Developing Institutional Research Data Management Strategies in Canada: Setting the Foundation for Stronger Partnerships and Collaborations
Bibliographic record
Abstract
The Government of Canada’s Tri-Agency formally launched the Research Data Management (RDM) Policy in March 2021 with the objective of supporting “Canadian research excellence by promoting sound data management and data stewardship practices”. A central component of this policy requires postsecondary institutions eligible to administer Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), or the Social Sciences and Humanities Research Council of Canada (SSHRC ) funds to create an institutional RDM strategy by March 2023. A national survey was developed to gauge institutions’ readiness for developing an institutional RDM strategy required by the Tri-Agency. The survey emphasized increasing participation from diverse institutions to ensure that future support and resources are developed to address the distinct needs of institutions. Recommendations from the survey report included increasing Tri-Agency involvement as institutions developed their institutional RDM strategies, encouraging institutions to collaborate, and the development of forums to provide support for disciplinary societies to have RDM conversations. As a result, three panel discussions covering the active stages (Initial, Planning, and Execution) of developing an institutional RDM strategy were successfully delivered through the Digital Research Alliance RDM (Alliance RDM) to a diverse range of institutions. Recognizing the needs of smaller institutions including CEGEPS, colleges, and polytechnics, an additional panel discussion was developed and delivered to this audience.
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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 | 0.203 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.043 | 0.019 |
| Scholarly communication | 0.042 | 0.016 |
| Open science | 0.010 | 0.036 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".