Four principles from software engineering to improve scientific reproducibility

Summary

Reproducibility is essential to scientific progress, yet most results cannot be reproduced. Today’s research papers rely heavily on programming analysis pipelines and statistical models. For decades, software engineering has evolved best practices on how to achieve high quality, reproducible code. However, scientists are not typically trained in these best practices. This post covers four fundamental principles that will promote greater reproducibility in scientific research: code review, testing, version control, and documentation.

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Photo by Conner Baker on Unsplash

The Problem of Irreproducible Research

Towards the end of my graduate career, I researched the reproducibility crisis as a side project, which for an increasingly disillusioned Ph.D. student was about as smart as pouring gasoline on a fire. The fact that some published research findings are false is not new nor particularly alarming. …

About

Patrick Beukema

Current: Machine learning/AI scientist. Previous: neuroscience Ph.D, Carnegie Mellon/U. Pitt.

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