Policy on Publishing Code: Encouraging Good Practice to Ensure Quality

Following on from our sponsorship of the Guide to Reproducible Code in Ecology and Evolution and our collaboration with rOpenSci, we have now released a new policy on publishing code. The main objective of this policy is to make sure that high quality code is readily available to our readers.

We’ve set out four key principles to help achieve this, as well as explaining what code outputs we publish, giving some examples of things that make it easier to review code, and giving some advice on how to store code once it’s been published. Below is a summary of some highlights of the policy, but you can find it in full on the Methods in Ecology and Evolution website. Continue reading

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A Guide to Reproducible Code in Ecology and Evolution

Post provided by Natalie Cooper and Pen-Yuan Hsing

Cover image by David J. Bird

The way we do science is changing — data are getting bigger, analyses are getting more complex, and governments, funding agencies and the scientific method itself demand more transparency and accountability in research. One way to deal with these changes is to make our research more reproducible, especially our code.

Although most of us now write code to perform our analyses, it’s often not very reproducible. We’ve all come back to a piece of work we haven’t looked at for a while and had no idea what our code was doing or which of the many “final_analysis” scripts truly was the final analysis! Unfortunately, the number of tools for reproducibility and all the jargon can leave new users feeling overwhelmed, with no idea how to start making their code more reproducible. So, we’ve put together the Guide to Reproducible Code in Ecology and Evolution to help. Continue reading

Making Your Research Reproducible with R

Post provided by Laura Graham

tweetReproducible research is important for three main reasons. Firstly, it makes it much easier to revisit a project a few months down the line, for example when making revisions to a paper which has been through peer review.

Secondly, it allows the reader of a published article to scrutinise your results more easily – meaning it is easier to show their validity. For this reason, some journals and reviewers are starting to ask authors to provide their code.

Thirdly, having clean and reproducible code available can encourage greater uptake of new methods. It’s much easier for users to replicate, apply and improve on methods if the code is reproducible and widely available

Throughout my PhD and Postdoctoral research, I have aimed to ensure that I use a reproducible workflow and this generally saves me time and helps to avoid errors. Along the way I’ve learned a lot through the advice of others, and trial and error. In this post I have set out a guide to creating a reproducible workflow and provided some useful tips. Continue reading