Embracing the value of research data: introducing the JCHLA/JABSC Data Sharing Policy
Bibliographic record
Abstract
Health sciences researchers are being asked to share their data more frequently due to funder policies, journal requirements, or interest from their peers. Health sciences librarians (HSLs) have simultaneously begun to provide support to researchers in this space through training, participating in RDM efforts on research grants, and developing comprehensive data services programs. If supporting researchers' data sharing efforts is a worthwhile investment for HSLs, it is crucial that we practice data sharing in our own research endeavours. Sharing data is a positive step in the right direction, as it can increase the transparency, reliability, and reusability of HSL-related research outputs. Furthermore, being able to identify and connect with researchers in relation to the challenges associated with data sharing can help HSLs empathize with their communities and gain new perspectives on improving support in this area. To that end, the Journal of the Canadian Health Libraries Association / Journal de l'Association des bibliothèques de la santé du Canada (JCHLA/JABSC) has developed a Data Sharing Policy to improve the transparency and reusability of research data underlying the results of its publications. This paper will describe the approach taken to inform and develop this policy.
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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 | 0.081 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.037 | 0.013 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.058 | 0.061 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".