There's no "I" in Research Data Management: Reshaping RDM Services Toward a Collaborative Multi-Stakeholder Model
Bibliographic record
Abstract
Objective: This article examines a reshaped service model for research data management (RDM) founded on centralized and cohesive collaboration between multiple stakeholders at a large research university in Canada. This initiative, along with a newly formed team dedicated to RDM service provision, is a joint effort by the institution’s Vice-Principal Research and Innovation (VPRI), Library, IT Services, and Research Ethics units.Methods: This article presents a single case study methodology. The authors reflect on services such as “query the panel” sessions where researchers across all disciplines bring their questions to representatives from the Library, IT, Research Ethics, and VPRI. This case study also highlights the use of Jira’s service desk software as a user management system. The authors also present descriptive statistics representing engagement with this new unit and our services.Results: Support for RDM requires expertise from multiple domains. With a collaborative approach as a guiding principle and a focus on establishing a small, but agile team comprised of a librarian along with stakeholders from IT and VPRI, it is possible to leverage resources and support for RDM from a broad range of units across an institution. Conclusions: At many institutions, RDM services are siloed within the library or an adjacent campus unit. New digital technologies have profoundly transformed academic research across all disciplines, necessitating the evolution of corresponding research data-related services. The authors will conclude by outlining specific lessons learned in reshaping digital research infrastructure-related services at their institution.
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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 | 0.171 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.025 | 0.064 |
| Scholarly communication | 0.037 | 0.049 |
| Open science | 0.007 | 0.039 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".