Report: Understanding and evaluating data discovery and access for restricted data in Canada: A metadata assessment of health data sources
Bibliographic record
Abstract
This project identified Canadian restricted and access-limited data sources (n=137) and evaluated a sub-sample (n=48) of restricted health data sources to measure how discoverable and accessible the datasets are to potential researchers. To identify common elements used by the data sources, we inventoried and mapped elements to existing metadata standards for discovery and access. Overall, for many data sources, there was incomplete information and only basic metadata provided about datasets. Across the sub-sample, none of the data sources had implemented any metadata standards for sharing information about restricted data, which poses significant barriers for the discovery and reuse of restricted health data in Canada. Based on these findings, stakeholders across the research data ecosystem should consider establishing key recommendations and set priorities for collaborators to improve restricted data discovery and access systems and policies in Canada. These key recommendations are included within this report, alongside a description of the project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.074 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".