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The Turing Way

This README.md file in also available in Dutch (README-Dutch), French (README-French.md), German (README-German.md), Indonesian (README-Indonesian), Italian (README-Italian), Korean (README-Korean), Portuguese (README-Portuguese), and Spanish (README-Spanish) (listed alphabetically).

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Cite Cite this using DOI

Welcome to The Turing Way project GitHub repository. This is where all the components of the project are developed, reviewed and maintained.

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 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.

The Turing Way project is a book, community, an open-source project and a culture of collaboration. This is shown in four illustrations, the first one showing the Turing Way book, the second showing how the community can grow, the third one showing two people collaborating on a pull request, the last one is showing a balance where reproducibility is valued more than the number of papers published.

The Turing Way is a book, a community and a global collaboration.

All stakeholders, including students, researchers, software engineers, project leaders and funding teams, are encouraged to use The Turing Way to understand their roles and responsibility of reproducibility in data science. You can read the book online, contribute to the project as described in our contribution guidelines and re-use all materials (see the License).

This is a screenshot of the online Turing Way book. It also shows one of the Turing Way illustrations at the beginning of the book. In this illustration, there is a road or path with shops for different data science skills. People can go in and out with their shopping cart and pick and choose what they need.

Screenshot of The Turing Way online book (use this image in a presentation)

Started 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.

Our moonshot goal is to make reproducibility "too easy not to do".

Table of Contents:

🎧 If you prefer an audio introduction to the project, our team member Rachael presented at the Open Science Fair 2019 in Porto and her demo was recorded by the Orion podcast. The Turing Way overview starts at minute 5:13.

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, software development, and continuous integration techniques: skills that are not widely taught or expected of academic researchers and data scientists. As these activities are not commonly taught, we recognise that the burden of requirement and new skill acquisition can be intimidating to individuals who are new to this world. 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" even for people who have never worked in this way before. 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.

The Team

The Turing Way is an open collaboration and community-driven project. 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. Long-term contributors of the project are considered part of the core contributors groups who take on various leadership roles in the project.

The project is coordinated by the co-lead investigators Kirstie Whitaker (founder) and Malvika Sharan, and hosted at The Alan Turing Institute. Anne Lee Steele is the Community Manager of The Turing Way since March 2022.

You can read The Turing Way acknowledgement process and Record of Contributions to learn about how we acknowledge your work and how our contributors are highlighted in the project. Please see the Contributors Table for the GitHub profiles of all our contributors.

Contributing

🚧 This repository is always a work in progress and everyone is encouraged to help us build something that is useful to the many. 🚧

Everyone who joins the project is expected to follow our code of conduct and to check out our contributing guidelines for more information on how to get started. We want to meet our contributors where they are. Therefore, we provide multiple entry points for you to contribute based on your interest, availability or skill requirements.

This image shows six of many kinds of contributions that anyone can make. These are: Develop and share, Maintain and improve, Share resources, Review and update, Make it global through translation, and Share best practices

Contributions include development and sharing of new chapters; maintenance and improvement of existing chapters; sharing The Turing Way resources; review and updating of previously developed materials; translating its chapter to help make this project globally accessible, and sharing best practices in research.

Community members are provided with opportunities to learn new skills, share their ideas and collaborate with others. They are also given mentorship opportunities in the project as they make their contributions to The Turing Way or other open source projects and are encouraged to mentor new participants of the project.

We have created a promotion pack to help you in presenting and sharing about The Turing Way in your network.

Citing The Turing Way

We release the latest version of The Turing Way through the project's Zenodo archive using DOI: 10.5281/zenodo.3233853. This DOI is a "concept DOI" which means it will always resolve to the latest version. If you need to cite a specific version you can find those DOIs at the zenodo page above. DOIs allow us to archive the repository and they are really valuable to ensure that the work is tracked in academic publications.

The citation will look something like this:

The Turing Way Community. (2021, November 10). The Turing Way: A handbook for reproducible, ethical and collaborative research. Zenodo. http://doi.org/10.5281/zenodo.3233853

To see our workflow for making releases for different versions and to suggest improvements, please head over to the release workflow document.

You can share the human-readable URL to a page in the book, for example, https://the-turing-way.netlify.app/reproducible-research/overview/overview-definitions.html, but be aware that the project is under development and therefore these links may update over time. You might want to include a web archive link such as https://web.archive.org/web/20191030093753/https://the-turing-way.netlify.com/reproducibility/03/definitions.html to make sure that you don't end up with broken links everywhere!

We really appreciate any references that you make to The Turing Way project in your and we hope it is useful. If you have any questions please get in touch.

