Sustainable computational science: the ReScience initiative

TLDR

Computational science lags behind reproducibility, as journals rarely require or execute source code, despite the view that full software environments and data constitute the true scholarship. ReScience aims to promote explicit replication of computational research by requiring open‑source implementations that can be reproduced from the article’s description. ReScience operates on GitHub, making each new implementation of a study available with comments, explanations, and software tests, thereby redefining the publishing workflow.

Abstract

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.