Surveying the landscape of CIHR-funded research data sharing practices: An analysis of the published literature
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
Background:As Canada increases requirements for research data management (RDM) and sharing, there is value in identifying how research data are shared, and what has been done to make them findable and reusable. This study aims to understand Canada’s data sharing landscape by reviewing how Canadian Institutes of Health Research (CIHR) funded data are shared, and comparing researchers’ data sharing practices to RDM and sharing best practices. Methods:We performed a descriptive analysis of CIHR-funded publications from PubMed and PubMed Central that were published between 1946 and Dec 31, 2019 and that indicated the research data underlying the results of the publication were shared. Each publication was analyzed to identify how and where data were shared, who shared data, and what documentation was included to support data reuse.Results:Of 4,144 CIHR-funded publications, 45.2% (n=1,876) included accessible data, 21.9% (n=909) stated data were available by request, 7.3% (n=304) stated data sharing was not applicable/possible, and we found no evidence of data sharing in 37.6% (n=1,558) of publications. Frequent data sharing methods included via a repository (n=1,549, 37.3%), within supplementary files (n=1,048, 25.2%), and by request (n=919, 22.1%). 13.1% (n=554) of publications included documentation that would facilitate data reuse.Interpretation:Our findings reveal that CIHR-funded publications largely lack the metadata, access instructions, and documentation to facilitate data discovery and reuse. Without measures to address these concerns, and enhanced support for researchers seeking to implement RDM and sharing best practices, most CIHR-funded research data will remain hidden, inaccessible, and unusable.
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 | 0.059 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.030 | 0.044 |
| Open science | 0.057 | 0.100 |
| Research integrity | 0.000 | 0.003 |
| 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