Citing The Turing Way Illustrations

This is an example of one of The Turing Way illustrations. It tries to shows the evolution towards an open science era

The Turing Way illustrations are created by artists from Scriberia as part of The Turing Way book dashes in Manchester on 17 May 2019, London on 28 May 2019 and 21 February 2020, and online on 27th November 2020 and 28th May 2021. They depict a variety of content from the handbook, collaborative efforts in the community and The Turing Way project in general. These illustrations are available on Zenodo (https://doi.org/10.5281/zenodo.3332807) under a CC-BY license.

When using any of the images, please include the following attribution:

This image was created by Scriberia for The Turing Way community and is used under a CC-BY licence.

The latest version from Zenodo can be cited as:

The Turing Way Community, & Scriberia. (2021, May 29). Illustrations from the Turing Way book dashes. Zenodo. https://doi.org/10.5281/zenodo.4906004

We have used a few of these illustrations in the Welcome Bot's responses to new members' contributions in this GitHub repository.

Get in Touch

Email

You can contact The Turing Way team by emailing theturingway@gmail.com.

You can also contact Anne Lee Steele (asteele@turing.ac.uk), Malvika Sharan (msharan@turing.ac.uk) or Kirstie Whitaker (kwhitaker@turing.ac.uk).

Chat

Connect with others and discuss your ideas on Slack using this invitation link.

We also have a Gitter chat room (if you prefer an open source alternative for chat) and we'd love for you to swing by to say hello at https://gitter.im/alan-turing-institute/the-turing-way. The room is also accessible with a Matrix account at #alan-turing-institute_the-turing-way:gitter.im.

Receive Updates

We have a tinyletter mailing list to which we send monthly project updates. Subscribe at https://tinyletter.com/TuringWay.

You can also follow us on Twitter (@turingway).

Contributors

Thanks goes to these wonderful people (emoji key):


Aakash Raj

🌍

Abel Siqueira

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Achintya Rao

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Adina Wagner

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Aditi Shenvi

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Afzal Ansari

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Ago3

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Ahmed Essam

πŸ›

Aida Mehonic

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Albert Hornos Vidal

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Alden Conner

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Alejandro Β©

πŸ–‹ πŸ€” 🚧 πŸ“–

Aleksandra Zaforemska

πŸ€” πŸ–‹

Alex Bird

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Alex Chan

πŸ€”

Alex Clarke

πŸ“–

Alexander Morley

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Ali Seyhun Saral

πŸ“–

Ali Seyhun Saral

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Ambreen Masud

πŸ€” πŸ–‹

Andrea PierrΓ©

πŸ›

Andrea SΓ‘nchez-Tapia (she/her)

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Andreea Avramescu

πŸ–‹

Andrei Alexandru

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Andrew Stewart

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Andrian Nobella

🌍

Angelo Varlotta

🌍

Aniketh Varma

πŸ›

Anna Hadjitofi

πŸ–‹ 🌍 βœ…

Anna Krystalli

πŸ’¬ πŸ’‘ πŸ‘€ πŸ€” βœ…

Annabel Elizabeth Whipp

πŸ€”

Anne Fouilloux

πŸ€” πŸ–‹

Anne Lee Steele

πŸ‘€ πŸ›

Arielle-Bennett

πŸ€” πŸ‘€ πŸ–‹

Arron Lacey

πŸ‘€

Aryan nath

πŸ›

Asma Kacem

πŸ›

Augustinas Sukys

πŸ€”

Barbara Vreede

πŸ–‹

Batool

πŸ€” πŸ–‹ 🌍 πŸš‡ πŸ‘€ 🚧 πŸ“–

Becki Green

πŸ€” πŸ–‹

Becky Arnold

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Benjamin Mummery

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Beth Montague-Hellen

πŸ“–

Bouwe Andela

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Brandon Lee

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Brigitta SipΕ‘cz

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Bruno Camino

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Callum Mole

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Cameron Trotter

πŸ€”

Camila Rangel Smith

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Carlos Martinez

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Carlos Vladimiro GonzΓ‘lez Zelaya

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Cassandra Gould van Praag

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Cem Ulus

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Chad Gilbert

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Chandler Klein

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Chanuki Illushka Seresinhe

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Charlotte Watson

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Chris Holdgraf

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Chris Markiewicz

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Chris Tomlinson

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Christina Hitrova

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Christopher Lovell

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Clare Liggins

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ClauFischer

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Colin Sauze

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Collin Schwantes

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DACNC

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DaisyParry

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Dan Hobley

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Dan Kerchner

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Danbee Kim

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Daniel Lintott

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Daniel Mietchen

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Daniel NΓΌst

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Danny Garside

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Danping

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David Foster

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David Gregg

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David Stansby

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DerienFe

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Diego Alonso Alvarez

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Dimitra Blana

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Dinesh kumar

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Ed Chalstrey

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Edwin Ajong

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Eirini Malliaraki

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Eirini Zormpa

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Elisa-on-GitHub

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Elizabeth DuPre

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Em K

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Enrico Glerean

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Eric Daub

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Eric Leung

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Eric R Scott

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Esther Plomp

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Evelina Gabasova

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Faruk D.

