An Assessment of the Material Measurement Laboratory at the National Institute of Standards and Technology Fiscal Year 2023 (2023) / Chapter Skim
Currently Skimming:
Summary
Summary
Pages 1-5
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 1... ...
The two offices manage programs related to NIST standard reference materials and NIST data products. The technical divisions engage in research and development of the measurement science, standards, technology, and data required to support the nation's need to design, develop, manufacture, and use materials.
|
From page 2... ...
All of these issues are discussed in the body of the report. Regarding facilities, this report notes MML-specific challenges and ties to the National Academies' 2023 report Technical Assessment of the Capital Facility Needs of the National Institute of Standards and Technology (NASEM 2023; hereafter the "Capital Facility Needs report")
|
From page 3... ...
This Summary highlights overarching, crosscutting themes that emerged over the course of the 2023 assessment. KEY RECOMMENDATIONS Adequacy of Facilities As highlighted in the National Academies' 2020 MML assessment, An Assessment of the Material Measurement Laboratory at the National Institute of Standards and Technology: Fiscal Year 2020 (NASEM 2021)
|
From page 4... ...
The balance of maintaining legacy standard reference materials versus redirecting limited resources toward producing standard reference materials and standard reference data in new and emerging areas presents both a challenge and an opportunity. MML notes that areas of new opportunities include the bioeconomy and engineering biology, data science and engineering, artificial intelligence, and the circular economy.
|
From page 5... ...
In addition, not all of the experimental measurement systems have a consolidated and automated data infrastructure, which results in a fragmented data infrastructure that, unfortunately, leads to discrete, inefficient, and stovepiped experimental data infrastructure across MML. These observations lead to opportunities to unify all computational systems, data formats, and data transmission protocols into a single, uniform platform for more efficient and effective data curation, storage, processing, transmission, and security management.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.