd virial coefficient calculated from pair potential"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/Hellmann_B3_extended.csv","format":"Comma-separated values","description":"Extension of the third virial coefficient calculations presented in R. Hellmann, J. Chem. Phys. 146, 054302 (2017) to temperatures below 190 K.","mediaType":"text/csv","title":"Extended values of 3rd virial coefficients from pair and 3-body potentials"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/Hellmann_B4_extended.csv","format":"Comma-separated values","description":"Extension of the fourth virial coefficient calculations presented in R. Hellmann, J. Chem. Phys. 146, 054302 (2017) to temperatures below 230 K.","mediaType":"text/csv","title":"Extended values of 4th virial coefficient calculated from pair and 3-body potentials"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/KartalDogan_VP.csv","format":"Comma-separated values","description":"Points for CO2 vapor pressure reported graphically in Figure 4 of A. Kartal Dogan, G. Bonnier, A. Uytun, I Kocas, and Y. Durgut, \"Toward Carbon Dioxide Vapor-Pressure Thermometer,\" Int. J. Thermophys. 32, 2230 (2011).","mediaType":"text/csv","title":"Vapor-pressure data from Kartal Dogan et al. (2011)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/Magee_Cv2.csv","format":"Comma-separated values","description":"More detailed information corresponding to 2-phase Cv data for CO2 reported in Table 2 of J.W. Magee and J.F. Ely, \"Specific Heats (Cv) of Saturated and Compressed Liquid and Vapor Carbon Dioxide,\" Int. J. Thermophys. 7, 1163 (1986).","mediaType":"text/csv","title":"Two-phase Cv data from Magee and Ely (1986)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/Albright_Cv.csv","format":"Comma-separated values","description":"Data for Cv near critical point of CO2 corresponding to P.C. Albright, T.J. Edwards, Z.Y. Chen, and J.V. Sengers, \"A scaled fundamental equation for the thermodynamic properties of carbon dioxide in the critical region,\" J. Chem. Phys. 87, 1717 (1987).Note information in file about temperature scales","mediaType":"text/csv","title":"Near-critical Cv data from Albright et al. (1987)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/Datchi_density.csv","format":"Comma-separated values","description":"High-pressure density data from XRD shown graphically (Fig. 3d) in F. Datchi et al., \"Structure of liquid carbon dioxide at pressures up to 10 GPa,\" Phys. Rev. B 94, 014201 (2016).","mediaType":"text/csv","title":"High-pressure fluid density data from Datchi et al. (2016)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/Giordano_Brillouin.csv","format":"Comma-separated values","description":"Speeds from Brillouin scattering from V.M. Giordano, F. Datchi, and A. Dewaele, \"Melting curve and fluid equation of state of carbon dioxide at high pressure and high temperature,\" J. Chem. Phys. 125, 054504 (2006).See file for relationship to thermodynamic sound speed.","mediaType":"text/csv","title":"Data from Brillouin scattering from Giordano et al. (2006)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2721/README.txt","description":"README file describing the 11 uploaded data files.","mediaType":"text/plain","title":"README file"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Physics:Thermodynamics","Chemistry:Chemical thermodynamics and chemical properties"],"issued":"2022-07-26","keyword":["CO2","carbon dioxide","thermodynamics","melting","heat capacity","sound speed","vapor pressure","virial coefficients","equation of state"]},{"identifier":"ark:/88434/mds2-2724","accessLevel":"public","contactPoint":{"hasEmail":"mailto:joe.magee@nist.gov","fn":"Joe W. Magee"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2724","description":"Measured electrolytic conductivity data at a pressure of 0.1 MPa and for temperatures from 293.15 K to 323.15 K for the following 10 Ionic Liquids:1-benzyl-3-methylimidazolium bis[(trifluoromethane)sulfonyl]amide1-[(4-methylphenyl)methyl]-3-ethylimidazolium bis[(trifluoromethyl)sulfonyl]amide1-[(4-methylphenyl)methyl]-3-butylimidazolium bis[(trifluoromethyl)sulfonyl]amide1-[(4-methylphenyl)methyl]-3-hexylimidazolium bis[(trifluoromethyl)sulfonyl]amide1-[(4-methylphenyl)methyl]-3-octylimidazolium bis[(trifluoromethyl)sulfonyl]amide1-[(4-methylphenyl)methyl]-3-decylimidazolium bis[(trifluoromethyl)sulfonyl]amide1-methyl-1-propylpyrrolidinium bis[(trifluoromethyl)sulfonyl]amide1-butyl-1-methylpyrrolidinium bis[(trifluoromethyl)sulfonyl]amide1-pentyl-1-methylpyrrolidinium bis[(trifluoromethyl)sulfonyl]amide1-hexyl-1-methylpyrrolidinium bis[(trifluoromethyl)sulfonyl]amideFor additional details, please see the associated publication: \"Electrolytic Conductivity Measurements for Ten IonicLiquids,\" Journal of Ionic Liquids (publisher: Elsevier)https://www.sciencedirect.com/journal/journal-of-ionic-liquids [ISSN 2772-4220].Tables S1 and S2 in the associated publication contain the electrolyticconductivity measurements for 10 ionic liquids.","language":["en"],"title":"Electrolytic Conductivity Measurements for Ten Ionic Liquids","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2724/EC_data.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2724/EC_data.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2724/README_ElecCondTenILsText_JoeMagee_2Nov2022.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2724/README_ElecCondTenILsText_JoeMagee_2Nov2022.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2724/Compound_map.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2724/Compound_map.csv.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Thermodynamics"],"issued":"2023-01-11","keyword":["electrolytic conductivity","measurements","ionic liquids"]},{"identifier":"ark:/88434/mds2-2725","accessLevel":"public","references":["https://doi.org/10.1080/19420862.2018.1544454","https://dx.doi.org/10.1021/acs.jcim.0c00081","https://doi.org/10.1016/j.chemolab.2020.103973"],"contactPoint":{"hasEmail":"mailto:robert.brinson@nist.gov","fn":"Robert Brinson III"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2725","description":"The NISTmAb Interlaboratory NMR Study is a joint effort between 25 organizations to evaluate the performance of two-dimensional (2D) heteronuclear NMR as used to characterize the high order structure (HOS) of proteins, with the aim of harmonizing 2D-NMR methods to support industrial use of NMR as applied to mAb therapeutics. This study features nearly equal representation between laboratories from academia, government, and industry, including 4 laboratories from regulatory agencies. In the study, each participating lab measured a series of NMR spectra on the NIST-Fab, which was derived by papain cleavage of the NISTmAb. A 20%-enriched 13C, uniformly enriched 15N NIST-Fab, produced from Pichia pastoris, served as the system suitability sample.This package contains all of the original time-domain data as measured by the collaborating partners, converted to NMRPipe format. It also includes corresponding spectra as processed at NIST, and the software scripts and processing parameters that were used to generate the spectra. The entire package is greater than 4 GB. The software NMRPipe, version 9.6 or later, is required for this data package.","language":["en"],"title":"NMR Interlaboratory Study of NISTmAb","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2725/data_package.tar.gz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2725/data_package.tar.gz.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Health:Pharmaceuticals","Bioscience","Standards:Reference materials"],"issued":"2022-08-25","keyword":["NMR","biopharmaceuticals","structure","fingerprint","comparability","biosimilarity","NISTmAb","RM 8671","Biosciences and Health","interlaboratory study","harmonization","chemometrics"]},{"identifier":"ark:/88434/mds2-2726","accessLevel":"public","references":["https://doi.org/10.23919/AMTA55213.2022.9955009"],"contactPoint":{"hasEmail":"mailto:matthew.simons@nist.gov","fn":"Matt Simons"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2726","description":"After recently completing a five year renovation, the NIST anechoic chamber for radio-frequency field strength measurements was returned to service. Tests of field probe transfer functions can now be performed from 10 kHz to 40 GHz. To validate that the chamber and measurement process maintained continuity, measurements of NIST field probes in the new chamber were compared with measurements in the chamber before renovation and in another test facility at NIST. An uncertainty analysis was carried out on the new measurement process.","language":["en"],"title":"Reinstatement of the NIST Field Strength Probe Calibration Service","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2726/Fig3_AMTA_500MHz.csv","format":"2 column csv file","description":"X axis is the first column, distance from calibration position in mm.Y axis is the second column, the relative response of the probe vs the calibration position in dB.This file is the RF = 500 MHz trace.","mediaType":"text/csv","title":"Fig3_a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2726/Fig3_AMTA_40GHz.csv","format":"2 column csv","description":"X axis is the first column, distance from calibration position in mm.Y axis is the second column, the relative response of the probe vs the calibration position in dB.This file is the RF = 40 MHz trace.","mediaType":"text/csv","title":"Fig3_b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2726/Fig2_AMTA.csv","format":"3 column csv","description":"X axis is the first column, RF frequency in MHz.Y axis is the second column, degree of equivalence in dB.Error bars are the third column, in dB.The first 6 rows are the 8 mm probes, the last 3 rows are the 6 mm probes","mediaType":"text/csv","title":"Fig2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2726/2726_README.txt","format":"txt","description":"readme file","mediaType":"text/plain","title":"README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Electronics:Electromagnetics"],"issued":"2022-11-29","keyword":["fields strength","electric field","volts/meter","antenna","robots","metrology"]},{"identifier":"ark:/88434/mds2-2727","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michaela.iorga@nist.gov","fn":"Michaela Iorga"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2727","description":"OSCAL Deep Diff is a CLI application and library that can produce schema-agnostic comparisons of JSON artifacts. The purpose of this tool is to compare OSCAL artifacts.","language":["en"],"title":"OSCAL-deep-diff","distribution":[{"accessURL":"https://github.com/usnistgov/oscal-deep-diff","format":"Web page","description":"The GitHub repository that the source code is housed in","title":"GitHub Repository"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2727/oscal-deep-diff.zip","format":"Zip file of source code","description":"Source code of OSCAL-deep-diff v1.0.0","mediaType":"application/zip","title":"OSCAL Deep Diff Source Code (v1.0.0)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1M","theme":["Information Technology:Data and informatics","Information Technology:Cybersecurity"],"issued":"2022-10-05","keyword":["oscal","diff","json","compliance"]},{"identifier":"ark:/88434/mds2-2728","accessLevel":"public","references":["https://doi.org/10.1016/j.jqsrt.2022.108324"],"contactPoint":{"hasEmail":"mailto:david.long@nist.gov","fn":"David Long"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This is the data presented in the figures of the paper \"The effects of advanced spectral line shapes on atmospheric carbon dioxide retrievals\" published in J. Quant. Spectrosc. Radiat. Transfer at https://doi.org/10.1016/j.jqsrt.2022.108324","language":["en"],"title":"The effects of advanced spectral line shapes on atmospheric carbon dioxide retrievals","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2728/Fig1.csv","format":".csv","description":"This is the data presented in Figure 1 of https://doi.org/10.1016/j.jqsrt.2022.108324. Cavity ring-down spectrum and corresponding residuals for 387.98(5) µmol/mol of CO2 in air at 100.8 kPa and 296.7 K evaluated with a range of line profiles.","mediaType":"text/csv","title":"Figure 1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2728/Fig2.csv","format":".csv","description":"This is the data presented in Figure 2 of https://doi.org/10.1016/j.jqsrt.2022.108324. Solar spectrum recorded at the Total Carbon Column Observing Network (TCCON) site in Lamont, OK at a solar zenith angle of 60.64° on 14 January 2012. Also shown are the corresponding residuals for a range of line profiles which were vertically offset for clarity.","mediaType":"text/csv","title":"Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2728/Fig3.csv","format":".csv","description":"This is the data presented in Figure 3 of https://doi.org/10.1016/j.jqsrt.2022.108324. Volume mixing ratio scale factor for the Lamont, OK Total Carbon Column Observing Network (TCCON) site for a range of line profiles and the corresponding volume mixing ratio scale factor for the SDVP using the present TCCON spectroscopic line list.","mediaType":"text/csv","title":"Figure 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2728/Fig4.csv","format":".csv","description":"This is the data presented in Figure 4 of https://doi.org/10.1016/j.jqsrt.2022.108324. CO2 profiles retrieved with GFIT2 from 65 solar spectra coincident within ±1 hour of an AirCore profile on Jan. 12, 2012 at the Lamont, Oklahoma TCCON site. Also included are the corresponding profiles for the SDVP using the the GGG2020 linelists as well as the aircraft-based prior profile.","mediaType":"text/csv","title":"Figure 4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2728/2728_README.txt","description":"This is the readme file","mediaType":"text/plain","title":"readme file"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Environment:Greenhouse gas measurements","Physics:Spectroscopy"],"issued":"2022-08-30","keyword":["greenhouse gases","carbon dioxide","remote sensing"]},{"identifier":"ark:/88434/mds2-2731","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2237"],"contactPoint":{"hasEmail":"mailto:adam.wunderlich@nist.gov","fn":"Adam Wunderlich"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2731","description":"This dataset consists of baseband in-phase/quadrature (I/Q) radio frequency recordings of Wi-Fi and Bluetooth radiated emissions in the 2.4 GHz and 5 GHz unlicensed bands collected with low-cost software defined radios.  A NIST technical note describing the data collection methods is pending publication. All I/Q captures are one second in duration, with a sampling rate of 30 mega samples per second (MS/s), and a center frequency of 2437 MHz for the 2.4 GHz band captures and 5825 MHz for the 5 GHz band captures.  In total, the data consist of 900 one second captures, organized into five Hierarchical Data Format 5 (HDF5) files, where each HDF5 file has a size of 20.1 GB and consists of 180 one second captures.  There is a metadata file associated with each data file in comma-separated values (CSV) format that contains relevant parameters such as center frequency, bandwidth, sampling rate, bit depth, receive gain, antenna and hardware information.  There are two additional CSV files containing estimated gain calibration and noise floor values.","language":["en"],"title":"Wi-Fi and Bluetooth I/Q Recordings in the 2.4 GHz and 5 GHz Bands with Low-Cost Software Defined Radios","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_bluetooth.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_bluetooth.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/5ghz_indoor.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/5ghz_indoor.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/5ghz_outdoor.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/5ghz_outdoor.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_outdoor.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_outdoor.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/noise_floor_data.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/5ghz_outdoor.h5","mediaType":"application/x-hdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/5ghz_outdoor.h5.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/5ghz_indoor.h5","mediaType":"application/x-hdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/5ghz_indoor.h5.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_outdoor.h5","mediaType":"application/x-hdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_outdoor.h5.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_indoor.h5","mediaType":"application/x-hdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_indoor.h5.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_bluetooth.h5","mediaType":"application/x-hdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_bluetooth.h5.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/noise_floor_data.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/gain_calibration_data.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/gain_calibration_data.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_indoor.csv","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/2_4ghz_indoor.csv.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/README.txt","mediaType":"text/csv"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2731/README.txt.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-08-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)"],"keyword":["I/Q Data","Wi-Fi","Bluetooth","wireless communications"]},{"identifier":"ark:/88434/mds2-2733","accessLevel":"public","contactPoint":{"hasEmail":"mailto:mehdi.dadfarnia@nist.gov","fn":"Mehdi Dadfarnia"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2733","description":"Sim-PROCESD is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems. The classes are designed to be extensible so the user can change their behavior to model more complex processes.In addition to modeling the behavior of existing systems, Sim-PROCESD is intended for use with simulation-based optimization and planning applications. For instance, users may be interested in evaluating alternative maintenance policies for a particular system. Estimating the expected system performance under each candidate policy will require a large number of simulation replications when the system is subject to a high degree of stochasticity. Sim-PROCESD therefore provides tools to make simulation replication easy.","language":["en"],"title":"Sim-PROCESD: Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-making","distribution":[{"accessURL":"https://github.com/usnistgov/simprocesd","format":"Github Repository URL","description":"Simulated-Production Resource for Operations & Conditions Evaluations to Support Decision-making","title":"Sim-PROCESD"},{"accessURL":"https://doi.org/10.18434/mds2-2733","title":"DOI Access for Sim-PROCESD"},{"accessURL":"https://pypi.org/project/simprocesd","description":"Python package index for SimPROCESD","title":"simprocesd pip install"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2024-08-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Factory operations planning and control","Manufacturing:Manufacturing systems design and analysis"],"issued":"2022-08-17","keyword":["discrete-event simulation","manufacturing","production","maintenance","python"]},{"identifier":"ark:/88434/mds2-2734","accessLevel":"public","contactPoint":{"hasEmail":"mailto:john.grantham@nist.gov","fn":"John Grantham"},"programCode":["006:045"],"@type":"dcat:Dataset","replaces":"ark:/88434/mds2-2734","landingPage":"https://data.nist.gov/od/id/mds2-2734","description":"The NIST Fingerprint Registration and Comparison Tool (NFRaCT) is a cross-platform GUI application which allows a user to load a pair of fingerprint images, find corresponding points in both images, register and crop the images, and finally compute a series of measurements on the registered images as described in NIST Special Publication 500-336","language":["en"],"title":"NIST Fingerprint Registration and Comparison Tool (NFRaCT)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/NFRaCT_macOS_x86_64_1.1.0.dmg","description":"NFRaCT 1.1.0 for macOS 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LTS","mediaType":"application/octet-stream","title":"NFRaCT_Ubuntu_x86_64_1.2.0.AppImage"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/NFRaCT_macOS_x86_64_1.2.0.dmg","description":"NFRaCT 1.2.0 for macOS 11.6+","mediaType":"application/octet-stream","title":"NFRaCT_macOS_x86_64_1.2.0.dmg"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/NFRaCT_Windows_x64_v1.2.0.zip","description":"NFRaCT 1.2.0 for Windows 10 (x64)","mediaType":"application/x-zip-compressed","title":"NFRaCT_Windows_x64_v1.2.0.zip"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/qt-everywhere-src-6.5.2.tar.xz","description":"Source code for the version of the Qt Framework used to build NFRaCT 1.2.0","mediaType":"application/octet-stream","title":"qt-everywhere-src-6.5.2.tar.xz"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/NFRaCT_Schema_1.2.0.xsd","description":"XML Schema Definition for results generated by NFRaCT version 1.2.0","mediaType":"application/xml","title":"NFRaCT_Schema_1.2.0.xsd"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/NFRaCT-Ubuntu_x86_64_1.3.3.AppImage","description":"NFRaCT 1.3.3 for Ubuntu 20.04 LTS","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/NFRaCT_macOS_arm_arch_1.3.3.dmg","description":"NFRaCT 1.3.3 for macOS 11.6+","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/NFRaCT_Windows_x64_v1.3.3.zip","description":"NFRaCT 1.3.3 for Windows 10 (x64)","mediaType":"application/x-zip-compressed"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/qt-everywhere-src-6.8.0.tar.xz","description":"Source code for the version of the Qt Framework used to build NFRaCT 1.3.3","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/NFRaCT_Schema_1.3.0.xsd","description":"XML Schema Definition for results generated by NFRaCT version 1.3.0","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/NFRaCT_Changelog.MD","mediaType":"text/x-web-markdown","title":"NFRaCT_Changelog"},{"downloadURL":"https://nigos.nist.gov/nfract/NFRaCT_README_1.0.0.md","description":"README file for NFRaCT 1.0.0","mediaType":"text/plain","title":"NFRaCT README"},{"accessURL":"https://doi.org/10.18434/mds2-2734","format":"text/html","description":"DOI Access for NIST Fingerprint Registration and Comparison Tool (NFRaCT)","title":"DOI Access for NIST Fingerprint Registration and Comparison Tool (NFRaCT)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2734/NFRaCT_Ubuntu_x86_64_1.1.0.AppImage","description":"NFRaCT 1.1.0 for Ubuntu 20.04 LTS","mediaType":"application/octet-stream","title":"NFRaCT_Ubuntu_x86_64_1.1.0.AppImage"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Biometrics"],"issued":"2025-02-05","keyword":["Biometrics","Fingerprint","Image Analysis"]},{"identifier":"ark:/88434/mds2-2748","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michaela.iorga@nist.gov","fn":"Michaela Iorga"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2748","description":"The OSCAL Catalog Authoring Tool (CAT) enables users to create, scope, tailor control catalogs by creating [OSCAL Profiles in JSON format](https://pages.nist.gov/OSCAL/reference/latest/profile/json-reference/) and subsequently create new catalogs using [profile resolution](https://pages.nist.gov/OSCAL/concepts/processing/profile-resolution/).","language":["en"],"title":"OSCAL Catalog Authoring Tool","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-08-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1M","theme":["Information Technology:Cybersecurity"],"issued":"2022-09-02","keyword":["GitHub","OSCAL","profile","catalog","controls"]},{"identifier":"ark:/88434/mds2-2751","accessLevel":"public","references":["https://doi.org/10.6028/NIST.TN.2178","https://doi.org/10.3384/ecp21181177","https://doi.org/10.1109/TNNLS.2021.3085358","https://doi.org/10.6028/NIST.TN.2037","https://doi.org/10.6028/NIST.TN.2025","https://doi.org/10.1016/j.est.2019.03.011","https://doi.org/10.6028/NIST.TN.1970","https://doi.org/10.6028/NIST.TN.1933"],"contactPoint":{"hasEmail":"mailto:amanda.pertzborn@nist.gov","fn":"Amanda Pertzborn"},"programCode":["006:045"],"@type":"dcat:Dataset","replaces":"ark:/88434/mds2-2751","description":"The Intelligent Building Agents (IBA) project is part of the Embedded Intelligence in Buildings Program in the Engineering Laboratory at the National Institute of Standards and Technology (NIST). A key part of the IBA Project is the IBA Laboratory (IBAL), a unique facility consisting of a mixed system of off the shelf equipment, including chillers and air handling units, controlled by a data acquisition system and capable of supporting building system optimization research under realistic and reproducible operating conditions.The database contains the values of approximately 300 sensors/actuators in the IBAL, including both sensor measurements and control actions, as well as approximately 850 process data, which are typically related to control settings and decisions. Each of the sensors/actuators has associated metadata. The metadata, sensors/actuators, and process data are defined on the \"metadata\", \"sensors\", and \"parameters\" tabs in the definitions file. Data are collected every 10 s.The database contains two dashboards: 1) Experiments - select data from individual experiments and 2) Measurements - select individual sensor/actuator and parameter data. The Experiments Dashboard contains three sections. The \"Experiment Data Plot\" shows plots of the sensor/actuator data selected in the second section, \"Experiment/Metadata\". There are plots of both scaled and raw data (see the meta data file for the conversion from raw to scaled data). Underneath the plots is a \"Download CSV\" button; select that button and a csv file of the data in the plot is automatically generated. In \"Experiment/Metadata\", first select an \"Experiment\" from the options in the table on the left. A specific experiment or type of experiment can be found by entering terms in the search box. For example, searching for the word \"Charge\" will bring up experiments in which the ice thermal storage tank is charged. The table of experiments also includes the duration of the experiment in minutes.Once an experiment is selected, specific sensor/actuator data points can be selected from the \"Measurements\" table on the right. These data can be filtered by subsystem (e.g., primary loop, secondary loop, Chiller1) and/or measurement type (e.g., pressure, flow, temperature). These data will then be shown in the plots at the top. The final section, \"Process\", contains the process data, which are shown by the subsystem. These data are not shown in the plots but can be downloaded by selecting the \"Download CSV\" button in the \"Process\" section. The Measurements Dashboard contains three sections. The \"Date Range\" section is used to select the time range of the data. The \"All Measurements\" section is used to select specific sensor/actuator data. As in the Experiments Dashboard, these data can be filtered by subsystem and/or measurement type. The scaled and raw values of the selected data are then plotted in the \"Historical Data Plot\" section. The \"Download CSV\" button underneath the plots will automatically download the selected data.","language":["en"],"title":"Intelligent Building Agents Project Data","distribution":[{"accessURL":"https://ibal.nist.gov","format":"Database","description":"The database contains the values of approximately 300 sensors/actuators in the IBAL, including both sensor measurements and control actions, as well as approximately 850 process data, which are typically related to control settings and decisions. Each of the sensors/actuators has associated metadata. The metadata, sensors/actuators, and process data are defined on the \"metadata\", \"sensors\", and \"parameters\" tabs in the definitions file. Data are collected every 10 s.","title":"IBAL Database"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-06-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://ibal.el.nist.gov","accrualPeriodicity":"R/P3M","theme":["Buildings and Construction:Building control systems","Buildings and Construction:Air conditioning and heating equipment"],"issued":"2022-12-01","keyword":["Building control systems; Heating","ventilation and air conditioning equipment; Machine Learning; Applied AI;"]},{"identifier":"ark:/88434/mds2-2753","accessLevel":"public","references":["https://doi.org/10.1038/s42003-021-02441-2"],"contactPoint":{"hasEmail":"mailto:thomas.cleveland@nist.gov","fn":"Thomas Cleveland"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2753","description":"Lipid nanoparticles (LNPs) were prepared as described (https://doi.org/10.1038/s42003-021-02441-2) using the lipids DLin-KC2-DMA, DSPC, cholesterol, and PEG-DMG2000 at mol ratios of 50:10:38.5:1.5. Four sample types were prepared: LNPs in the presence and absence of RNA, and with LNPs ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 ?L of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper). Grids were then incubated for 30 s at 298 K and 100% humidity before blotting and plunge-freezing into liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific). Grids were imaged at 200 kV using a Talos Arctica system equipped with a Falcon 3EC detector (Thermo Fisher Scientific). A nominal magnification of 45,000x was used, corresponding to images with a pixel count of 4096x4096 and a calibrated pixel spacing of 0.223 nm. Micrographs were collected as dose-fractionated ?movies? at nominal defocus values between -1 and -3 ?m, with 10 s total exposures consisting of 66 frames with a total electron dose of 12,000 electrons per square nanometer. Movies were motion-corrected using MotionCor2 (https://doi.org/10.1038/nmeth.4193), resulting in flattened micrographs suitable for downstream particle segmentation. A total of 38 images were manually segmented into particle and non-particle regions. Segmentation masks and their corresponding images are deposited in this data set.","language":["en"],"title":"Segmentation of lipid nanoparticles from cryogenic electron microscopy images","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2753/readme.txt","mediaType":"text/plain","title":"Readme"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2753/R111_S404_pH4_1p5pcPEG_noRNA.zip","format":".zip archive containing TIFF images","description":"CryoEM images of lipid nanoparticles and their segmentation masks","mediaType":"application/x-zip-compressed","title":"KC2 LNPs at pH 4, without RNA"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2753/R111_S406_pH4_1p5pcPEG_RNA.zip","format":".zip archive containing TIFF images","description":"CryoEM images of lipid nanoparticles and their segmentation masks","mediaType":"application/x-zip-compressed","title":"KC2 LNPs at pH 4, with RNA"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2753/R111_S405_pH7_1p5pcPEG_noRNA.zip","format":".zip archive containing TIFF images","description":"CryoEM images of lipid nanoparticles and their segmentation masks","mediaType":"application/x-zip-compressed","title":"KC2 LNPs at pH 7, without RNA"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2753/R111_S407_pH7_1p5pcPEG_RNA.zip","format":".zip archive containing TIFF images","description":"CryoEM images of lipid nanoparticles and their segmentation masks","mediaType":"application/x-zip-compressed","title":"KC2 LNPs at pH 7, with RNA"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-08-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Software research","Information Technology:Computational science","Physics:Biological physics","Nanotechnology:Nanobiotechnology","Bioscience:Biomaterials"],"issued":"2023-02-01","keyword":["Lipid Nanoparticle","LNP","cryogenic electron microscopy","CryoEM","machine learning","AI","mRNA"]},{"identifier":"ark:/88434/mds2-2754","accessLevel":"public","references":["https://doi.org/10.1557/jmr.2009.0122"],"contactPoint":{"hasEmail":"mailto:yvonne.gerbig@nist.gov","fn":"Yvonne Gerbig"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2754","description":"The effect of the crystallographic orientation on the primary and secondary phase transformations of single-crystal silicon (Si) during indentation was investigated in a statistical instrumented-indentation study using a spherical diamond probe with a nominal tip radius of 5 µm. The primary phase transformation from the Si-I to Si-II phase were initiated above a threshold pressure during loading and assumed to be reflected as change in slope or a plateau-like discontinuity in the loading curve (pop-in event). Secondary transformations to polycrystalline high-pressure phases (Si-XII and Si-III) and/or amorphous Si (a-Si) occurred during unloading. It is believed that elbow events correspond to the presence of a-Si; pop-out and kink pop-out events were associated with Si-XII and Si-III phases. The presence of and the pressure at which phase-transformation events occurred during indentation were analyzed and compared for three crystallographic orientations: Si(001), Si(110), and Si(111).In load sequence indentations, the applied maximum force was varied from (20, 25, 30, 45, 60, 80, 100, 150 to 200) mN to study its effect on the phase transformation for the three orientations. In these tests, the force was increased and decreased at fixed (un)loading rates of 5 mN/s. For the majority of the tests, the maximum force was held constant for 5 s before unloading. In selected tests, the force was immediately decreased after reaching its maximum value. For each maximum force, 50 indentation tests were performed.In the partial-unload series, indentations were carried out in the multiple partial unloading technique to study the onset of the primary phase transformation