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Federico Nanni

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Ferran Gonzalez Hernandez

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Florian Gilcher

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Frances Cooper

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Frances Madden

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Froguin99

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FrozenLines

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Fuad Reza Pahlevi

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Georgia

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Georgia Atkinson

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Georgia Tomova

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Georgiana Elena

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Gertjan van den Burg

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Gianni Scolaro

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Giulia Crocioni

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Graham Lee

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Greg Kiar

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Gustavo Becelli do Nacimento

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Heidi Seibold

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Hieu Hoang

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Iain

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Ian Hinder

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Ikko Ashimine

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Isil Bilgin

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Ismael-KG

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JKasmire

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James Kent

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James Myatt

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James Robinson

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James Thomas

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Jamie J Quinn

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Jason Gates

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Javier Moldon

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Jay Dev Jha

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Jennifer Ding

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Jeremy Crampton

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Jeremy Leipzig

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Jessica

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Jessy Provencher

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Jez Cope

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Jill Wang

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Jim Circadian

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Jim Madge

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Joanna Leng

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Joe Early

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Joe Fennell

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Johanna Bayer

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Joshua Teves

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Julia Guiomar Niso GalΓ‘n

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Julien Colomb

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Katherine Dixey

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Kelly-dot

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Kesson Magid

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Kevin Kunzmann

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Kim De Ruyck

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Kim De Ruyck

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Kirstie Whitaker

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Kristijan Armeni

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Krunal Rank

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Lachlan Mason

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Laura Acion

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Laura Carter

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Lenka

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Liberty Hamilton

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Lion-admin

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Louise Bowler

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Lovkush

πŸ›

Luca Bertinetto

🌍

Luigi Scalzone

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Luke Conibear

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Luna

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Lupe CaMay

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Malvika Sharan

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Marcos Ellys Rocha Honorato

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Maria Eriksson

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Maria del Mar Quiroga

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Mariam-ke

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Mariana V.

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Mariona

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Mark Woodbridge

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Markus LΓΆning

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Martin O'Reilly

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Martina G. Vilas

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Mateusz Kuzak

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Matthew Evans

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Max Joseph

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Melissa Black

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Michael Grayling

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Miguel Rivera

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Mustafa Anil Tuncel

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Nadia Soliman

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Naomi Penfold

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Nathan Begbie

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Neha Moopen

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Neil Chue Hong

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Nick Barlow

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Nico

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NicolΓ‘s Alessandroni

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Nina

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Nomi Harris

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NotActuallyACat

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Obi Thompson Sargoni

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Oleg Lavrovsky

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Oliver Clark

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Oliver Forrest

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Oliver Hamelijnck

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Oliver Strickson

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Oscar Corcho

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Oscar Giles

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Pablo RodrΓ­guez-SΓ‘nchez

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Patricia Herterich

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Patrick Mineault

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Paul Dominick Baniqued

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Paul Owoicho

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Paula Andrea Martinez

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Pedro Pinto da Silva

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Philip Darke

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Phillip Crout

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Pierre Grimaud

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Pooja Gadige

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Pranav Mahajan

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Przemek Dolata

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Rachael Ainsworth

πŸ“– πŸ“‹ πŸ€” πŸ’¬ πŸ‘€ πŸ“’

Radka Jersakova

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Radovan Bast

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Rafaela Queiroz

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Rahul Thakare

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Raniere Silva

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Raul Palma

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Reina Camacho Toro

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Remi Gau

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Reshama Shaikh

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Richard Gilham

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Richard Plant

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Risa Ueno

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Robert Precious

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Robin Long

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Romero Silva

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Rose Sisk

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SYU-NING

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Samuel Guay

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Samuel Nastase

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Sangram K Sahu

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Sara King

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Sarah Gibson

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Sarah Stewart

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Shashank

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Sparkler

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Stephen Eglen

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Tim Powell

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Veronika Cheplygina

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Warrick Ball

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Will Hulme

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Yanina Bellini Saibene

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Host repository for The Turing Way: a how to guide for reproducible data science

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