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The Turing Way Guide to Reproducible, Ethical and Collaborative Research

Authors: Sharan, Malvika;

The Turing Way Guide to Reproducible, Ethical and Collaborative Research

Abstract

As researchers, we make complex choices around project design and decision-making throughout the lifecycle of our research. We are expected to ensure that our research objects are easily accessed, openly examined and built upon by others in future work. Although open and transparent reporting helps to make sure that scientific work can be trusted, we must also integrate considerations of the societal and ethical implications of our work. Especially when these considerations impact people's lives. Furthermore, reproducible research practices are important for enabling independent verification of research methods, underlying data, analysis code and workflows. All these require an understanding of research best practices and skills that are often not widely taught or explored among academic researchers. In this talk, I will introduce The Turing Way - an open source community-driven guide to reproducible, ethical and inclusive data science and research. Our goal is to provide all the information that researchers, industry professionals and community leaders need at the start of their projects to ensure that they maintain the highest reproducible and ethical standards at all stages of development. All attendees will leave the talk understanding the many dimensions of openness and how they can participate in an inclusive, kind and inspiring open source ecosystem as they collaboratively seek to improve research culture. All questions and contributions are welcome at GitHub repository: https://github.com/alan-turing-institute/the-turing-way.

This talk will be given by Malvika Sharan at SORTEE 2021 on 12 July: https://www.sortee.org/malvika_sharan/. The talk will be chaired by Malika Ihle.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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