Development of a dictionary of information items inspired by common data models to support health research data access requests
Bibliographic record
Abstract
A network of organizations collaborates to provide services to facilitate multi-regional research across Canada. To streamline the data access process, the network is developing a dictionary of information items (InfoItems), providing a label and definition that represent a semantic sourced from an ontology (e.g., “human birth date” from a demography ontology). This allows for a standardized and shareable means to express data requirements. Inspired by health-research common data models (CDMs) like those from the Observational Medical Outcomes Partnership (OMOP) and the Canadian Network for Observational Drug Effects (CNODES), approximately 60 InfoItems were developed by the network. As a pilot exercise, four data centres mapped their data assets to the InfoItems to ensure they are relevant to the Canadian health data context. This has laid the foundation for InfoItems as a basic data harmonization mechanism. The new InfoItems-based data dictionary promotes the standardization of data asset contents across the network’s data centres. Researchers can use the dictionary to specify a project’s data requirements, establishing details like data availability across multiple regions, for inclusion in their data access request. The InfoItems dictionary is pivotal in simplifying data access requests, promoting multi-regional research in Canada. This resource can simultaneously safeguard the quality of analytical results and optimize data extraction into existing data models (e.g., health-research CDMs). Ongoing improvements in this area aim to enhance user experience and facilitate quality research.
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 | 0.032 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".