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Reproducible research is necessary to ensure that scientific output can be trusted and built upon in future work. But conducting reproducible research requires skills in data management, software development, version control, and continuous integration techniques, that are usually not taught or expected of academic researchers. The Turing Way is an open-source, community-led handbook that supports this knowledge in an accessible and comprehensible form for everyone. Its moonshot goal is to make reproducible research “too easy not to do”. In addition to discussing different approaches for reproducibility, it also provides material on ethical practices in data science, inclusive collaborative work, and effective communication and management of research projects. The handbook has so far been collaboratively built with the help of more than 175 people from different disciplines and career stages within data research, who have contributed to the project’s online repository (https://github.com/alan-turing-institute/the-turing-way). This talk will give an overview of The Turing Way book, project, and community, and will show how you can get involved in its development.
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citations 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). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |