Reference

Glossary

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Please check our jargon busting section for the glossary of terms we use in this course.

Reference and further reading

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The content of this course borrows from or references the following work, which we also recommend for further reading.

• A Beginner’s Guide to Conducting Reproducible Research, Jesse M. Alston, Jessica A. Rick, Bulletin of The Ecological Society of America 102 (2) (2021), https://doi.org/10.1002/bes2.1801

• The Turing Way Community. (2022). The Turing Way: A handbook for reproducible, ethical and collaborative research. Zenodo. https://doi.org/10.5281/zenodo.3233853

• The Turing Way’s “Guide for Reproducible Research”, online book - part of the The Turing Way: A handbook for reproducible, ethical and collaborative research

• “Ten reproducible research things” tutorial

• Reproducibility for Everyone’s (R4E) resources, community-led education initiative to increase adoption of open research practices at scale

• [FORCE11’s FAIR 4 Research Software (FAIR4RS) Working Group][fair4rs-working-group/]

• Training materials on different aspects of research software engineering (including open source, reproducibility, research software testing, engineering, design, continuous integration, collaboration, version control, packaging, etc.), compiled by the INTERSECT project

• Automating assessment of the FAIR Principles for Research Software (FAIR4RS)

• “Good Enough Practices in Scientific Computing” course

• CodeRefinery - training and e-Infrastructure for research software development

• Curated resources by the Framework for Open and Reproducible Research Training (FORRT)

• Simon Hettrick. (2018). softwaresaved/software_in_research_survey_2014: Software in research survey (1.0). Zenodo. https://doi.org/10.5281/zenodo.1183562 - also see the related blogpost

• Barker, M., Chue Hong, N.P., Katz, D.S. et al.  Introducing the FAIR Principles for research software. Sci Data 9, 622 (2022). https://doi.org/10.1038/s41597-022-01710-x

• Wilkinson, M., Dumontier, M., Aalbersberg, I. et al.  The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18.

• CodeRefinery: Reproducible research - preparing code to be usable by you and others in the future

• The FAIR Cookbook, online recipes for life scientists that help make and keep data FAIR

• Top 10 FAIR Data & Software Things - brief guides for different disciplines that can be used by the research community to understand how they can make their research (data and software) more FAIR

• 10 easy things to make your research software FAIR, poster, part of Top 10 FAIR Data & Software Things

• Five recommendations for FAIR software, by the Netherlands eScience Center and DANS

• Short online courses on various aspects of research software (including FAIR), by the NeSC Research Software Support

• Awesome Research Software Registries, a list of research software registries (by country, organisation, domain and programming language) where research software can be registered to help promote its discovery

• Software Carpentry’s Git Novice lesson

• “How Git Works” course on Pluralsight

• A cookiecutter software project template to kickstart a modern best-practice Python project with FAIR metadata

• A self-assessment checklist for FAIR research software, by the Netherlands eScience Center and Australian Research Data Commons

• Open source definition, by the Open Source Initiative

• CODECHECK, an approach for independent execution of computations underlying research articles