https://github.com/fhdsl/github_automation_for_scientists
This course walks through why's and the how's for using automation to boost scientific software development process.
https://github.com/fhdsl/github_automation_for_scientists
activities audience-software-developers automation category-software-dev course github-actions itn-course launched-nov2023 templates troubleshooting
Last synced: 11 months ago
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This course walks through why's and the how's for using automation to boost scientific software development process.
- Host: GitHub
- URL: https://github.com/fhdsl/github_automation_for_scientists
- Owner: fhdsl
- License: cc-by-4.0
- Created: 2023-08-31T12:13:41.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2025-06-06T19:42:17.000Z (about 1 year ago)
- Last Synced: 2025-06-06T20:53:54.108Z (about 1 year ago)
- Topics: activities, audience-software-developers, automation, category-software-dev, course, github-actions, itn-course, launched-nov2023, templates, troubleshooting
- Language: CSS
- Homepage: http://hutchdatascience.org/GitHub_Automation_for_Scientists/
- Size: 134 MB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 6
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Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Code of conduct: code_of_conduct.md
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README
# GitHub Automation for Scientists
This course covers how to use GitHub actions for scientific software development. We encourage the recognition that scientific software can take many forms that can all benefit from the concepts of continuous integration and continuous deployment.
## Target Audience
The course is intended for students in the biomedical sciences and researchers who use informatics tools in their research
_This course is written for individuals who:_
- Are comfortable with GitHub and know how to make a pull request
- Wish to save time and enhance their scientific projects using automation
- Perhaps previously tried to learn about GitHub Actions but felt overwhelmed on how to get started
## Prerequisites
This course builds on concepts introduced in the [Reproducibility](https://jhudatascience.org/Reproducibility_in_Cancer_Informatics/introduction.html) and [Advanced Reproducibility](https://jhudatascience.org/Adv_Reproducibility_in_Cancer_Informatics/introduction.html) courses from the ITCR Training Network. If you are unfamiliar with GitHub and/or do not have an account, we'd suggest you start with those courses by using the links.