Teaching Research Data Management Skills Using Resources and Scenarios Based on Real Data
Bibliographic record
Abstract
The need for researchers to enhance their research data management skills is currently high, in line with expectations for sharing and reuse of research data. Data librarians and data services specialists increasingly provide data management training to researchers. It is widely known that effective learning of skills is best achieved through active learning by making processes visible, through directly experiencing methods and through critical reflection on practice. The organisers of this workshop each apply these methods when teaching good data practices to academic audiences, making use of exercises, case studies and scenarios developed from real datasets. We will showcase recent examples of how we have developed existing qualitative and quantitative datasets into rich teaching resources and fun scenarios to teach research data management practices to doctoral students and advanced researchers; how we use these resources in hands-on training workshops and what our experiences are of what works and does not work. Participants will then actively develop ideas and data management exercises and scenarios from existing data collections, which they can then use in teaching research data management skills to researchers.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".