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Sustainable computational science: the ReScience initiative.

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|November 1, 2021
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Summary
This summary is machine-generated.

Computational science research reproducibility is often poor, with code and data sharing lacking. ReScience journal addresses this by promoting open-source implementations and explicit replication for verifiable scientific scholarship.

Keywords:
Computational scienceGitHubOpen peer-reviewOpen sciencePublicationReplicableReproducibleSustainable

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Area of Science:

  • Computational Science
  • Scientific Publishing
  • Software Engineering

Background:

  • Traditional scientific publishing struggles with computational research reproducibility.
  • Authors often provide code archives, but true reproducibility remains a challenge.
  • The scholarship lies in the full computational environment, not just the article.

Purpose of the Study:

  • To highlight the inadequacy of current practices in computational research reproducibility.
  • To introduce ReScience as a novel journal addressing these challenges.
  • To promote explicit replication and open-source implementations in computational science.

Main Methods:

  • ReScience utilizes a GitHub-based platform for publishing.
  • It encourages new, open-source implementations of computational studies.
  • The journal emphasizes the inclusion of comments, explanations, and software tests.

Main Results:

  • ReScience facilitates the explicit replication of published computational research.
  • It ensures that original research can be reproduced from its description.
  • The journal fosters a different publishing workflow focused on verifiable results.

Conclusions:

  • ReScience offers a solution to the reproducibility crisis in computational science.
  • Open-source implementations and replication are key to genuine scientific scholarship.
  • The journal's unique publishing model enhances the integrity of computational research.