The New England Collaborative Data Management Curriculum Pilot at the University of Manitoba: A Canadian Experience
Bibliographic record
Abstract
Canada’s federal funding agencies are following the directions of funding agencies in the United States and United Kingdom, and will soon require a data management plan in grant applications. The University of Manitoba Libraries in Canada has started planning and implementing research data services, and education is seen as a key component. In June 2014, the New England Collaborative Data Management Curriculum (NECDMC) (Lamar Soutter Library, University of Massachusetts Medical School 2014) was piloted and used to provide data management training for a group of subject librarians at the University of Manitoba Libraries, in combination with information about data-related policies of the Canadian funding agencies and the University of Manitoba. The seven NECDMC modules were delivered in a seminar style, with emphasis on group discussions and Canadian content. The benefits of NECDMC – adaptability and flexible framework – should be weighed against the challenges experienced in the pilot, mainly the significant amount of time needed to create local content and complement the existing curriculum. Overall, the pilot showed that NECDMC is a good, thorough introduction to data management, and that it is possible to adapt NECDMC to the local and Canadian settings in an effective way.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 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".