From policy to practice: progress towards data- and code-sharing in ecology and evolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it