Across Canada, across Disciplines: Research Data Management Practices and Needs in the Social Sciences and Humanities
Bibliographic record
Abstract
Across Canada, ten universities (to date) have worked together to survey their research communities in order to better understand research data management practices and needs. This work builds on a previous collaborative effort designed to delve into RDM habits of researchers in engineering and science, by expanding to researchers in the humanities and social sciences. This session will discuss the survey results from participating universities, providing insight into the Canadian RDM landscape while highlighting disciplinary differences and notable results. Survey sections include working with research data, data sharing, funding mandates and research data management services. Information generated by this survey will help inform Canadian institutional services, infrastructure and policies. Participating universities at the time of writing include: Dalhousie University, McGill University, Queen's University, Ryerson University, University of Alberta, University of British Columbia, University of Ottawa, University of Toronto, University of Waterloo, and the University of Windsor. The session will also discuss the collaboration process, which resulted in the development of a clearinghouse of generic survey documents (questionnaires, ethics review documents) that will be housed by Portage, Canada's emerging national RDM infrastructure project. These documents can be used by other institutions to conduct similar studies. Future initiatives include a further survey of researchers in the health and medical sciences.Additional authors arenbsp;Marjorie Mitchell,nbsp;University of British Columbia andnbsp;Matthew Gertler,nbsp;University of Toronto.
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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 | 0.038 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.032 |
| Science and technology studies | 0.042 | 0.007 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".