Report on Research Data Management Faculty & Postdoctoral Survey, University of Toronto
Bibliographic record
Abstract
In order to become better prepared to support research data management practices at University of Toronto in sciences and engineering, librarians at University of Toronto Libraries conducted a research study of all ranks of faculty and postdoctoral fellows at the Faculty of Applied Science and Engineering as well as at the Canadian Institute for Theoretical Astrophysics, the Dunlap Institute for Astronomy & Astrophysics, and the Departments of Astronomy and Astrophysics, Chemistry, Computer Science, Earth Sciences, Mathematics, Physics, and Statistical Sciences in the Faculty of Arts and Science. The goals of the study were to determine how University of Toronto science and engineering faculty and postdoctoral fellows manage and share research data beyond their project, determine how University of Toronto Libraries might help to facilitate data management activities, and understand some of the differences in research data management practices and needs across disciplines and sub-disciplines. The research study was conducted April 2015. This report describes the background, survey design, aggregated results, conclusions and further steps.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
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".