The Turing Way

An open source and community-led handbook for reproducible, ethical and collaborative data science

Research areas


Reproducible research is work that can be independently verified. In practice, it means sharing the data and code that were used to generate published results – yet this is often easier said than done. The Turing Way is a handbook to reproducible, ethical and collaborative data science. We involve and support a diverse community of contributors to make data science accessible, comprehensible and effective for everyone.

Our moonshot goal is to 'make reproducibility too easy not to do'!

Explaining the science

In the ideal case, all published results should be independently verifiable and suitable for other researchers to build upon. For this to happen, the data and code that support the publication need to be made available in an easy-to-use and open format. 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's goal is to provide all the information that researchers and data scientists in academia, industry and the public sector need at the start of their projects to ensure that they are easy to reproduce at the end. Our resources are designed to support researchers as they develop their projects, apply coding skills and handle data, with an aim to produce work that will be regarded as gold-standard examples of trustworthy and reusable research.

Project aims

Launched in 2019 as a lightly opinionated guide to data science, The Turing Way has since expanded into a series of guides on Reproducible Research, Project Design, Communication, Collaboration and Ethical Research. Each guide offers chapters on a range of topics covering best practices, guidance and recommendations. These chapters have been co-authored by contributors who are students, researchers, educators, community leaders, policy-makers and professionals from diverse backgrounds, lived experiences and domain knowledge.

The Turing Way is an open collaboration and community-driven project. The Turing Way's mission is to involve everybody in data science and research infrastructure roles: the developers of the code (research engineers, postdocs and doctoral students), their supervisors and the business team members who coordinate these projects. Everyone who contributes to this book, no matter how small or big their contributions are, is recognised in this project as a contributor and a community member.


We recognise that the burden of requirement and new skill acquisition can be intimidating to individuals who are new to data science. Therefore the format of The Turing Way chapters is kept modular for the reader to dip in and out of, depending on their level of experience in the various topics. The project will help to answer questions that researchers don't always ask: "How do I ensure that my code's existing functionality doesn't change as I extend the codebase?", "How do I make my project easy for someone else to run?", and many more.

Senior team members – Turing fellows, program directors and managers – will be catered for with key points tailored towards managing reproducible research projects highlighted for each topic covered. The project will build and curate checklists for what can be done to ensure all project outputs are reproducible. A chapter on Binder will be of interest to supervisors who want to regularly review their students' code and will include the technical details of how to set up a BinderHub that will be useful for research software engineers.

Recent updates

The Turing Way book currently hosts 200 live pages across 50 chapters that have been co-created by over 300 contributors.

A recent version of the book can be cited as: The Turing Way Community. (2021). The Turing Way: A handbook for reproducible, ethical and collaborative research (1.0.1). Zenodo.

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Overview of resources

The Turing Way guides

The Turing Way hosts chapters on research topics cetegorised across the following community-produced guides:

  1. Guide to reproducible research
  2. Guide to ethical research
  3. Guide to project design
  4. Guide for communication
  5. Guide for collaboration
  6. Community Handbook


Since 2019, we have developed reusable illustrations collaboratively with The Turing Way Book Dash participants using the scribing service of Scriberia. These have been shared for community use on Zenodo and have been downloaded nearly 10000 times to use in different Open Source materials. A new set of illustrations are released after each Book Dash event that can be reused under CC-BY 4.0 license with the appropriate citation: “This image was created by Scriberia for The Turing Way community and is used under a CC-BY 4.0 Licence. Zenodo. DOI: 10.5281/zenodo.3332807.

Community calls and events

Community and mentorship support

The Turing Way community is dedicated to making collaborative, reusable and transparent research accessible. That means investing in the socio-technical skills required to work in a team, to build something more significant than any individual could deliver alone. As an openly developed project, we welcome any and all questions, comments and contributions via the GitHub repository. The Turing Way members regularly offer guidance, review support and mentoring for facilitating contributions by new members.

We have also collaborated with Open Life Science (OLS) to support our members to receive mentoring and training on applying open research principles in the work within The Turing Way.

In 2020 and 2021, multiple projects in the Turing and The Turing Way community successfully graduated from OLS. To support this collaboration in 2021, the Turing Online Training Grant was offered to the Open Life Science in association with The Turing Way. In 2022, The Turing Way team is collaborating with the Training Team to adopt Open Life Science training model in delivering The Turing Way resources via a peer-mentoring programme, as well as co-create a Data Science Educators' Programme.

Selected reports

Selected articles


      Researchers and collaborators

      Previous contributors

      Contact info

      Contact the project lead Kirstie Whitaker, co-lead Malvika Sharan and community manager Anne Lee Steele.


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