Computational Reproducibility via Containers in Psychology

Keywords: computational-reproducibility, reproducibility, methods, containers, code-ocean


Scientific progress relies on the replication and reuse of research. Recent studies suggest, however, that sharing code and data does not suffice for computational reproducibility —defined as the ability of researchers to reproduce “par- ticular analysis outcomes from the same data set using the same code and software” (Fidler and Wilcox, 2018). To date, creating long-term computationally reproducible code has been technically challenging and time-consuming. This tutorial introduces Code Ocean, a cloud-based computational reproducibility platform that attempts to solve these problems. It does this by adapting software engineering tools, such as Docker, for easier use by scientists and scientific audiences. In this article, we first outline arguments for the importance of computational reproducibility, as well as some reasons why this is a nontrivial problem for researchers. We then provide a step-by-step guide to getting started with containers in research using Code Ocean. (Disclaimer: the authors all worked for Code Ocean at the time of this article’s writing.)


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