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National Institute of Standards and Technology
NIST … is an agency in the Technology
Administration of the US Department of
Commerce.
NIST Assets Include:

3,000 employees
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1,600 guest researchers
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$760 million annual budget
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NIST Laboratories -- National
measurement standards - $400 million budget
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Advanced Technology Program -- $570 million current R&D
partnerships with industry
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Manufacturing Extension Partnership -- 400 centers nationwide to
help small manufacturers
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Baldrige National Quality Award
NIST Laboratories
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CSTL/NIST Goals
Measurement Standards
Establish CSTL as the pinnacle of the national traceability and
international comparability structure for measurements in chemistry,
chemical engineering and biotechnology, and provide the fundamental
basis of the nation’s measurement system
Chemical and Process Information
Assure that U.S. industry has access to accurate and
reliable data and predictive models to determine the
chemical and physical properties of materials and
processes
Measurement Science
Anticipate and address next generation measurement needs of the
nation, by performing cutting-edge research
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• Explosive growth in the need for reliable
physical, chemical, biochemical, and materials
property data to support process and product
optimization and discovery.
Millions of Pages
• combinatorial methods
• high throughput experimentation
• doubling of the volume of published thermodynamic data in the
last 10 yrs
1.6
Growth in monthly use
Chemistry WebBook
1.2
0.8
0.4
0
7/1996
1/2003
Information & Knowledge Management
Knowledge
Knowledge
Development
Information
Information Synthesis
Data Collection
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Reliable Data
Information
• Reliable Data
• suitably documented
• established uncertainty
– first step in the creation of information
– core of NIST mission
“...critically evaluated data on well-characterized
substances...”
Standard Reference Data Act
• Trustworthy information “on-demand”:
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– data collection
• autonomous input at publication
• federation of data archives world-wide
– data management
• metadata, uncertainty, provenance
– data mining
– data evaluation – best value, “self-improving” archives
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expert systems
comprehensive archives
non-linear, multi-variate, constrained, statistical optimization
self-consistent, reflexive annotation of uncertainty
– “virtual measurements”
• first principles computations
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PrIMe – Process Informatics in a
Community Collaboration for the
Development of Combustion Models
• Needs
– reliable hardware
– modern data-management tools
– sustained experimental data collection and
evaluation
– scientific tools for mechanism generation, model
reduction, dataset analysis, …
Challenges
• 100’s gigabytes - federated world-wide
– semantics & syntax – legacy
• mirror archives or only query federation?
– real-time access to comprehensive archive
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non-standard, legacy database schema
non-standard or non-existent provenance
quality measures and annotation
metadata update upon evaluation
– enable dynamic self-consistency
• computation in a distributed environment
• architectures for “on-demand” first principles
computational estimation
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