Data Tools for Environmental Metrology

Summary

A major goal of the NIST Biorepository is to address large scale interdisciplinary questions about the environment. Each sample contained in the bank can be used for a wide variety of analyses. Answering these questions requires harmonization across data streams to allow for statistical assessment. Active research includes a variety of data use and processing tools to address these issues, including the Marine Sample Tracking and Analytical Reporting database, which will eventually make results obtained by NIST available to a broad audience of potential collaborators.

Description

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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 interpretation stage. Whether it's properly parsing instrument output, processing mass spectral data, finding archived data necessary to complete (or begin) a project, or simply being overwhelmed by the sheer terabytes of information generate, friction in the data processing aspect of environmental metrology at NIST reduces productivity by increasing time-to-completion and quality control constraints. By removing roadblocks and friction points from data processing tasks, NIST scientists can focus more closely on areas where they can make the most impact: project planning, data interpretation, and communication of our science. Major focuses of the tools developed at the Hollings Marine Laboratory seek to not only reduce friction points and processing time, but also to increase confidence in our data products by assuring data integrity through process automation.

Ongoing Projects:

• Marine Sample Tracking and Analytical Reporting (STAR)
• Environmental Metrology Measurement Assistant (EMMA)
• Lipidomics Data Simplifier (LipiDS)
• Stock Tracking and Assessment of Chemical Solutions (TrACS)

Associated Publications 

1. Mahynski, N. A., Ragland, J. M., Schuur, S. S., and Shen, V. K., "Building Interpretable Machine Learning Models to Identify Chemometric Trends in Seabirds of the North Pacific Ocean," Environ. Sci. Technol., 56, 14361-14374 (2022). 

2. Ulmer, C. Z., Ragland, J. M., Koelmel, J. P., Heckert, A., Jones, C. M., Garrett, T. J., Yost, R. A., and Bowden, J. A., "LipidQC: Method Validation Tool for Visual Comparison to SRM 1950 Using NIST Interlaboratory Comparison Exercise Lipid Consensus Mean Estimate Values," Analytical Chemistry, 89, 13069-13073 (2017). 

3. Ulmer, C. Z., Koelmel, J. P., Ragland, J. M., Garrett, T. J., and Bowden, J. A., "LipidPioneer : A Comprehensive User-Generated Exact Mass Template for Lipidomics," Journal of the American Society for Mass Spectrometry, 28, 562-565 (2017).