Better together: Collaborating on a community-led initiative to develop a survey of Canadian Dataverse administrators
Bibliographic record
Abstract
In response to the open science movement and the growth of funder and journal policies, researchers are increasingly looking for support in depositing and sharing their research data. Responding to this need by developing accessible and inclusive services and infrastructure, Borealis is a publicly accessible, multi-disciplinary, bilingual, national research data repository, based on the open-source Dataverse software, provided in partnership with regional academic library consortia and the Digital Research Alliance of Canada. The shared infrastructure supports over 65 Canadian institutions, each managing a locally-branded collection and providing local support to researchers. With support of the Borealis team and the Dataverse North Expert Group, a national-level community of Dataverse administrators is coalescing, consisting of librarians or other information specialists. This presentation will highlight the results of a community-driven initiative to survey Canadian Dataverse administrators to develop a better understanding of this community - who they are, the service models they support, their experiences using the Dataverse software, and the challenges they face supporting researchers; as well as to surface unique perceptions and perspectives of this emerging national community. Understanding both the infrastructure and the community’s collaborative approach lays important groundwork to move forward with engaging smaller institutions, such as community colleges, as well as historically marginalized populations–both as data custodians and potential depositors. The presentation will highlight the process of developing this community-informed survey, including the formation of a working group of community members and diverse stakeholders from across Canada, questionnaire development and pre-testing. The Canadian Dataverse administrator community represents a unique effort to build equitable data sharing infrastructure that is national in scope and reflective of community needs. The presenters will conclude by sharing preliminary aggregated results and discuss the importance of collaborative approaches to implementing data repository infrastructure in a way that encourages continuing adaptability to diverse community needs.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.048 | 0.008 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".