Understanding the challenges associated with finding and accessing restricted data in Canada: a mixed methods study
Bibliographic record
Abstract
Data that are restricted are historically challenging for researchers to find and even more difficult to access. While efforts to support open data have expanded in Canada, the same cannot be said for restricted data. To better understand the landscape of restricted data in Canada, this study aimed to accomplish two primary goals: (1) identify data sources where data were restricted and (2) assess a subset of health sciences data sources to determine how well they make their data discoverable and accessible. Our study identified 137 Canadian data sources, where 48 health sciences sources were evaluated for discoverability/accessibility. Data sources received poor grades with respect to data discovery due to a lack of metadata standards (38/48, 79%), an inability to find datasets through searching and browsing (32/46, 70%), and a lack of data documentation to support reuse (27/48, 56%). The absence of pricing information (31/48, 65%) and opaque dataset restrictions (25/48, 52%) were identified as key barriers to the data access request process. This study highlights significant room for improvement with respect to improving the discovery of and access to restricted data in Canada and makes recommendations for how to better support restricted data sources on a national scale.
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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 | 0.049 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".