Venturing Beyond our Silos: Results from a Survey of Canadian Data Repository Administrators
Bibliographic record
Abstract
The academic library community has a long history of collaboration demonstrated by the global adoption of common standards in our work. Despite the widespread use of the same standards and platforms, too few examples exist of academic libraries navigating beyond silos to shared infrastructure and services. As the scope of academic libraries grows to support research data as part of the scholarly resources that we steward, is it possible to develop shared research data management (RDM) services and infrastructures collaboratively? Six years have passed since a recommendation was made by Canada's national research library association to establish a national data repository that would provide a robust, scalable, and affordable shared service. Building a national repository together would harness available but limited and distributed expertise in RDM, and encourage the collective creation and reuse of materials supporting training, user support, and outreach. Since 2019, Borealis began formally offering the service nationally, governed in partnership with regional academic library consortia, which now supports over 70 Canadian institutions, each managing a locally-branded collection and providing local support to researchers.Has this push for greater equity been realized by Borealis and what are the experiences of its institutional administrators? This presentation reports the results of a community-led survey of Canada's Borealis administrators, focusing on their individual perspectives on challenges, barriers, and needs of the emerging community. Overall, the results of the survey highlight a unique effort to build equitable data sharing infrastructure that is national in scope and reflective of community needs. Understanding both the infrastructure demands and the needs for support from our community provides insights on how to plan future programs and software development initiatives. Learning from our experiences and our community can benefit other national and international large-scale repository initiatives in charting the future of data.
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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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".