RDM goes OER: Community-Sourcing a Canadian Open Educational Resource on Research Data Management
Bibliographic record
Abstract
Canada’s federal funding agencies launched their much-anticipated policy on Research Data Management in early 2021. In response, during the summer of 2021 a group of data experts from Canada’s data community began to discuss the need to develop a Research Data Management (RDM) resource that would work for teaching RDM in Canada. Canada has its own set of regulations, standards, and resources, as well as two official languages, French and English, and due to the lack of an appropriate teaching resource, instructors have been cobbling together a mix of outdated texts, articles, and web pages from obscure Canadian organizations to teach their classes. A small group of librarians took on leadership of the project and decided to develop the textbook as an open, edited, and peer-reviewed collection of chapters. What followed was something of a wild ride as we learned about pedagogy, plain language, translation, applying for academic funding, and working as part of a bilingual project team. Throughout we focused on equity, from ensuring that both languages were treated equally to making sure we had appropriate coverage of Indigenous data concerns. Maintaining the enthusiasm and sense of involvement and ownership of the full community while trying to guide the project towards a cohesive outcome has been a continual balancing act and we will share some of the lessons we learned as well as some issues we still struggle with. Despite the challenges, we are fortunate to have the expertise of this diverse community as we develop “RDM in the Canadian Context: a Textbook for Practitioners and Learners”, with anticipated release of the English version in Summer 2023, with a French edition to follow. We hope to inspire similar efforts elsewhere, and all our material will be open for reuse and adaptation!
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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 | 0.033 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.017 |
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".