Static program analysis

Code review

Mathematics

Software development

Operating system

Replication (statistics)

Statistics

Software engineering

Engineering

Political science

Reuse

Set (abstract data type)

Programming language

Disclaimer

Data science

Code (set theory)

Software

Computer science

Cloud computing

Waste management

Reproducibility

Meta-Psychology

&#x0D; &#x0D; &#x0D; 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.)&#x0D; &#x0D; &#x0D;

Computational Reproducibility via Containers in Psychology