Practices Before Policy: Research Data Management Behaviours in Canada
Bibliographic record
Abstract
In anticipation of the then forthcoming Tri-Agency Research Data Management Policy, a consortium of professionals from Canadian university libraries surveyed researchers on their research data management (RDM) practices, attitudes, and interest in data management services. Data collected from three surveys targeting researchers in science and engineering, humanities and social sciences, and health sciences and medicine were compiled to create a national dataset. The present study is the first large-scale survey investigating researcher RDM practices in Canada, and one of the few recent multi-institutional and multidisciplinary surveys on this topic. This article presents the results of the survey to assess researcher readiness to meet RDM policy requirements, namely the preparation of data management plans (DMPs) and data deposit in a digital repository. The survey results also highlight common trends across the country while revealing differences in practices and attitudes between disciplines. Based on our survey results, most researchers would have to change their RDM behaviors to meet Tri-Agency RDM policy requirements. The data we gathered provides insights that can help institutions prioritize service development and infrastructure that will meet researcher needs.
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.
How this classification was reachedexpand
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 | 0.048 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.030 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".