Why sharing data and code during peer review can enhance behavioral ecology research

Implementing code review in the scientific workflow: Insights from ecology and evolutionary biology

Code review increases reliability and improves reproducibility of research. As such, code review is an inevitable step in software development and is common in fields such as computer science. However, despite its importance, code review is noticeably lacking in ecology and evolutionary biology. This is problematic as it facilitates the propagation of coding errors and a reduction in reproducibility and reliability of published results. To address this, we provide a detailed commentary on how to effectively review code, how to set up your project to enable this form of review and detail its possible implementation at several stages throughout the research process. This guide serves as a primer for code review, and adoption of the principles and advice here will go a long way in promoting more open, reliable, and transparent ecology and evolutionary biology.

Why don't we share data and code? Perceived barriers and benefits to public archiving practices

The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.

A survey of researchers' code sharing and code reuse practices, and assessment of interactive notebook prototypes

This research aimed to understand the needs and habits of researchers in relation to code sharing and reuse; gather feedback on prototype code notebooks created by NeuroLibre; and help determine strategies that publishers could use to increase code sharing. We surveyed 188 researchers in computational biology. Respondents were asked about how often and why they look at code, which methods of accessing code they find useful and why, what aspects of code sharing are important to them, and how satisfied they are with their ability to complete these tasks. Respondents were asked to look at a prototype code notebook and give feedback on its features. Respondents were also asked how much time they spent preparing code and if they would be willing to increase this to use a code sharing tool, such as a notebook. As a reader of research articles the most common reason (70%) for looking at code was to gain a better understanding of the article. The most commonly encountered method for code sharing-linking articles to a code repository-was also the most useful method of accessing code from the reader's perspective. As authors, the respondents were largely satisfied with their ability to carry out tasks related to code sharing. The most important of these tasks were ensuring that the code was running in the correct environment, and sharing code with good documentation. The average researcher, according to our results, is unwilling to incur additional costs (in time, effort or expenditure) that are currently needed to use code sharing tools alongside a publication. We infer this means we need different models for funding and producing interactive or executable research outputs if they are to reach a large number of researchers. For the purpose of increasing the amount of code shared by authors, PLOS Computational Biology is, as a result, focusing on policy rather than tools.

Paths towards greater consensus building in experimental biology

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.