Shifting Horizons: A Literature Review of Research Data Management Train-the-Trainer Models for Library and Campus-Wide Research Support Staff in Canadian Institutions
Bibliographic record
Abstract
Objective – In consideration of emerging national Research Data Management (RDM) policy and infrastructure, this literature review seeks answers to the following questions: 1) What is the most effective way for a Canadian research university to build capacity among library and campus-wide research support staff, with a view towards providing coordinated RDM support services for our researcher community?2) What international training models and course offerings are available and appropriate for a local context?3) What national guidelines and best practices for pedagogical design and delivery can be adapted for a local context? Methods – This literature review synthesizes a total of 13 sources: 9 articles, 2 book chapters, and 2 whitepapers. The whitepapers were selected for a narrative literature review because of their focus on case studies detailing train-the-trainer models. Within the 13 sources we found 14 key case studies. This review serves as a supplement to the 2017 CARL Portage Training Expert Group white paper, “Research Data Management Training Landscape in Canada,” the focus of which was to identify RDM training gaps in order to recommend a coordinated approach to RDM training in a national environment. Results – The narrative review of case studies revealed three thematic areas. Firstly, pedagogical challenges were identified, including the need to target training to RDM support staff such as librarians and researchers, as they comprise distinct groups of trainees with divergent disciplinary vocabularies and incentives for training. Secondly, the case studies cover a broad range of pedagogical models including single or multiple sessions, self-directed or instructor-led, in-person or online instruction, and a hybrid of the two. Finally, RDM training also emerged as a key factor in community building within library staff units, among service units on campus, and with campus research communities. Conclusion – RDM training programs at local institutions should be guided by a set of principles aligned with the training methods, modes of assessment, and infrastructure development timeline outlined in a national training strategy. When adapting principles and training strategies to a local context, the following trends in the literature should be considered: librarians and researchers must have meaningful incentives to undertake training in RDM or to join a community of practice; disciplinary-specific instruction is preferable to general instruction; a librarian’s own training opportunities will influence their ability to provide discipline-specific RDM instruction to researchers; in-person training opportunities improve learning retention and produce beneficial secondary effects, whereas online instruction is most effective when paired with an in-person component; generalized third-party RDM training should be adapted to local context to be meaningful. Future directions for RDM training will integrate into open access and digital scholarship training, and into cross-disciplinary, open science communities of practice.
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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 | 0.046 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.030 | 0.054 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".