What Makes a Good Health Related Research Data Repository? Findings from a Scoping Review
Bibliographic record
Abstract
Introduction There is a strong need for having a focal point such as a data repository platform to ensure that evidence produced in local context can be collected, managed, protected and disseminated to relevant parties in timely manner. In establishing such platform, understanding the best practices and major characteristics of established data repositories on health-related research data worldwide would be the first crucial step. A scoping review was thus performed with the objective of mapping the characteristics of online-based health-related research data repositories that are being practised globally. Methodology A three phase processes was performed which include; identifying relevant articles from major online databases, namely Pubmed, CINAHL, Web of Science and Google Scholar; charting the data, and; collating and summarising the data from the articles collected. Results A total of 46 final articles were identified and analysed. Majority of the articles discussed on platforms which are based in the United States and Canada (n=26), combination of more than one country (n=9), Europe (n=8), Africa (n=2), and Asia (n=1). Three major themes emerged that describe the characteristics of these data repositories, namely; Data deposit and archive; Data retrieval and access; Data policy and governance. Discussion/Conclusion The scoping review has revealed the major characteristics of established health-related research data repositories that are being practised globally. It is thus imperative to adopt these characteristics in local data repository platforms to enhance the function of these platforms especially for organisations that produce and manage local research products such as the National Institutes of Health (NIH) Malaysia. [Disclaimer: Abstract text might vary slightly from what is displayed in the e-poster]
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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 | 0.182 | 0.426 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.043 | 0.055 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".