This repository contains a 3 hour carpentries format lesson covering Good Enough Practices in Scientific Computing (Wilson et al., 2017): "a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill".
The workshop is targeted at a broad audience of researchers who want to learn how to be more efficient and effective in their data analysis and computing, whatever their career stage. Examples of our target audience are in learner profiles.
This fork for CSIRO Ag&Food Data School strips out a few lesson redundancies and adds some intro to available collaboration tools.
We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.
We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.
Please see the current list of issues for ideas for contributing to this
repository. For making your contribution, we use the GitHub flow, which is
nicely explained in the chapter Contributing to a Project in Pro Git
by Scott Chacon.
Look for the tag 
We are developing this lesson in 2023, led by a team from Edinburgh Carpentries. This lesson development was led by the ED-DaSH consortium at The University of Edinburgh, as a collaboration between BioRDM and Edinburgh Carpentries. Ed-DaSH was supported by the "Data driven life science skills development - equipping society for the future" UKRI-MRC grant MR/V039075/1.
Current maintainers of this lesson are
- Edward Wallace @ewallace
- Alison Meynert @ameynert
- Emma Wilson @emma-wilson
- Allen Lee @alee
A list of contributors to the lesson can be found in AUTHORS
To cite this lesson, please consult with CITATION