Multi-site research and data sharing
Bibliographic record
Abstract
Context and aims Researchers increasingly need to share their data. This requires both adherence to Australia’s robust privacy legislation and preparation of comprehensive data management plans. This paper outlines the data-sharing issues managed by IMPACT, a 6-site Canadian-Australian collaborative research program designed to improve access to primary health care for vulnerable individuals. Each site used a common protocol to evaluate its own intervention, with the aim of pooling data across the sites. Ethics applications were submitted in each site. Methods Consultations were conducted with key informants within one Australian university (UNSW Sydney) and external informants to develop a data sharing plan. The authors reflect upon the process and have identified lessons for others wanting to share data. Findings Data sharing for a multi-site multi-country study was complex. University policies and infrastructure have been changing, not all sharing tools were available and support personnel were still learning how to implement policies related to data sharing. Furthermore, site-specific ethics applications did not specify that the data was part of a larger study. Consequently, the other 5 sites were deemed as external. We needed multiple consultations with ethics, IT, and data governance units to understand data classification (patient data is inherently sensitive), who needed access, and how access could be enabled. Bringing these support units together assisted a common understanding – this had not been previous practice. Innovative contribution to policy, practice and/or research Early consultations with university ethics and data governance units is recommended for planning data sharing – particularly for patient data and complex projects.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchOpen science Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchScholarly communicationOpen science Domain: Reproducibility · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.395 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.017 | 0.036 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.007 | 0.035 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".