Public Engagement and other Essential Requirements for Data Trusts, Data Repositories and Other Data Collaborations.
Bibliographic record
Abstract
ObjectiveTo test and refine a list of 12 minimum specification essential requirements (min specs) for data trusts, data repositories, and other data collaborations that had been generated and published by a team of 19 Canadians in 2020. ApproachWe convened an international team of more than 50 people to discuss, test, and refine the 12 min specs. Twenty-three (23) organizations tested the min specs and five analysis sub-teams were formed to identify commonalities and differences in terms of how the min specs are being fulfilled, and ways to improve the min specs. In parallel, we worked with Canada’s CIO Strategy Council to develop the voluntary standard “CAN/CIOSC 100-7 Operating Model for Responsible Data Stewardship” based on the updated min specs. ResultsThe list of min specs increased from 12 to 15: one for Legal, five for Governance, four for Management, two for Data Users, and three for Stakeholder & Public Engagement. The main changes were the division of requirements that had initially been grouped together under Stakeholder & Public Engagement, one new Governance min spec focused on Indigenous data sovereignty, one new Management min spec focused on data documentation, and multiple changes to make the min specs more precise and directive. The CAN/CIOSC 100-7 standard is progressing through committees and approvals and on track to be finalized by summer 2022. To our knowledge, it will be the first standard that identifies public engagement as a requirement for data trusts, data repositories, or other data collaboratives. ConclusionsIncluding international team members in the testing and refinement of the min specs led to significant improvements. The process we used may also benefit other teams and organizations who are working to progress from frameworks and principles to practical guidance.
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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 | 0.148 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".