during loading. In this technique, the force was stepwise increased, but before continuing to the next, greater, force value, the force was partially released. The resulting force-displacement curve had two branches corresponding to the fully loaded and partially unloaded state. For elastic deformation, the two branches coincided, but they diverged on plastic deformation, which was associated with the start of the primary phase transformation for Si. The maximum indentation forces applied was 50 mN or 100 mN (in a few selected tests on Si(001)). For each orientation, 50 indentation tests were performed.The indentation moduli of the three Si orientations were determined at maximum indentation loads guaranteeing a purely elastic response of the materials: 20 mN for Si(001) respective 15 mN for Si(110) and Si(111). In each test, the indentation force was linearly increased to the maximum value, then held constant for 5 s and afterwards linearly decreased. The (un)loading rates were fixed at 5 mN/s. For each orientation, 25 indentation tests were performed.The raw experimental indentation data collected in this study are compiled in datasets A through E of this data publication. In this context, raw indentation data are defined as being direct from the instrument corrected for machine compliance and thermal drift. Note: Outliers in indentation curves were not included in the data sets.The aforementioned indentation datasets built the foundation of and serve as companion to the paper: Y.B Gerbig, S.J. Stranick, D.J. Morris, M.D. Vaudin, R.F. Cook, J. Mater. Res. 24/3, 1172 - 1183 (2009) https://doi.org/10.1557/jmr.2009.0122.More details about data collection and processing than already described in this summary can be found in the paper. Data directly underlying figures 1, 2, 3, 5, and 6 of the companion paper are compiled in datasets F through J of this data publication. The accompanying Readme document contains details about organization, content and format of the individual data sets.","language":["en"],"title":"Nanoindentation study on the phase transformation of single crystal silicon of different crystallographic orientations using a spherical indenter 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In addition, the Tool Catalog also provides a picture of the digital forensics tool landscape, showing where there are gaps, i.e., functions for which there are no tools or techniques.","language":["en"],"title":"Computer Forensic Tools and Techniques Catalog","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-08-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1M","theme":["Forensics:Digital and multimedia evidence"],"issued":"2022-09-07","keyword":["Computer Forensics Tools and Techniques Catalog","Tool Catalog","Forensic Catalog"]},{"identifier":"ark:/88434/mds2-2760","accessLevel":"public","contactPoint":{"hasEmail":"mailto:saadi.habib@nist.gov","fn":"Saadi Habib"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2760","description":"Macro-scale tensile data on additively manufactured (AM) IN625 in the as-built microstructural condition at room temperature and at different strain rates are provided in this data publication. The specimens are machined in accordance with ASTM-E8 subsize specimen with a gauge width of 6 mm and a gauge thickness of 4 mm. Three specimens are tested at a nominal strain rate of 0.001 1/s and 2 specimens are tested at a nominal strain rate of 0.01 1/s on a servo hydraulic material testing machine using a constant crosshead displacement rate of 0.03175 mm/s and 0.3175 mm/s, respectively. Stereo digital image correlation (DIC) is used to report a 25.4 mm gauge length virtual extensometer strain up to fracture for each experiment. A separate comma-separated-value (csv) file for each experiment is provided which contains time, engineering strain, and engineering stress data. The first row in the csv file gives the column name. The second row in the csv file gives the SI unit of the column. Please see SUMMARY_AMBench_2022_Tensile_data.pdf file for further details regarding the additive manufacturing procedures of the specimens, the composition of the IN625, and the measurements.","language":["en"],"title":"AM Bench 2022 ASTM E8 Macroscale Tension at Different Strain Rates on As-built IN625","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2760/SpecimenT3_stress_strain.csv","description":"T3 Data File","mediaType":"text/csv","title":"T3 Data File"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2760/SpecimenT4_stress_strain.csv","description":"T4 Data File","mediaType":"text/csv","title":"T4 Data File"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2760/SpecimenT5_stress_strain.csv","description":"T5 Data File","mediaType":"text/csv","title":"T5 Data File"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2760/SpecimenT6_stress_strain.csv","description":"T6 Data File","mediaType":"text/csv","title":"T6 Data File"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2760/SpecimenT7_stress_strain.csv","description":"T7 Data File","mediaType":"text/csv","title":"T7 Data File"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2760/README_AMBench_2022_Tensile_data.txt","description":"README File","mediaType":"text/plain","title":"README File"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2760/SUMMARY_AMBench_2022_Tensile_data.pdf","description":"A summary for AM Bench 2022 ASTM E8 macroscale tension procedures and measurements at different strain rates on as-built IN625","mediaType":"application/pdf","title":"Summary File"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-08-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Metals","Materials:Materials characterization","Manufacturing:Additive manufacturing"],"conformsTo":"https://www.astm.org/e0008_e0008m-22.html","issued":"2023-03-07","keyword":["AM Bench","benchmark","mechanical characterization","strain rate","inconel 625","additive manufacturing","superalloy"]},{"identifier":"ark:/88434/mds2-2762","accessLevel":"public","references":["https://doi.org/10.1016/j.bpj.2023.03.002"],"contactPoint":{"hasEmail":"mailto:robert.dejaco@nist.gov","fn":"Robert De Jaco"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2762","description":"The purpose of this software and data is to enable reproduction and facilitate extension of the computational results associated with the following referenceDeJaco, R. F.; Majikes, J. M.; Liddle, J. A.; Kearsley, A. J. Binding, Brightness, or Noise? Extracting Temperature-dependent Properties of Dye Bound to DNA. Biophysical Journal, 2023, https://doi.org/10.1016/j.bpj.2023.03.002.The software and data can also be found at https://github.com/usnistgov/dye_dna_plates.","language":["en"],"title":"Software and Data associated with \"Binding, Brightness, or Noise? Extracting Temperature-dependent Properties of Dye Bound to DNA\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2762/singlehtml.zip","description":"This compressed file contains documentation of this software and dataset in the HTML format. It can be viewed by downloading, extracting, and opening the index.html file with a browser.","mediaType":"application/x-zip-compressed","title":"Documentation in HTML format"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2762/DyeDNAPlates_v0.7.zip","description":"This compressed folder contains all software and data required to reproduce the manuscript. The version is 0.7.","mediaType":"application/x-zip-compressed","title":"Version 0.7 of the software"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2762/dye_dna_plates-1.0.1.tar.gz","description":"Version 1.0.1 of code/software.","mediaType":"application/gzip","title":"Version 1.0.1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2762/dye_dna_plates-1.0.1.zip","mediaType":"application/x-zip-compressed","title":"Version 1.0.1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2762/REPRODUCEME.md","description":"Step-by-step instructions enabling reproduction of paper","mediaType":"application/octet-stream","title":"File describing reproduction procedure"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-03-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics","Metrology","Health","Chemistry","Bioscience"],"issued":"2022-09-02","keyword":["fluorescence","numerical-optimization","mathematical-modeling","fluorescence-data","total-least-squares","python","intercalating dyes","noise-removal"]},{"identifier":"ark:/88434/mds2-2767","accessLevel":"public","references":["https://doi.org/10.1007/s40192-024-00372-4","https://doi.org/10.18434/mds2-2715","https://doi.org/10.18434/mds2-2692","https://doi.org/10.18434/mds2-2711"],"contactPoint":{"hasEmail":"mailto:lyle.levine@nist.gov","fn":"Lyle E. Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2767","description":"These sets of automated serial sectioning electron backscatter diffraction (EBSD) and X-ray computed tomography (XRCT) measurements were designed to produce spatially registered, multi-modal datasets within a region of interest (ROI) of an additive manufacturing benchmark test series (AM Bench) nickel alloy 718 specimen produced using laser powder bed fusion (PBF-LB) additive manufacturing (AM).  The serial sectioning data are in the form of 2D slices which were reconstructed and registered to make 3D data sets that can be analyzed and visualized using open source software. The ROI includes part of a 2.5 mm leg and extends into the baseplate. These measurements are part of an extended dataset associated with the AMB2022-01 set of AM Bench measurements. AMB2022-01 includes powder characterization, detailed information about the build geometry and process, in situ measurements during the build, ex situ measurements of the residual stresses, part distortion following partial removal from the build plate, location-specific microstructure characterization from as-built and heat treated specimens, and microstructure evolution during post-build heat treatments. Detailed descriptions of the AMB2022-01 benchmark measurements and measurement results may be found through the AM Bench webpage at www.nist.gov/ambench.  The serial sectioning EBSD and XRCT data in this data publication are also spatially registered with the detailed build file and in situ thermography measurements obtained during the build.","language":["en"],"title":"AM Bench 2022: IN718 Serial Sectioning and X-ray Computed Tomography Measurement Data","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2024-10-25 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Manufacturing:Additive manufacturing"],"issued":"2024-11-15","keyword":["AM Bench","additive manufacturing","serial sectioning","EBSD","XRCT"]},{"identifier":"ark:/88434/mds2-2771","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jessica.staymates@nist.gov","fn":"Jessica Staymates"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2771","description":"The main data is a webpage with a list of MML Accolade winners from 2022. There is also a csv file with the recipients' names, workplaces, and recognition category/citation.","language":["en"],"title":"2022 MML Accolades Program","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2771/MML%20Accolades%20winners%202022%20-%20for%20DOI.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2771/MML%20Accolades%20winners%202022%20-%20for%20DOI.csv","format":"2022 MML Accolade winners csv file","description":"2022 MML Accolade winners csv file","mediaType":"text/csv","title":"2022 MML Accolade list of winners"},{"accessURL":"https://www.nist.gov/mml/mml-accolades/2022-accolades","format":"html","description":"A website with the list of 2022 MML Accolade awardees","title":"MML Accolades 2022"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Performance Excellence"],"issued":"2022-09-07","keyword":["accolades","MML","recognition","peer-to-peer","gala","award"]},{"identifier":"ark:/88434/mds2-2773","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.jungwirth@nist.gov","fn":"Nick Jungwirth"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2773","description":"Included here are figures and other relevant data from the paper \"A distributed theory for contactless interconnects at terahertz frequencies\".  Abstract: Here we test a multimodal model for distributed contactless interconnects by comparing it to 3D full-wave simulations. In comparison to 3D simulations, the model offers insight into how the interconnect works and reduces the computational cost of estimating the interconnect?s performance. We predict the performance of four distributed contactless interconnects and find good agreement between our multimodal model and 3D simulations up to 1 THz. All the interconnects have less than 1 dB insertion loss in their first pass bands, highlighting the opportunity offered by contactless interconnects.","language":["en"],"title":"Data for \"A distributed theory for contactless interconnects at terahertz frequencies\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2773/Fig3_TableI_Data.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Scattering parameters of distributed contactless interconnect"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2773/Fig2_Data.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Simulated distributed circuit parameters"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2773/Fig1.tif","description":"(a) 3D design of broadside-coupled CPWs and associated two-port network model of the interconnect. (b) Cross-sectional geometry of the translucent plane in (a). Distributed circuit model in the conductor representation (c) and modal representation (d) for N=2 coupled lines.","mediaType":"image/tiff","title":"Figure 1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2773/Fig2.tif","description":"Conductor-representation distributed circuit parameters of resistance (a), inductance (b), conductance (c) and capacitance (d) per unit length.","mediaType":"image/tiff","title":"Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2773/Fig3.tif","description":"Interconnect transmission (S_12) predicted by full-wave simulations (red curves) and our analytical, parameter-free, two-mode model (dashed curves) for l_c= 500 µm (a), 250 µm (b), 125 µm (c), and 60 µm (d). The transmission band maxima (f_n) and bandwidth (?f_n) increase as the coupling length decreases.","mediaType":"image/tiff","title":"Figure 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2773/README.txt","description":"A readme document for data submitted for \"A distributed theory for contactless interconnects at terahertz frequencies\".","mediaType":"text/plain","title":"Readme"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-12-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Electronics:Electromagnetics","Metrology:Electrical/electromagnetic metrology"],"issued":"2023-02-09","keyword":["On-Wafer","microwave","amplifier","photomixers","frequency combiner","frequency comb","electronics","terahertz"]},{"identifier":"ark:/88434/mds2-2775","accessLevel":"public","references":["https://link.springer.com/article/10.1007/s40192-024-00361-7","https://link.springer.com/article/10.1007/s40192-024-00355-5","https://doi.org/10.1007/s40192-024-00372-4","https://doi.org/10.18434/mds2-2718","https://doi.org/10.18434/mds2-2716"],"contactPoint":{"hasEmail":"mailto:lyle.levine@nist.gov","fn":"Lyle E. Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2775","description":"The following data files include microstructure measurement results associated with the 2022 Additive Manufacturing Benchmark test series (AM Bench 2022) AMB2022-03 set of benchmarks. These AMB2022-03 benchmarks explore a range of individual and overlapping melt pool behaviors using individual laser tracks and 2D arrays of laser tracks (pads) on solid metal IN718 plates. For the individual laser tracks, a range of laser parameters was used, with variations in laser power, speed, and spot diameter. For the laser pads, the laser scan patterns and most of the laser parameters match those used for the 2.5 mm legs from the AMB2022-01 3D builds. The laser tracks and pads were cross sectioned at different locations and examined using scanning electron microscopy (SEM) electron backscatter diffraction (EBSD) and energy dispersive spectroscopy (EDS). Descriptions and measurement data for all of the other AMB2022-03 measurements may be found on the AM Bench website at www.nist.gov/ambench.The AM Bench measurements metadata catalog provides both a web search interface and API access to extensive linked data associated with these measurements (see Data Access link to explore this related resource).  The SciServer AM Bench collaborative compute platform provides a mechanism for exploring and analyzing the AM Bench datasets directly on a data server without the need to download large datasets (see Data Access link to explore this related resource).","language":["en"],"title":"AM Bench 2022: Cross sectional microstructure of single laser tracks produced using different processing conditions and 2D arrays of laser tracks (pads) on solid plates of nickel alloy 718","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-04-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Metals"],"issued":"2024-04-29","keyword":["additive manufacturing","benchmarks","AM Bench 2022","LPBF","EBSD","EDS"]},{"identifier":"ark:/88434/mds2-2776","accessLevel":"public","contactPoint":{"hasEmail":"mailto:paulina.kuo@nist.gov","fn":"Paulina Kuo"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2776","description":"Optical spectrum dataset for backward-wave spontaneous-parametric downconversion (BW SPDC) observed in sub-micron, periodically poled KTiOPO4 (PPKTP).","language":["en"],"title":"Optical spectra data for backward-wave spontaneous-parametric downconversion","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try01_monoch_scan_sig_2mmslit.dat","format":"text","mediaType":"text/plain","title":"Monochromator scan (signal wavelength)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try02_monoch_scan_pump_2mmslit_2ndorder.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (pump)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try04_monoch_scan_idl_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (idler)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try06_monoch_scan_idl_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (idler)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try07_monoch_scan_pump_2mmslit_2ndorder.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (pump)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try08_monoch_scan_sig_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (signal)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try10_monoch_scan_sig_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (signal)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try11_monoch_scan_pump_2ndorder_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (pump)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try12_monoch_scan_idler_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (idler)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try13_monoch_scan_sig_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (signal)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try14_monoch_scan_pump_2ndorder_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (pump)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try15_monoch_scan_idler_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (idler)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try16_monoch_scan_sig_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (signal)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try17_monoch_scan_pump_2ndorder_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (pump)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/try18_monoch_scan_idler_2mmslit.dat","format":"text","mediaType":"text/plain","title":"monochromator scan (idler)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/2776_README.txt","format":"text","description":"readme.txt file containing details about the dataset","mediaType":"text/plain","title":"README file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2776/plot_BW_SPDC_data.ipynb","format":"jupyter notebook","description":"Jupyter notebook that reads data files and plots the data","mediaType":"text/x-python","title":"Plots of BW SPDC spectral scans (Jupyter notebook/python)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Optical physics","Physics:Quantum information science"],"issued":"2022-12-01","keyword":["quantum communication","entangled-photon generation","nonlinear optics"]},{"identifier":"ark:/88434/mds2-2777","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.jungwirth@nist.gov","fn":"Nick Jungwirth"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2777","description":"Included here are figures and other relevant data from the paper \"Distributed contactless interconnects for millimeter-wave heterogeneous integration\", submitted to TMTT Letters. Abstract: State-of-the-art integrated circuits leverage dissimilar materials to optimize system performance. Such heterogeneous integration often involves multiple chips electrically coupled to one another via bump bonds or wire-bond interconnects. While these interconnects are a mature technology for low-frequency operation (< 100 GHz), they have stringent fabrication requirements and are prone to failure during operation in the terahertz range (300 GHz to 10 THz). Next-generation integrated circuits require alternative interconnect topologies that are less sensitive to fabrication tolerances and conditions, are more robust, and have superior high-frequency performance. Here, we demonstrate distributed coupling to 325 GHz between broadside-coupled coplanar waveguides without bump bonds, wire bonds, or direct metal-to-metal bonding. The insertion loss of these contactless interconnects was approximately 1.4 dB at the maximum in the passbands at 63 GHz, 93 GHz, and 120 GHz. This interconnect topology enables robust integration of low-cost silicon with high-speed compound semiconductors for terahertz communications networks to improve reliability and increase yield.","language":["en"],"title":"Data for \"Distributed contactless interconnects for millimeter-wave heterogeneous integration\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2777/Fig2.tif","description":". Fabrication process flow for the interconnect test structures investigated in this work.  Side view perspectives are cross-sections of the dashed vertical line in the corresponding plan view. Micrographs are optical microscope images of the interconnect test structure at each fabrication step.","mediaType":"image/tiff","title":"Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2777/Fig3.tif","description":". Simulated and experimentally measured transmission (S_12, (a)) and reflection (S_11, (b)) coefficients for interconnect test structures with coupling lengths l_c equal to (top row) 500 ?m, (middle row) 250 ?m, and (bottom row) 125 ?m. The maximum simulated transmission in each passband is indicated by the blue circular marker in (a). The dashes lines in (a) represent the net insertion loss of the transmission line regions and therefore set an upper bound on the transmission of the back-to-back configuration.","mediaType":"image/tiff","title":"Figure 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2777/Figure_3_Data.xlsx","description":"Measured and simulated scattering parameter data for interconnects in a back-to-back condiguration.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure 3 dataset"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2777/README.txt","description":"A txt document detailing the specifics of this dataset","mediaType":"text/plain","title":"readme"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-12-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Electronics:Electromagnetics","Metrology:Electrical/electromagnetic metrology"],"issued":"2023-02-09","keyword":["On-Wafer","microwave","amplifier","photomixers","frequency combiner","frequency comb","electronics","terahertz"]},{"identifier":"ark:/88434/mds2-2778","accessLevel":"public","contactPoint":{"hasEmail":"mailto:bruce.bandini@nist.gov","fn":"Bruce Bandini"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2778","description":"NFRL registers two fingerprint images based on a pair of corresponding control-points. It uses this control-pointpair of pixel locations within the images to translate and rotate the Moving image to the Fixed image.Runtime configuration parameters include:* moving and fixed image data in 8-bits-per-pixel grayscale (preferable but not required)* two-pairs of corresponding control points (pixel coordinates).The fingerprint-image rigid-registration process is performed in two steps:1. Translation of the Moving image to the Fixed image using the \"first\" pair of control-points (the unconstrained pair)2. Rotation of the Moving image around the Fixed image control-point (the translation \"target\" location) based on theangle-difference determined by the \"second\" pair of control-points (the constrained pair).Both final images, a few interim images, and registration metadata generated during the registration processare made available to the using software:* Final registered Moving image* Final registered Fixed image* Registered, padded, overlaid image (colorized)* Registered, padded, Moving image (grayscale)* Padded, Fixed image (grayscale)* Summed, registered, dilated overlaid image (the \"blob\")* Process metadata available in both text and XML format.The two Final images are registered.  They are cropped to the region-of-interest that is the smallest area of \"overlap\"per the registration.  Therefore, these two images have identical width and height which enables analysis using metricslike PSNR (Peak Signal to Noise Ratio).","language":["en"],"title":"NIST Fingerprint Image Registration Library (NFRL). Registers a pair of fingerprint images using two pairs of control-points (pixel locations).  Registration is rigid; translation and rotation are performed without scaling.","distribution":[{"accessURL":"https://github.com/usnistgov/NFRL","format":"Source code in plain text","description":"NFRL registers two fingerprint images based on a pair of corresponding control-points. It uses this control-point pair of pixel locations within the images to translate and rotate the Moving image to the Fixed image.","title":"NFRL NIST Fingerprint Image Registration Library"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2778/NFRL.zip","format":"plain text","description":"Source code","mediaType":"application/x-zip-compressed","title":"NFRL NIST Fingerprint Image Registration Library"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Biometrics","Information Technology:Conformance testing"],"issued":"2023-02-01","keyword":["fingerprint","image","registration","NFRL","control-points","translation","rotation","overlap","overlay","Otsu","moving","fixed","grayscale","png","bmp"]},{"identifier":"ark:/88434/mds2-2779","accessLevel":"public","references":["https://doi.org/10.1021/acs.jced.3c00104"],"contactPoint":{"hasEmail":"mailto:tara.fortin@nist.gov","fn":"Tara Fortin"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Data to accompany the paper \"Vapor and Liquid (p-rho-T-x) Measurements of Binary Refrigerant Blends Containing R-32, R-152a, R-227ea, R-1234yf, and R 1234ze(E)\" published in Journal of Chemical & Engineering Data. Included are experimental data for two compositions each of four binary refrigerant blends: R-32 + R1234yf, R-32 + R-1234ze(E), R-1234yf + R-152a, and R-1234ze(E) + R-227ea. Both the averaged data, which are presented in Tables 3-10 of the above manuscript, and the full replicate data, which are presented in Tables S1-S8 of the accompanying Supporting Information file, are included.","language":["en"],"title":"Data to accompany the paper entitled \"Vapor and Liquid (p-rho-T-x) Measurements of Binary Refrigerant Blends Containing R-32, R-152a, R-227ea, R-1234yf, and R-1234ze(E)\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2779/R32EtalDensityData.zip","description":"Re-generated download URL","mediaType":"application/zip","title":"R32EtalDensityData"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-02-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Thermochemical properties"],"issued":"2023-06-13","keyword":["density","hydrofluorocarbons","hydrofluoroolefins","refrigerant blends"]},{"identifier":"ark:/88434/mds2-2780","accessLevel":"public","references":["https://doi.org/10.1021/acs.jced.3c00103"],"contactPoint":{"hasEmail":"mailto:tara.fortin@nist.gov","fn":"Tara Fortin"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2780","description":"Data to accompany the paper \"Vapor and Liquid (p-rho-T-x) Measurements of Binary Refrigerant Blends Containing R-134a, R-1234yf, and R-1234ze(E)\" published in Journal of Chemical & Engineering Data. Included are experimental data for two compositions each of three binary refrigerant blends: R 1234yf + R134a, R-134a + R-1234ze(E), and R-1234yf + R-1234ze(E). Both the averaged data, which are presented in Tables 3-8 of the above manuscript, and the full replicate data, which are presented in Tables S1-S6 of the accompanying Supporting Information file, are included. ","language":["en"],"title":"Data to accompany the paper entitled \"Vapor and Liquid (p-rho-T-x) Measurements of Binary Refrigerant Blends Containing R-134a, R-1234yf, and R-1234ze(E)\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2780/R134aEtalDensityData.zip","mediaType":"application/x-zip-compressed"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-02-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Thermochemical properties"],"issued":"2023-06-13","keyword":["density","hydrofluorocarbons","hydrofluoroolefins","refrigerant blends"]},{"identifier":"ark:/88434/mds2-2781","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2781","description":"Round 9 Test DatasetThis is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 210 Sentiment Classification, Named Entity Recognition, and Extractive Question Answering AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - nlp-summary-jan2022-test","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1voAHxVT1wfAKfhlqBkIXERjWFHmgs6gy?usp=drive_link","title":"nlp-summary-jan2022-test"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-01-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research"],"issued":"2022-11-09","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2782","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2782","description":"Round 9 Holdout DatasetThis is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 410 Sentiment Classification, Named Entity Recognition, and Extractive Question Answering AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - nlp-summary-jan2022-holdout","distribution":[{"accessURL":"https://drive.google.com/drive/folders/1Pkh41erON1xFNlHQBu1wWtlhP3rt6FqI?usp=drive_link","title":"nlp-summary-jan2022-holdout"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-01-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Software research","Information Technology:Cybersecurity"],"issued":"2023-04-07","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2783","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2783","description":"Round 10 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of object detection AIs trained on the COCO dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 144 AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.","language":["en"],"title":"Trojan Detection Software Challenge - object-detection-jul2022-train","distribution":[{"accessURL":"https://drive.google.com/drive/folders/11khneOUXZPSMHPPObNSyl1ObLsO6ohoV?usp=drive_link","title":"object-detection-jul2022-train"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Cybersecurity","Information Technology:Software research"],"issued":"2023-01-12","keyword":["Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"]},{"identifier":"ark:/88434/mds2-2785","accessLevel":"public","contactPoint":{"hasEmail":"mailto:william.krekelberg@nist.gov","fn":"William P. Krekelberg"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"A python package to analyze ``lnPi`` data from Transition Matrix Monte Carlo(TMMC) simulation.  The main output from TMMC simulations, ``lnPi``, provides a means to calculate a host of thermodynamicproperties.  Moreover, if ``lnPi`` is calculated at a specific chemical potential, it can be reweighted to providethermodynamic information at a different chemical potential.  The python package``tmmc-lnpy`` provides a wide array of routines to analyze ``lnPi`` data.  These include:* Reweighting to arbitrary chemical potential* Segmenting ``lnPi`` (to identify unique phases)* Containers for interacting with several values of  ``lnPi`` in a vectorized way.* Calculating thermodynamic properties from these containers* Calculating limits of stability, and phase equilibrium","language":["en"],"title":"tmmc-lnpy:  A python package to analyze Transition Matrix Monte Carlo lnPi data.","distribution":[{"accessURL":"https://github.com/usnistgov/tmmc-lnpy","title":"tmmc-lnpy:  A python package to analyze Transition Matrix Monte Carlo lnPi data."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2785/README.pdf","mediaType":"application/pdf","title":"README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P3M","theme":["Chemistry:Theoretical chemistry and modeling","Chemistry:Chemical thermodynamics and chemical properties"],"issued":"2022-10-07","keyword":["Python","Molecular simulation","Data analysis","Transition Matrix Monte Carlo","Statistical mechanics"]},{"identifier":"ark:/88434/mds2-2787","accessLevel":"public","contactPoint":{"hasEmail":"mailto:william.krekelberg@nist.gov","fn":"William P. Krekelberg"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"cmomy is a python package to calculate central moments and co-moments in a numerical stable and direct way. Behind the scenes, cmomy makes use of Numba to rapidly calculate moments. cmomy provides utilities to calculate central moments from individual samples, precomputed central moments, and precomputed raw moments.  