From FAIR to AI-FAIR Metadata-First Infrastructure for AI-Enabled Science
- Published
- Source
- arXiv
- Paper number
- 1414
- Field
- Research
- arXiv ID
- 2611.03327
Key points
- The authors diagnose that data infrastructure in ecology and biodiversity science is fragmented and the flow of value remains largely unidirectional.
- They define the AI-FAIR metadata criteria as four categories: structural, scientific, provenance and governance metadata.
- They propose the Question-Data-Model triangle with bidirectional edges, so improvements made by models return to the source data providers.
- They built an automated evaluation tool where an agent retrieves metadata and rates checklist items while scoring stays deterministic.
- Data publishers have begun piloting the evaluation tool to establish baseline scores for their own data.
Paper links
External research summaries. These are not HDATF publications or measured product results.