Mapping Canadian institutional research data management strategies: a cross-sectional study
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
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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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.008 | 0.006 |
| 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