Developing Institutional Research Data Management Strategies in Canada: Setting the Foundation for Stronger Partnerships and Collaborations
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
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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