Improving the Discovery of Restricted Data in Canada: Identifying Metadata Commonalities Across Restricted Data Sources
Bibliographic record
Abstract
Background The challenge of finding and accessing restricted data for research purposes is a known issue; specifically, researchers encounter barriers when identifying prospective data sources, when locating and understanding available data within those sources, and when discerning whether they are eligible to access it. A prior study conducted by the authors of this presentation identified that many restricted data sources do not make use of metadata to ensure their data are findable and accessible. Methods To assess the readiness of restricted data sources to utilize a metadata standard, this study identified common elements of both dataset descriptions and access requirements/procedures across 48 restricted health data sources. These elements were subsequently mapped to current metadata standards (e.g. DataCite) to determine how closely they matched the elements in these existing standards. Results Our findings indicate that many restricted data sources already provide dataset information that aligns closely with existing metadata standards, that data sources would benefit from adopting metadata standards to improve the discovery of their data, and that generally, it would be possible for these data sources to adopt an existing common metadata standard to describe their data. Access information provided by these data sources, however, is not adequately supported by existing standards. To ensure that the access requirements/procedures needed to acquire restricted datasets can be discoverable and transparent, metadata standards bodies will need to revise their schemas to include more descriptive access information. This revision would also provide researchers – who collect restricted data and must comply with funder and publisher data sharing policies – with standard guidelines for describing their data access request processes in more detail. Conclusion This presentation will discuss our findings in detail, articulate key challenges in assigning metadata to restricted data, and suggest recommendations for improving the discovery of and access to restricted data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".