How DASH enables external data linkage to support multi-regional research
Bibliographic record
Abstract
A network of organizations works together to support multi-regional health research across Canada. The network comprises 13+ provincial/territorial and pan-Canadian data centres, which collectively hold 500+ data assets. Although the network actively pursues new administrative or clinical data assets, linking to external research data is also a growing need in the contemporary research landscape. Data from the network’s centres can be linked to external data sources such as that from: researchers’ trials or studies; disease or population-based registries; and data sources from other organizations or custodians. Consultations held with data centers clarified their processes for linking to external data, by identifying and mapping local linkage features to a general linkage model. Local processes for data linkage, including necessary agreements and approval steps are now modelled and documented, and available to researchers and the data centres as a resource. This information helps streamline data access and linkages to data assets across the network and externally. Operationally, the network is currently working on 11 data access requests involving linkage to external data, of which three are expected to deliver final data to researchers by spring 2024. Collaboration with data centres, affiliated organizations, and researchers are foundational in the development of linkage models across the network. These models play a critical role in making linkages across data sources within Canada more efficient and standardized. Data linkages across data assets support the utility of data and health innovation.
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 | 0.055 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.020 |
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".