How FAIR-R Is Your Data? Enhancing Legal and Technical Readiness for Open and AI-Enabled Reuse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it