The New England Collaborative Data Management Curriculum Pilot at the University of Manitoba: A Canadian Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.023 |
| Open science | 0.013 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it