Understanding the Challenges Associated with Finding and Accessing Restricted Data in Canada: A Mixed Methods Study
Bibliographic record
Abstract
INTRODUCTIONThis study aimed to identify Canadian access-limited data sources and evaluate a subset of restricted health sciences data sources to determine how well they make their data discoverable and accessible.MATERIALS AND METHODSA search was conducted across Canadian sectors and national experts were consulted to identify access-limited data sources. A subset of restricted health sciences data sources (n=48) was evaluated using a rubric to assess how well they make their data discoverable and accessible. The rubric assigned data sources a grade of A through C denoting how well they met certain discoverability and access criteria. The degree to which data sources demonstrated consistency between their discovery and access grades was assessed using Kendall’s rank correlation coefficient.RESULTS137 Canadian data sources were identified. Restricted health sciences sources received poor data discovery grades due to a lack of metadata (38/48, 79%), an inability to search/browse datasets (32/46, 70%), and lack of data documentation to support interpretability and reuse (27/48, 56%). Low data accessibility grades were assigned for the lack of transparent pricing information (31/48, 65%) and opaque data restriction criteria (25/48, 52%). Data sources with higher discovery scores had higher access scores on average (tau-b=0.31, p=0.0059).DISCUSSIONThis study highlights areas for improvement with respect to the discovery of and access to Canadian restricted data. Developing metadata standards to accommodate restricted data access procedures, improving data infrastructure to support restricted data, and expanding data documentation training for restricted data custodians are necessary to ensure that restricted data is discoverable, accessible, and reusable in the future.
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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 | 0.040 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".