Systematic reviewers' perspectives on sharing review data, analytic code, and other materials: A survey
Bibliographic record
Abstract
Background: There are many benefits of sharing data, analytic code, and other materials, yet these items are infrequently shared among systematic reviews (SRs). It is unclear which factors influence authors' decisions to share data, code, or materials when publishing their SRs. Therefore, we aimed to explore systematic reviewers' perspectives on the importance of sharing review materials and factors that might influence such practices. Methods: = 4671) using Qualtrics. Quantitative responses were summarized using frequency analysis. Free-text answers were coded using an inductive approach. Results: = 417). Most participants supported routine sharing of search strategies (84%) but fewer for analytic code (43%) or files documenting data preparation (38%). Most participants agreed that normative practices within the discipline were an important facilitator (78%). Major perceived barriers were lack of time (62%) and suitable sharing platforms (31%). Few participants were required by funders (19%) or institutions (17%) to share data, and only 12% of participants reported receiving training on data sharing. Commonly perceived consequences of data sharing were lost opportunities for future publications (50%), misuse of data (48%), and issues with intellectual property (40%). In their most recent reviews, participants who did not share data cited the lack of journal requirements (56%) or noted the review did not include any statistical analysis that required sharing (29%). Conclusion: Certain types of review materials were considered unnecessary for sharing, despite their importance to the review's transparency and reproducibility. Structural barriers and concerns about negative consequences hinder data sharing among systematic reviewers. Normalization and institutional incentives are essential to promote data-sharing practices in evidence-synthesis research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.547 | 0.618 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".