The Turing Way: Reproducible, Inclusive, Collaborative Data Science
Creators: name: Whitaker, Kirstie
Publisher: name: Zenodo
Year: 2019

description: <em><strong>Slides from Kirstie's keynote at PyData Cambridge on 16 November 2019</strong></em> <strong>Abstract</strong>: Reproducible research is necessary to ensure that scientific work can be trusted. By sharing data, analysis code and the computational environment used to generate the results, researchers can more effectively stand on the shoulders of their peers and colleagues and deliver high quality, trustworthy and verifiable outputs. This requires skills in data management, library sciences, software development, and continuous integration techniques: skills that are not widely taught or expected of academic researchers. Skills that are unreasonable, in fact, to expect in one individual team member. Even worse, they are not sufficient for ethical, transparent, collaborative, participatory and well designed data science! <em>The Turing Way</em> is a handbook to support students, their supervisors, industry data scientists, team leaders, funders, journal editors, and policy makers in ensuring that reliable and impactful data science is "too easy not to do". It includes training material on version control, analysis testing, collaborating in distributed groups, open and transparent communication skills, and effective management of diverse research projects. <em>The Turing Way</em> 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. In this talk, Kirstie Whitaker, lead developer of <em>The Turing Way</em>, will take you on a whirlwind tour of the chapters that already exist and the directions in which we're continuing to develop including ethical considerations,research project design, scoping across a broad range of incentives and ways of working, and effective communication strategies. All participants will leave the talk knowing that "Every Little Helps" when making their work reproducible, where to ask for help as they start or continue their open research journey, and how they can contribute to improve <em>The Turing Way</em> for future readers. <strong>Bio</strong>: Kirstie Whitaker is a research fellow at the Alan Turing Institute (London, UK) and senior research associate in the Department of Psychiatry at the University of Cambridge. Her work covers a broad range of interests and methods, but the driving principle is to improve the lives of neurodivergent people and people with mental health conditions. Dr Whitaker uses magnetic resonance imaging to study child and adolescent brain development and participatory citizen science to educate non-autistic people about how they can better support autistic friends and colleagues. She is the lead developer of <em>The Turing Way</em>, an openly developed educational resource to enable more reproducible data science. Kirstie is a passionate advocate for making science "open for all" by promoting equity and inclusion for people from diverse backgrounds, and by changing the academic incentive structure to reward collaborative working. She is the chair of the Turing Institute's Ethics Advisory Group, a Fulbright scholarship alumna and was a 2016/17 Mozilla Fellow for Science. Kirstie was named, with her collaborator Petra Vertes, as a 2016 Global Thinker by Foreign Policy magazine. You can find more information at her lab website: whitakerlab.github.io. <strong>Useful links</strong> The book: https://the-turing-way.netlify.com Github repository: https://github.com/alan-turing-institute/the-turing-way Contributing guidelines: https://github.com/alan-turing-institute/the-turing-way/blob/master/CONTRIBUTING.md Online Collaboration Cafe outline and schedule: https://github.com/alan-turing-institute/the-turing-way/blob/master/project_management/online-collaboration-cafe.md Gitter chat room: https://gitter.im/alan-turing-institute/the-turing-way Mailing list: https://tinyletter.com/TuringWay
description: 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.