Data-sharing practices in publications funded by the Canadian Institutes of Health Research: implications for health sciences librarians
Bibliographic record
Abstract
Objective: Funding bodies such as Canada's Tri-Agency have implemented requirements for grant recipients to encourage improved research data management (RDM) practices and data sharing. Consequently, RDM and data sharing have become a higher priority for researchers and stakeholders supporting the research process, including librarians. Health sciences research can present special challenges to those wishing to share and use research data, as access to sensitive data must be restricted. This study examines the data sharing practices of researchers funded by the Canadian Institutes of Health Research (CIHR) in recent years. Methods: We ran a search of PubMed Central to identify papers funded by CIHR that were published between 2020 and 2023 and had associated data. From the resulting records, we drew a sample of 368 articles. Using Qualtrics for each article, we recorded if and how data was shared and what types of documentation were provided alongside the data. Results were exported to and analyzed using Microsoft Excel. Results: We found that 69% of papers included a data availability statement. 34% of articles made at least some data readily accessible, while 31% indicated that some data was available via request or application. Only 9% of articles supplied the kinds of documentation that would support reuse of the data. Conclusion: Those seeking to reuse Canadian health sciences research data continue to face significant hurdles. We offer ideas for health sciences librarians looking to support researchers in their efforts to make data available and usable while respecting restrictions required due to ethical considerations.
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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 | 0.393 | 0.668 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.031 | 0.090 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.033 | 0.025 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".