Institutional RDM Strategies: A Canadian Context
Bibliographic record
Abstract
In March 2021, the Canadian federal funding agencies (Tri-Agencies) announced a Research Data Management (RDM) Policy requiring research institutions eligible to administer funding to develop and publish an institutional RDM strategy by March 2023. The Institutional RDM Strategy Review Group (composed of representatives from the Tri-Agencies, Digital Research Alliance of Canada’s Research Intelligence Expert Group, and University of Ottawa Researchers) has since collated all published institutional RDM strategies and conducted a quantitative description on institutions’ submission status and a qualitative analysis on characteristics of the strategies.This presentation will report on the preliminary findings of our study, focusing on the current RDM environment and initiatives at Canadian institutions that are reflected in their RDM strategies, such as the context of RDM strategy development, RDM governance, RDM related guidelines and policies, and RDM engagement strategies. Our presentation will speak to the readiness of Canadian research institutions to meet the agencies' RDM Policy’s incoming requirements for Data Management Plan (DMP) creation and data deposit. We will also report on how Canadian institutions recognize and discuss Indigenous data sovereignty, disciplinary RDM requirements, and EDI issues related to RDM.The results of our mapping provide an important step in the ongoing implementation of Canadian RDM activities and to ensure Canada’s continued leadership and innovation agenda. Our understanding of institutional strategies obtained from this work will serve to identify important organizational and infrastructural advancements, but also gaps in national policy, support services, and community infrastructure. We will highlight the previous efforts and future needs for a national level coordination and collaboration to foster RDM communities of practice and reduce duplication of effort.
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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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.058 | 0.027 |
| Scholarly communication | 0.032 | 0.009 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".