About this resource

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

    Good enough practices in scientific computing

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

    Abstract

    Computers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don’t know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.

    Keywords Computer Software · Control Systems · Data Management · Data Processing · Programming Languages · Reproducibility · Software Tools · Source Code