Surveying the landscape of CIHR-funded research data sharing practices: An analysis of the published literature
Bibliographic record
Abstract
This study will aim to accomplish two specific goals by assessing the availability of health sciences research datasets funded by the Canadian Institutes of Health Research (CIHR). The first goal will be to understand the Canadian data sharing landscape by reviewing how and where Canadian health sciences researchers share their data. The second goal will be to compare Canadian researchers’ current data sharing practices to the Tri-agency’s proposed framework for research data management and sharing. The information gathered from this study will be used to identify gaps within the Canadian data sharing landscape, and help inform the future development of data policy, infrastructure and research data management support by highlighting the key challenges and opportunities with respect to data sharing in a Canadian context.
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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 | 0.194 | 0.371 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.074 | 0.160 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".