A call for clean code to effectively communicate science

Filazzola, Alessandro; Lortie, Christopher J.

doi:10.1111/2041-210x.13961

Cited by 15 publications

(14 citation statements)

References 57 publications

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“…If there are different options for estimation approaches and an author uses a nonstandard approach, for example, using restricted maximum likelihood (REML) instead of maximum likelihood (ML) in fitting a generalized linear model, authors should report the method used. As mentioned previously, if unique code was developed as part of a study, authors should provide the code along with the manuscript (Culina et al, 2020; Filazzola & Lortie, 2022). Many readers find it useful to walk through analyses with the code provided, leading to a better understanding of what was done.…”

Section: Methodsmentioning

confidence: 99%

Writing statistical methods for ecologists

Davis

Kay

2023

Ecosphere

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The Methods section is a key component of any ecology research publication containing detailed information on how the data were collected and analyzed. However, descriptions of which statistical methods were used and how they were applied can substantially vary and may not provide enough information for the analyses to be reproducible. Computational and statistical programming advances have allowed ecological researchers without a strong statistical or mathematical background to access and use increasingly complex statistical methods. Thus, statistical methods are written by and need to be accessible to researchers across a range of quantitative expertise. Poorly written Methods sections can incorrectly inflate the strength of or call into question the results of an analysis. Although there are resources available, we have not found one that is specific to writing statistical methods, includes all the elements we discuss, and is targeted for ecologists. Here we provide guidelines for ecological researchers when writing statistical methods and review frequent errors made in Statistical Methods sections. We highlight some common dos and don'ts when writing Statistical Methods sections and present a simple checklist to help guide authors with their writing to ensure reproducibility. We illustrate the use of this guidance with two examples.

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Section: Methodsmentioning

confidence: 99%

Writing statistical methods for ecologists

Davis

Kay

2023

Ecosphere

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“…We recognize that even open science tools such as the R programming language and Python vary between versions, instances, and operating systems. Consequently, practices associated with clean and reproducible coding should also be reviewed to ensure that reasonable documentation is provided (Filazzola & Lortie, 2022 ).…”

Section: What Data and Code Should We Require?mentioning

confidence: 99%

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

Jenkins

Beckerman

Bellard

et al. 2023

Ecology and Evolution

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“…This can result in distinct differences in code style and practices amongst the community, including code legibility and documentation. Accordingly, custom scripts can be inaccessible to other users (Filazzola and Lortie, 2022). Although increasingly requested by journals, code is also not always provided as supplementary material nor made available in online repositories (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Even when code is available, it might be poorly documented or written in a way that is specific to the dataset being analysed, and as such it may require extensive reworking before it can be applied to other data. Consequently, researchers are often forced to 'reinvent the wheel', putting time and effort into writing code that already exists, but is unavailable, inaccessible, and/or difficult to repurpose (Filazzola and Lortie, 2022). Such issues are exacerbated by the absence of community standards for how data should be prepared for analyses; differing approaches utilised by different researchers result in a lack of consistency between studies, making comparison between results challenging.…”

Section: Introductionmentioning

confidence: 99%

palaeoverse: a community-driven R package to support palaeobiological analysis

Jones¹,

Gearty²,

Allen³

et al. 2023

Preprint

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1. The open-source programming language ‘R’ has become a standard tool in the palaeobiologist’s toolkit. Its popularity within the palaeobiology community continues to grow, with published articles increasingly citing the usage of R and R packages. However, there are currently a lack of agreed standards for data preparation and available frameworks to support implementation of such standards. Consequently, data preparation workflows are often unclear and not reproducible, even when code is provided. Moreover, due to a lack of code accessibility and documentation, palaeobiologists are often forced to ‘reinvent the wheel’ to find solutions to issues already solved by other members of the community.2. Here, we introduce palaeoverse, a community-driven R package to aid data preparation and exploration for quantitative palaeobiological research. The package is freely available and has three core principles: (1) streamline data preparation and analyses; (2) enhance code readability; and (3) improve reproducibility of results. To develop these aims, we assessed the analytical needs of the broader palaeobiological community using an online survey, in addition to incorporating our own experiences.3. In this work, we first report the findings of the survey which shaped the development of the package. Subsequently, we describe and demonstrate the functionality available in palaeoverse and provide usage examples. Finally, we discuss the resources we have made available for the community and the future plans for the broader palaeoverse project.4. palaeoverse is the first community-driven R package in palaeobiology, developed with the intention of bringing palaeobiologists together to establish agreed standards for high-quality quantitative research. The package provides a user-friendly platform for preparing data for analysis with well-documented open-source code to enhance transparency. The functionality available in palaeoverse improves code reproducibility and accessibility, which is beneficial for both the review process and future research.

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A call for clean code to effectively communicate science

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 15 publications

References 57 publications

Writing statistical methods for ecologists

Writing statistical methods for ecologists

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

palaeoverse: a community-driven R package to support palaeobiological analysis

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