Research Data Management in the Canadian Context: A Guide for Practitioners and Learners
Bibliographic record
Abstract
With the recent release of a Research Data Management (RDM) policy by Canada’s Tri-Agency, RDM has become crucially important. All researchers who apply for grants to fund data-related research must now meet requirements including writing Data Management Plans and preparing data for archiving. Libraries have traditionally supported RDM, and RDM is occasionally taught in Canadian library schools. Given the heightened attention to RDM, the need for greater education and the number of courses is likely to increase. However, at present there are no suitable teaching resources for the Canadian context. A comprehensive, peer-reviewed educational resource suited to the unique Canadian regulatory context and appropriate for use in classrooms does not exist. As a response to this need, a number of Canadian academics and librarians are creating a peer-reviewed, copy-edited open textbook, translated to both French and English languages and published via Pressbooks. It will include interactive media and self-assessment activities. As the resource will be an OER, instructors, students, and professionals can use the resource as-is or customize it to meet needs. This resource will offer a comprehensive, peer-reviewed academic educational resource suited to the Canadian context. Topics will include: the Canadian context for RDM, an introduction to active data management and curation, management of specific data types, repository selection and cloud storage, sensitive data, privacy, and deidentification, theory and principles, and more.
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 | 0.036 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.019 |
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".