Paths towards greater consensus building in experimental biology
Bibliographic record
Abstract
In a recent editorial, the Editors-in-Chief of Journal of Experimental Biology argued that consensus building, data sharing, and better integration across disciplines are needed to address the urgent scientific challenges posed by climate change. We agree and expand on the importance of cross-disciplinary integration and transparency to improve consensus building and advance climate change research in experimental biology. We investigated reproducible research practices in experimental biology through a review of open data and analysis code associated with empirical studies on three debated paradigms and for unrelated studies published in leading journals in comparative physiology and behavioural ecology over the last 10 years. Nineteen per cent of studies on the three paradigms had open data, and 3.2% had open code. Similarly, 12.1% of studies in the journals we examined had open data, and 3.1% had open code. Previous research indicates that only 50% of shared datasets are complete and re-usable, suggesting that fewer than 10% of studies in experimental biology have usable open data. Encouragingly, our results indicate that reproducible research practices are increasing over time, with data sharing rates in some journals reaching 75% in recent years. Rigorous empirical research in experimental biology is key to understanding the mechanisms by which climate change affects organisms, and ultimately promotes evidence-based conservation policy and practice. We argue that a greater adoption of open science practices, with a particular focus on FAIR (Findable, Accessible, Interoperable, Re-usable) data and code, represents a much-needed paradigm shift towards improved transparency, cross-disciplinary integration, and consensus building to maximize the contributions of experimental biologists in addressing the impacts of environmental change on living organisms.
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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 | 0.690 | 0.703 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.015 | 0.067 |
| Scholarly communication | 0.042 | 0.079 |
| Open science | 0.016 | 0.060 |
| Research integrity | 0.040 | 0.072 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".