Project Name:

AI-Ready Data Products to Facilitate Discovery and Use

Contractor: BrightQuery, Inc.

Lessons Learned

1. Data Availability and Format
○ Consolidated historical data and revisions are critical for accessibility and usability.
2. AI and ML Challenges
○ Commercial AI tools struggle with statistical and time-series data, particularly revisions.
○ Time must be treated as multidimensional, capturing both the period and the timestamp.
3. Standards and Discoverability
○ Schema.org and Croissant standards enhance data discoverability but require additional depth for analytics.
4. Knowledge Graph Development
○ Triplication is essential for building knowledge graphs but lacks standardization for entity denitions and time-series data representation.
5. Granularity and Interoperability
○ More granular data enhances interoperability but may be affected by changes in methodology or categorization.

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Disclaimer: America’s DataHub Consortium (ADC), a public-private partnership, implements research opportunities that support the strategic objectives of the National Center for Science and Engineering Statistics (NCSES) within the U.S. National Science Foundation (NSF). These results document research funded through ADC and is being shared to inform interested parties of ongoing activities and to encourage further discussion. Any opinions, findings, conclusions, or recommendations expressed above do not necessarily reflect the views of NCSES or NSF. Please send questions to ncsesweb@nsf.gov.