FAIRification of biomedical research data
Bibliographic record
Abstract
The Findable, Accessible, Interoperable, and Reusable guiding principles promote Findability, Accessibility, Interoperability, and Reuse of data to enhance data management and stewardship. In biomedicine, particular ethical, legal, and technical barriers complicate research data sharing. To help researchers overcome these challenges, we propose a framework of FAIRification from three dimensions - scientific, technical, and legal/ethical. We advocate for prospective FAIRification of study data, starting with a strong emphasis on planning for data-sharing from the beginning. Reflective questions throughout the process guide researchers to reflect on their situation. Researchers should assess resources and feasibility, secure technical and legal support, consider stakeholder needs, and devise an appropriate data sharing process. Given the sensitivity of biomedical data, confidentiality and security require careful attention. The data sharing strategy should be finalized before the study starts and documented in relevant study materials. Technical preparation for data sharing follows planning. Data should be well-documented with a data dictionary and metadata to facilitate reuse and provided in an accessible format. The data can be hosted on a repository to promote sharing and reuse. While a secure repository provides the technical foundation for data protection, effective administration is required to enforce data use agreements and licensing. We also discuss the importance of subsequent management upon data upload. Continued support for researchers and data maintenance are essential for effective reuse. Examples and resources to facilitate FAIRification are included to help researchers navigate challenges and ensure biomedical data are FAIR and reusable.
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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 | 0.580 | 0.577 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.013 | 0.049 |
| Scholarly communication | 0.036 | 0.053 |
| Open science | 0.010 | 0.049 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".