Published March 8, 2022 | Version v1
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Paths towards greater consensus building in experimental biology

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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, crossdisciplinary integration, and consensus building to maximize the
contributions of experimental biologists in addressing the impacts of environmental change on living organisms.

Notes

This project was supported by the European Union's Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement no. 838237-OPTIMISE.

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