From policy to practice: progress towards data- and code-sharing in ecology and evolution
Bibliographic record
Abstract
Data and code are essential for ensuring the credibility of scientific results and facilitating reproducibility, areas in which journal sharing policies play a crucial role. However, in ecology and evolution, we still do not know how widespread data- and code-sharing policies are, how accessible they are, and whether journals support data and code peer review. Here, we first assessed the clarity, strictness and timing of data- and code-sharing policies across 275 journals in ecology and evolution. Second, we assessed initial compliance to journal policies using submissions from two journals: Proceedings of the Royal Society B (Mar 2023–Feb 2024: n = 2340) and Ecology Letters (Jun 2021–Nov 2023: n = 571). Our results indicate the need for improvement: across 275 journals, 22.5% encouraged and 38.2% mandated data-sharing, while 26.6% encouraged and 26.9% mandated code-sharing. Journals that mandated data- or code-sharing typically required it for peer review (59.0% and 77.0%, respectively), which decreased when journals only encouraged sharing (40.3% and 24.7%, respectively). Our evaluation of policy compliance confirmed the important role of journals in increasing data- and code-sharing but also indicated the need for meaningful changes to enhance reproducibility. We provide seven recommendations to help improve data- and code-sharing, and policy compliance.
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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 | 0.609 | 0.790 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.016 | 0.042 |
| Scholarly communication | 0.052 | 0.075 |
| Open science | 0.013 | 0.031 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier 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".