Development of a framework to facilitate a data assembly plan for multi-regional research
Bibliographic record
Abstract
A network of organizations works together to facilitate multi-regional research across Canada. This network is streamlining the traditionally burdensome data access process, a major part of which is a project’s data assembly plan (DAP). A framework is proposed for the development of a DAP usable by researchers and multiple data centres across Canada. The network of 13 provincial/territorial and pan-Canadian data centres collaborated to understand variations in data request processes and local requirements. During this collaboration, partners used an iterative approach to review local forms, processes, and undertake consultations with the research community, and to identify critical components of the DAP. In April 2022, the network launched the centrally provisioned, standardized DAP form, which to date has been deployed by approximately five projects. Users have found the DAP’s unique aggregation and documentation capabilities particularly helpful. Overall, the preliminary feedback from researchers has been positive. The DAP has allowed aggregation of specific details about a project’s data requirements: cohort definition(s), data extraction(s) and analytical plans, in a single unified form. The DAP is an important component in streamlining the process for requesting data from multiple provinces/territories, organizations, and data sources. The DAP has ensured consistency across the network’s data centres that are providing data, supporting data linkage, and helping safeguard the quality of analytical results. Further process improvements are anticipated to address user experience feedback and to promote quality research.
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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 | 0.165 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".