Data Culture in Canada: Perceptions and Practice Across the Disciplines
Bibliographic record
Abstract
Amid the increasing recognition of the value of research data, federal granting agencies are developing formal policies to advance the data culture in Canada. In order to better support their research communities, a consortium of Canadian universities surveyed researchers to identify research data management (RDM) practices, needs and attitudes. The consortium’s previous efforts characterized the data culture in distinct disciplines with individual surveys targeting researchers in science and engineering, humanities and social sciences, and health sciences and medicine. The data collected from the three surveys have been compiled to create a national dataset, which enables a deeper understanding of the Canadian RDM landscape. This poster presents the analysis of the national dataset, giving an overall picture of data sharing, data preservation, data management planning and interest in data management services. The results highlight trends in common practices across the country while revealing any unique practices and attitudes between disciplines, regions, researcher ranks and types of institutions. Informed by the survey findings, institutional policy, service, and infrastructure development can be aligned with funding agency requirements and effective data stewardship practices. Additionally, this publicly available national dataset will support future analysis in building sustainability in a national RDM strategy.
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 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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".