It also provides routines to perform bootstrap resampling based on raw data, or precomputed moments.  cmomy has numpy array and xarray DataArray interfaces.","language":["en"],"title":"cmomy: A python package to calculate and manipulate Central (co)moments.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2787/README.pdf","mediaType":"application/pdf"},{"accessURL":"https://github.com/usnistgov/cmomy","title":"cmomy: A Python package to calculate and manipulate Central (co)moments."}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/PT1S","theme":["Mathematics and Statistics:Numerical methods and software"],"issued":"2022-10-07","keyword":["Numerical methods","python programming","statistics"]},{"identifier":"ark:/88434/mds2-2788","accessLevel":"public","contactPoint":{"hasEmail":"mailto:dean.delongchamp@nist.gov","fn":"Dean DeLongchamp"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2788","description":"Polarized Resonant Soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool to measure structure in complex, chemically heterogeneous systems. P-RSoXS combines principles of X-ray scattering and X-ray spectroscopy; this combination provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials. Quantitative extraction of orientation information from the P-RSoXS pattern data is challenging because the scattering processes depend on properties that are represented as three-dimensional tensors with heterogeneities at nanometer to sub-nanometer length scales. We overcome this challenge by developing an open-source virtual instrument that uses Graphical Processing Units (GPUs) to simulate P-RSoXS patterns from real-space material representations with nanoscale resolution. Our computational framework -- called CyRSoXS -- is designed to maximize GPU performance, including algorithms that minimize both communication and memory footprints. We demonstrate the accuracy and robustness of our approach by validating against an extensive set of test cases, which include both analytical solutions and numerical comparisons, demonstrating a speedup of over three orders to the current state-of-the-art P-RSoXS simulation software. Such fast simulations open up a variety of applications that were previously computationally infeasible, including (a) pattern fitting, (b) co-simulation with the physical instrument for operando analytics, data exploration, and decision support, (c) data creation and integration into machine learning workflows, and (d) utilization in multi-modal data assimilation approaches. Finally, we abstract away the complexity of the computational framework from the end-user by exposing CyRSoXS to Python using Pybind. This democratizes usage by enabling seamless integration with various Python libraries, and also eliminates I/O requirements for large-scale parameter exploration and inverse design.","language":["en"],"title":"CyRSoXS: A GPU-accelerated virtual instrument for Polarized Resonant Soft X-ray Scattering (P-RSoXS)","distribution":[{"accessURL":"https://github.com/usnistgov/cyrsoxs","format":"Github Repository","description":"A github repository containing the CyRSoXS C++/CUDA source code and documentation","title":"CyRSoXS Github Repository"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-19 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Condensed matter","Mathematics and Statistics:Numerical methods and software","Materials:Modeling and computational material science","Materials:Materials characterization","Materials:Polymers","Materials:Composites","Chemistry:Molecular characterization","Electronics:Organic electronics"],"issued":"2023-03-07","keyword":["polymer","Python","C++","CUDA","polymer nanocomposite","polymer solution","X-ray scattering","software","tool","computation"]},{"identifier":"ark:/88434/mds2-2789","accessLevel":"public","references":["https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935077"],"contactPoint":{"hasEmail":"mailto:yao.ma@nist.gov","fn":"Yao Ma"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This dataset provides RF data from software defined ratio (SDR) measurement results of a few cases:  A. one 4G LTE link, B. two LTE links, C. one LTE link and one Wi-Fi link. The LTE links were emulatedby USRP B210 units and an open-source software (srsRAN), and the Wi-Fi link was emulated by a pair ofWi-Fi commercial development boards.  This dataset includes metadata and performance results (ina spreadsheet format) and  I/Q baseband sample data (in a binary float point format). Though specifictrade names are mentioned, they should not be construed as an endorsement of that product. Other productsmay work as well or better.The spreadsheet files provide the mapping among some system parameters (such as the SDR received powerand SINR) and key performance indicators (KPIs), such as throughput and packet drop rate. The I/Q datafiles provide the digital samples of the received signals at the receivers (LTE or Wi-Fi).This dataset can be used to support research topics such as multi-cell LTE system performance evaluationand optimization, spectrum sensing and signal classification, and AI and machine learning, beside others.","language":["en"],"title":"LTE and Wi-Fi coexistence measurement data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2789/LTE_WiFi_data.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2789/2789_README%20v1.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2789/LTE_WiFi_data.zip","description":"This dataset includes metadata and performance results (in a spreadsheet format) and  I/Q baseband sample data (in a binary float point format).","mediaType":"application/zip","title":"Dataset zip file"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2789/2789_README%20v1.txt","mediaType":"text/plain","title":"Readme file"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)","Metrology:Electrical/electromagnetic metrology"],"issued":"2022-12-05","keyword":["Software defined radio (SDR)","hardware measurement","wireless coexistence","spectrum sharing","wireless communications and networks","4G","Wi-Fi"]},{"identifier":"ark:/88434/mds2-2790","accessLevel":"public","contactPoint":{"hasEmail":"mailto:allan.harvey@nist.gov","fn":"Allan H. Harvey"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Parameters, input scripts, and molecular dynamics simulation results corresponding to the simulations performed for the paper \"Entropy Scaling for Viscosity for Molecular Models of Molten Salts\" by J.M. Young, I.H. Bell, and A.H. Harvey, submitted to J. Chem. Phys. in September 2022.","language":["en"],"title":"Simulations for Entropy Scaling Behavior of Molecular Models of Molten Salts","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2790/README.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2790/ModelParameters.pdf","format":"PDF document","description":"Listing of model parameters used for molecular models of molten salts","mediaType":"application/pdf","title":"Model parameters"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2790/SimulationResults.zip","format":"zip file","description":"zip file containing csv tables of all entropy scaling data","mediaType":"application/x-zip-compressed","title":"Simulation results"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2790/SimulationScripts.tar.gz","format":"tar.gz","description":"tar file containing example simulation input scripts","mediaType":"application/gzip","title":"Simulation scripts"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Theoretical chemistry and modeling","Physics:Thermodynamics","Chemistry:Chemical thermodynamics and chemical properties"],"issued":"2022-10-03","keyword":["entropy","molecular dynamics","molten salt","viscosity"]},{"identifier":"ark:/88434/mds2-2792","accessLevel":"public","references":["https://nvlpubs.nist.gov/nistpubs/TechnicalNotes/NIST.TN.2203.pdf"],"contactPoint":{"hasEmail":"mailto:selvarajah.ramesh@nist.gov","fn":"Selvarajah Ramesh"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2792","description":"The National Institute of Standards and Technology conducted a series of large compartment fire tests to investigate the behavior and fire-induced failure mechanisms of the full-scale composite floor assemblies with the two-story steel gravity frame, two bays by three bays in plan. This report presents the experimental design and results from the second fire experiment (Test #2) conducted at the National Fire Research Laboratory. The Test #2 was aimed to investigate the influence of the slab reinforcement on the structural integrity of the 9.1 m × 6.1 m steel-concrete composite floor subjected to combined mechanical loads and compartment fire exposure. The fire test bay was situated on the ground floor in the middle edge bay of the two-story test building. The floor slab in the test bay was reinforced with the No.3 deformed bars placed 30 cm on center (230 mm2/m). The test floor was hydraulically loaded to 2.7 kPa to mimic the code-prescribed gravity loads for fire conditions. The natural gas burners created a peak gas temperature exceeding 1100 °C below the test floor. The test fire lasted about 131 min, but the hydraulic loading was not removed until the test floor cooled down over 2 hours. This experimental study confirmed that the steel reinforcement played a vital role in maintaining the integrity of the composite floor under prolonged compartment fire exposure. The mid-panel vertical displacement increased at a rate less than 1 mm/°C as the protected steel beams were heated to 850 °C on average. The peak vertical displacement of the test slab was recorded 475 mm surpassing the displacement limit prescribed in the standard fire test. Although the test slab developed extensive surface cracks, it successfully contained the test fire underneath while sustaining the imposed loads simultaneously. The test floor retained the post-fire flexural strength exceeding 90 % of the ambient design strength of the composite secondary beam prior to fire exposure. The experimental results presented in this report can be used for validation of predictive models to perform parametric studies incorporating the variability in the steel reinforcement scheme (area, spacing, and material) for safer and cost-effective composite floor construction for fire safety.","language":["en"],"title":"Data from Fire Resilience of a Steel-Concrete Composite Floor System: Full-Scale Experimental Evaluation for Influence of Slab Reinforcement (Test #2)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2792/NFRL_CompositeFloorTest_No2_DataSet.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Test #2 Data Set"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Structural fire resistance"],"keyword":["fire","composite floor","steel beam","shear connection","steel building","fire resistance","passive fire protection"]},{"identifier":"ark:/88434/mds2-2796","accessLevel":"public","contactPoint":{"hasEmail":"mailto:paritosh.manurkar@nist.gov","fn":"Paritosh Manurkar"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2796","description":"In this paper, we have demonstrated the importance of choosing the correct reference plane for applications such as over-the-air (OTA) modulated-signal measurements at millimeter-wave frequencies. We have employed a modulated-signal source at 44 GHz for this demonstration. The measurements have been performed using NIST's calibrated sampling oscilloscope and are traceable to the primary standards. The EVM values and distributions are obtained after complete uncertainty analyses. The source and oscilloscope mismatch measurements have been performed on a vector network analyzer (VNA) and are also shown here after complete uncertainty analyses. Each dataset shown in the paper has been obtained after running 1000 Monte-Carlo simulations.","language":["en"],"title":"Recommended Practices for Calibrated Millimeter-Wave Modulated-Signal Measurements","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/2796_README.txt","description":"It provides a description of the uploaded datasets.","mediaType":"text/plain","title":"README file"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/Figure2a_sourceMM_measurementRP.txt","description":"Source mismatch at the measurement reference plane","mediaType":"text/plain","title":"Figure2a_sourceMM_measurementRP"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/Figure2a_sourceMM_sourceRP.txt","description":"Source mismatch at the source reference plane","mediaType":"text/plain","title":"Figure2a_sourceMM_sourceRP"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/Figure2b_EVM_values.xlsx","description":"Monte-Carlo EVM values for plotting with EVM distribution","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure2b_EVM_values"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/Figure2b_histogram_measurementRP.txt","description":"EVM distribution at the measurement reference plane","mediaType":"text/plain","title":"Figure2b_histogram_measurementRP"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/Figure2b_histogram_sourceRP.txt","description":"EVM distribution at the source reference plane","mediaType":"text/plain","title":"Figure2b_histogram_sourceRP"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/Figure3_EVM_values.xlsx","description":"Monte-Carlo EVM values for plotting with the EVM distribution","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure3_EVM_values"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/Figure3_histogram.txt","description":"EVM distribution","mediaType":"text/plain","title":"Figure3_histogram"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/Figure4a_50GHz_module.txt","description":"50-GHz module oscilloscope mismatch","mediaType":"text/plain","title":"Figure4a_50GHz_module"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/Figure4a_67GHz_module.txt","description":"67-GHz module oscilloscope mismatch","mediaType":"text/plain","title":"Figure4a_67GHz_module"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2796/Figure4c_EVM_values.xlsx","description":"Monte-Carlo means and 95% confidence limits plotted for 0dB, 10dB, 20dB channel attenuations when no fixed attenuator was inserted and when 3dB and 6dB fixed attenuators were inserted before the oscilloscope adapters","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure4c_EVM_values"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)"],"issued":"2022-11-29","keyword":["Digitally modulated signals","error vector magnitude","predistortion","reference planes","uncertainty analysis."]},{"identifier":"ark:/88434/mds2-2799","accessLevel":"public","contactPoint":{"hasEmail":"mailto:evan.black@nist.gov","fn":"Evan Black"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2799","description":"This tool computes the expected link capacity in data rate (Mbit/s) for Sidelink considering 5G New Radio (NR) and Long Term Evolution (LTE) communication standards, as defined by the The 3rd Generation Partnership Project (3GPP). Users can input the respective configuration parameters and evaluate the resulting capacity.The tool provides side-by-side chart plotting between NR and LTE Sidelink capacities for easy comparison. Data and generated charts can be exported to be used outside the tool.In the current release, the tool applies to the public safety band, Band 14/n14 (the 700 MHz frequency band).","language":["en"],"title":"5G NR/LTE Sidelink Capacity Estimator","distribution":[{"accessURL":"https://gitlab.nist.gov/gitlab/wnd-publicsafety/5g/sidelink-capacity-tool","description":"Internal repository, for review only","title":"NIST GitLab"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2799/5G-NR-LTE-Sidelink-Capacity-Estimator.zip","description":"A copy of the repository for review","mediaType":"application/zip","title":"Source Code"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Public Safety:Public safety communications research"],"issued":"2022-12-05","keyword":["5G","NR","4G","LTE","Sidelink","3GPP","Band 14","Public Safety"]},{"identifier":"ark:/88434/mds2-2800","accessLevel":"public","contactPoint":{"hasEmail":"mailto:william.krekelberg@nist.gov","fn":"William P. Krekelberg"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"`analphipy` is a python package to calculate metrics for classical models for pair potentials. It provides a simple and extendable api for pair potentials creation. Several routines to calculate metrics are included in the package. The main features of `analphipy` are 1)  Pre-defined spherically symmetric potentials. 2)  Simple interface to extended to user defined pair potentials. 3) Routines to calculate Noro-Frenkel effective parameters. 4) Routines to calculate Jensen-Shannon divergence.","language":["en"],"title":"analphipy: A python package to analyze pair-potential metrics.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2800/README.pdf","mediaType":"application/pdf","title":"README"},{"accessURL":"https://github.com/usnistgov/analphipy","title":"analphipy: A python package to analyze pair-potential metrics"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-26 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P3M","theme":["Chemistry:Chemical thermodynamics and chemical properties"],"issued":"2022-10-07","keyword":["Python","Statistical Mechanics"]},{"identifier":"ark:/88434/mds2-2801","accessLevel":"public","contactPoint":{"hasEmail":"mailto:chandler.becker@nist.gov","fn":"Chandler A. Becker"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This repository contains scripts and supporting information for a set of demonstrations related to the 2022 AM-Bench challenge series.In particular, the data used in this demonstration comes from the 2018 AM-Bench challenge documented at https://www.nist.gov/ambench/amb2018-02-description.The script queries the AM-Bench 2018 repository located at https://ambench.nist.gov/, then processes the returned XML-based results, does some image processing, and generates plots of melt pool depths. This work was originally done by Miyu Mudalamane (University of Delaware) during a 2021 Summer Undergraduate Research Fellowship at NIST supervised by Chandler Becker and Gretchen Greene (NIST Office of Data and Informatics, ODI).It was later adapted as this notebook by Jordan Raddick (Johns Hopkins University) in consultation with NIST ODI staff.Additional datasets and challenge problem documentation are available through the NIST Public Data Repository record at https://data.nist.gov/od/id/6D6EC9B3A4147BE2E05324570681EEC91931 with an associated publication (https://link.springer.com/article/10.1007/s40192-020-00169-1) documenting the experiments.","language":["en"],"title":"AM-Bench image analysis demonstration","distribution":[{"accessURL":"https://github.com/usnistgov/ambench-sciserver-outreach","format":"Python scripts in a GitHub repository","description":"Scripts to process and analyze melt track cross-sections in images from the 2018 AM-Bench challenge (https://www.nist.gov/ambench)","title":"AM-Bench image analysis demonstration"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Mathematics and Statistics:Image and signal processing","Manufacturing:Additive manufacturing","Materials:Metals"],"issued":"2022-10-20","keyword":["additive manufacturing","benchmark","AM-Bench"]},{"identifier":"ark:/88434/mds2-2803","accessLevel":"public","references":["https://doi.org/10.1021/acs.iecr.2c01924"],"contactPoint":{"hasEmail":"mailto:aaron.rowane@nist.gov","fn":"Aaron Rowane"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2803","description":"Workflow: The data that falls into this category are gathered from a measurement program and are published in the archival literature.","language":["en"],"title":"Thermal Conductivity of Binary Mixtures of 1,1,1,2-Tetrafluoroethane(R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2803/SupplementalFiles.zip","description":"Thermal conductivity data for binary mixtures of R-134a, R-1234yf, and R-1234ze(E)","mediaType":"application/x-zip-compressed","title":"Thermal Conductivity of Binary Mixtures of 1,1,1,2-Tetrafluoroethane(R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2803/2803_README.txt","description":"A readme describing the thermal conductivity data supplied on this page","mediaType":"text/plain","title":"Readme for thermal conductivity data of R-1234yf/134a, R-1234yf/1234ze(E), and R-134a/1234ze(E) mixtures"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2803/AllTCXData.csv","format":".csv file which is a machine readable format. Opening the file in excel provides a human readable format.","description":"Data included are the components in the binary mixture and their composition listed with the measured temperature, pressure, and thermal conductivity values. Additionally included in the data set are densities calculated to determine the thermal conductivity.","mediaType":"text/csv","title":"Thermal conductivity data for R-1234yf/134a, R-1234yf/1234ze(E), and R-134a/1234ze(E) mixture"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Chemical thermodynamics and chemical properties"],"issued":"2022-10-03","keyword":["Advanced Materials","Energy","Environment and Climate","Physical Infrastructure","Safety","Security and Forensics"]},{"identifier":"ark:/88434/mds2-2804","accessLevel":"public","contactPoint":{"hasEmail":"mailto:aaron.rowane@nist.gov","fn":"Aaron Rowane"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants","language":["en"],"title":"Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2804/R32_R1234yf_R1234ze%28E%29_SOSData.csv","format":"The data are in .csv format for easy export into data analysis tools. Opening data in excel allow for the data to be viewed in human readable format.","description":"Unaveraged speed of sound data for R-32/1234yf and R-32/1234ze(E). Each state point contains up to four speed of sound values derived from triplicated, back-to-back, speed of sound measurements. Included are the REFPROP fluid names, composition, temperature, pressure, and  speed of sound. Additionally included are the standard deviations and uncertainties in temperature, pressure, and speed of sound measurements.","mediaType":"text/csv","title":"Unaveraged speed of sound data for R-32/1234yf and R-32/1234ze(E) binary mixtures"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2021-11-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Chemical thermodynamics and chemical properties"],"issued":"2022-10-03","keyword":["Advanced Materials","Energy","Environment and Climate","Physical Infrastructure","Safety","Security and Forensics"]},{"identifier":"ark:/88434/mds2-2805","accessLevel":"public","references":["https://www.nist.gov/publications/analyzing-5g-nr-u-and-wigig-coexistence-multiple-beam-directional-lbt"],"contactPoint":{"hasEmail":"mailto:yao.ma@nist.gov","fn":"Yao Ma"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2576","description":"This project  produces  synthetic  datasets of  spectrum sharing simulation results (I/Q data, metadata, and KPIs).","language":["en"],"title":"Dataset for  paper Y. Ma, S. Mosleh and J. Coder, \"Analyzing 5G NR-U and WiGig Coexistence with Multiple-Beam Directional LBT,\" 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), 2022, pp. 272-275, doi: 10.1109/CCNC49033.2022.9700690","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2576/2576_README.txt","description":"The readme file that explains the released data.","mediaType":"text/plain","title":"Readme file of the data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2576/CCNC22.zip","description":"The zip file that contains the simulation data and a figure.","mediaType":"application/x-zip-compressed","title":"The simulation data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-03-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)"],"issued":"2024-09-26","keyword":["wireless coexistence","spectrum sharing","machine learning","wireless communications and networks","4G","5G","6G","Wi-Fi"]},{"identifier":"ark:/88434/mds2-2807","accessLevel":"public","references":["https://doi.org/10.1515/pac-2021-1002"],"contactPoint":{"hasEmail":"mailto:ala.bazyleva@nist.gov","fn":"Ala Bazyleva"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2807","description":"The data files provide the literature experimental data used to plot the figures, develop and/or verify the models in the following article:A. Bazyleva, W. E. Acree Jr., V. Diky, G. T. Hefter, J. Jacquemin, M. C. F. Magalhães, J. W. Magee, D. K. Nordstrom, J. P. O'Connell, J. D. Olson, I. Polishuk, K. A. G. Schmidt, J. M. Shaw, J. P. M. Trusler, R. D. Weir. Reference Materials for Phase Equilibrium Studies. 2. Solid-Liquid Equilibria (IUPAC Technical Report). Pure Appl. Chem., 2022, accepted for publication.","language":["en"],"title":"Literature experimental data for \"Reference Materials for Phase Equilibrium Studies. 2. Solid-Liquid Equilibria (IUPAC Technical Report)\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2807/Readme.txt","format":"txt file","description":"The correspondence between the figures in the article and the data files is specified","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2807/DataFiles.zip","format":"zip file","description":"Literature experimental data used for figure plotting, model development and/or verification","mediaType":"application/x-zip-compressed","title":"Literature experimental data used for figure plotting, model development and/or verification"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-09-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Chemical thermodynamics and chemical properties","Standards:Reference data","Physics:Thermodynamics"],"issued":"2023-03-17","keyword":["Reference materials","Phase equilibrium","Solid-liquid equilibrium","Experimental method corroboration","Solubility"]},{"identifier":"ark:/88434/mds2-2808","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jacob.pawlik@nist.gov","fn":"Jacob Pawlik"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2808","description":"Broadband S-parameter measurements of Parylene C microwave microfluidic devices from 100 MHz - 110 GHz. The Parylene C devices consisted of 400 nm platinum coplanar waveguides (CPW) (50 um center conductor, 5 um gaps, 200 um ground planes) deposited on fused silica with 6.5 um of Parylene C on top, capped with a PDMS microfluidic layer aligned over the CPWs. The dimensions of the PDMS microfluidic channels were approximately 210 um wide by 100 um deep. The total CPW line length was 10.000 mm and the channel length was 4.160 mm, where the channel was aligned over the center of the CPW. We measured the S-parameters of Parylene C devices filled with three different fluid conditions at different intervals over a 2 month period: H2O at 20 degrees Celsius, 1xPBS (phosphate-buffered saline) at 20 degrees Celsius, and 1xPBS at 37 degrees Celsius. We obtained measurements of both the fluid-filled and empty channel at each measurement day. We measured broadband S-parameters with a vector network analyzer and extender heads at a source power of -17 dBm on a temperature-controlled probe station. The S-parameters were calibrated to the probe tips with measurements of a gold reference chip in combination with multiline TRL in the NIST Microwave Uncertainty Framework. The differences in S-parameters from day 0 were plotted over time to observe changes in the dielectric properties of the Parylene C device during soaking.","language":["en"],"title":"S-parameters of Parylene C microwave microfluidic devices","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2808/Corrected%20S%20parameters%20of%20Parylene%20C%20devices.zip","format":".s2p","description":"Corrected S-parameters of Parylene C devices soaked in H2O at 20oC, 1xPBS at 20oC, or 1xPBS at 37oC for 2 months. S-parameters were corrected using Au reference chips measurements and the NIST Microwave Uncertainty Framework","mediaType":"application/x-zip-compressed","title":"Corrected S-parameters of Parylene C devices"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2808/Raw%20S%20parameters%20of%20Parylene%20C%20devices.zip","format":".s2p","description":"Uncorrected S-parameters of Parylene C devices soaked in H2O at 20oC, 1xPBS at 20oC, or 1xPBS at 37oC for 2 months.","mediaType":"application/x-zip-compressed","title":"Raw S parameters of Parylene C devices"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2808/Repeatability_uncertainty.mat","format":".mat","description":"Uncertainty in S-parameters determined by taking the standard deviation of corrected S-parameter measurements of a Pt coplanar waveguide over 8 measurement days.","mediaType":"application/octet-stream","title":"Repeatability uncertainty"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2808/Compile_allplots_switch.m","format":".mat","description":"A Matlab script to plot S-parameters for figures in a manuscript","mediaType":"text/plain","title":"Matlab script to compile S-parameter results"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2808/Parylene_all_switch.mat","format":".mat","description":"A compiled data structure of corrected S-parameters for Parylene C devices","mediaType":"application/octet-stream","title":"Compiled S-parameter data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2808/2808_README_edit.txt","mediaType":"text/plain","title":"README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Biomaterials","Electronics:Electromagnetics","Materials:Polymers","Metrology:Electrical/electromagnetic metrology","Physics:Spectroscopy"],"issued":"2023-01-04","keyword":["Parylene C","Microwave microfluidic spectroscopy","broadband dielectric spectroscopy","S-parameters","biomedical devices","implantable devices"]},{"identifier":"ark:/88434/mds2-2809","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jacob.pawlik@nist.gov","fn":"Jacob Pawlik"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2809","description":"We obtained stylus profilometry measurements of Parylene C devices after soaking in different fluid conditions in a microfluidic environment. The Parylene C devices consist of platinum coplanar waveguides (400 nm Pt, 50 um center conductor, 5 um gaps, 200 um ground planes) with 6.5 um of Parylene C deposited on top. A PDMS microfluidic layer was aligned on top of the 10.00 mm CPW such that the 4.00 mm channel was centered on the CPW. The Parylene C device was subjected to one of three fluid conditions over a 2 month soaking period: H2O at 20 degrees Celsius, 1xPBS (phosphate-buffered saline) at 20 degrees Celsius, or 1xPBS at 37 degrees Celsius. The cross-sectional profile of the CPW was measured in three locations along the channel before and after soaking in each fluid. Here we give these topographical profiles for each Parylene C device we measured.","language":["en"],"title":"KLA stylus profilometry of Parylene C devices","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2809/Profilometry.zip","description":"Cross-sectional profiles of Pt CPWs coated in Parylene C and soaked in different microfluidic fluid conditions.","mediaType":"application/x-zip-compressed","title":"KLA profilometry of Parylene C devices"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2809/2809_README_edit.txt","mediaType":"text/plain","title":"README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Biomaterials","Electronics:Electromagnetics","Materials:Materials characterization","Metrology:Electrical/electromagnetic metrology","Physics:Spectroscopy"],"issued":"2023-01-04","keyword":["Parylene C","Microwave microfluidic spectroscopy","S-parameters","dielectric spectroscopy","profilometry","biomedical devices","implantable devices"]},{"identifier":"ark:/88434/mds2-2811","accessLevel":"public","contactPoint":{"hasEmail":"mailto:richard.candell@nist.gov","fn":"Rick Candell Jr."},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2811","description":"Time-sensitive networking (TSN) is an emerging topic for the advancement of wireless networking for industrial applications.  TSN, as defined under the umbrella of IEEE 802.1 working group standards, addresses issues related to providing deterministic communications over IEEE 802-based Local Area Networks (LANs).  TSN was originally designed to support real-time audio/video applications over Ethernet providing better reliability and lower, more deterministic latency with traffic shaping capabilities.  TSN has since expanded its scope and applicability to other applications such as those in industrial environments and automotive.  Industrial examples include machine-machine communications for robot control, end-effector actuation, real-time sensing, and safety integrated systems.  Applications utilizing an wireless local area network (WLAN) can also benefit from scheduling and traffic shaping as defined in the 802.1Qbv standard; however, factors such as clock stability, synchronization, resource requirements and protocol options come into play when selecting a schedule to support multiple application types on the same network.  In this article, we present a scenario for a collaborative robot heavy lift operation, in which, two robots communicate over an IEEE 802.11 WLAN with TSN capabilities to lift a rigid body in three dimensions.  Scheduling is performed using 802.1Qbv over WLAN with the robot operating system (ROS) used as the software middleware utilizing the transport control protocol (TCP).  As a part of the research, we describe our process for schedule selection to accommodate the time-sensitive traffic of the robotic scenario while allowing an industrial internet of things (IIoT) high data rate traffic to coexist.  We then provide an analysis of the impacts of TSN schedule selection on the operational performance of the collaborative robot application.  