Migrating from Nesstar to Borealis, the Canadian Dataverse Repository
Bibliographic record
Abstract
The Canadian ODESI repository (https://www.odesi.ca) aggregates 6,000+ social science datasets including microdata and census data from Statistics Canada, Canadian polling agencies, and other sources. Developed and maintained by Scholars Portal, it is the outcome of ongoing collaboration between members of the Ontario Council of University Libraries (OCUL, https://www.ocul.on.ca). After 15 years providing data access and tools to the Canadian research community, the repository infrastructure is due for an upgrade. In early 2022, the platform's back-end began migrating from Nesstar to Dataverse, a state-of-the art, DDI-compliant repository platform. The current project phase involves migrating five data collections into a testing instance of Borealis, the Canadian Dataverse Repository, and a concurrent redevelopment of the ODESI front-end. This presentation will share and discuss our initial findings, with a primary focus on Dataverse and DDI metadata interoperability issues—alongside other common migration challenges like data/metadata quality, migration workflow management, stakeholder and institutional change management, and sustainability. Our aim is to formulate a migration approach that is community-based, in collaboration and dialogue with the Canadian and international RDM, Dataverse and DDI communities.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".