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
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.
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 | 0.237 | 0.473 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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