The Turing Way: Reproducible Research and Beyond

Presentation on The Turing Way for the COMPUTE research school at Lund University on 2021-03-22. Website: https://compute.lu.se Course material: https://github.com/mlund/jupyter-course Abstract: Reproducible research is necessary to ensure that scientific work can be trusted. Funders and stakeholders are beginning to require that publications and research outreach include access to the underlying data and 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. The Turing Way is a handbook to support research professionals, stakeholders, funders, students and their supervisors in ensuring that reproducible research is "too easy not to do". It includes training material on version control, analysis testing, and open and transparent communication with future users. Beyond just the reproducibility aspect of research, The Turing Way has expanded into a series of volumes sharing insights on building data science projects that are open, collaborative, inclusive and ethical. 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 Bio: Sarah Gibson is a Research Software Engineer at The Alan Turing Institute where she implements software best practices to translate academic research into real world solutions through the Turing's collaborative network. She is a core contributor to The Turing Way and is also an operator and maintainer for the Binder project and runs a BinderHub cluster at the Turing which receives traffic from mybinder.org. 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