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Material Measurement Laboratory

Material Data Division

NIST's Material Data Division provides modern data infrastructure, data science capabilities, and modeling and simulation expertise to enable the Material Measurement Laboratory's technical staff to carry out NIST’s mission.

The Material Data Division (MDD), formerly the Office of Data and Informatics, provides modern data infrastructure, data science capabilities, and modeling and simulation expertise to enable the Material Measurement Laboratory (MML) technical staff to carry out NIST’s mission. In partnership with U.S. industry, federal agencies, and other stakeholders, MDD develops reference data to:

  1. Establish the accuracy of measurement methods, including those evolving from emerging automated laboratories technologies, and theoretical approaches, including modeling, simulation, and AI/ML, that describe chemical, materials, and biological systems essential to the competitiveness of the U.S. economy, and
  2. Help fill key data gaps germane to MML’s core competencies in critical emerging technologies. MDD supports dissemination of reference data to stakeholders and manages the NIST’s Standard Reference Data program.

News and Updates

120 Years of Data at NIST

In celebration of the 50 th anniversary of the founding of the Journal of Physical and Chemical Reference Data (JPCRD), two NIST scientists have published a...

Events

Projects and Programs

Map of the Northern hemisphere Pacific Ocean, with environmental specimen sampling sites indicated with data points in the Hawaiian Islands, Alaska, and Bering Sea locations.

Classification Methods for the NIST Biorepository

Ongoing
NIST has been archiving biological and environmental specimens, including marine animal and environmental samples, for over 40 years. Currently, these biospecimens are stored at cryogenic temperatures in the NIST Biorepository located at the Hollings Marine Laboratory in Charleston, SC. Various...
Axial Diffusion in Pores is controlled by Local Density

Connecting Thermodynamic and Dynamic Properties of Bulk and Confined Fluids

Ongoing
The dynamic properties of bulk fluids remain a challenge to predict. Predicting the dynamic properties of confined fluids represents a greater scientific and engineering challenge. Developing simple heuristics or even robust correlations will be helpful in developing and understanding technologies...
component 1

Consistency Analysis and Uncertainty in ‘omic Data

Ongoing
‘Omics is increasingly moving out of the laboratory and towards use in industrial and commercial applications. For instance, in biomanufacturing, there is a need for quality control when developing biotherapeutics, which will almost certainly require a machine-learning classifier to separate...
Big data analysis report on 3D effects.AI technology generated image

Data Tools for Environmental Metrology

Ongoing
A recent addition to operations in the NIST Biorepository, we seek out places where NIST scientists get bogged down by the complexity of their data. Research at NIST can generated some of the most complex data in the world, at times requiring six months or more of processing to get to the data...

Awards

Contacts

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