The Experience of Establishing Data Sharing & Linkage Platforms for Administrative, Research and Community-Service Data
Bibliographic record
Abstract
INTRODUCTION: Innovative data platforms (e.g. biobanks, repositories) continually emerge to facilitate data sharing. Extant and emerging data platforms must navigate myriad tensions for successful data sharing and re-use. Two Alberta data platforms navigated such processes and factors regarding administrative, research and nonprofit data: the Child & Youth Data Laboratory (CYDL) and Secondary Analysis to Generate Evidence (SAGE). OBJECTIVES: To clarify the social and policy factors that influenced CYDL and SAGE establishment and implementation, and the relationships, if any, between these factors and data type. METHODS: This paper involves a qualitative secondary analysis of two developmental evaluations on CYDL and SAGE establishment. Six-years post-implementation, the CYDL evaluation entailed document review; website user analysis; interviews (n=30); online stakeholder survey (n=260); and an environmental scan. One-year post implementation, the SAGE evaluation included 15 interviews and document review. We used thematic analysis and comparisons with the literature to identify key factors. RESULTS: Three (not mutually exclusive) categories of social and policy factors influenced the navigation towards CYDL and SAGE realization: trusting relationships; sustainability amidst readiness; and privacy within social context. For these platforms to be able to manage, link or share data, trust had to be fostered and maintained across multiple, dynamic and intersecting relationships between primary data producers, data subjects, secondary users and institutions. Platform sustainability required capacity building and innovation. Privacy and information sharing evolved culturally and correspondingly for these data platforms, which required constant flexibility and awareness. CONCLUSIONS: This analysis calls for more empirical research on the value of data re-use or the detriment in not re-using data. While the culture of information sharing is progressing towards greater openness and capacity for data sharing and re-use, successful data platforms must advocate, facilitate and mobilize analysis and innovation using data re-use while being cognizant of social and policy influences.
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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 | 0.044 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".