Implementing and Learning from a Summer Research Data Management Training Program for Student Researchers
Bibliographic record
Abstract
Background This study explores a library-led research data management (RDM) training program at a Canadian post-secondary institution that targeted students participating in summer research assistantships as well as their faculty supervisors. This paper describes the program in detail and shares findings from a student reflection assignment about practicing RDM for the first time. Methods The RDM training program included four requirements: attending an introductory RDM session; attending a data management plan (DMP) workshop; submitting a DMP for feedback; and completing a reflection assignment. Where consent was obtained (n=19), reflection assignments were analyzed using a qualitative content analysis approach. Results 35 faculty supervisors registered 53 students to participate. 62.2% (n=33) of students completed all components of the program. Perceived benefits of completing a DMP included improved project planning, supporting best practices, potential for data reuse, and team communication. Perceived challenges included the inflexibility of DMPs, difficulty populating DMPs, demands on researchers’ time, and lack of long-term utility. 73.6% of students (n=14/19) reported that building a DMP helped them with their summer projects. Conclusion Through instruction, practical engagement, and reflection within the context of real-world research, the program supported participants in learning about and practicing RDM, and provided insights for academic librarians who wish to refine or develop training in their local contexts as they continue to navigate emerging expectations from funders and publishers.
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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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".