Mapping Canadian institutional research data management strategies: a cross-sectional study
Bibliographic record
Abstract
In March 2021, Canada’s three federal research funding agencies introduced the Tri-Agency Research Data Management (RDM) Policy, with the objective of promoting sound RDM and data stewardship practices at research institutions. Among the requirements of the Policy, each post-secondary institution and research hospital eligible to administer agency funds was required to publish an institutional RDM strategy. This study presents a cross-sectional mapping of published institutional strategies ( n = 211) in response to the Tri-Agency RDM Policy requirement. We extracted information pertaining to institutional characteristics, institutional needs, and support models for data management planning and data deposit. Our analysis of institutional strategies indicates that developing RDM expertise among researchers (84%, n = 177) and research support staff (61%, n = 129) is of high priority. We also found that most institutions did not describe activities to promote behavioural changes and foster a broader culture of RDM among researchers; only 6% of institutional strategies ( n = 12) explored shifting incentives and rewards. A mapping of institutional RDM strategies is an important step to identify potential gaps in responding to the Policy. We find that further efforts are needed to address consultation gaps, resource constraints, and support for data management plans and data deposit to foster a robust and effective RDM culture at Canadian research institutions.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".