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The Turing Way: A Handbook for Reproducible Data Science

The Turing Way Community; Becky Arnold; Louise Bowler; Sarah Gibson; Patricia Herterich; Rosie Higman; Anna Krystalli; Alexander Morley; Martin O'Reilly; Kirstie Whitaker

Reproducible research is necessary to ensure that scientific work can be trusted. Funders and publishers are beginning to require that publications include access to the underlying data and the analysis code. The goal is to ensure that all results can be independently verified and built upon in future work. This is sometimes easier said than done.

Sharing these research outputs means understanding data management, library sciences, software development, and continuous integration techniques: skills that are not widely taught or expected of academic researchers and data scientists. The Turing Way is a handbook to support students, their supervisors, funders and journal editors in ensuring that reproducible data science is "too easy not to do".

It will include training material on version control, analysis testing, and open and transparent communication with future users, and build on Turing Institute case studies and workshops.

This project is openly developed and any and all questions, comments and recommendations are welcome at our github repository:

Release log

  • v0.0.4: Continuous integration chapter merged to master.
  • v0.0.3: Reproducible environments chapter merged to master.
  • v0.0.2: Version control chapter merged to master.
  • v0.0.1: Reproducibility chapter merged to master.
This work was supported by The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "Tools, Practices and Systems" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1.
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