Assessing data deposits in an institutional repository (U of T Dataverse in Borealis)
Bibliographic record
Abstract
U of T Dataverse, the University of Toronto's data repository, is one of the largest institutional dataverse collections in Borealis with over 1000 published datasets. It currently follows a self-deposit model that allows U of T researchers to deposit and publish their data without intervention unless requested. Considering increased usage of the repository and new and developing data deposit policies, we conducted an assessment of U of T Dataverse to (1) review the quality of published datasets, and (2) understand who is using the repository. This was accomplished by analyzing the monthly Borealis metrics and conducting a quality assessment of select datasets’ structure and associated metadata. Ultimately, this assessment will be used to help conceptualize curation services and identify resources to develop that would enable high-quality data deposits. In this presentation we will discuss our approach to this assessment, preliminary findings, and how this project will shape our approach to service and resource development. Overall, this project will allow us to better understand disciplinary trends relating to who is (and isn’t) using U of T Dataverse, help develop clear processes and guidelines, and inform training and departmental outreach. Longer-term, it will help us estimate the effort and time required to provide curation services, inform priorities for repository development, and help us anticipate the impact any change in national policy may have in demand for institutional services.
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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 | 0.132 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".