The data provided within this data set was collected as a result of experiments conducted under this research effort.","language":["en"],"title":"Measurement Data From \"Operational Impacts of IEEE 802.1Qbv Scheduling on a Collaborative Robotic Scenario\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2811/IECON_2022_measurements.zip","description":"This data set includes operational and network measurement data taken from a collaborative robot application employing wireless communications with IEEE 802.1Qbv scheduling.","mediaType":"application/x-zip-compressed","title":"Measurement Data for IEEE 802.1Qbv Scheduling on a Collaborative Robotic Scenario"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Factory communications","Advanced Communications:Wireless (RF)"],"issued":"2022-10-28","keyword":["Wireless","TSN","factory communications","IEEE 802.11","IEEE 802.1Qbv","WLAN"]},{"identifier":"ark:/88434/mds2-2812","accessLevel":"public","contactPoint":{"hasEmail":"mailto:isaac.leventon@nist.gov","fn":"Isaac Leventon"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2812","description":"A set of 6 experiments was performed on poly(methyl methacrylate), PMMA. In each test, samples (i.e., 2.44 m tall, 0.61 m wide slabs of PMMA mounted in a parallel panel configuration) were exposed to a propane burner (nominal heat release rate, HRR = 60 kW), which was turned off after sustained flaming was observed across the panel walls. Flames were allowed to spread upward across the panels and continue burning until self-extinction following complete sample burnout.Measurement data obtained in this test series includes:Time-resolved measurements of fire size (kW), soot generation, and gaseous species (CO and CO2) production;Spatially resolved measurements of flame to wall heat transfer [kW/m2];Radiative heat flux at a distance[kW/m2];Initial and final sample mass;Photographs and video of material ignition and fire growth behaviorData will be made publicly available on the MaCFP Repo: https://github.com/MaCFP/macfp-db/tree/master/Fire_Growth/NIST_Parallel_Panel","language":["en"],"title":"Experimental Measurements for Fire Model Validation - Parallel Panel Tests on PMMA","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/Burner_Fill.png","description":"Propane burner cross section/schematic","mediaType":"image/png","title":"Burner_Fill"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/Burner_HF_Centerline_gravel.csv","description":"Time-resolved measurements of total heat flux from the propane burner to the centerline of inert panel walls when the burner is in its original configuration (i.e., filled only with pea gravel; Figs. 2a,c). This burner configuration was used for PMMA tests R1-R5","mediaType":"text/csv","title":"Burner_HF_Centerline_gravel"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/Burner_HF_Centerline_sand_multi-layer.csv","description":"Time-resolved measurements of total heat flux from the propane burner to the centerline of inert panel walls when the burner is in its multi-layered configuration (i.e., filled with a 0.15 m deep layer of pea gravel, topped by 0.075 m layer of sand, and a 0.025 m thick sheet of porous, flexible insulation; Figs. 2b,d). This burner configuration was used for PMMA test R6","mediaType":"text/csv","title":"Burner_HF_Centerline_sand_multi-layer"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/Burner_heatflux.png","description":"Measured total flame heat flux from the propane burner to inert panel walls (height-resolved data, along the centerline of panels)","mediaType":"image/png","title":"Burner_heatflux"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/Burner_heatflux_colormap.png","description":"Spatially resolved measurements (across the width of panel walls) of total flame heat flux, qburner [kW/m2] at steady state (multilayer configuration)","mediaType":"image/png","title":"Burner_heatflux_colormap"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/Burner_steadyHF_Width_multi-layer.csv","description":"Width-resolved measurements of total heat flux (at quasi-steady state) from the propane burner to inert panel walls when the burner was in its multi-layered configuration (i.e., filled with a 0.15 m deep layer of pea gravel, topped by 0.075 m layer of sand, and a 0.025 m thick sheet of porous, flexible insulation). This burner configuration was used for PMMA testf R6","mediaType":"text/csv","title":"Burner_steadyHF_Width_multi-layer"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/PMMA_HRR_qrad_R2.csv","description":"HRR and heat flux at a distance measurements from Test R2","mediaType":"text/csv","title":"PMMA_HRR_qrad_R2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/PMMA_HRR_qrad_R3.csv","description":"HRR and heat flux at a distance measurements from Test R3","mediaType":"text/csv","title":"PMMA_HRR_qrad_R3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/PMMA_HRR_qrad_R6.csv","description":"HRR and heat flux at a distance measurements from Test R6","mediaType":"text/csv","title":"PMMA_HRR_qrad_R6"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/PMMA_flame_spread_heatflux.png","description":"Representative images of flame structure and height-resolved measurements of total flame to wall heat flux (as measured by an array of water-cooled, Schmidt-Boelter heat flux gauges) during upward flame spread over cast poly(methyl methacrylate), PMMA","mediaType":"image/png","title":"PMMA_flame_spread_heatflux"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/PMMA_heatflux.csv","description":"Measured height-resolved total (flame to wall) heat flux profiles (and calculated expanded uncertainties) at seven heat release rates","mediaType":"text/csv","title":"PMMA_heatflux"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/Panel_Assembly.png","description":"Schematic of the NIST Parallel Panel Apparatus","mediaType":"image/png","title":"Panel_Assembly"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2812/README.md","description":"README","mediaType":"application/octet-stream","title":"README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-04 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Fire:Fire dynamics and science"],"issued":"2022-11-02","keyword":["fire modeling","flame spread"]},{"identifier":"ark:/88434/mds2-2813","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michaela.iorga@nist.gov","fn":"Michaela Iorga"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2813","description":"Demonstrations and utilities using XSLT in the web browser. XSLT 1.0 is supported with no dependencies on external libraries. Source code is in XML, XSLT, Javascript and Typescript.","language":["en"],"title":"XSLT Blender","distribution":[{"accessURL":"https://github.com/usnistgov/xslt-blender","description":"Source code for XSLT Blender demonstrations","title":"XSLT Blender - Github repository"},{"accessURL":"https://pages.nist.gov/xslt-blender/","description":"Demonstrations of XSLT in the browser.","title":"XSLT Blender - Portal (demonstrations)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"R/P1M","theme":["Standards:Documentary standards","Information Technology:Data and informatics","Information Technology:Cybersecurity"],"conformsTo":"https://www.w3.org/TR/REC-xml/","issued":"2022-11-18","keyword":["XML","XSLT","client-side processing","document processing","declarative programming"]},{"identifier":"ark:/88434/mds2-2815","accessLevel":"public","contactPoint":{"hasEmail":"mailto:vladimir.aksyuk@nist.gov","fn":"Vladimir Aksyuk"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2815","description":"Data from figure 4 \"Measuring photonic optomechanical devices\" in manuscript \"Integrated photonic optomechanical atomic force microscopy probes batch fabricated using deep UV photolithography,\" in Journal of Microelectromechanical Systems, doi: 10.1109/JMEMS.2023.3247300.","language":["en"],"title":"Data for manuscript: Integrated photonic optomechanical atomic force microscopy  probes batch fabricated using deep UV photolithography","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2815/Fig4b_experimental_data.csv","description":"Broad wavelength scan for transverse magnetic (TM) polarized modes of a typical disk-cantilever device (w = 150 nm, G = 180 nm )","mediaType":"text/csv","title":"Experimental data for figure 4(b)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2815/Fig4c_experimental_data.csv","description":"High-Q optical resonance indicated with an red arrow in (b)","mediaType":"text/csv","title":"Experimental data for Figure 4c"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2815/Fig4c_fit_data.csv","description":"Lorentzian fit Q=64000","mediaType":"text/csv","title":"Fit data for Figure 4c"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2815/Fig4d_experimental_data_10um.csv","description":"Transducer non-contact noise spectral density S_d for two devices with different dimensions. Device with a 10 micrometer disk (upper, red)  presents a lowest in-plane mode eigenfrequency of ?_0/2? ? 5 MHz and damping coefficient ?/2? ? 0.34 MHz while the 5 micrometer disk device (lower, blue) presents ?_0/2? ? 25 MHz and damping coefficient ?/2? ? 0.28 MHz.","mediaType":"text/csv","title":"Experimental data for figure 4d, 10 micrometer disk."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2815/Fig4d_experimental_data_5um.csv","description":"Transducer non-contact noise spectral density S_d for two devices with different dimensions. Device with a 10 micrometer disk (upper, red)  presents a lowest in-plane mode eigenfrequency of ?_0/2? ? 5 MHz and damping coefficient ?/2? ? 0.34 MHz while the 5 micrometer disk device (lower, blue) presents ?_0/2? ? 25 MHz and damping coefficient ?/2? ? 0.28 MHz.","mediaType":"text/csv","title":"Experimental data for Figure 4d, 5 micrometer disk"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Nanotechnology:Nanofabrication/manufacturing","Nanotechnology:Nanomechanics","Nanotechnology:Nanophotonics"],"issued":"2022-10-14","keyword":["integrated photonics","cavity optomechanical sensors"]},{"identifier":"ark:/88434/mds2-2816","accessLevel":"public","references":["https://doi.org/10.1145/3551659.3559049"],"contactPoint":{"hasEmail":"mailto:chunmei.liu@nist.gov","fn":"Chunmei Liu"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This 5G NR Sidelink Link-Level Simulator (LLS) is developed by National Institute Standards and Technology (NIST) developers by leveraging the MATLAB-based Vienna 5G NR Link Level Simulator. It requires the Vienna 5G NR LLS Release 1.2, licensed by Institute of Telecommunications TU-Wien and available at https://www.tuwien.at/etit/tc/en/vienna-simulators/vienna-5g-simulators/. It also requires MATLAB version 2021b with the following toolboxes: communication toolbox, 5G tool-box, and signal blocks.","language":["en"],"title":"5G New Radio Sidelink Link-Level Simulator","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2816/5g-new-radio-sidelink-link-level-simulator-master.zip","description":"This is the zip file of the patch file, the readme, and the documentation.","mediaType":"application/zip","title":"5G New Radio Sidelink Link-level Simulator"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-11 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Mobile","Advanced Communications:Wireless (RF)","Public Safety:Public safety communications research"],"issued":"2022-12-05","keyword":["Sidelink","link-level simulator","5G New Radio","Device-to-Device communications","wireless communication","communication range"]},{"identifier":"ark:/88434/mds2-2817","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jacob.pawlik@nist.gov","fn":"Jacob Pawlik"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2817","description":"Electromagnetic simulations of Parylene C devices, described in \"The influence of intrinsic water and ion permeation of the dielectric properties of Parylene C films\". We simulated various geometries of the device with different fluids inside and calculated S-parameters from extracted RLCG results. We also provide scripts to work up the simulation results into S-parameter plots.","language":["en"],"title":"Electromagnetic simulations of Parylene C devices","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2817/Parylene%20C%20CPW%20Simulation%20resources%20for%20MIDAS.zip","mediaType":"application/x-zip-compressed","title":"Electromagnetic simulations of Parylene C devices"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2817/2817_README_edit.txt","description":"Added line in \"data use\" to indicate which programs are required to open the data","mediaType":"text/plain","title":"README_edit"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Biomaterials","Electronics:Electromagnetics","Materials:Materials characterization","Physics:Spectroscopy"],"issued":"2023-01-04","keyword":["Parylene C","Microwave microfluidic spectroscopy","S-parameters","dielectric spectroscopy","profilometry","biomedical devices","implantable devices"]},{"identifier":"ark:/88434/mds2-2819","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nicholas.ritchie@nist.gov","fn":"Nicholas Ritchie"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2819","description":"A SQLite database containing mass absorption coefficient (both discrete and continuous), atomic sub-shell binding energy, X-ray energy, jump ratio, ground-state occupancy, atomic relaxation rate following core shell ionization and X-ray linewidth data.The data is in the common SQLite format and also available in SQL format. SQLite is an open-source database which is supported on many different platforms.This database represents a compilation of data from other sources.   Each datum is labeled with a literature reference which represents the source. The references are listed in the LIT_REFERENCES table with associated BIBTEX reference data. The two exceptions to this rule are the FFAST and FFAST_EXTRA tables which are associated with the Chantler2005 reference.","language":["en"],"title":"X-ray Properties Database in SQLite Format","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2819/atomic_database.db.sql","format":"SQLite SQL","description":"An atomic properties database in SQL format (SQLite dialect)","mediaType":"application/octet-stream","title":"Atomic properties in SQL format"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2819/atomic_database.db","format":"SQLite Database Format","description":"A database of atomic properties in SQLite binary format","mediaType":"application/octet-stream","title":"Atomic database in SQLite binary format"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2819/atomic_database.zip","format":"JSON in ZIP","description":"A database of atomic properties in JSON format","mediaType":"application/x-zip-compressed","title":"Atomic database tables in JSON format"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2819/2819_README.txt","format":"Text","description":"A file which describes the content of the data files.","mediaType":"text/plain","title":"READ_ME.TXT"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-14 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Analytical chemistry","Materials:Materials characterization","Materials:Modeling and computational material science","Physics:Spectroscopy","Physics:Atomic, molecular, and quantum"],"issued":"2022-12-01","keyword":["mass absorption coefficient","atomic sub-shell binding energy","X-ray energy","jump-ratio","ground-state occupancy","atomic relaxation rate","X-ray linewidth"]},{"identifier":"ark:/88434/mds2-2820","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.8216","https://doi.org/10.6028/NIST.IR.8245","https://doi.org/10.6028/NIST.IR.8277","https://doi.org/10.6028/NIST.IR.8295","https://doi.org/10.6028/NIST.IR.8340","https://doi.org/10.6028/NIST.IR.8288","https://doi.org/10.6028/NIST.IR.8314","https://doi.org/10.6028/NIST.IR.8400","https://doi.org/10.6028/NIST.IR.8444","https://doi.org/10.6028/NIST.IR.8443","https://doi.org/10.6028/NIST.SP.1286pt1","https://doi.org/10.6028/NIST.SP.1286pt2","https://doi.org/10.6028/NIST.SP.1286pt3","https://doi.org/10.6028/NIST.SP.1286pt4","https://publicsafety.nist.gov/"],"contactPoint":{"hasEmail":"mailto:yee-yin.choong@nist.gov","fn":"Yee-Yin Choong"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2820","description":"The Usability Team of the National Institute of Standards and Technology's (NIST) Public Safety Communications Research (PSCR) program works to identify issues faced by first responders surrounding the use of their existing and emerging public safety communication technology. The team conducted an exploratory, sequential, mixed-methods study to gather insights into first responders' needs for and problems experienced with communication technology. The multi-phase study included in-depth interviews with 193 first responders in Phase 1, followed by a nationwide survey of 7,182 first responders in Phase 2, across four public safety disciplines, Communication Center & 9-1-1 Services (COMMS), Emergency Medical Services (EMS), Fire Service (FF), and Law Enforcement (LE).The data consists of two datasets: (1) Phase 1 data from 193 interviews with first responders from four disciplines (COMMS, EMS, FF, LE) including direct quotes from interviewees categorized by codes/subcodes with demographic information included; (2) Phase 2 survey data from 7,182 first responders from four disciplines (COMMS, EMS, FF, LE) including their responses on what technology they have and use, along with their needs for and problems experienced with communication technology; demographic information is also included.","language":["en"],"title":"PSCR Usability Project_Voices of First Responders","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2820/PSCR%20Usability%20Project_Phase%201_Interview_Quotes.xlsx","description":"The data were extracted from over 20,000 first responder quotes from the first responder interview transcripts in Phase 1 of the PSCR Usability Project - Voices of First Responders. Phase 1 of the project was a qualitative exploration of first responders and their contexts of work. The interviews included 193 first responders across the country, from four public safety disciplines: Communication Center & 9-1-1 Services (COMMS), Emergency Medical Services (EMS), Fire Service (FF), and Law Enforcement (LE). All interview recordings were transcribed by a professional  transcription service; as such, the accuracy of transcription text is dependent on the transcriber. To protect the identity of the first responders interviewed, all quotes have been reviewed for personally identifiable information (PII) and all identifiers redacted.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"PSCR Usability Project_Phase 1_Interview_Quotes"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2820/PSCR%20Usability%20Project_Phase%202_Survey_Responses.xlsx","description":"The data were from the responses of the Phase 2, nationwide survey - completed by 7,182 first responders across four public safety disciplines: Communication Center & 9-1-1 Services (COMMS), Emergency Medical Services (EMS), Fire Service (FF), and Law Enforcement (LE). Phase 2 was a quantitative survey that focused on what technology first responders have and use, along with their needs for and problems experienced with communication technology. To protect the identity of the first responders interviewed and surveyed, all open-ended survey responses have been reviewed for personally identifiable information (PII) and all identifiers redacted.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"PSCR Usability Project_Phase 2_Survey_Responses"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2820/PSCR_Usability_Project_datamap.xlsx","description":"PSCR_Usability_Project_datamap","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"PSCR_Usability_Project_datamap"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2820/PSCR_Usability_Project_README.txt","description":"PSCR_Usability_Project_README","mediaType":"text/plain","title":"PSCR_Usability_Project_README"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology","Information Technology:Usability and human factors","Public Safety","Public Safety:Public safety communications research"],"issued":"2022-12-21","keyword":["Communication technology","First responders","Public safety","Usability","Human Factors","Mixed-methods research"]},{"identifier":"ark:/88434/mds2-2824","accessLevel":"public","references":["https://dx.doi.org/10.1109/JMW.2022.3232076"],"contactPoint":{"hasEmail":"mailto:dylan.williams@nist.gov","fn":"Dylan Williams"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2824","description":"This repository contains all of the data published in the figures of the paper \"On-Wafer Vector-Network-Analyzer Measurements at mK Temperatures.\" Manuscript DOI: 10.1109/JMW.2022.3232076Manuscript ID: JMW-2022-0108Manuscript Title: On-Wafer Vector-Network-Analyzer Measurements at mK TemperaturesPublished by: Institute of Electrical and Electronics Engineers (IEEE)","language":["en"],"title":"On-Wafer Vector-Network-Analyzer Measurements at mK Temperatures","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%204.zip","format":"text in a zip file","description":"Data for Figure 4","mediaType":"application/x-zip-compressed","title":"Figure 4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%208.zip","format":"Text files in a zip file","description":"Data for Figure 8","mediaType":"application/x-zip-compressed","title":"Figure 8"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%206a.zip","format":"Text files in a zip file","description":"Data for Figure 6a","mediaType":"application/x-zip-compressed","title":"Figure 6a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%205a.zip","format":"text files in a zip file","description":"Data in Figure 5a","mediaType":"application/x-zip-compressed","title":"Figure 5a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%205b.zip","format":"Text files in a zip file","description":"Data for Figure 5b","mediaType":"application/x-zip-compressed","title":"Figure 5b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/2824_README.txt","format":"Text","description":"A README data file for this data set, including explanations for data location and organization.","mediaType":"text/plain","title":"README file for this data set"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%206b.zip","format":"Text files in a zip file","description":"Data for Figure 6b","mediaType":"application/x-zip-compressed","title":"Figure 6b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%207a.zip","format":"Text files in a zip file","description":"Data for Figure 7a","mediaType":"application/x-zip-compressed","title":"Figure 7a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%207b.zip","format":"Text files in a zip file","description":"Data for Figure 7b","mediaType":"application/x-zip-compressed","title":"Figure 7b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%208a.zip","format":"Text files in a zip file","description":"Data for Figure 8a","mediaType":"application/x-zip-compressed","title":"Figure 8a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%208b.zip","format":"Text files in a zip file","description":"Data for Figure 8b","mediaType":"application/x-zip-compressed","title":"Figure 8b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%209a.zip","format":"Text files in a zip file","description":"Data for Figure 9a","mediaType":"application/x-zip-compressed","title":"Figure 9a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%209b.zip","format":"Text files in a zip file","description":"Data for Figure 9b","mediaType":"application/x-zip-compressed","title":"Figure 9b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%2011a.zip","format":"Text files in a zip file","description":"Data for Figure 11a","mediaType":"application/x-zip-compressed","title":"Figure 11a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%2011b.zip","format":"Text files in a zip file","description":"Data for Figure 11b","mediaType":"application/x-zip-compressed","title":"Figure 11b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure%2010b.zip","format":"Text files in zip file","description":"Data used in Figure 10b","mediaType":"application/x-zip-compressed","title":"Figure 10b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure10a.zip","format":"Text files in a zip file","description":"Data in Figure 10a","mediaType":"application/x-zip-compressed","title":"Figure 10a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2824/Figure10c.zip","format":"text files in zip file","description":"Data for Figure 10c","mediaType":"application/x-zip-compressed","title":"Figure 10c"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Electronics:Superconducting electronics","Electronics:Electromagnetics"],"issued":"2023-04-07","keyword":["Microwave calibrations","on-wafer measurement","cryogenic","dilution refrigerator","vector network analysis."]},{"identifier":"ark:/88434/mds2-2827","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nader.moayeri@nist.gov","fn":"Nader Moayeri"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"NIST carried out a comprehensive BLE RSI data collection campaign under various operational scenarios.  In some scenarios, the transmitting and receiving BLE radios were in line-of-sight (LOS) or each other and in others they were in non-line-of-sight (NLOS).  The distance between the radios were measured and recorded in each scenario.  in NLOS scenarios, data was collected were walls made of different construction materials were blocking the direct path from the transmitter to the receiver.  In LOS scenarios, we looked at the effect of a person being between the radios and how that affected the received signal.  It is valuable to make this data, which is well-documented, available to the R&D community so that they can develop and evaluate their own proximity detection algorithms.","language":["en"],"title":"Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI) Data for Proximity Detection in COVID-19 Electronic Contact Tracing","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2827/BLE_RSSI_Data.zip","format":"zip","description":"Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI) Data for Proximity Detection in COVID-19 Electronic Contact Tracing","mediaType":"application/zip","title":"Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI) Data for Proximity Detection in COVID-19 Electronic Contact Tracing"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2827/2827_README.txt","mediaType":"text/plain","title":"Read Me File for BLE RSSI Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Networking","Information Technology:Mobile","Information Technology:Internet of Things","Information Technology:Healthcare IT","Information Technology:Data and informatics","Electronics:Sensors","Advanced Communications:Wireless (RF)"],"issued":"2022-10-28","keyword":["Bluetooth Low Energy (BLE); COVID-19; Distance Estimation; Electronic Contact Tracing; Exposure Notification; Infectious Diseases; Pandemics; Path Loss Models; Proximity Detection; Received Signal Strength Indicator (RSSI); Receiver Operating Characteristic (ROC) Curve; Smartphones; Viterbi Algorithm; Wearable Devices"]},{"identifier":"ark:/88434/mds2-2829","accessLevel":"public","contactPoint":{"hasEmail":"mailto:elizabeth.kelley@nist.gov","fn":"Elizabeth Kelley"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2829","description":"The files include dynamic light scattering (DLS), small angle neutron scattering (SANS), and neutron spin echo spectroscopy (NSE) data that were analyzed in writing the manuscript. The SANS data were collected on the NGB30 SANS and NG3 vSANS instruments at the NIST Center for Neutron Research (NCNR) and have been reduced to absolute intensity using standard procedures. The NSE data were collected on the NGA NSE instrument at the NCNR and have been corrected for the instrument resolution and solvent signal. Together these data were used to analyze the nanoscale bending fluctuations in tail-asymmetric lipid membranes.The files for the different samples are labelled with the same abbreviations for the lipid chemical names used in the manuscript.DPPC: 1,2-dipalmitoyl-sn-glycerco-3-phosphophochloine (di 16:0 PC)MSPC: 1-myrisotyl-2-stearoyl-sn-glycerol-3-phosphocholine (14:0-18:0 PC)SMPC: 1-stearoyl-2-myristoyl-sn-glycero-3-phosphocholine (18:0-14:0 PC)","language":["en"],"title":"Data used in writing \"Nanoscale bending dynamics in mixed-chain lipid membranes\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2829/X_symmetry_data.zip","format":"All data are given as .txt. files","description":"The files include dynamic light scattering (DLS), small angle neutron scattering (SANS), and neutron spin echo spectroscopy (NSE) data analyzed in the manuscript.  The SANS data have been reduced to absolute intensity, and the NSE data have been corrected for the instrument resolution and solvent signal.","mediaType":"application/x-zip-compressed","title":"Data files analyzed in the writing of the manuscript"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Neutron Research"],"issued":"2023-02-09","keyword":["lipid vesicles","small angle neutron scattering (SANS)","dynamic light scattering (DLS)","neutron spin echo spectroscopy (NSE)","membrane fluctuations"]},{"identifier":"ark:/88434/mds2-2830","accessLevel":"public","contactPoint":{"hasEmail":"mailto:samuel.forry@nist.gov","fn":"Samuel P. Forry"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2830","description":"This repository contains all submissions made to the Mosaic Standards challenge from the period of the challenge opening through December 31st, 2019. The Mosaic Standards Challenge asked the microbiome research community to participate in determining the level of variation due to wet-lab protocols by sequencing a set of samples and providing the resulting files. Each participant ordered one or more kits, where each kit contained five fecal samples and two predetermined DNA mixtures. All samples were identical across all kits; in other words, the samples labled \"#1\" provided to each lab were identical to each other. Participants in the challenge sequenced any number of the provided samples and provided both the raw sequencing result files, and the details of their protocol. Protocol details were provided by answering a set of pre-specified questions in a metadata spreadsheet upon submission of each sample.","language":["en"],"title":"Mosaic Standards 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Microbial measurements"],"keyword":["microbiome; methodology; metagenomic sequencing;"]},{"identifier":"ark:/88434/mds2-2831","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.majurski@nist.gov","fn":"Michael Paul Majurski"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2831","description":"Round 11 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of image classification AIs trained on synthetic image data build from Cityscapes. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 288 AI models using a small set of model architectures. 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.pdf manuscript","description":"A NIST Tech Note containing a description of the Generator Fleet Characteristics Model","mediaType":"application/pdf","title":"Generator Fleet Characteristics Model (NIST TN 2246)"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-27 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Energy:Electric power / smart grid"],"spatial":"The GFCM uses operations and reanalysis data that cover the contiguous United States.","issued":"2023-02-06","keyword":["Economics; Electricity; Generator Fleet; Infrastructure; Interoperability; Operations; Power Systems; Resilience; Smart Grid; Synchronous Inertia."],"temporal":"2019-01-01/2021-12-31"},{"identifier":"ark:/88434/mds2-2837","accessLevel":"public","contactPoint":{"hasEmail":"mailto:andrew.rotunno@nist.gov","fn":"Drew 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Data is taken as an oscilloscope average of 5 photodiode voltage traces, with frequency offsets given by a simultaneous reference cell (not included). Some data are given as 2-D arrays, with axes of laser detuning across a waterfall of field strength.  Some data represents theory eigen-energies of the system, for comparison. This paper will be submitted to Physical Review Letters.","language":["en"],"title":"Data for the article  \" Pseudo-resonant Detection of `Low Frequency' VHF Electric Fields via Rabi Matching with Autler-Townes Splitting in Rydberg Atoms\"","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-10-28 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Atomic, molecular, and quantum","Physics:Spectroscopy"],"issued":"2022-12-01","keyword":["Rydberg atoms","atomic physics","receivers","fields strength","electric field","volts/meter"]},{"identifier":"ark:/88434/mds2-2842","accessLevel":"public","references":["https://doi.org/10.1121/10.0014628"],"contactPoint":{"hasEmail":"mailto:marc.valdez@nist.gov","fn":"Marc Valdez"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2842","description":"This dataset contains CSV files for the figures in the paper titled \"On-grid compressive sampling for spherical field measurements in acoustics\" in The Journal of the Acoustical Society of America. In this paper, we derive a compressive sampling method for spherical harmonic/spherical wavefunction or Wigner D-function series with sparse coefficients. Applications of these sparse expansions include spherical field measurements in acoustics and spherical near-field antenna measurements, to name a couple. The figures that this dataset is for are examples demonstrating the following: example acoustic field coefficients in the spherical harmonic/spherical wavefunction basis; relationships between spherical harmonic/spherical wavefunction/Wigner D-function coefficient sparsity and spatial Fourier coefficient sparsity; example compressive sampling reconstruction using our proposed compressive sampling method with and without noise; and comparisons between classical Nyquist sampling and our proposed compressive sampling method.","language":["en"],"title":"Data for \"On-grid compressive sampling for spherical field measurements in acoustics\" in The Journal of the Acoustical Society of America.