Welcome to 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.
A bit more background
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, 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.
The book itself
The book that you are reading is a jupyter book. Jupyter books render markdown documents and jupyter notebooks as static html web pages. They are easy to read and navigate…but also easy to edit and extend!
🚧 Under construction 🚧
Watch this space for a little more information on how to contribute to the Turing Way!
🚧 Under construction 🚧
The Turing Way Community
The Turing Way is built by an incredible team…..and you!
The lead investigator for this project is Dr Kirstie Whitaker. She is a research fellow at the Alan Turing Institute and senior research associate in the Department of Psychiatry at the University of Cambridge.
Our core contributors are, in alphabetical order:
- Rachael Ainsworth
- Becky Arnold
- Louise Bowler
- Sarah Gibson
- Patricia Herterich
- Rosie Higman
- Anna Krystalli
- Alexander Morley
- Martin O’Reilly
You can see all of our incredible contributors in our README file, and screengrabbed below.
Check out our contributing guidelines on how you can join this fantastic group and help us build the most useful book we can!