Supporting the Canadian DMP landscape: An overview of DMPEG activities and a new DMP Assessment Rubric!
Bibliographic record
Abstract
The Digital Research Alliance of Canada’s Data Management Planning Expert Group (DMPEG) develops and delivers publicly available DMP-related guidelines, best practices, content, and resources for supporting researchers and research excellence across Canada, including DMP templates, examples, and guidance materials. DMPEG also supports the ongoing development, maintenance, and sustainability of DMP Assistant, a freely available bilingual web-based tool providing templates, questions, and guidance for supporting researchers with their DMP needs.This session offers an overview of DMPEG activities and resources, notably highlighting a new DMP assessment rubric developed specifically to support researchers in developing quality DMPs and meeting requirements at the funding application stage, including those implemented by the Tri-Agency, Canada’s national funders of research. An overview of the assessment rubric and its content, along with an accompanying Simplified DMP template that it is standardized to, will be provided. Information regarding DMP Assistant, including its key features and how to access and use it will also be provided, along with additional resources developed by DMPEG, including DMP examples. Future work and directions will additionally be discussed.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".