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_1_sorted_coefficients_case_2a.csv","description":"This file contains the data for supplemental figure 1. It shows     the relative magnitude in dB of the sorted Fourier and Wigner     D function coefficients for case 2a as described in the file     header and paper. The data is organized as described in the header    (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_1_sorted_coefficients_case_2a.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_2_sorted_coefficients_case_2a.csv","description":"This file contains the data for supplemental figure 2. It shows     the coefficient normalized error in dB of the sorted Fourier and     Wigner D function coefficients for case 2a as described in the     file header and paper. The data is organized as described in the     header (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_2_sorted_coefficients_case_2a.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_3_sorted_coefficients_case_2b.csv","description":"This file contains the data for supplemental figure 3. It shows     the relative magnitude in dB of the sorted Fourier and Wigner     D function coefficients for case 2b as described in the file     header and paper. The data is organized as described in the header    (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_3_sorted_coefficients_case_2b.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_4_sorted_coefficients_case_2b.csv","description":"This file contains the data for supplemental figure 4. It shows     the coefficient normalized error in dB of the sorted Fourier and     Wigner D function coefficients for case 2b as described in the     file header and paper. The data is organized as described in the     header (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_4_sorted_coefficients_case_2b.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_5_sorted_coefficients_case_2c.csv","description":"This file contains the data for supplemental figure 5. It shows     the relative magnitude in dB of the sorted Fourier and Wigner     D function coefficients for case 2c as described in the file     header and paper. The data is organized as described in the header    (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_5_sorted_coefficients_case_2c.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_4a_SW_coefs_DirPat_CUBE_1098Hz.csv","description":"This file contains the data for figure 4a. It shows the magnitude     of the spherical wavefunction coefficients for the DirPat CUBE     driver 1 loudspeaker at 1098Hz. The data is organized as described    in the header (first row) of the csv file.","mediaType":"text/csv","title":"Figure_4a_SW_coefs_DirPat_CUBE_1098Hz.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_4b_SW_coefs_DirPat_CUBE_1400Hz.csv","description":"This file contains the data for figure 4b. It shows the magnitude     of the spherical wavefunction coefficients for the DirPat CUBE     driver 1 loudspeaker at 1400Hz. The data is organized as described    in the header (first row) of the csv file.","mediaType":"text/csv","title":"Figure_4b_SW_coefs_DirPat_CUBE_1400Hz.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_6_sorted_coefficients_case_2c.csv","description":"This file contains the data for supplemental figure 6. It shows     the coefficient normalized error in dB of the sorted Fourier and     Wigner D function coefficients for case 2c as described in the     file header and paper. The data is organized as described in the     header (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_6_sorted_coefficients_case_2c.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_7_sorted_coefficients_case_3a.csv","description":"This file contains the data for supplemental figure 7. It shows     the relative magnitude in dB of the sorted Fourier and Wigner     D function coefficients for case 3a as described in the file     header and paper. The data is organized as described in the header    (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_7_sorted_coefficients_case_3a.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_8_sorted_coefficients_case_3a.csv","description":"This file contains the data for supplemental figure 8. It shows     the coefficient normalized error in dB of the sorted Fourier and     Wigner D function coefficients for case 3a as described in the     file header and paper. The data is organized as described in the    header (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_8_sorted_coefficients_case_3a.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_9_sorted_coefficients_case_3b.csv","description":"This file contains the data for supplemental figure 9. It shows     the relative magnitude in dB of the sorted Fourier and Wigner     D function coefficients for case 3b as described in the file     header and paper. The data is organized as described in the header    (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_9_sorted_coefficients_case_3b.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_10_sorted_coefficients_case_3b.csv","description":"This file contains the data for supplemental figure 10. It shows     the coefficient normalized error in dB of the sorted Fourier and    Wigner D function coefficients for case 3b as described in the     file header and paper. The data is organized as described in the     header (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_10_sorted_coefficients_case_3b.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_4c_SW_coefs_DirPat_CUBE_1895Hz.csv","description":"This file contains the data for figure 4c. It shows the magnitude     of the spherical wavefunction coefficients for the DirPat CUBE     driver 1 loudspeaker at 1895Hz. The data is organized as described    in the header (first row) of the csv file.","mediaType":"text/csv","title":"Figure_4c_SW_coefs_DirPat_CUBE_1895Hz.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_3_data_s_D_verus_average_s_F_100_trials.csv","description":"This file contains the data for Figure 3, which shows the sorted     concentrations of the Fourier basis sparsity as a function of     Wigner D-function sparsity for different sparsity levels where     the Wigner D-function coefficients are set to 1 at random     positions. The data is organized as described in the header     (first row) of the csv file.","mediaType":"text/csv","title":"Figure_3_data_s_D_verus_average_s_F_100_trials.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_5a_sorted_coefficients_case_1a.csv","description":"This file contains the data for figure 5a. It shows the relative     magnitude in dB of the sorted Fourier and Wigner D function     coefficients for case 1a as described in the file header and     paper. The data is organized as described in the header (first     row) of the csv file.","mediaType":"text/csv","title":"Figure_5a_sorted_coefficients_case_1a.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_5b_sorted_coefficients_case_1a.csv","description":"This file contains the data for figure 5b. It shows the     coefficient normalized error in dB of the sorted Fourier and     Wigner D function coefficients for case 1a as described in the    file header and paper. The data is organized as described in the     header (first row) of the csv file.","mediaType":"text/csv","title":"Figure_5b_sorted_coefficients_case_1a.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_6a_sorted_coefficients_case_1b.csv","description":"This file contains the data for figure 6a. It shows the relative     magnitude in dB of the sorted Fourier and Wigner D function     coefficients for case 1b as described in the file header and     paper. The data is organized as described in the header (first     row) of the csv file.","mediaType":"text/csv","title":"Figure_6a_sorted_coefficients_case_1b.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_6b_sorted_coefficients_case_1b.csv","description":"This file contains the data for figure 6b. It shows the     coefficient normalized error in dB of the sorted Fourier and     Wigner D function coefficients for case 1b as described in the     file header and paper. The data is organized as described in the     header (first row) of the csv file.","mediaType":"text/csv","title":"Figure_6b_sorted_coefficients_case_1b.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_11_classical_Nyquist_RelativeError_vs_n_meas_with_noise.csv","description":"This file contains the data for figure 11. It shows the relative    error (dB) for classical Fourier sampling as a function of sample    grid density for cases 1a, 2a, and 3a from the paper. The data is    organized as described in the header (first row) of the csv file.","mediaType":"text/csv","title":"Figure_11_classical_Nyquist_RelativeError_vs_n_meas_with_noise.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_12a_RelativeError_vs_n_phys_meas_vs_grid_dens.csv","description":"This file contains the data for figure 12a. It shows the relative    error (dB) for Fourier based compressive sampling as a function of    sample grid density and number of measurements for case 1a from     the paper. The data is organized as described in the header (first    row) of the csv file.","mediaType":"text/csv","title":"Figure_12a_RelativeError_vs_n_phys_meas_vs_grid_dens.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_12b_RelativeError_vs_n_phys_meas_vs_grid_dens.csv","description":"This file contains the data for figure 12b. It shows the relative    error (dB) for Fourier based compressive sampling as a function of    sample grid density and number of measurements for case 2a from     the paper. The data is organized as described in the header (first    row) of the csv file.","mediaType":"text/csv","title":"Figure_12b_RelativeError_vs_n_phys_meas_vs_grid_dens.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_12c_RelativeError_vs_n_phys_meas_vs_grid_dens.csv","description":"This file contains the data for figure 12c. It shows the relative    error (dB) for Fourier based compressive sampling as a function of    sample grid density and number of measurements for cases 3a from     the paper. The data is organized as described in the header (first    row) of the csv file.","mediaType":"text/csv","title":"Figure_12c_RelativeError_vs_n_phys_meas_vs_grid_dens.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_13_coherence_vs_sample_dense_vs_grid_dens.csv","description":"This file contains the data for figure 13. It shows the average     coherence of the 2DDFT CS measurment matrix as a function of grid    density and average sample number. The average is over 25 trials.     The data is organized as described in the header (first row) of     the csv file.","mediaType":"text/csv","title":"Figure_13_coherence_vs_sample_dense_vs_grid_dens.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_11_sorted_coefficients_case_3c.csv","description":"This file contains the data for supplemental figure 11. It shows     the relative magnitude in dB of the sorted Fourier and Wigner     D function coefficients for case 3c as described in the file     header and paper. The data is organized as described in the header    (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_11_sorted_coefficients_case_3c.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/supp_figure_12_sorted_coefficients_case_3c.csv","description":"This file contains the data for supplemental figure 12. It shows     the coefficient normalized error in dB of the sorted Fourier and    Wigner D function coefficients for case 3c as described in the     file header and paper. The data is organized as described in the     header (first row) of the csv file.","mediaType":"text/csv","title":"supp_figure_12_sorted_coefficients_case_3c.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/README.txt","description":"This is a \"read me\" file that contains and overview of the dataset.","mediaType":"text/plain","title":"README.txt"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_7a_sorted_coefficients_case_1c.csv","description":"This file contains the data for figure 7a. It shows the relative    magnitude in dB of the sorted Fourier and Wigner D function     coefficients for case 1c as described in the file header and     paper. The data is organized as described in the header (first     row) of the csv file.","mediaType":"text/csv","title":"Figure_7a_sorted_coefficients_case_1c.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_7b_sorted_coefficients_case_1c.csv","description":"This file contains the data for figure 7b. It shows the     coefficient normalized error in dB of the sorted Fourier and     Wigner D function coefficients for case 1c as described in the     file header and paper. The data is organized as described in the     header (first row) of the csv file.","mediaType":"text/csv","title":"Figure_7b_sorted_coefficients_case_1c.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_8a_actual_vs_CS_field_C1a.csv","description":"This file contains the data for figure 8a. It shows the near-field    reconstruction (relative magnitude in dB) using Fourier based     compressive sensing for an acoustic field (case 1a from the     paper). The data is organized as described in the header (first     row) of the csv file.","mediaType":"text/csv","title":"Figure_8a_actual_vs_CS_field_C1a.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_8b_CS_field_RelativeError_C1a.csv","description":"This file contains the data for figure 8b. It shows the relative      error (dB) of the near-field reconstruction using Fourier based       compressive sensing for an acoustic field (case 1a from the       paper). The data is organized as described in the header (first       row) of the csv file.","mediaType":"text/csv","title":"Figure_8b_CS_field_RelativeError_C1a.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_9_rel_err_vs_n_phys_meas_Fourier_vs_WingerD.csv","description":"This file contains the data for figure 9. It shows the coefficient    relative error as a function of measurement number for Fourier and    Wigner D function based compressive sensing for acoustic fields     (case 1a, 2a, 3a from the paper). The data is organized as     described in the header (first row) of the csv file.","mediaType":"text/csv","title":"Figure_9_rel_err_vs_n_phys_meas_Fourier_vs_WingerD.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_10_RelativeError_vs_n_phys_meas_c1a_Fourier_vs_WingerD_grid_dens_1_to_4.csv","description":"This file contains the data for figure 10. It shows the     coefficient relative error as a function of measurement number for    Fourier and on-grid Wigner D function based compressive sensing     for the acoustic fields in case 1a as the sampling grid density is    increased. The data is organized as described in the header (first    row) of the csv file.","mediaType":"text/csv","title":"Figure_10_RelativeError_vs_n_phys_meas_c1a_Fourier_vs_WingerD_grid_dens_1_to_4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_14a_RelativeError_vs_sample_dense_vs_grid_dens.csv","description":"This file contains the data for figure 14a. It shows the relative    error (dB) for Fourier based compressive sampling as a function of    sample grid density and sample density for cases 1a from the     paper. The data is organized as described in the header (first     row) of the csv file.","mediaType":"text/csv","title":"Figure_14a_RelativeError_vs_sample_dense_vs_grid_dens.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_14b_RelativeError_vs_sample_dense_vs_grid_dens.csv","description":"This file contains the data for figure 14b. It shows the relative    error (dB) for Fourier based compressive sampling as a function of    sample grid density and sample density for cases 2a from the     paper. The data is organized as described in the header (first     row) of the csv file.","mediaType":"text/csv","title":"Figure_14b_RelativeError_vs_sample_dense_vs_grid_dens.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2842/Figure_14c_RelativeError_vs_sample_dense_vs_grid_dens.csv","description":"This file contains the data for figure 14c. It shows the relative    error (dB) for Fourier based compressive sampling as a function of    sample grid density and sample density for cases 3a from the     paper. The data is organized as described in the header (first     row) of the csv file.","mediaType":"text/csv","title":"Figure_14c_RelativeError_vs_sample_dense_vs_grid_dens.csv"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-11-09 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)","Mathematics and Statistics:Image and signal processing"],"issued":"2023-01-11","keyword":["compressive sensing","compressive sampling","sparse signal processing","far-field pattern","near-field pattern","antenna characterization","Wigner D-functions","spherical harmonics","acoustic fields;"]},{"identifier":"ark:/88434/mds2-2843","accessLevel":"public","contactPoint":{"hasEmail":"mailto:benjamin.neely@nist.gov","fn":"Ben Neely"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2843","description":"Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomicists. There are now a wide variety of robust and inexpensive enrichment strategies to generate phosphoproteomes, while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities (ABRF) umbrella, the Proteomics Standards Research Group (sPRG) has worked to develop a multipathway phosphopeptide prototype mixture based on a pool of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This prototype mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/IGF-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. We describe a characterization of the standard spiked into a HeLa tryptic digest stimulated with both EGF and IGF1 to activate the MAPK and PI3K/AKT/mTOR pathways. We demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently by the co-authors with this prototype mixture with data independent acquisition.","language":["en"],"title":"Gas phase fractionation data independent acquisition analysis of a phosphopeptide mixture","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2843/2019-06-26_sPRG_GPF_7.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2843/2019-06-26_sPRG_GPF_8.raw.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2843/2019-06-26_sPRG_GPF_1.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2843/2019-06-26_sPRG_GPF_2.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2843/2019-06-26_sPRG_GPF_3.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2843/2019-06-26_sPRG_GPF_4.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2843/2019-06-26_sPRG_GPF_5.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2843/2019-06-26_sPRG_GPF_6.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2843/2019-06-26_sPRG_GPF_7.raw","mediaType":"application/octet-stream"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2843/2019-06-26_sPRG_GPF_8.raw","mediaType":"application/octet-stream"},{"accessURL":"https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=d9d03f9b21c442f8929056b348c2ac6d","format":"raw 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00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Proteomics"],"issued":"2023-04-13","keyword":["gas phase fractionation","phosphopeptides","post-translational modification localization"]},{"identifier":"ark:/88434/mds2-2844","accessLevel":"public","contactPoint":{"hasEmail":"mailto:tasshi.dennis@nist.gov","fn":"Tasshi Dennis"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2844","description":"Data presented is part of the journal manuscript \"Optically Distributing Remote Two-node Microwave Entanglement using Doubly Parametric Quantum Transducers.\"  Data includes graphical plots generated from numerical models and computations for various network topologies which illustrate their thresholds for achieving quantum information transfer.","language":["en"],"title":"Optically Distributing Remote Two-node Microwave Entanglement using Doubly Parametric Quantum Transducers","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2844/Nth_vs_Da_opt_Db_small.csv","description":"Modeled and computed network entanglement thresholds for preserving quantum information for the eight network topologies illustrated in Table 1 as a function of optical cooperativity where microwave cooperativity is small.  Quantum information is preserved above the curves and separable below.  In the spreadsheet, column A is unitless optical cooperativity for the horizontal axis, and for the vertical axis the thermal occupation for the topologies of Extrinsic Optical Downconversion (column B), Extrinsic Microwave Downconversion (column C), Intrinsic Optical Downconversion (column D), Intrinsic Microwave Downconversion (column E), Extrinsic Optical Swapping (column F), Extrinsic Microwave Swapping (column G), Intrinsic Optical Swapping (column H), Intrinsic Microwave Swapping (column I).","mediaType":"text/csv","title":"Nth_vs_Da_opt_Db_small"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2844/Nth_vs_Da_opt_Db_big.csv","description":"Modeled and computed network entanglement thresholds for preserving quantum information for the eight network topologies illustrated in Table 1 as a function of optical cooperativity where microwave cooperativity is large.  Quantum information is preserved above the curves and separable below.  In the spreadsheet, column A is unitless optical cooperativity for the horizontal axis, and for the vertical axis the thermal occupation for the topologies of Extrinsic Optical Downconversion (column B), Extrinsic Microwave Downconversion (column C), Intrinsic Optical Downconversion (column D), Intrinsic Microwave Downconversion (column E), Extrinsic Optical Swapping (column F), Extrinsic Microwave Swapping (column G), Intrinsic Optical Swapping (column H), Intrinsic Microwave Swapping (column I).","mediaType":"text/csv","title":"Nth_vs_Da_opt_Db_big.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2844/N_vs_Ea_opt.csv","description":"Modeled and computed network entanglement thresholds for preserving quantum information for the eight network topologies illustrated in Table 1 as a function of optical device loss.  Quantum information is preserved above the curves and separable below.  In the spreadsheet, column A is unitless optical device transmissivity (equivalent to loss) for the horizontal axis, and for the vertical axis the normalized thermal occupation for the topologies of Extrinsic Optical Downconversion (column B), Extrinsic Microwave Downconversion (column C), Intrinsic Optical Downconversion (column D), Intrinsic Microwave Downconversion (column E), Extrinsic Optical Swapping (column F), Extrinsic Microwave Swapping (column G), Intrinsic Optical Swapping (column H), Intrinsic Microwave Swapping (column I).","mediaType":"text/csv","title":"N_vs_Ea_opt.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2844/En_vs_Et_realistic_device.csv","description":"Modeled and computed logarithmic negativity of the final microwave-microwave state generated by each network topology considered, using recently reported electro-optic-mechanical transducer parameters plotted as a function of optical loss external to the transducers.  In the spreadsheet, column A is unitless optical transmissivity (equivalent to loss) for the horizontal axis, and for the unitless vertical axis column B is Extrinsic Optical Downconversion with 10 dB squeezing, column C is Extrinsic Optical Downconversion with 3 dB squeezing, column D is Extrinsic Optical Swapping with 10 dB squeezing, column E is Intrinsic Microwave Downconversion, and column F is Intrinsic Microwave Swapping.","mediaType":"text/csv","title":"En_vs_Et_realistic_device.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2844/En_vs_Et_asymmetric.csv","description":"Modeled and computed logarithmic negativity of the final microwave-microwave state generated by each network topology considered, using recently reported electro-optic-mechanical transducer parameters plotted as a function of optical loss external to the transducers where the optical loss may be distributed asymmetrically within the network links. In the spreadsheet, column A is unitless optical transmissivity (equivalent to loss) for the horizontal axis, and for the unitless vertical axis column B is extrinsic optical symmetric, column C is intrinsic microwave symmetric, column D is intrinsic microwave asymmetric, column intrinsic microwave plus extrinsic optical asymmetric.","mediaType":"text/csv","title":"En_vs_Et_asymmetric.csv"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2844/2844_README.txt","description":"MIDAS generated Read Me file","mediaType":"text/plain","title":"28844_ReadMe.txt"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-11-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Quantum communications","Physics:Atomic, molecular, and quantum","Physics:Quantum information science","Physics:Optical physics"],"issued":"2023-01-19","keyword":["quantum networks","quantum computing","superconducting","transduction","squeezed state","entanglement"]},{"identifier":"ark:/88434/mds2-2846","accessLevel":"public","contactPoint":{"hasEmail":"mailto:felix.kim@nist.gov","fn":"Felix Kim"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2846","description":"X-ray computed tomography (XCT) datasets with known ground truth pores were developed using realistic XCT simulation. Non-overlapping spherical pores of varying sizes are randomly distributed in a cylindrical part near surfaces and within the core. Ground truth data, ground truth binary data, and reconstructed data for three different signal-to-noise ratios (SNRs) are provided. The data set can be used for evaluation and comparison of image segmentation/detection algorithms.","language":["en"],"title":"Simulated X-ray computed tomography (XCT) and ground truth images of cylindrical sample with randomly distributed spherical pores","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2846/Ground%20Truth.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2846/Ground%20Truth.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2846/Reconstruction1.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2846/Reconstruction1.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2846/Reconstruction2.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2846/Reconstruction2.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2846/Reconstruction3.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2846/Reconstruction3.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2846/Dataset-Notes.docx","description":"The file provides relevant information about the datasets.","mediaType":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"Readme file"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2846/Ground%20Truth%20Binary.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2846/Ground%20Truth%20Binary.zip.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-07-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Statistical analysis","Mathematics and Statistics:Image and signal processing","Manufacturing:Additive manufacturing"],"issued":"2023-07-20","keyword":["X-ray computed tomography","defect","flaw","additive manufacturing","image segmentation","evaluation metrics","ground truth data","Non-destructive evaluation"]},{"identifier":"ark:/88434/mds2-2849","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jerome.cheron@nist.gov","fn":"Jerome Cheron"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"data published in paper \"A Tunable 220 GHz Comb Generator Realized with an Ultrawideband Mixer in a InP HBT Technology\"","language":["en"],"title":"A Tunable 220 GHz Comb Generator Realized with an Ultrawideband Mixer in a InP HBT Technology","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2849/fig4a.txt","mediaType":"text/plain","title":"RF performance of the mixer when the IF is swept from 10 MHz to 50 GHz with a CW signal.  LO frequency is set at 140 GHz."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2849/fig4b.txt","mediaType":"text/plain","title":"RF performance of the mixer when the IF is swept from 10 MHz to 50 GHz with a CW signal.  LO frequency is set at 180 GHz."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2849/fig5a.txt","mediaType":"text/plain","title":"RF performance when the mixer is fed with a broadband comb (pulse) at the IF input. IF: 500 MHz tone spacing, LO frequency is 140 GHz."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2849/fig5b.txt","mediaType":"text/plain","title":"RF performance when the mixer is fed with a broadband comb (pulse) at the IF input. IF: 500 MHz tone spacing, LO frequency is 180 GHz."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2849/fig5c.txt","mediaType":"text/plain","title":"RF performance when the mixer is fed with a broadband comb (pulse) at the IF input. IF: 2 GHz tone spacing, LO frequency is 140 GHz."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2849/fig5d.txt","mediaType":"text/plain","title":"RF performance when the mixer is fed with a broadband comb (pulse) at the IF input. IF: 2 GHz tone spacing, LO frequency is 180 GHz."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2849/2849_README.txt","mediaType":"text/plain","title":"Readme"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-11-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications","Electronics"],"issued":"2022-12-06","keyword":["on-wafer calibration. Comb generator","millimeter-wave","monolithic microwave integrated-circuit (MMIC)","ultrawideband mixer."]},{"identifier":"ark:/88434/mds2-2850","accessLevel":"public","references":["https://iopscience.iop.org/article/10.1088/0953-4075/47/9/093001"],"contactPoint":{"hasEmail":"mailto:zachary.levine@nist.gov","fn":"Zachary H. Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2850","description":"The Maxwell-Bloch equations for a two-level system are solved in a particular case.  The example follows that of P. Siddons, \"Light propagation through atomic vapours,\" Journal of Physics B: Atomic, Molecular, and Optical Physics 47, 093001 (2014).  In the reference, the optical intensity, population of the upper state and coherence are given for light with a carrier frequency which is on resonance.  Here, the same example is worked, but the result is given at intermediate times as well as at the entrance and exit faces as in the example.   The solution is found using Mathematica's NDSolve for the time dimension and the Method of Lines for propagation in space.","language":["en"],"title":"Maxwell-Bloch Equations for Two-Level System","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2850/maxwellBlochWolfDemo03.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2850/maxwellBlochWolfDemo03.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2850/2850_README.txt","format":"ASCII text file  (*.txt)","description":"Information about the software in this record are given","mediaType":"text/plain","title":"README file for Maxwell-Bloch Equations for Two-Level System"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2850/maxwellBlochWolfDemo03.zip","format":"zip file, Wolfram Mathematica 13.0 notebook, pdf","description":"This file will unzip to a directory (or folder) called maxwellBlochWolfDemo03.   Its contents is maxwellBlochWolfDemo03.nb and maxwellBlochWolfDemo03.pdf.  These are a Wolfram Mathematica 13.0 notebook written in the style of a Wolfram Demonstration and a static pdf thereof.","mediaType":"application/x-zip-compressed","title":"Maxwell-Bloch Equations for Two-Level System zip file"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-11-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics"],"issued":"2023-02-01","keyword":["Quantum Optics ; Quantum Memory ; Theory ; Simulation ; Maxwell-Bloch Equations ; Two-Level System"]},{"identifier":"ark:/88434/mds2-2851","accessLevel":"public","contactPoint":{"hasEmail":"mailto:adam.creuziger@nist.gov","fn":"Adam Creuziger"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2851","description":"This data set includes data and metadata about a metallurgical analysis and conservation of turbine blades from a recovered F-1 engine from the Apollo 16 Saturn V rocket. This data set includes:  images of the as recovered turbine blades, cross sections of the recovered turbine blades after vacuum impregnation, x-ray fluorescence (XRF) and energy dispersive spectroscopy (EDS) measurements of the recovered turbine blades.","language":["en"],"title":"Data Publication: Metallurgical Analysis and Conservation of Turbine Blades from Recovered Apollo F-1 Engines","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"License \"TerraMareConservationLicense.txt\" covers:- All data and images in the 'Treatment-Images' folder- File \"TMC F-1 Engines Conservation Report Final.pdf\" in the \"Reports\" folderAll other files and folders are covered under \"NISTLicense.txt\"","modified":"2023-09-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Metals"],"issued":"2023-10-19","keyword":["Space Program; Superalloy; Turbine; marine corrosion; metallography"]},{"identifier":"ark:/88434/mds2-2853","accessLevel":"public","contactPoint":{"hasEmail":"mailto:dale.newbury@nist.gov","fn":"Dale E. Newbury"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"EDS spectra measured at E0 = 5 keV for 75 materials are provided along with the standards used for quantification with the NIST DTSA-II software for electron-excited X-ray microanalysis with energy dispersive spectrometry.The data sets are organized according to the analytical instrument platform used, and an EDS detector configuration appropriate to each EDS spectrometer is provided . Each folder contains spectra for that specific material and the standards used as well as an Excel file summarizing the DTSA-II results in terms of the raw mass concentrations, normalized mass concentrations, and atomic concentrations. Each calculated compositional value is accompanied by the uncertainty budget as estimated by DTSA-II.An Excel file (5keV_accuracy_summary_DTSA-II) containing an overall summary of results for all 75 materials is provided.","language":["en"],"title":"Data to accompany \"Low Electron Beam Energy X-ray Microanalysis: The Adventure Continues!