From Principles to Practice: Enabling FAIRification from laboratories to Research Infrastructures
Bibliographic record
Abstract
Preprint in Embargo until submission. Targetted journal: Patterns: Cell Press This paper presents an analysis of 12 case studies showcasing the implementation of the FAIR principles (Findable, Accessible, Interoperable, Reusable) across diverse disciplinary contexts. It identifies key challenges such as the burden of metadata entry, inconsistent standards, repository sustainability, and difficulties in tracking data provenance, and proposes actionable recommendations tailored and targeted to the roles and responsibilities of key stakeholders, addressing e.g., data producers, data stewards, infrastructure providers, repository managers, and policymakers. The recommendations are designed to be actionable at three complementary levels: (1) by research teams and laboratories, (2) through institutional governance and support structures, and (3) via engagement with international ecosystems and initiatives developing tools for FAIR support and assessment. This paper highlights the importance of promoting wider use of existing FAIR tools, guidances and initiatives, tailoring practices to community needs, and using self-assessment tools not merely for compliance, but as mechanisms for continuous improvement. Ultimately, the authors encourage the development of tailored FAIR checklists and the use of European and international infrastructures to align FAIRification efforts, emphasising the role of FAIR in fostering efficient, transparent and reproducible science.
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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 | 0.268 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.014 | 0.041 |
| Scholarly communication | 0.039 | 0.052 |
| Open science | 0.007 | 0.047 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".