Presenter: Allen Lee
Part of the Session on Model Reuse
1 Like
Related Links
Software Sustainability Institute: https://www.software.ac.uk/
Software and Data Carpentry workshops: https://software-carpentry.org/workshops/
Lorena Barba’s essential skills for reproducible research computing: https://barbagroup.github.io/essential_skills_RRC/
Example DOI issuing digital repositories:
- https://osf.io
- https://figshare.com
- https://zenodo.org
- https://www.softwareheritage.org/
- https://datadryad.org
- https://f1000research.com/
Learn Git:
- http://swcarpentry.github.io/git-novice/
- https://try.github.io
- https://www.atlassian.com/git/tutorials/what-is-version-control
- http://think-like-a-git.net/
Testing:
- https://jrsmith3.github.io/python-testing-style-guidelines.html
- https://software-carpentry.org/blog/2014/10/why-we-dont-teach-testing.html
Docker:
References
Boettiger, Carl. (2014). An introduction to Docker for reproducible research, with examples from the R environment. https://arxiv.org/abs/1410.0846
Chue Hong, Neil (2014). Minimal information for reusable scientific software. https://doi.org/10.6084/m9.figshare.1112528.v1
Goodman, Steven N., et al (2016). What does research reproducibility mean? https://doi.org/10.1126/scitranslmed.aaf5027
Wilkinson, Mark D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. http://doi.org/10.1038/sdata.2016.18
Wilson, Greg, et al. (2017). Good Enough Practices in Scientific Computing. https://doi.org/10.1371/journal.pcbi.1005510
Great collection of resources/links!
As a computer scientist who teaches a software development course, I could easily follow (& agree with) the various points in your presentation. I am curious though, for people who don’t come from a CS background, how easy it was to follow all of the tech. jargon… any ecologists or economists or astronomers out there who want to weigh in, or ask for additional clarification about any of the content?