\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2853/5keV_Analysis_Spectrum_Archive.zip","format":"ZIP file","description":"A collection of 5 keV electron excited X-ray spectra measured on an energy dispersive X-ray spectrometer with the necessary standards and meta-data.","mediaType":"application/x-zip-compressed","title":"5 keV X-ray spectra"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-12-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Metrology:Amount of substance","Materials:Metals","Materials:Materials characterization","Materials:Composites","Materials:Ceramics","Chemistry:Analytical chemistry"],"conformsTo":"https://www.iso.org/standard/56211.html","issued":"2023-01-11","keyword":["SEM-EDS","SEM","EDS","scanning electron microscopy","energy dispersive X-ray spectrometry","X-ray spectrometry","silicon-drift detector","SDD","low beam energy","low keV"]},{"identifier":"ark:/88434/mds2-2854","accessLevel":"public","contactPoint":{"hasEmail":"mailto:samuel.berweger@nist.gov","fn":"Samuel Berweger"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2854","description":"Measurement of the effect of electric field inhomogeneity on electromagnetically induced transparency in a waveguide-based cesium atomic vapor cell. Used for the figure for the Photonics West 2023 manuscript.","language":["en"],"title":"Electric field inhomogeneity","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2854/data.zip","description":"Data for the figure in the Photonics West 2023 manuscript","mediaType":"application/x-zip-compressed","title":"Data for the figure in the Photonics West 2023 manuscript"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2854/README.txt","format":".txt","mediaType":"text/plain","title":"Readme for dataset"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-12-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Spectroscopy","Physics:Atomic, molecular, and quantum","Advanced Communications:Wireless (RF)"],"issued":"2023-01-05","keyword":["Rydberg atoms","atomic physics","receivers","fields strength","electric field","volts/meter"]},{"identifier":"ark:/88434/mds2-2857","accessLevel":"public","references":["https://doi.org/10.6028/NIST.AMS.100-43"],"contactPoint":{"hasEmail":"mailto:jordan.weaver@nist.gov","fn":"Jordan Weaver"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2857","description":"Gas speed measurements using hot-wire anemometers (HWA) on a commercial laser powder bed fusion (LBPF) machine were recorded as a function of position (X, Y, Z) and nozzle type (standard and grid).","language":["en"],"title":"Hot-wire anemometer gas speed measurements in a commercial laser powder bed fusion machine","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2857/Speed_grid_nozzle.csv","mediaType":"text/csv","title":"Speed Measurements for Grid Nozzle"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2857/Photos.zip","description":"Photos of measurement setup, machine, and nozzles","mediaType":"application/x-zip-compressed","title":"Photos"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2857/TSI-Alnor-8465-datasheet.pdf","mediaType":"application/pdf","title":"Anemometer specification"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2857/Temperature.csv","mediaType":"text/csv","title":"Temperature measurements"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2857/Speed_standard_nozzle.csv","mediaType":"text/csv","title":"Speed Measurements for Standard Nozzle"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2857/2857_README.txt","description":"Read me file","mediaType":"text/plain","title":"ReadMe"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-12-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing"],"issued":"2023-01-03","keyword":["additive manufacturing","gas flow","anemometer","machine qualification"]},{"identifier":"ark:/88434/mds2-2862","accessLevel":"public","contactPoint":{"hasEmail":"mailto:edward.sisco@nist.gov","fn":"Edward Sisco"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2862","description":"This dataset contains raw datafiles that support the development of rapid gas chromatography mass spectrometry (GC-MS) methods for seized drug analysis. Files are provided in the native \".D\" format collected from an Agilent GC-MS system. Files can be opened using Agilent proprietary software or freely available software such as AMDIS (which can be downloaded at chemdata.nist.gov). Included here is data of seized drug mixtures and adjudicated case samples that were analyzed as part of the method development process for rapid GC-MS. Information about the naming of datafiles and the contents of each mixture and case sample can be found in the associated Excel sheet (\"File Names and Comments.xlsx\").","language":["en"],"title":"Data to Support the Development of Rapid GC-MS Methods for Seized Drug Analysis","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2862/NIST%20Public%20Data%20Repository%20%28Rapid%20GC-MS%20of%20Seized%20Drugs%29.zip","format":"Zip folder containing raw \".D\" files","description":"Representative datafiles from the development of rapid GC-MS methods for seized drug analysis.","mediaType":"application/x-zip-compressed","title":"Rapid GC-MS Datafiles"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2862/File%20Names%20and%20Comments.xlsx","description":"Excel sheet with information on the samples analyzed in each of the GC-MS datafiles.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"File Names and Comments"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-12-13 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Forensics:Drugs and toxicology"],"issued":"2023-02-01","keyword":["Forensics","Seized Drug","GC-MS"]},{"identifier":"ark:/88434/mds2-2863","accessLevel":"public","contactPoint":{"hasEmail":"mailto:michael.riley@nist.gov","fn":"Michael A. Riley"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2863","description":"Projectile velocity data, from tests using the National Institute of Justice (NIJ) body armor standard (NIJ-0101.06) test threats, collected over a numbers of years at the NIST ballistics laboratory, that was used to assess the uncertainty associated with a chronograph/light screen system.","language":["en"],"title":"Historic test velocity data used for chronograph/light screen system uncertainty analysis.","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2863/ShotData.csv","format":"CSV","description":"This data file contains the projectile velocity data, from tests using the National Institute of Justice (NIJ) body armor standard (NIJ-0101.06) test threats, collected over a numbers of years at the NIST ballistics laboratory, that was used to assess the uncertainty associated with a chronograph/light screen system.","mediaType":"text/csv","title":"Test velocity data used for assessing uncertainty of a chronograph/light screen system."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2863/README.TXT","format":"ASCII text","description":"An explanation of the data file format and contents.","mediaType":"text/plain","title":"Explanation of data file format"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-12-15 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Public Safety:Law enforcement","Standards:Documentary standards"],"issued":"2023-03-10","keyword":["ballistics","velocity measurement"]},{"identifier":"ark:/88434/mds2-2867","accessLevel":"public","references":["https://doi.org/10.1016/j.fsigen.2023.102872"],"contactPoint":{"hasEmail":"mailto:sarah.riman@nist.gov","fn":"Sarah Riman"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2867","description":"This data repository is meant to provide the supplementary files, tables, and figures included in the peer-reviewed research article entitled: \"Sequence-based allelic variations and frequencies for 22 autosomal STR Loci in the Lebanese population\". The article can be found at the following link (https://doi.org/10.1016/j.fsigen.2023.102872) and describes the sequencing of the 22 autosomal Short Tandem Repeat (aSTR) loci, using the PowerSeq 46GY System Prototype, in 195 individuals of self-reported Lebanese admixed ancestry. The supplemental files contain the sequence strings for each allele at each autosomal STR locus, length- and sequence- based allelic frequencies, quality control metrics for the sequencing runs, flanking region polymorphisms, as well as population and forensic genetic statistics. Any future changes to the supplements will be listed in the \"Change Log\" tab within each spreadsheet.","language":["en"],"title":"Sequence-based allelic variations and frequencies for 22 autosomal STR loci in the Lebanese population - Supplementary material","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Figure%203.pptx","mediaType":"application/vnd.openxmlformats-officedocument.presentationml.presentation","title":"Lebanese population structure using unsupervised clustering"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20File%201.config","mediaType":"application/octet-stream","title":"STRait Razor v3 configuration file for the 22 autosomal    STRs identified by the PowerSeq 46GY assay"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20File%201.config","description":"STRait Razor v3 configuration file for the 22 autosomal","mediaType":"application/octet-stream","title":"Supplementary File 1 - STRait Razor v3 Config"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20File%202.txt","mediaType":"text/plain","title":"GRCh38 coordinate ranges used to adjust the sequences identified by both PowerSeq 46GY and ForenSeq Signature kits"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%201.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Quality control metrics for the sequencing runs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%202.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Average coverage of allelic sequences"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%203.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Average Hb across sequencing runs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%204.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Sequence-based allelic frequencies"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%205.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Allelic gains by sequencing"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%206.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Flanking region polymorphisms"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%207.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Length-based allelic frequencies"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%208.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Polymorphism Information Content, Power of discrimination, and Power of exclusion"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%209.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Match probability and Typical Paternity Index"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%2010.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Linkage disequilibrium"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%2011.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"AMOVA test among and within the 5 studied populations"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%2012.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Locus-by-locus FST and p-values"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%2013.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Pairwise FST estimates and permutation test across five populations"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/2867_README.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20File%202.txt","description":"Sequence ranges for the comparison of the PowerSeq 46GY kit to the ForenSeq Signature kit","mediaType":"text/plain","title":"Supplementary File 2 - Sequence Ranges"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%201.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Supplementary Table 1.  Quality control metrics for the sequencing runs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%203.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Supplementary Table 3. Average Hb across sequencing runs"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%204.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Supplementary Table 4. Sequence-based allelic frequencies"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%205.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Supplementary Table 5. Allelic gains by sequencing"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%206.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Supplementary Table 6. Flanking region polymorphisms"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%207.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Supplementary Table 7. Length-based allelic frequencies"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%208.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Supplementary Table 8. Polymorphism Information Content, Power of discrimination, and Power of exclusion"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%209.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Supplementary Table 9. Match probability and Typical Paternity Index"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Table%2010.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Supplementary Table 10. Linkage disequilibrium"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Figure%201.pptx","mediaType":"application/vnd.openxmlformats-officedocument.presentationml.presentation","title":"MDS plot for Lebanese and four U.S. populations"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2867/Supplementary%20Figure%202.pptx","mediaType":"application/vnd.openxmlformats-officedocument.presentationml.presentation","title":"Population tree for Lebanese and four U.S. populations"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-04-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Forensics:DNA and biological evidence"],"issued":"2023-04-12","keyword":["Lebanon","Lebanese population","Next Generation Sequencing","Autosomal STR loci","Sequence variations","Population STRUCTURE","PowerSeq 46GY"]},{"identifier":"ark:/88434/mds2-2868","accessLevel":"public","contactPoint":{"hasEmail":"mailto:kate.remley@nist.gov","fn":"Kate Remley"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2868","description":"The data correspond to the paper Practical Correlation-Matrix Approaches for Standardized Testing of Wireless Devices in Reverberation Chambers. Abstract: We extend the autocorrelation-based approaches currently used in standards to full correlation matrix-based approaches in order to identify correlation between both spatially adjacent and non-adjacent samples in reverberation-chamber measurements. We employ a scalar metric that allows users to identify the number of effectively uncorrelated samples in new types of stirring sequences. To make these approaches practical and enhance their accuracy, we implement a thresholding technique that retains correlation related to important aspects of chamber configuration such as loading and undermoded conditions. We develop a method to propagate uncertainty in the complex correlation coefficients through to the number of effective samples for a given reverberation-chamber set-up by use of a bootstrap technique that is accurate even for highly skewed distributions of correlation coefficients. We further apply this method in a sensitivity studyregarding the choice threshold value. Agreement with existing approaches in determining the number of effectively uncorrelated samples is presented for a measurement example where spatially adjacent samples are utilized. Examples are then illustrated for non-spatially-adjacent correlated samples at microwave and millimeter-wave frequencies.","language":["en"],"title":"Correlation-Matrix Approaches for Testing Wireless Devices in Reverberation Chambers","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig4b_NeffvsFreq_NoThr_WThr_FullCorr_0Abs.xlsx","description":"Thresholded (top black curves) and unthresholded (bottom red curves) values of Neff plotted as a function of frequency. Curves were computed using the Full Correlation Matrix approach for three correlation-matrix calculation bandwidths. The unthresholded values significantly underestimate Neff.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure 4b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig2a_normal_autocorrelation_different_peak_widths_diff_freqs.csv","description":"Autocorrelation curves for a stirring sequence with only mechanical mode stirrers, measured at three different frequencies.","mediaType":"text/csv","title":"Figure 2a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig2b_spiky_autocorrelation.csv","description":"Autocorrelation for a stirring sequence with an antenna switch and mechanical mode stirrers.","mediaType":"text/csv","title":"Figure 2b"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig5_Boot_w_replace_Nb10000_1550To5550_HistData.zip","format":"Excel files with labelled columns","description":"Bootstrapping approach to approximate the uncertainty in the estimation of Neff. 10,000 bootstrap samples computed from 400 complex S-parameter measurements of the stirring sequence are given for center frequencies between 1.55 GHz and 5.55 GHz. The center frequency of 3.55 GHz is shown in Fig. 5. Data include Excel files with the histogram data and the bootstrap metrics.","mediaType":"application/x-zip-compressed","title":"Figure 5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig8_CorrelationMatrices_950MHz_5550MHz.zip","format":"Excel files with rows and columns corresponding to correlation matrix entries","description":"360 × 360 correlation matrices of |r_ij|^2 and zoom-in on 50 × 50 segments prior to thresholding, computed over two different frequency ranges with the Full Correlation Matrix approach (a) 750 MHz - 1.15 GHz and (b) 5.55 GHz - 5.95 GHz. There are more significantly correlated samples in the lower bands, resulting in a smaller value of Neff.","mediaType":"application/x-zip-compressed","title":"Figure 8"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig11_NeffvsFreq_3GPP_0Abs.zip","format":"Excel files with labelled columns","description":"The number of effective samples Nind computed with the IEC/3GPP approach as a function of frequency for an unloaded chamber. One file has the values at individual frequencies. The other file has the values averaged over 400 MHz.","mediaType":"application/x-zip-compressed","title":"Figure 11"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig12_CorrMatrix_1Row_AbsSq_Dir1_650MHzFc_500MHzBW.xlsx","format":"Excel file with one column corresponding to the correlation matrix values","description":"Data from one row of the correlation matrix of |r^corr_ij |^2 for Case 1, the unswitched case.","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","title":"Figure 12"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig10_Boot_w_replace_Nb10000_1050To5550_Metrics.zip","format":"For each Excel file, the nominal Neff values are in Col. 2 and the 95% confidence interval is in Col. 7","description":"Number of effective samples N^corr_eff and the 95% confidence interval as a function of frequency for the data from Fig. 9, as determined from the bootstrap method described in Section II.D. 10,000 bootstrap samples were used to generate the data. The confidence intervals are below 10 samples except for the rapid change in N^corr_eff for the zero-absorber case.","mediaType":"application/x-zip-compressed","title":"Figure 10"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig6_Histogram_rij_And_NeffwThr_fc5550_HistData.zip","format":"For each .xlsx file: Col. 1: x-axis data; Col. 2: Counts for histogram; Col. 3: Neff as function of threshold","description":"The link between the distribution of correlation coefficients, the threshold value and Neff. Left axis: Histogram of |rij |^2. Right axis: Neff as a function of threshold value. (a) Unloaded chamber (CBW = 613 kHz); (b) chamber loaded with five RF absorbers (CBW = 3.3 MHz); (c) chamber loaded with eleven RF absorbers (CBW = 6.7 MHz); (d) five absorber case with 180 stirring-sequence samples obtained with a wider spatial step.","mediaType":"application/x-zip-compressed","title":"Figure 6"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig14_Neff_And_Uncert_mmWave_AllMethods_2Abs_600MHzBW.zip","format":"Excel files with labeled columns","description":"Estimate of Neff using all three approaches and the bootstrap 95% confidence interval for the Full Correlation Matrix approach as a function of frequency for a millimeter-wave-band measurement. File \"Fig14_Neff_mmWave_AllMethods_2Abs_600MHzBW.xlsx\": Neff for two-absorber case averaged or computed over 600 MHz. File \"Fig14_Boot_fc28GHzto39p9GHz_601from601_Nb1000_Metrics.xlsx\": Bootstrap metrics including the 95% confidence interval for N^corr_eff with 1000 bootstrap samples.","mediaType":"application/x-zip-compressed","title":"Figure 14"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig13_Neff_4Cases.zip","format":"Three Excel files with labelled columns for each approach. The frequency averaging bandwidth is given in the name of the file.","description":"The number of effective samples as a function of frequency for the IEC/3GPP approach, Circular-Shift Matrix approach and Full Correlation Matrix approach. Plots show three switching cases.  Data include four switching cases from Table 2 in the paper: Case 1 (not switched), Case 3 (200 ms switching time), Case 4 (500 ms switching time) and Case 5 (1 s switching time). The IEC/3GPP overestimates the number of effective samples for the unswitched case because it cannot assess the non-spatially adjacent samples.","mediaType":"application/x-zip-compressed","title":"Figure 13"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig4a_NeffvsFreq_NoThr_WThr_CircShiftCorr_0Abs.zip","format":"Excel file with labeled columns.","description":"Thresholded (top black/blue curves) and unthresholded (bottom red/black curves) values of Neff plotted as a function of frequency. Curves were computed using the Circular-Shift Matrix approach. The thin (blue and red) lines represent the computed value of Neff at each frequency, and the thicker (black) line is the average taken over 400 MHz.","mediaType":"application/x-zip-compressed","title":"Figure 4a"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig9_NISTData_Neff_AllAbs_5550MHzMax_400MHzBW.zip","format":"Excel files with column headings labeled.","description":"Number of effective samples Neff for the three methods discussed in Section II for the case where correlation between stirring-sequence samples is adjacent or near-adjacent. Three rotational stirring mechanisms were moved simultaneously in 1-degree steps. Measurements were performed with three different amounts of RF absorber present in the chamber. The threshold value was rlim = 0.3082. Data in the attached files includes Neff values for the Circular-Shift Correlation Matrix approach and the Full Correlation Matrix approach with and without thresholding.","mediaType":"application/x-zip-compressed","title":"Figure 9"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig15abc_CorrMatrix_mmWave_2Abs_28p4GHz.zip","format":"Excel files with labeled columns","description":"Unthresholded correlation matrix for a large reverberation chamber loaded with two RF absorbers (CBW = 4.0 MHz): File Fig15a: Correlation matrix and zoom-in on 100 samples of the correlation matrix of |r^corr_ij |^2 computed over a 600 MHz bandwidth. Files Fig15b: One row of the correlation matrix of |r^circ_ij |^2 computed at three individual frequencies. Files Fig15c: One row of the correlation matrix of |r^corr_ij |^2 computed at three center frequencies over a 600 MHz bandwidth.","mediaType":"application/x-zip-compressed","title":"Figure 15"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig3_rlim_variation.csv","format":"Excel file with labeled columns","description":"IEC/3GPP threshold rlim that compensates for the use of a finite number of samples in determining the number of uncorrelated samples in reverberation-chamber measurements.","mediaType":"text/csv","title":"Figure 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig7_Neff_Boot_w_replace_wthresh_Nb10000_fc3550_HistData.zip","format":"Excel files with labeled columns","description":"Bootstrapping approach illustrating the sensitivity of Neff to choice of threshold. The 10,000 bootstrap samples were randomized with choices of threshold ranging from 0.34 to 0.40, along with the randomization as in Fig. 5, with a center frequency of 3.55 GHz. The data for the histograms are included along with the bootstrap metrics at several center frequencies.","mediaType":"application/x-zip-compressed","title":"Figure 7"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/Fig9_MatlabScript_FullCorrMatrix.zip","format":".m file with script; .mat files with data","description":"Matlab script and NIST-measured complex S-parameter data used to compute the results in Fig. 9 with the Full Correlation Matrix approach. The script plots Neff as a function of frequency, the correlation matrix, and a histogram of the correlation coefficients. The script can be modified by users for their own data.","mediaType":"application/x-zip-compressed","title":"Figure 9 Matlab Script"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2868/2868_README.txt","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-12-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)"],"issued":"2023-02-24","keyword":["cellular device measurements; metrology for wireless systems; millimeter-wave metrology; millimeter-wave wireless device; mobile communications; modulated signals; over-the-air measurements; reverberation chamber; uncertainty; wireless systems"]},{"identifier":"ark:/88434/mds2-2888","accessLevel":"public","contactPoint":{"hasEmail":"mailto:thomas.kolibaba@nist.gov","fn":"Thomas Kolibaba"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2888","description":"Videos and .stl files from Synergistic Fire Resistance of Nanobrick Wall Coated 3D Printed Photopolymer Lattices.The videos are from the fire tests of 3D printed photopolymer parts, from which time series still images were acquired. In these tests, printed parts (with or without a nanocoating) are subjected to direct blowtorch exposure until part failure (defined as when the part shatters). Most videos include time after torch removal while the part continues to burn.The .stl files are the files used to 3D print the parts studied in this work. They include objects with and without handles (to facilitate coating in a dipping robot) and with varying internal geometries, used to study the influence of part surface area on its flammability.The video files should be able to be played on any video playing software.The .stl files will need to be opened in computer aided design (CAD) software or in the slicer software of a 3D printer.","language":["en"],"title":"Supporting Info for Synergistic Fire Resistance of Nanobrick Wall Coated 3D Printed Photopolymer Lattices","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2888/Electronic%20Supporting%20FIles.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2888/Electronic%20Supporting%20FIles.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2888/readme.txt","format":".txt","description":"A document describing the contents of this MIDAS upload.","mediaType":"text/plain","title":"Readme Document"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-12-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing","Materials:Polymers","Fire:Materials flammability"],"issued":"2023-02-27","keyword":["Fire protection","layer-by-layer","additive manufacturing"]},{"identifier":"ark:/88434/mds2-2895","accessLevel":"public","contactPoint":{"hasEmail":"mailto:gary.howarth@nist.gov","fn":"Gary Howarth II"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2895","description":"The NIST Excerpts Benchmark Data are a set of target data for deidentification algorithms. The data are configured to work with \"SDNist: Synthetic Data Report Tool\", a package for evaluating synthetic data generators: https://github.com/usnistgov/SDNist. An installation of SDNist will download the data resources automatically. Jan 2025 -- Benhcmark Excerpts: - NIST American Community Survey (ACS) Data Excerpts, 24 demographic features over 40k records,- NIST Survey of Business Owners (SBO) Data Excerpts, 130 demographic and financial features over 161k recordsThe data are curated subsets of U.S. Census Bureau products.","language":["en"],"title":"NIST Excerpts Benchmark Data","distribution":[{"accessURL":"https://github.com/usnistgov/SDNist/tree/main/BenchmarkData","format":"A data respository","description":"The NIST Data Excerpts are curated subsets of publicly released tabular data sets, drawn from real households and businesses in the U.S. The Excerpts serve as benchmark data for the [SDNist v2: Deidentified Data Report Tool](https://github.com/usnistgov/SDNist/) .","title":"NIST Excerpt Benchmark Data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2025-01-31 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"describedBy":"https://github.com/usnistgov/SDNist/tree/main/BenchmarkData","theme":["Information Technology:Privacy","Information Technology:Data and informatics","Information Technology:Software research"],"issued":"2023-06-02","keyword":["privacy","synthetic data","demographic data","American Community Survey","SDNist"]},{"identifier":"ark:/88434/mds2-2905","accessLevel":"public","contactPoint":{"hasEmail":"mailto:benjamin.place@nist.gov","fn":"Benjamin Place"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2905","description":"Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs).Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.","language":["en"],"title":"Database Infrastructure for Mass Spectrometry - Per- and Polyfluoroalkyl Substances","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2905/ERD.png","format":"Portable Network Graphics (PNG) image file","description":"The entity relationship diagram (ERD) associated with the dimspec_nist_pfas.sqlite database file.","mediaType":"image/png","title":"Entity Relationship Diagram"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2905/DIMSpec%20for%20PFAS_1.0.1.20230615_data_dictionary.json","format":"Javascript Object Notation (JSON)","description":"A data dictionary in javascript object notation (JSON) to accompany the dimspec_nist_pfas.sqlite database file.","mediaType":"application/json","title":"Data Dictionary"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2905/LICENSE.pdf","format":"Portable Document File (PDF)","description":"The license for use and reuse accompanying these data.","mediaType":"application/pdf","title":"Use Licence"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2905/README.pdf","format":"Portable Document File (PDF)","description":"The README file containing information about this dataset, and links to other resources supporting it.","mediaType":"application/pdf","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2905/dimspec_nist_pfas.sqlite","format":"SQLite","description":"A DIMSpec compliant database of high-resolution accurate mass spectrometry data for per- and polyfluorinated alkyl substances.","mediaType":"application/octet-stream","title":"DIMSpec for PFAS"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-07-05 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Data and informatics","Environment:Environmental health","Chemistry:Analytical chemistry"],"issued":"2023-10-02","keyword":["per- and polyfluoroalkyl substances","PFAS","database","mass spectrometry","SQLite","emerging contaminants","high resolution mass spectrometry","non-targeted analysis","NTA","HRMS"]},{"identifier":"ark:/88434/mds2-2908","accessLevel":"public","contactPoint":{"hasEmail":"mailto:robert.mcmichael@nist.gov","fn":"Robert D. McMichael"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://pages.nist.gov/optbayesexpt/","description":"Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters.  Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters.  Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties.   