About this resource

    Type of resources
    Reading
    Education level(s)
    Graduate / Professional
    Primary user(s)
    student, teacher
    Subject area(s)
    Computer Science, Information Science, Social Science
    Language(s)
    English
    Cluster
    Transparency and Reproducibility in Computation and Analysis
    Last updated
    1 January 2020

    Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions

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    Level Graduate / Professional · For student, teacher · Language English

    Abstract

    Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable).

    Keywords Best Practices · Computational Science · Replicability · Reproducibility of Results · Reproducible Research · Reproducible Science · Software Development