Reproducibility in ecology and evolution: Minimum standards for data and code

Jenkins, G. C.; Beckerman, Andrew P.; Bellard, Céline; Benítez‐López, Ana; Ellison, Aaron M.; Foote, Christopher G.; Hufton, Andrew L.; Lashley, Marcus A.; Lortie, Christopher J.; Ma, Zongmin; Moore, Allen J.; Narum, Shawn R.; Nilsson, Johan; O’Boyle, Bridget; Provete, Diogo B.; Razgour, Orly; Rieseberg, Loren H.; Riginos, Cynthia; Santini, Luca; Sibbett, Benjamin; Peres‐Neto, Pedro R.

doi:10.1002/ece3.9961

Cited by 42 publications

(27 citation statements)

References 28 publications

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“…To be fully effective, however, the data and code must be accompanied by appropriate metadata and documentation. In their call for journals to commit to improved policies around data and code, Jenkins et al (2023) describe minimum standards for archiving data and code, to ensure accessibility and useability, while Roche et al (2020) describe best practices, including adherence to ‘FAIR' (findable, accessible, interoperable, and reusable) principles (Wilkinson et al 2016), and also provide an up‐to‐date list of open‐access resources providing guidance on managing and publishing open data, including sensitive data. Indigenous data sovereignty and governance should also be a priority in many connectivity conservation projects, and for this Carroll et al (2021) provide guidance on complementing FAIR principles with CARE principles.…”

Section: Discussionmentioning

confidence: 99%

“…An effective and comparatively low‐barrier first step is making all code and all non‐sensitive raw (unmanipulated) data openly available, ideally alongside metadata and documentation that are sufficiently detailed such that independent researchers could repeat the original analyses and achieve comparable outputs (c.f. Wilson et al 2017, Jenkins et al 2023). This step is essential for improving the transparency and reliability of connectivity models.…”

Section: Introductionmentioning

confidence: 99%

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Four steps to strengthen connectivity modeling

Riordan‐Short

Pither

2023

Ecography

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Maintaining and restoring ecological connectivity is considered a global imperative to help reverse the decline of biodiversity. To be successful, practitioners need to be guided by connectivity modeling research that is rigorous and reliable for the task at hand. However, the methods and workflows within this rapidly growing field are diverse and few have been carefully scrutinized. We propose four steps that should be consistently undertaken in connectivity modeling studies in order to improve rigour and utility: 1) describe the type of connectivity being modeled, 2) assess the uncertainty and sensitivity of model parameters, 3) validate the model outputs, ideally with independent data and 4) make non‐sensitive raw data and code openly available to enhance computational reproducibility. We reviewed the literature to determine the extent to which studies included these four steps. We focused on studies that generated novel landscape connectivity outputs using circuit theory and restricted our assessment to studies concerning terrestrial mammals. Among 181 studies meeting our search criteria, 39% communicated the type of connectivity being modeled, 18% conducted some form of sensitivity or uncertainty analysis (or both), 18% of studies attempted to validate their connectivity model outputs and only 7% used fully independent data to do so. Lastly, 13% of the studies made all raw data available, 2% provided all required code and only two studies provided both. Our findings highlight a clear need and opportunity to improve the reliability, reproducibility and utility of connectivity modeling research. We provide a checklist that researchers can consult and include with outputs. This will help practitioners make more informed decisions and ensure limited resources for connectivity conservation and restoration are allocated appropriately.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Four steps to strengthen connectivity modeling

Riordan‐Short

Pither

2023

Ecography

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“…However, there is hope to be found here. As Culina et al (2020) have shown, journals requiring code to be shared are increasing in number yearly, and as a field, we already have improved substantially (Jenkins et al, 2023; Mislan et al, 2016). In some cases, journals have implemented far stricter (and rightly so) data and code requirements along with assigning corresponding data editors (Bolnick, 2022).…”

Section: Post‐publication: Reviewing Code After Publicationmentioning

confidence: 99%

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

Ivimey-Cook,

Pick,

Bairos-Novak

et al. 2023

Journal of Evolutionary Biology

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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.

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“…Over the years, we expanded the policy to include code, programming scripts, and software, as well as detailed metadata. Earlier this year, members of the MEC and Molecular Ecology Resources ( MER ) Editorial Board (along with editors from other journals) contributed to an article in Ecology and Evolution that established minimum standards for data and code in ecology and evolution journals (Jenkins et al., 2023). While the recommendations largely reinforce what we are already doing, the article provides a description of best practices for archiving of data, metadata, programming scripts, and software, such that analyses published in a given study can be fully replicated.…”

Section: Editorial Announcementsmentioning

confidence: 99%

Editorial 2024

Rieseberg,

Warschefsky,

Burton

et al. 2023

Molecular Ecology

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Molecular Ecology (MEC) continues to be one of the largest and most influential journals in the fields of ecology and evolution. In 2022, a total of 434 citable items were published in the journal, ranking third out of 53 journals in Clarivate's list of Evolutionary Biology journals and sixth of 171 Ecology journals. A similar pattern is seen for total citations, where MEC was cited 40,823 times (third in Evolutionary Biology and fourth in Ecology). Additional metrics include journal impact factor (4.9; eighth in Evolutionary Biology) and EigenFactor (0.030, fifth in Evolutionary Biology). The latter metric measures the number of times articles from the journal published in the past five years have been cited in the focal year. Google Scholar's h5-index, which is the h-index for articles published over the past five years, offers a longer term measure of journal influence. Molecular Ecology has an h5-index of 72, which ranks fourth among ecology journals. Lastly, journal downloads demonstrate wide interest in research published in MEC, reaching close to 1.9 million in 2021 and 2022. | EDITORIAL ANNOUN CEMENTS | Minimum standards for data and code in ecology and evolution journalsMolecular Ecology was among the first journals in ecology and evolution to require that data supporting the results of published papers be archived in an appropriate public archive (Rieseberg et al., 2010). Over the years, we expanded the policy to include code, programming scripts, and software, as well as detailed metadata. Earlier this year, members of the MEC and Molecular Ecology Resources (MER) Editorial Board (along with editors from other journals) contributed to an article in Ecology and Evolution that established minimum standards for data and code in ecology and evolution journals (Jenkins et al., 2023). While the recommendations largely reinforce what we are already doing, the article provides a description of best practices for archiving of data, metadata, programming scripts, and software, such that analyses published in a given study can be fully replicated. We will be linking the recommendation to our author guidelines. We also will be transitioning to requiring data and code be made available to editors and reviewers during peer review.

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Reproducibility in ecology and evolution: Minimum standards for data and code

Cited by 42 publications

References 28 publications

Four steps to strengthen connectivity modeling

Four steps to strengthen connectivity modeling

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

Editorial 2024

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