A TCP socket interface allows the software to be used from experimental control software written in other programming languages. Code is developed in Python, and shared via GitHub's USNISTGOV organization.","language":["en"],"title":"Optimal Bayesian Experimental Design Version 1.2.0","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2908/Review%20README.txt","format":"plain text","description":"Special instructions for reviewers","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2908/obe-repo.zip","format":"ZIP","description":"Zip file of ORE Repo","mediaType":"application/gzip","title":"ORE Repo Files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2908/nist-pages.zip","format":"ZIP","description":"Zip with files from NIST pages","mediaType":"application/gzip","title":"NIST Pages Files"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-10 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Experiment design"],"issued":"2023-02-22","keyword":["GitHub pages template","experimental design","Bayesian","optbayesexpt","python","adaptive measurement"]},{"identifier":"ark:/88434/mds2-2909","accessLevel":"restricted public","references":["https://doi.org/10.6028/NIST.IR.8382"],"contactPoint":{"hasEmail":"mailto:gregory.fiumara@nist.gov","fn":"Gregory Fiumara"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2909","description":"The images and values in this dataset are used to ensure conformance with ISO/IEC 29794-4, which may be purchased from https://www.iso.org/standard/62791.html. Values were computed with NIST's NFIQ 2 software, https://github.com/usnistgov/NFIQ2. When compiling the NIST NFIQ 2 source code or developing their own alternate implementation of quality metrics contained within ISO/IEC 29794-4, Annex A of the standard requires that the developer ensure that software generate values within 1% of these values when using these images as input.","language":["en"],"title":"NIST Fingerprint Image Quality (NFIQ) 2 Conformance Test Set","distribution":[{"accessURL":"https://nigos.nist.gov/datasets/nfiq2_conformance/","title":"Request Dataset"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2909/nfiq2_conformance_expected_output.csv","description":"Expected output from NFIQ 2 when run against the conformance dataset.","mediaType":"text/csv","title":"NFIQ 2 Conformance Expected Output"},{"accessURL":"https://data.nist.gov/od/rp?@id=ark:/88434/mds2-2909","description":"This page provides a registration form that must be completed before downloading the data.","title":"Gateway for Registered Data Access"},{"accessURL":"https://nigos.nist.gov/datasets/nfiq2_conformance/","description":"Visit this website to request the NFIQ 2 Conformance Test Set images.","title":"Request Images"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Purchase is not required for data downloading. Users must complete registration form to download data.","modified":"2021-11-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Information Technology:Biometrics"],"conformsTo":"https://www.iso.org/standard/83827.html","issued":"2023-02-01","keyword":["biometric","conformance","fingerprint","quality"]},{"identifier":"ark:/88434/mds2-2910","accessLevel":"public","contactPoint":{"hasEmail":"mailto:robert.dejaco@nist.gov","fn":"Robert De Jaco"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2910","description":"The purpose of this code and data is to enable reproductionand facilitate extension of the computationalresults associated with following workDeJaco, R. F.; Roberts, M. J.; Romsos, E. L.; Vallone, P. M.; Kearsley, A. J. Reducing Bias and Quantifying Uncertainty in Fluorescence Produced by PCR*Under Review*, 2023.","language":["en"],"title":"Software and data associated with ``DeJaco, R. F.; Roberts, M. J.; Romsos, E. L.; Vallone, P. M.; Kearsley, A. J. Reducing Bias and Quantifying Uncertainty in Fluorescence Produced by PCR*Under Review*, 2023.''","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2910/code-v0.0.1.zip","mediaType":"application/x-zip-compressed","title":"Zipped version of all files"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2910/bias-uq-pcr-v0.9.1.zip","mediaType":"application/octet-stream","title":"Updated version of dataset"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Nanotechnology:Nanobiotechnology","Physics:Biological physics","Mathematics and Statistics:Uncertainty quantification","Mathematics and Statistics:Modeling and simulation research","Information Technology:Data and informatics","Information Technology:Computational science","Information Technology:Biometrics","Health:Clinical diagnostics","Chemistry:Theoretical chemistry and modeling","Chemistry:Chemical engineering and processing","Bioscience:Genomic measurements"],"issued":"2023-02-06","keyword":["Polymerase Chain Reaction","Uncertainty Quantification","Stochastic Branching Process"]},{"identifier":"ark:/88434/mds2-2911","accessLevel":"public","contactPoint":{"hasEmail":"mailto:james.booth@nist.gov","fn":"Jim Booth"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"Figures and relevant data from the paper \"Broadband Electromagnetic Properties of Engineered Flexible Absorber Materials\" are found here . The paper was published on Advanced Materials Technologies in 2023. ABSTRACT: Flexible and stretchable materials have attracted significant interest for applications in wearable electronics and bioengineering fields. Recent developments also incorporate mounted and embedded microwave circuits, components, and systems with engineered flexible materials that operate over a broadband frequency range (~1 to 100 GHz). Here we demonstrate a simple, low-cost, flip-chip technique where flexible materials are placed on top of coplanar waveguide (CPW) transmission lines for material property measurement. We apply on-wafer error correction and de-embedding techniques to determine broadband electromagnetic properties of the material-loaded transmission line segments. Finite-element simulations of material-loaded devices were employed along with the broadband measurements to estimate the electromagnetic material properties. To demonstrate this technique, we fabricated flexible polydimethylsiloxane (PDMS) composites with varying concentrations of Barium Hexaferrite (BaM) nanoparticles for potential applications in electromagnetic shielding and quantified the complex permittivity and permeability of the composites up to 110 GHz using our broadband scattering-parameter measurements. We fit the frequency-dependent permeability to models describing the ferromagnetic resonance of barium hexaferrite (BaM) nanoparticles in PDMS and estimated the constituent nanoparticle properties using the Maxwell-Garnett mixing model. This study paves way to exploit a wide range of engineered materials in flexible, wearable, and biomedical electronics applications and presents a convenient methodology to extract important broadband electromagnetic properties for applications such as electromagnetic shielding.","language":["en"],"title":"Broadband Electromagnetic Properties of Engineered Flexible Absorber Materials","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%201.png","description":"PDMS composites with 30% and 60% (weight ratio) barium hexaferrite were directly placed on test chips with CPWs for flip chip measurements. The Reference die is used for on-wafer calibration with necessary devices to perform multiline through-reflect-line (mTRL) and series resistor calibration [Orloff2011]. Test chip has 8 identical transmission lines of 11 mm. Schematic diagrams of the top-view and cross section of the composite (superstrate) loaded transmission line are shown on the bottom and right-hand of the image.","mediaType":"image/png","title":"Figure 1: Reference chip, air and composite loaded test chips subject to measurement."},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%202.png","description":"The image depicts the electric field vectors for gold transmission lines on quartz substrate that are exaggerated for clarity (Image not to scale). The fields emanate from the signal line and ends on ground planes.","mediaType":"image/png","title":"Figure 2: Frequency dependent R, L, C, G (per unit length) transmission line model that is used for simulations and calculations"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%204.png","description":"The cables and probes from the vector network analyzer (VNA) are connected to the reference plane during measurement. The TRL calibration procedure translates 50 Ohm reference plane to probe pads. The air-loaded distance (L) of the transmission line is de-embedded to obtain RLCG distributed circuit parameters for BaM material loaded CPW space.","mediaType":"image/png","title":"Figure 4: Material loaded transmission lines and de-embedding in test wafer"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%203a.csv","description":"The impact of substrate and superstrate material properties on CPW transmission line capacitance, inductance, and resistance calculated from finite-element simulations.","mediaType":"text/csv","title":"Differential substrate capacitance vs. substrate permittivity"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%203b.csv","description":"The impact of substrate and superstrate material properties on CPW transmission line capacitance, inductance, and resistance calculated from finite-element simulations.","mediaType":"text/csv","title":"Differential superstrate capacitance vs. superstrate permittivity"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%203c.csv","description":"The impact of substrate and superstrate material properties on CPW transmission line capacitance, inductance, and resistance calculated from finite-element simulations.","mediaType":"text/csv","title":"Differential inductance vs. superstrate permeability"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%205a.csv","mediaType":"text/csv","title":"Calculated and measured distributed circuit parameters - Inductance per unit length"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%205b.csv","mediaType":"text/csv","title":"Calculated and measured distributed circuit parameters - Resistance per unit length"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%206a.csv","description":"Frequency dependence for the bare test chip (air), two PDMS samples, three 30% BaM samples, and the BaM 60% sample. These values are obtained by fixing RL and optimizing CG to fit the measured, de-embedded data. The conductance is negligible when compared to capacitance values.","mediaType":"text/csv","title":"Frequency dependence of the capacitance per unit length"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%206b.csv","description":"Frequency dependence for the bare test chip (air), two PDMS samples, three 30% BaM samples, and the BaM 60% sample. These values are obtained by fixing RL and optimizing CG to fit the measured, de-embedded data. The conductance is negligible when compared to capacitance values.","mediaType":"text/csv","title":"Frequency dependence of the conductance per unit length (LDUT)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%207a.csv","description":"Real permittivity for two PDMS samples, three 30% BaM samples, and the BaM 60% sample.","mediaType":"text/csv","title":"Calculated real effective permittivity (εeff) vs. frequency"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%207b.csv","description":"Imaginary permittivity for two PDMS samples, three 30% BaM samples, and the BaM 60% sample.","mediaType":"text/csv","title":"Calculated imaginary effective permittivity (εeff) vs. frequency"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%208a.csv","description":"Calculated effective permeability (μeff) vs. frequency for three 30% BaM samples, and the BaM 60% sample.","mediaType":"text/csv","title":"Calculated and extracted real part of effective permeability for samples"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%208b.csv","description":"Calculated effective permeability (μeff) vs. frequency for three 30% BaM samples, and the BaM 60% sample.","mediaType":"text/csv","title":"Calculated and extracted imaginary part of effective permeability for samples"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/Figure%209.csv","mediaType":"text/csv","title":"Estimates for the free-space attenuation constant for engineered BaM-PDMS composites"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2911/README.txt","mediaType":"text/plain","title":"Readme File for Figures with Description"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-17 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Electronics:Electromagnetics","Advanced Communications:Wireless (RF)","Materials:Materials characterization"],"issued":"2023-05-01","keyword":["Flexible electronics","microwave circuits","Barium hexaferrite","PDMS composites","permittivity","permeability"]},{"identifier":"ark:/88434/mds2-2913","accessLevel":"public","contactPoint":{"hasEmail":"mailto:marcus.mendenhall@nist.gov","fn":"Marcus Mendenhall"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2913","description":"The data from this instrument consists of sets of measurements of Xray intensity as a function of diffraction angle.  All of it is collected and is stored in JSON files. The files are highly self-descriptive of their content, and contain angles, intensities, temperatures, and other environmental parameters, along with metadata.","language":["en"],"title":"Supplementary data from the NIST  lattice comparator  associated with paper \"The NIST Silicon Lattice Comparator Upgrade\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2913/640g_silicon_example_data.zip","format":"zipped JSON files and python code","description":"JSON data files and python code to process them","mediaType":"application/zip","title":"Example measurement of silicon lattice parameter"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2913/calib.zip","format":"zipped file with historical angular calibration data","description":"Data files and python code used to process angular calibration data","mediaType":"application/zip","title":"Calibration of angular interferometer"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2913/202106_EIB_interferometer_tests.zip","format":"zipped text files","description":"Dataset and python code used to measure periodic error in angular interferometer","mediaType":"application/zip","title":"Interferometer short-period compensation data"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Materials:Materials characterization","Standards:Reference materials","Standards:Reference data"],"issued":"2023-03-10","keyword":["Single crystal lattice standards; lattice comparator; delta-d; silicon"]},{"identifier":"ark:/88434/mds2-2915","accessLevel":"public","references":["https://doi.org/10.1021/acs.est.3c10307"],"contactPoint":{"hasEmail":"mailto:israel.lopezcoto@nist.gov","fn":"Israel Lopez Coto"},"programCode":["006:047"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2915","description":"These files represent four different versions of a high resolution (0.02° x 0.02°) methane emissions inventory covering the New York-Newark urban area and its surroundings. Each specific inventory version has an associated README.docx file that provides details on the methodology used to compile that inventory version. Each inventory version is provided in 10 separate files, with each file containing emissions from a specific sector. Total methane emissions for a given inventory version can be calculated by adding the emissions from these 10 files together","language":["en"],"title":"A high-resolution sectoral inventory for use in inverse modelling of New York City methane emissions","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2915/A1_VS1_WC_SN_2022-04-21.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2915/A1_VS1_WC_SN_2022-04-21.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2915/VL_VS1_S1_SS_2022-04-21.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2915/VL_VS1_S1_SS_2022-04-21.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2915/V5_VS1_S1_SS_2022-04-21.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2915/V5_VS1_S1_SS_2022-04-21.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2915/AL_AS1_WC_SS_2022-04-21.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2915/AL_AS1_WC_SS_2022-04-21.zip.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-04-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Environment:Greenhouse gas measurements"],"spatial":"New York, 75.7°W/39.2°N, 72.1°W/42.0°N","issued":"2024-05-20","keyword":["Methane","emissions","GHG","New York","high resolution","NG","Natural Gas"]},{"identifier":"ark:/88434/mds2-2916","accessLevel":"public","contactPoint":{"hasEmail":"mailto:robert.horansky@nist.gov","fn":"Rob Horansky"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2916","description":"The raw data from this experiment are recorded voltage waveforms versus time from a calibrated equivalent time sampling oscilloscope, touchstone files from network analysis, and waveforms in both frequency and time domain from a vector receiver.  As such, the raw data are text files in MATLAB format.The text files recorded from the oscilloscope end with .mat and can have varying numbers of columns depending on the number of scope channels that were recorded.","language":["en"],"title":"Data for Plots in Work with Title: A Measurement-Referenced Error Vector Magnitude for Counterfeit Cellular Device Detection","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2916/result_Fig3_top.csv","mediaType":"application/vnd.ms-excel","title":"Data for Fig 1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2916/result_Fig3_middle.csv","mediaType":"application/vnd.ms-excel","title":"Data for Fig 3 Middle"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2916/result_Fig3_bottom_UE_Model_1.csv","description":"Constellation Plot with complex values","mediaType":"application/vnd.ms-excel","title":"Data for Fig 3 Bottom UE Model 1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2916/result_Fig3_bottom_UE_Model_2.csv","mediaType":"application/vnd.ms-excel","title":"Data for UE Model 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2916/result_Fig3_bottom_UE_Model_3.csv","mediaType":"application/vnd.ms-excel","title":"Data for Constelaltion Plot for UE Model 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2916/2916_README.txt","format":"Text","description":"Contains Information regarding the data.","mediaType":"text/plain","title":"Readme File"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-18 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Advanced Communications:Wireless (RF)"],"issued":"2023-02-17","keyword":["metrology for wireless systems; millimeter-wave wireless devices","modulated signals; over-the-air; wireless systems; millimeter-wave metrology; correlated uncertainty; Microwave Uncertainty Framework"]},{"identifier":"ark:/88434/mds2-2917","accessLevel":"public","references":["https://www.doi.org/10.1364/ol.482597"],"contactPoint":{"hasEmail":"mailto:david.long@nist.gov","fn":"David Long"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2917","description":"This is the data underlying Optics Letters publication \"Single-modulator, direct frequency comb spectroscopy via serrodyne modulation\"","language":["en"],"title":"Data underlying Optics Letters \"Single-modulator, direct frequency comb spectroscopy via serrodyne modulation\" DOI: 10.1364/OL.482597","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2917/Figure2.csv","description":"This is the data underlying figure 2 from https://doi:10.1364/OL.482597","mediaType":"text/csv","title":"Figure 2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2917/Figure3.txt","description":"This is the data underlying Figure 3 of https://doi:10.1364/OL.482597","mediaType":"text/plain","title":"Figure 3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2917/Figure4.csv","description":"This is the data underlying Figure 4 of https://doi:10.1364/OL.482597","mediaType":"text/csv","title":"Figure 4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2917/Figure5.csv","description":"This is the data underlying Figure 5 of https://doi:10.1364/OL.482597","mediaType":"text/csv","title":"Figure 5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2917/2696_README%20%281%292.txt","mediaType":"text/plain","title":"Readme file"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-19 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Optical physics"],"issued":"2023-04-12","keyword":["Optical frequency comb","serrodyne","cavity optomechanics"]},{"identifier":"ark:/88434/mds2-2918","accessLevel":"public","contactPoint":{"hasEmail":"mailto:stian.romberg@nist.gov","fn":"Stian Romberg"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2918","description":"Thermoset composite structures printed at room temperature using direct ink writing often collapse during thermal post-curing. This behavior suggests that the rheological properties that govern structural stability (i.e., storage modulus and/or yield stress) are sensitive to both temperature and conversion. The rheo-Raman instrument provides a way to directly link rheological properties, temperature, and conversion. Using this technique, we characterized how the yield stress and storage modulus evolve as a function of conversion at different temperatures and filler contents of fumed silica. This data set focuses on a diglycidyl ether of bisphenol A (DGEBA) epoxy resin (Epon 826, Hexion, Ohio, USA) cured with Jeffamine D-230 (Huntsman Corporation, Texas, USA). Three resins with fumed silica (Cabot Corporation, Massachusetts, USA) mass fractions of 0 %, 5 %, and 10 % were cured and observed isothermally at 70 °C and 100 °C. Rheological and Raman data were obtained, analyzed, and then combined to determine how the yield stress and storage modulus evolve with conversion at different temperatures. These results motivated a two-step schedule designed to prevent a reduction in rheological properties during curing while quickly driving the reaction to high conversion. The two-step schedule began at 70 °C then ramped to 100 °C and is also included in this dataset. This data is described in: Romberg, S.K., & Kotula, A.P. (2023) Simultaneous rheology and cure kinetics dictate thermal post-curing of thermoset composite resins, National Institute of Standards and Technology, submitted for publication.","language":["en"],"title":"Simultaneous rheology and cure kinetics dictate thermal post-curing of thermoset composite resins for material extrusion","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2918/README.txt","format":"text","description":"This text file describes the files in the dataset.","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2918/ALL%20DATA.zip","format":"zip","description":"This zip file includes all of the data described in the README file.","mediaType":"application/x-zip-compressed","title":"Dataset - Simultaneous rheology and cure kinetics dictate thermal post-curing of thermoset composites for material extrusion"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing","Physics:Spectroscopy","Metrology:Flow metrology and rheology","Materials:Polymers","Materials:Composites","Materials","Manufacturing:Additive manufacturing"],"issued":"2023-03-07","keyword":["rheo-Raman","thermoset composite","yield stress fluid","cure kinetics","direct ink writing","thermal post-cure"]},{"identifier":"ark:/88434/mds2-2919","accessLevel":"public","contactPoint":{"hasEmail":"mailto:angela.stelson@nist.gov","fn":"Angela Stelson"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2919","description":"S-parameter,  Matlab Analysis files for \"Quantifying the Effect of Guest Binding on Host Environment Associated Data\"Abstract:The environment around a host-guest complex is defined by of intermolecular interactions between solvent molecules and counter ions. These interactions govern both the solubility of these complexes and the rates of reactions confined within them11. Such noncovalent2 interactions between solvent molecules and ions are challenging to detect by standard analytical chemistry techniques. We use microwave microfluidic spectroscopy to quantify the hydration and ion pairing of a FeII4L4 coordination cage with a set of guest molecules having widely varying physicochemical properties3,4. Significantly, we observed that the environment around a host-guest complex changes depending on the identity of the encapsulated guest. The impact of different guest properties on host ion pairing and hydration was determined through microwave microfluidic measurements paired with principal component analysis. This analysis showed that introducing guest molecules into solution displaced counterions that were bound to the cage, and the water solubility of the guest has the greatest impact on the solvent and ion-pairing dynamics surrounding the host. We also observed that cage-counterion pairing is well-described by a single ion-pairing type, with a one-step reaction model independent of the type of cargo, and that the ion-pairing association constant is reduced for cargo with higher water solubility. Looking beyond this study, microwave microfluidics elucidates structure-property relationships that connect the host interior cavity to its external environment, and these measurements enable improved design of host-guest systems for chemical separations, catalysis, and more.","language":["en"],"title":"Quantifying the Effect of Guest Binding on Host Environment Associated Data","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2919/Figs_data_plots.zip","description":"Figure ad Data files and scripts","mediaType":"application/x-zip-compressed","title":"Figures and Associated Data for Quantifying the Effect of Guest Binding on Host Environment"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2919/2919_README%20v2.txt","mediaType":"text/plain","title":"README file"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2022-12-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Chemical thermodynamics and chemical properties"],"issued":"2023-02-02","keyword":["Microwave Microfluidics","permittivity","loss","ionic conductivity","ion pairing"]},{"identifier":"ark:/88434/mds2-2920","accessLevel":"public","contactPoint":{"hasEmail":"mailto:arun.moorthy@nist.gov","fn":"Arun Moorthy"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2920","description":"The NIST DART-MS Forensics Database is an evaluated collection of in-source collisionally-induced dissociation (is-CID) mass spectra of compounds of interest to the forensics community (e.g. seized drugs, cutting agents, etc.). The is-CID mass spectra were collected using Direct Analysis in Real-Time (DART) Mass Spectrometry (MS), either by NIST scientists or by contributing agencies noted per compound. The database is provided as a general-purpose structure data file (.SDF). For users on Windows operating systems, the .SDF format library can be converted to NIST MS Search format using Lib2NIST and then explored using NIST MS Search v2.4 for general mass spectral analysis. These software tools can be downloaded at https://chemdata.nist.gov. The  database is now (09-28-2021) also  provided in R data format (.RDS) for use with the R programming language.This database, also commonly referred to as a library, is one in a series of high-quality mass spectral libraries/databases produced by NIST (see NIST SRD 1a, https://dx.doi.org/10.18434/T4H594).","language":["en"],"title":"NIST DART-MS Forensics Database (is-CID)","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/NIST-DARTMS-Forensics-update-2021-01.SDF","format":"A structure data file (SDF) readable in any plain text editor.","description":"The NIST DART-MS Forensics library (is-CID mass spectra) updated as of January 19th, 2021. Includes in-source collisionally-induced dissociation (is-CID) mass spectra of 760 compounds of interest for the forensics computes.","mediaType":"text/plain","title":"NIST DART-MS Forensics Library (is-CID) - updated 2021/01"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/NIST_DART-MS_Forensics_Cicada.SDF.sha256","mediaType":"text/plain","title":"SHA256 File for Updated NIST DART-MS Forensics Database Cicada (2021-06-22)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/210622_HeLibrary_Cicada_Notes.pdf.sha256","mediaType":"text/plain","title":"SHA256 File for Release Notes for NIST DART-MS Forensics Database Cicada (2021-06-22)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/210622_HeLibrary_Cicada_Notes.pdf","mediaType":"application/pdf","title":"Release Notes for NIST DART-MS Forensics Database Cicada (2021-06-22)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/NIST_DART-MS_Forensics_Cicada.SDF","format":"SDF (text file)","description":"Updated NIST DART-MS Forensics Database including spectra of 830 compounds of interest.","mediaType":"text/plain","title":"Updated NIST DART-MS Forensics Database Cicada (2021-06-22)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/210915_He_Pos_Library_Dragonfly.zip.sha256","mediaType":"text/plain","title":"SHA256 File for NIST DART-MS Forensics Database (He Pos Dragonfly)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/210915_He_Pos_Library_Dragonfly.zip","description":"This is a new update of the NIST DART-MS Forensics Database with spectra measured with helium gas in positive ion mode. The library is  provided  as both a general-purpose structure data file (.SDF) for use with any text editor and as an R data file (.RDS) for use with the R programming language.","mediaType":"application/zip","title":"NIST DART-MS Forensics Database (He Pos Dragonfly)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/220120_He_Pos_Library_Earthworm.zip","description":"This is the latest (2022-Jan-20) of the NIST DART-MS Forensics Database with is-CID mass spectra measured with helium gas in positive ion mode. The library is provided as both a general-purpose structure data file (.SDF) for use with any text editor and as an R data file (.RDS) for use with the R programming language.","mediaType":"application/zip","title":"NIST DART-MS Forensic Database - Earthworm"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/220315_HeLibrary_Firefly.zip","description":"This is the latest (2022-March-15) of the NIST DART-MS Forensics Database with is-CID mass spectra measured with helium gas in positive ion mode. The library is provided as both a general-purpose structure data file (.SDF) for use with any text editor and as an R data file (.RDS) for use with the R programming language.","mediaType":"application/zip","title":"DART-MS Forensics Database Firefly"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/NIST-DARTMS-Forensics-v1.SDF","mediaType":"text/plain","title":"NIST-DARTMS-Forensics-v1.SDF"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/NIST-DARTMS-Forensics-v1.SDF.sha256","mediaType":"text/plain","title":"SHA-256 file for NIST-DARTMS-Forensics-v1.SDF"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/230104_He_Pos_Library_Grasshopper.zip","description":"Positive mode database collected using helium as the source gas. In this folder is the .RDS format of the database (compatible with the NIST/NIJ Data Interpretation Tool), the .SDF format of the database (compatible with NIST MS Search), and the release notes which outline the new compounds added to the database.","mediaType":"application/x-zip-compressed","title":"NIST DART-MS Forensics Database - Version Grasshopper"},{"accessURL":"https://doi.org/10.18434/mds2-2313","title":"DOI Access for NIST DART-MS Forensics Database 2020"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2313/NIST-DARTMS-Forensics-update-2021-01.SDF.sha256","mediaType":"text/plain","title":"SHA256 File for NIST DART-MS Forensics Library (is-CID) - updated 2021/01"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2020-10-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Chemistry:Analytical chemistry"],"issued":"2023-05-19","keyword":["Standard Reference Data","Mass Spectra","Ion Fragmentation","Mass Spectrometry","NIST Mass Spectral Libraries","Chemical Identification","Biosciences and Health","Security and Forensics"]},{"identifier":"ark:/88434/mds2-2922","accessLevel":"public","contactPoint":{"hasEmail":"mailto:vladimir.aksyuk@nist.gov","fn":"Vladimir Aksyuk"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2922","description":"Experimental and modeling data from the manuscript: 'Integrating planar photonics for multi-beam generation and atomic clock packaging on chip', C. Ropp, et.al., accepted for publication in Light: Science & Application, 2023.","language":["en"],"title":"Data for the manuscript: Integrating planar photonics for multi-beam generation and atomic clock packaging on chip.","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-20 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Nanotechnology:Nanophotonics","Physics:Atomic, molecular, and quantum","Physics:Optical physics","Physics:Time and frequency"],"issued":"2023-01-23","keyword":["integrated photonics","atomic clock","photonic-atomic interface","metasurfaces","extreme mode converters","hybrid integration"]},{"identifier":"ark:/88434/mds2-2923","accessLevel":"public","contactPoint":{"hasEmail":"mailto:jordan.weaver@nist.gov","fn":"Jordan Weaver"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2923","description":"Single-track laser scans were produced with Yb-fiber lasers on bare plates of IN625 and IN718 using three different laser powder bed fusion machines. The laser power, scan speed, and laser spot  diameter varied. Tracks were cross-sectioned and metallographically prepared. Optical micrographs were taken on etched samples. Melt pool depth and width measurements were made on optical micrographs. The dataset includes optical micrographs and melt pool width and depth measurements. These are supplemental experiments to the single-track laser scans for Additive Manufacturing Benchmark 2018 and 2022  challenges (https://www.nist.gov/ambench/am-bench-data-and-challenge-problems-0). Some of the data is associated with publications (1) https://doi.org/10.1016/j.jmapro.2021.10.053  and (2) https://doi.org/10.1007/s40192-022-00289-w.Users are strongly encouraged to first review the ?Master_TrackList_Measuremetns.xlsx? file for description of each image file.","language":["en"],"title":"Single-track laser scan cross-sectional micrographs on IN625 and IN718 bare plates with melt pool depth and width measurements","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2923/Micrographs%2FIN625_20181026_SpotSize%2FSpotSize_B_03_m-Image%20Export-10.tif","mediaType":"image/tiff","title":"Micrographs/IN625_20181026_SpotSize/SpotSize_B_03_m-Image Export-10"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2923/Micrographs%2FIN625_20181026_SpotSize%2FSpotSize_B_04-Image%20Export-47.tif","mediaType":"image/tiff","title":"Micrographs/IN625_20181026_SpotSize/SpotSize_B_04-Image 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Export-48"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-09-21 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Additive manufacturing"],"issued":"2023-09-21","keyword":["melt pool","laser powder bed fusion","process maps","microstructure"]},{"identifier":"ark:/88434/mds2-2926","accessLevel":"public","contactPoint":{"hasEmail":"mailto:vladimir.aksyuk@nist.gov","fn":"Vladimir Aksyuk"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2926","description":"'Beating thermal noise in a dynamic signal measurement by a nanofabricated cavity-optomechanical sensor', Mingkang Wang, Diego J. Perez-Morelo, Georg Ramer, Georges Pavlidis, Jeffrey J. Schwartz, Liya Yu, Robert Ilic, Andrea Centrone, and Vladimir A. Aksyuk, Science Advances 9, eadf759, 2023.DOI:10.1126/sciadv.adf7595Thermal fluctuations often impose both fundamental and practical measurement limits on high-performance sensors, motivating the development of techniques that bypass the limitations imposed by thermal noise outside cryogenic environments. Here, we theoretically propose and experimentally demonstrate a measurement method that reduces the effective transducer temperature and improves the measurement precision of a dynamic impulse response signal. Thermal noise limited, integrated cavity-optomechanical atomic force microscopy probes are used in a photothermal induced resonance measurement to demonstrate an effective temperature reduction by a factor of ? 