The Turing Way
The Turing Way is a lightly opinionated guide to reproducible data science.
Our goal is to provide all the information that researchers need at the start of their projects to ensure that they are easy to reproduce at the end.
This also means making sure PhD students, postdocs, PIs and funding teams know which parts of the "responsibility of reproducibility" they can affect, and what they should do to nudge data science to being more efficient, effective and understandable.
Table of contents:
About the project
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, sofware 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: https://github.com/alan-turing-institute/the-turing-way.
This is the (part of) the project team planning work at the Turing Institute. For more on how to contact us, see the ways of working document.
Get in touch
You can contact the PI of the Turing Way project - Kirstie Whitaker - by email at email@example.com.