Bridging evaluation and implementation: Using results from a survey of research data repository administrators to anchor community-driven initiatives
Bibliographic record
Abstract
Two years have passed since we launched a survey of research data repository administrators in Canada. The goal of the survey was to identify gaps in the data repository services landscape that might be collaboratively addressed by a national community of research data management librarians, data specialists, data repository managers, and infrastructure providers. This presentation will focus on how results of this survey have helped to steer the launch of new community-driven initiatives. Three primary gaps identified by the survey include capacity or support for developing curation models, preservation planning and workflows, and support for sensitive data deposit in the context of limited staffing capacity. Regarding curation and preservation, we will discuss how the survey results supported the relaunch of a community initiative to update and develop new resources and documentation for Canadian institutional research data repositories seeking to apply for CoreTrustSeal (CTS) certification or to benchmark their services. CTS requirements mandate specific levels of preservation and curation activities that align with gaps identified in our survey results. Borealis resources on how members of our national shared research data repository infrastructure may implement service models to meet CTS requirements also provides guidance on the resources and capacity required for harmonizing curation models to international standards. We will also discuss how the survey results have helped to shape the work of a collaborative group of librarians and data repository administrators aiming to draft guidelines and a checklist for sensitive data deposit as contextually defined by a combination of institutional and national policies, regulations, and frameworks. For example, we will discuss standardizing the guidelines to common levels of risk related to research data. This presentation will also address variations in institutional-level staffing models and how we plan to use a longitudinal survey design to track shifts in readiness and capacity over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".