25; i.e., from room temperature down as low as ? 12 K, without cryogens. The method improves the experimental measurement precision and throughput by > 2x, approaching the theoretical limit of ? 3.5x improvement for our experimental conditions. The general applicability of this method to dynamic measurements leveraging thermal-noise-limited harmonic transducers will have a broad impact across a variety of measurement platforms and scientific fields.","language":["en"],"title":"Data for manuscript: Beating thermal noise in a dynamic signal measurement by a nanofabricated cavity-optomechanical 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The emissions are generated using a bottom-up/engineering approach and use the results generated by the Vulcan Project (version 3), an effort to quantify space/time-resolved FFCO2 emissions for the entire United States landscape, as the starting point for the more detailed Hestia analysis. A large number of local data sources are combined to best estimate FFCO2 emissions at fine scales such as air quality emissions data, traffic flow data, building information, sociodemographic information, and fuel statistics. The native spatial resolution of the Hestia data product is a combination of points, lines, and polygons dictated primarily by the underlying data sources and the Vulcan FFCO2 emissions outputs. The output made available here places this information into a regularized grid (0.01 degrees latitude x 0.01 degrees longitude) at hourly and annual temporal resolutions.  The files below are NetCDF format files that are compressed with tar and gzip (*.tgz).  Each contains Hestia for all sectors for one calendar year indicated in the filename. Annual files contain annual means, while hourly files contain hourly emissions for that calendar year.","language":["en"],"title":"Hestia Fossil Fuel Carbon Dioxide (FFCO2) Data Product --  NE corridor domain, Version 1.0 Beta, 0.01 degree grid","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2010.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2010.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2011.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2011.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2012.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2012.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2013.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2013.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2014.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2014.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2015.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.annual.2015.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2010.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2010.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2011.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2011.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2012.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2012.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2013.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2013.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2014.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2014.tgz.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2015.tgz","mediaType":"application/gzip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2930/NE.corr.hourly.2015.tgz.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-23 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Environment:Greenhouse gas measurements"],"spatial":"-77.6/39.8 (upper left coordinate lon/lat)-76.3/38.5 (lower right coordinate lon/lat)","issued":"2023-02-21","keyword":["Greenhouse gas measurements and models","emission inventories","urban greenhouse gas domes","GHG concentration measurements","fossil fuel carbon dioxide"],"temporal":"2010-01-01/2015-12-31"},{"identifier":"ark:/88434/mds2-2932","accessLevel":"public","contactPoint":{"hasEmail":"mailto:manuel.castellanosbeltran@nist.gov","fn":"Manuel Beltran"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2932","description":"Most data generated are averaged heterodyne IQ voltages of a reflectometry of a superconducting cavity dispersively coupled to a transmon qubit, where the phase shift of the cavity probe tone is used to infer the qubit state. These data are collected while performing various parameter sweeps to track the qubit state evolution in response to various stimuli.There is also simulation data used to model qubit state evolution when driven with digital pulses.","language":["en"],"title":"Coherence-limited digital control of a superconducting qubit using a Josephson pulse generator at 3 K","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2932/figures.zip","format":"Data is in csv files as json files for the simulation","description":"Data for the initial submission of the paper \"Coherence-limited digital control of a superconducting qubit using a Josephson pulse generator at 3~K \"","mediaType":"application/x-zip-compressed","title":"apl_JPG_version1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2932/README_APL_specialissue_paper.txt","format":"csv and json","description":"Description of the data in the file figures.zip","mediaType":"text/plain","title":"Read me file"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-01-30 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Physics:Quantum information science"],"issued":"2023-02-09","keyword":["Quantum computing","scalable","cryogenic control","Josephson junction (JJ)","Single Flux Quantum (SFQ)","transmon","superconductor"]},{"identifier":"ark:/88434/mds2-2933","accessLevel":"public","contactPoint":{"hasEmail":"mailto:lyle.levine@nist.gov","fn":"Lyle E. Levine"},"programCode":["006:045"],"@type":"dcat:Dataset","description":"This resource is the implementation in XML Schema [1] of a data model that describes the Additive Manufacturing Benchmark 2022 series data. It provides a robust set of metadata for the build processes and their resulting specimens and for measurements made on these in the context of the AM Bench 2022 project.The schema was designed to support typical science questions which users of a database with metadata about the AM Bench results might wish to pose. The metadata include identifiers assigned to build products, derived specimens, and measurements; links to relevant journal publications, documents, and illustrations; provenance of specimens such as source materials and details of the build process; measurement geometry, instruments and other configurations used in measurements; and access information to raw and processed data as well as analysis descriptions of these datasets.This data model is an abstraction of these metadata, designed using the concepts of inheritance, normalization, and reusability of an object oriented language for ease of extensibility and maintenance. It is simple to incorporate new metadata as needed.A CDCS [2] database at NIST was filled with metadata provided by the contributors to the AM Bench project. They entered values for the metadata fields for an AM Bench measurement, specimen or build process in tabular spreadsheets. These entries were translated to XML documents compliant with the schema using a set of python scripts. The generated XML documents were loaded into the database with a persistent identifier (PID) assigned by the database.[1] https://www.w3.org/XML/Schema[2] https://www.nist.gov/itl/ssd/information-systems-group/configurable-data-curation-system-cdcs/about-cdcs","language":["en"],"title":"Additive Manufacturing Benchmark 2022 Schema","distribution":[{"accessURL":"https://ambench2022.nist.gov/","title":"AM Bench 2022 Metadata Catalog"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2933/AMMeasurement.xsd","format":"xsd","description":"The schema for the metadata describing characterization measurements conducted in the AMBench 2022 project.","mediaType":"text/xml","title":"AMMeasurement.xsd"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2933/AMBuild.xsd","format":"xsd","description":"The schema for the metadata describing samples and build processes used in the AM Bench 2022 project.","mediaType":"text/xml","title":"AMBuild.xsd"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2933/AMReference.xsd","format":"xsd","description":"The schema  which contains a generic resource type 'AMResource' in the AM Bench project such as samples and measurements.  AMResource is the ultimate base type for other more explicit AM Bench documents.","mediaType":"text/xml","title":"AMReference.xsd"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2933/AMDocs.xsd","format":"xsd","description":"The schema which contains a root element for an AM Bench XML document. It wraps the more specific AM Bench resource documents including specimens and measurements.","mediaType":"text/xml","title":"AMDocs.xsd"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2933/2933_README.txt","format":"Plain text","description":"Description of the data files included in this data record","mediaType":"text/plain","title":"README file"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-09-12 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Materials:Materials characterization:Materials:Modeling and computational material science"],"issued":"2024-01-18","keyword":["data model","additive manufacturing","build plate","material composition","laser absorptivity","radiography","mechanical testing"]},{"identifier":"ark:/88434/mds2-2934","accessLevel":"public","contactPoint":{"hasEmail":"mailto:gregory.vogl@nist.gov","fn":"Gregory W. Vogl"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2934","description":"An experiment was set up within a machine tool at the National Institute of Standards and Technology (NIST) to test vision-based thermal drift tracking methods. A wireless microscope within a tool holder in the spindle is used to capture videos of image targets attached to the worktable. For each target, one video is captured during spindle rotation orthogonal to the worktable and another video is captured during axis translation orthogonal to the worktable. Data are collected periodically so that the three-dimensional thermal error at each target location may be determined via image analysis.","language":["en"],"title":"Thermal Drift Monitoring Experiment 01","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/Data.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/Data.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/G-code.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/G-code.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/Other.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/Other.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/Pictures%20and%20Videos.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/Pictures%20and%20Videos.zip.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/README.pdf","mediaType":"application/pdf"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/README.pdf.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/Thermal%20Images.zip","mediaType":"application/zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2934/Thermal%20Images.zip.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-02-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Manufacturing:Machining"],"issued":"2023-05-15","keyword":["Thermal error","Machine tool","Monitoring","Manufacturing","Microscope"]},{"identifier":"ark:/88434/mds2-2937","accessLevel":"public","references":["https://doi.org/10.1007/978-3-030-05710-7_29","https://doi.org/10.1145/3323873.3325051"],"contactPoint":{"hasEmail":"mailto:george.awad@nist.gov","fn":"George Awad"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2937","description":"The V3C2 dataset (drawn from a larger V3C video dataset) is composed of 9760 Vimeo videos (1.6 TB, 1300 h) with Creative Commons licenses and mean duration of 8 min. All videos will have some metadata available e.g., title, keywords, and description in json files. The dataset has been segmented into 1,425,454 short video segments according to the provided master shot boundary files. In addition, Keyframes and thumbnails per video segment have been extracted and available. For more details, please consult the TRECVID website: trecvid.nist.gov","language":["en"],"title":"Vimeo Creative Commons Collection (V3C2)","distribution":[{"accessURL":"https://www-nlpir.nist.gov/projects/tv2019/pastdata/ad.hoc.search/V3C_Org.Form.txt","description":"Please fill, sign, and submit this data agreement and once processed, you will be notified with the access information to download the dataset.","title":"Data Agreement"},{"accessURL":"https://trecvid.nist.gov/past.data.table.html","description":"Please use this URL to access the 2019, 2020, and 2021 queries and ground truth files used with the V3C1 dataset.","title":"Queries and Ground truth"},{"downloadURL":"https://www-nlpir.nist.gov/projects/tv2022/pastdata/ad.hoc.search/V3C2.master.shot.reference/","description":"Readme for the master shot reference:http://www-nlpir.nist.gov/projects/tv2019/pastdata/ad.hoc.search/V3C1.shotNames.readme","mediaType":"text/plain","title":"V3C1 master shot reference"},{"accessURL":"https://www-nlpir.nist.gov/projects/tv2022/avs.html","format":"plain text","description":"This is the AVS (Ad-hoc Video Search) Task guidelines page. The guidelines highlight the task definition, run formats, conditions, and metrics.","title":"The Ad-hoc Video Search Task guidelines"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"rights":"Signed agreement is required","modified":"2018-12-02 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Data and informatics"],"issued":"2023-02-13","keyword":["TRECTRECVID video searchcontent-based video retrieval"]},{"identifier":"ark:/88434/mds2-2938","accessLevel":"public","contactPoint":{"hasEmail":"mailto:paritosh.manurkar@nist.gov","fn":"Paritosh Manurkar"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2938","description":"We present an experimental approach to design an over-the-air (OTA) millimeter-wave system for measuring error vector magnitude (EVM) with associated uncertainties that include correlations and nonlinearities. Our approach uses a variable waveguide attenuator at the output of a modulated-signal source at 44 GHz and provides traceable measurements on a calibrated equivalent-time sampling oscilloscope. The conductor-based EVM measurements and associated uncertainties presented here serve as a baseline for the eventual OTA-based EVM. We also discuss a noise based EVM estimation technique as a simple tool for planning OTA EVM measurements, but without a complete knowledge of measurement uncertainties.","language":["en"],"title":"Dynamic Range by Design in OTA EVM measurements","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2938/2938_README.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2938/2938_README.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2938/data_for_Fig2_2023.04.22_final.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2938/data_for_Fig2_2023.04.22_final.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2938/data_for_Fig3_2023.04.22_final.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2938/data_for_Fig3_2023.04.22_final.txt.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-04-22 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Advanced Communications:Wireless (RF)"],"issued":"2024-01-03","keyword":["Digitally modulated signals","error vector magnitude","over-the-air measurements","uncertainty analysis","wireless system."]},{"identifier":"ark:/88434/mds2-2939","accessLevel":"public","contactPoint":{"hasEmail":"mailto:samuel.forry@nist.gov","fn":"Samuel P. Forry"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2939","description":"This repository provides analysis code and results produced during evaluation of metagenomic sequencing (MGS) data collected through the Mosaic Standards Challenge. The Mosaic Standards Challenge asked participating laboratories analyze the same set of 7 samples using their own favored MGS laboratory methods. Each lab submitted their raw sequencing results and protocol information. The resulting MGS data was analyzed through a common bioinformatic pipeline and then evaluated to determine the effects of methodological choices. ","language":["en"],"title":"Variability and Bias in Microbiome Metagenomic Sequencing: an Interlaboratory Study Comparing Experimental Protocols","license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-02-03 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Bioscience:Microbial measurements"],"issued":"2023-03-02","keyword":["microbiome; methodology; metagenomic sequencing"]},{"identifier":"ark:/88434/mds2-2940","accessLevel":"public","contactPoint":{"hasEmail":"mailto:isaac.leventon@nist.gov","fn":"Isaac Leventon"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2940","description":"A set of 10 anaerobic gasification experiments was conducted on the poly(methyl methacrylate), PMMA, made available to participants in the MaCFP-2 Workshop. In each test, samples (i.e., PMMA discs of approximate dimensions: 7 cm diameter, 5.8 mm thickness) were exposed to radiant heating (nominally 50 kW m-2 across their top surface) in an anaerobic environment. Samples were insulated at their back surface and continuously heated until complete decomposition was observed. Test boundary conditions (e.g., time- and spatially-resolved measurements of incident radiant heat flux; chamber wall temperatures) were carefully characterized.Six additional tests were also conducted to measure the temperature rise of inert materials (Copper and Kaowool PM Insulation) exposed to the same conditions (i.e., Nitrogen flow rate + incident radiant heat flux) as used during tests on PMMA samples. These test results may be used to validate material thermophysical properties and boundary conditions (e.g., convection heat transfer) controlling heat transfer in this system.","language":["en"],"title":"Experimental Measurements for Pyrolysis Model Validation - Anaerobic Gasification of PMMA Under External Thermal Radiation","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Mass.pdf","description":"Time-resolved measurements of PMMA Mass when exposed to an incident radiant heat flux of 50 kW m-2.","mediaType":"application/pdf","title":"PMMA Mass vs time (q50)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_MLR.pdf","description":"Time-resolved measurements of PMMA Mass Loss Rate when exposed to an incident radiant heat flux of 50 kW m-2.","mediaType":"application/pdf","title":"PMMA MLR vs time (q50)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Temperature.pdf","description":"Time-resolved measurements of PMMA back surface temperature when exposed to an incident radiant heat flux of 50 kW m-2.","mediaType":"application/pdf","title":"PMMA Temperature vs. time (q 50)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MACFP-3-Gasification_README.md","description":"Description of test goals, apparatus setup and calibration, and measurement results.","mediaType":"application/octet-stream","title":"README"},{"accessURL":"https://github.com/MaCFP/macfp-db/","format":"A github repository containing .csv files with experimental data and a .md text file describing these measurements","description":"Anaerobic gasification of poly(methyl methacrylate) under external thermal radiation in the NIST Gasification Apparatus. Measurement data includes back surface temperature and sample mass loss rate of ~5.8mm thick, 7cm diameter PMMA discs","title":"Anaerobic gasification of poly(methyl methacrylate) under external thermal radiation"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Mass_R3.csv","description":"Measured sample mass of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R3)","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Mass_R3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Mass_R4.csv","description":"Measured sample mass of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R4)","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Mass_R4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Mass_R5.csv","description":"Measured sample mass of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R5)","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Mass_R5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Temp_R1.csv","description":"Measured back surface temperature of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R1)","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Temp_R1"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Temp_R2.csv","description":"Measured back surface temperature of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R2)","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Temp_R2"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Temp_R3.csv","description":"Measured back surface temperature of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R3)","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Temp_R3"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Temp_R4.csv","description":"Measured back surface temperature of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R4)","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Temp_R4"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/MaCFP-PMMA_Gasification_q50_Temp_R5.csv","description":"Measured back surface temperature of MaCFP-PMMA during anaerobic pyrolysis when exposed to 50 kW/m2 of external radiant heating (test repetition R5)","mediaType":"text/csv","title":"MaCFP-PMMA_Gasification_q50_Temp_R5"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/Black-Copper_q50_Temp.csv","description":"Temperature rise of 1/8 in. black copper disc when exposed at top surface to 50 kW/m2 incident radiation and insulated at back with 1.125 in. Kaowool PM insulation board. Temperature values represent an average of 3 tests conducted in triplicate; uncertainties (Uc) represent 2 stdev_mean of Temp values recorded in triplicate tests in a +/- 1 s time window (i.e., 3 time steps averaged across 3 tests)","mediaType":"text/csv","title":"Black-Copper_q50_Temp"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/Black-Insulation_q50_Temp.csv","description":"Temperature rise of 5x 1/4 in. thick Kaowool PM board insulation discs when exposed at top surface (painted with medtherm optical black coating, emissivity = 0.95) to 50 kW/m2 incident radiation. Temperature measurements are recorded at 3 locations (x, distance from top surface); each column / value represents an average of 3 tests conducted in triplicate.Uncertainties (Uc) at each location represent 2 stdev_mean of Temp values recorded at that specific location in triplicate tests in a +/- 1 s time window (i.e., 3 time steps averaged across 3 tests; 9 total values)","mediaType":"text/csv","title":"Black-Insulation_q50_Temp"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/Gasification_Apparatus_Schematic_w-sample.pdf","description":"Schematic of gasification apparatus with highlight of sample/insulation assembly","mediaType":"application/pdf","title":"Gasification_Apparatus_Schematic_w-sample"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/q50_normalized.pdf","description":"normalized (q*=q\"/q\"steady) time-response of gasification apparatus heat flux across the sample's surface","mediaType":"application/pdf","title":"Time-averaged, normalized incident heat flux (i.e., q*=q\"/q\"{240s-300s}) at r = 0, 2.5, 5.0 and 7.1 cm (21 locations)"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2940/q50_hfmap.pdf","description":"contourf plot of heat flux distribution across sample's surface at steady state (target flux = 50 kW m-2)","mediaType":"application/pdf","title":"Measured steady state heat flux profile across the sample's surface for tests conducted at 50 kW m-2"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-02-06 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Fire:Materials flammability","Fire:Fire modeling"],"issued":"2023-02-17","keyword":["fire modeling","material properties","model validation","pyrolysis"]},{"identifier":"ark:/88434/mds2-2942","accessLevel":"public","contactPoint":{"hasEmail":"mailto:nikolasdale.barrera@nist.gov","fn":"Nick 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interfaces","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2942/Microwave%20characterization%20of%20Parylene%20C%20dielectric%20and%20barrier%20properties%20dataset.zip","format":"csv, .mat, .m","description":"Data for paper titled \"Microwave characterization of Parylene C dielectric and barrier properties\"","mediaType":"application/x-zip-compressed","title":"Microwave characterization of Parylene C dielectric and barrier properties dataset.zip"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2942/readme.txt","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2942/readme.txt.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2942/2942_README.txt","format":".txt","description":"Contains file format descriptions of the Microwave characterization of Parylene C dielectric and barrier properties dataset","mediaType":"text/plain","title":"README file describing Microwave characterization of 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The documents come from CommonCrawl.  The IE annotations in three schemas are by MITRE and ARLIS.  The IR queries and relevance judgments were done at NIST, and NIST was asked by IARPA to distribute the data in its final form.  The tasks are all cross-language from English into one of Arabic, Farsi, Russian, Chinese, and Korean","language":["en"],"title":"IARPA BETTER (Better Extraction from Text Towards Enhanced Retrieval) information extraction and information retrieval datasets.","distribution":[{"downloadURL":"https://ir.nist.gov/better/","mediaType":"application/octet-stream","title":"The BETTER datasets"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-02-24 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Information Technology:Data and informatics"],"issued":"2024-04-22","keyword":["information extraction; information retrieval; cross-language information retrieval"]},{"identifier":"ark:/88434/mds2-2948","accessLevel":"public","references":["https://doi.org/10.1109/ECTC51909.2023.00225"],"contactPoint":{"hasEmail":"mailto:aaron.forster@chips.gov","fn":"Aaron M. 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Han, Investigation of Cure Kinetics of Advanced Epoxy Molding Compound Using Dynamic Heating Scan: An Overlooked Second Reaction, 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC), Orlando, Florida, May 30 - June 2, 2023. https://doi.org/10.1109/ECTC51909.2023.00225","language":["en"],"title":"Cure Kinetics of Advanced Epoxy Molding Compound Using Dynamic Heating Scan","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%203%20ECTC.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%203%20ECTC.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%204%20ECTC.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%204%20ECTC.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%205%20ECTC.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%205%20ECTC.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%206%20ECTC.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%206%20ECTC.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%207%20ECTC.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%207%20ECTC.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%208%20ECTC.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%208%20ECTC.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2948/2948_README.txt","mediaType":"text/plain","title":"README"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%201%20ECTC.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%201%20ECTC.xlsx.sha256","mediaType":"text/plain"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%202%20ECTC.xlsx","mediaType":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2948/Figure%202%20ECTC.xlsx.sha256","mediaType":"text/plain"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-03-01 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"theme":["Chemistry:Thermochemical properties","Chemistry:Analytical chemistry","Chemistry:Chemical engineering and processing","Materials:Polymers","Materials:Composites","Materials:Materials characterization"],"issued":"2023-03-10","keyword":["differential scanning calorimetry","thermal gravimetric analysis","heat of reaction","glass transition","thermosets","filled polymers","advanced packaging","semiconductors"]},{"identifier":"ark:/88434/mds2-2950","accessLevel":"public","references":["https://doi.org/10.1002/smll.202301987"],"contactPoint":{"hasEmail":"mailto:jason.killgore@nist.gov","fn":"Jason Killgore"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2950","description":"Digital light processing (DLP) vat photopolymerization (VP) additive manufacturing (AM) uses patterned UV light to selectively cure a liquid photopolymer into a solid layer. 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Jupyter notebooks for various steps in process are also provided.","language":["en"],"title":"A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-nets and Generative Adversarial Networks","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/mds2-2950/Jupyter_notebooks.zip","mediaType":"application/zip","title":"Jupyter_notebooks"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2950/2950_README.txt","mediaType":"text/plain","title":"2950_README"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2950/raw_print_data.zip","mediaType":"application/zip","title":"raw_print_data"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2950/photomasks.zip","mediaType":"application/zip","title":"photomasks"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2950/modified_pix2pix.zip","mediaType":"application/zip","title":"modified_pix2pix"},{"downloadURL":"https://data.nist.gov/od/ds/mds2-2950/training_pairs.zip","mediaType":"application/zip","title":"training_pairs"}],"license":"https://www.nist.gov/open/license","bureauCode":["006:55"],"modified":"2023-03-07 00:00:00","publisher":{"@type":"org:Organization","name":"National Institute of Standards and Technology"},"accrualPeriodicity":"irregular","theme":["Mathematics and Statistics:Statistical analysis","Materials:Polymers","Manufacturing:Additive manufacturing"],"issued":"2023-07-20","keyword":["3D Printing","Additive Manufacturing","Machine Learning","Generative Adversarial Network","Photopolymer"]},{"identifier":"ark:/88434/mds2-2951","accessLevel":"public","references":["https://doi.org/10.6028/NIST.IR.7814","https://eprintspublications.npl.co.uk/5412/"],"contactPoint":{"hasEmail":"mailto:adam.creuziger@nist.gov","fn":"Adam Creuziger"},"programCode":["006:045"],"@type":"dcat:Dataset","landingPage":"https://data.nist.gov/od/id/mds2-2951","description":"This data publication includes electron backscatter diffraction (EBSD) data used to support the development of ISO 13067 \"Microbeam Analysis - Electron Backscatter Diffraction - Measurement of Average Grain Size\" as part of a Versailles Project on Advanced Materials and Standards (VAMAS) work area. EBSD data was collected on two commercially pure titanium samples. Two different scanning electron microscopes were used, and two staff independently performed measurements of 5 selected areas. The reports, data summaries, and raw data were provided back to the VAMAS work group for evaluation.  Methods, discussion, and data summaries from these data sets are contained in the referenced NIST Internal Report (NIST IR). The referenced National Physical Laboratory (NPL) report is a summary of the round robin.","language":["en"],"title":"Data Publication: Data from NIST IR \"Report on VAMAS Round Robin of ISO 13067: Microbeam Analysis - Electron Backscatter Diffraction - Measurement of Average Grain Size\"","distribution":[{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2951/Hitachi%20final%20dataset%2F2011_4_18%2Fsample%2023%204-18%2002-Euler%20-cleaned%20Map.bmp","description":"Re-generated download URL","mediaType":"image/bmp","title":"Hitachi final dataset/2011_4_18/sample 23 4-18 02-Euler -cleaned Map"},{"downloadURL":"https://data.nist.gov/od/ds/ark:/88434/mds2-2951/Hitachi%20final%20dataset%2F2011_4_18%2Fsample%2023%204-18%2002Before.jpg","description":"Re-generated download URL","mediaType":"image/jpeg","title":"Hitachi final dataset/2011_4_18/sample 23 4-18 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