How FAIR-R Is Your Data? Enhancing Legal and Technical Readiness for Open and AI-Enabled Reuse
Bibliographic record
Abstract
Title: How FAIR-R Is Your Data? Enhancing Legal and Technical Readiness for Open and AI-Enabled Reuse Authors: Katharina Miller, Vanessa Guzek (Miller International Knowledge, MIK), partner in Horizon Europe project IP4OS Conference: Open Science Conference 2025, Hamburg Description :This contribution, to be presented at the Open Science Conference 2025 in Hamburg, introduces the concept of FAIR-R as an evolution of the FAIR data principles (Findable, Accessible, Interoperable, Reusable). While FAIR focuses on technical openness, FAIR-R adds a crucial dimension: datasets must also be Responsibly licensed and legally ready for reuse in artificial intelligence (AI) and machine learning workflows. The presentation provides: A quick overview of FAIR vs. FAIR-R. Key licensing red flags that block reuse, such as missing licenses, NonCommercial (NC) or NoDerivatives (ND) clauses, or lack of machine-readable metadata. Common AI-specific barriers, including sensitive data, restrictive license clauses, proprietary formats, and insufficient traceability. Participants of the session applied a lightweight FAIR-R checklist to evaluate real datasets, identifying both technical and legal gaps that limit responsible reuse. The outcomes contribute to improving dataset readiness for Open Science and AI-driven research, offering practical guidance for researchers, institutions, and policymakers. Funding Acknowledgment:This work is part of the Horizon Europe project IP4OS (Grant Agreement No. 101188026), funded by the European Union. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EU nor REA can be held responsible for them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.138 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.036 | 0.059 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".