ReproduciblePython ๐๐ฑโ๐ค
Materials associated with the PyCon 2018 workshop on reproducible analysis in Python.
The proposal for this workshop can be found in the proposal.md file.
Slides
๐๏ธ The slides for the workshop can be found here:
- Online html version: interactive slides
- PDF version
๐ฌ Discussion
We will encourage discussions over the workshop, for this purpose we will be using an Etherpad. Click on the following link: https://public.etherpad-mozilla.org/p/ReproduciblePython
๐๏ธ The content
This material covers the basics of reproducible workflows in Python and is provided in the following sections:
- Setup: installation instructions for the workshop
- Setting up projects: advise on best practices to set up projects with a reproducibility-first approach
- Working with data: information on how to use, archive, and share data
- Processing data, workflows: producing automated wokrflows
- All things testing: introduction to testing of standalone scripts and Jupyter notebooks
- Making code public: how to share your code and being credited for it
๐ฆ Additional materials
These are complementary materials that you can follow at your own pace if you wanted to dive further.
- Getting started with Docker: introduction to containers and usage of repo2docker
- Sharing your interactive notebooks using Binder
- Introduction to Datalad
Solutions
The solutions to the tutorial can be found in the solutions folder. Make sure to read the solutions README first
๐ฅ๏ธ What do I need for this workshop?
The installation instructions can be found at http://bitsandchips.me/ReproduciblePython/Setup.html
Acknowledgements
The development of this material was funded by OpenDreamKit, a Horizon2020 European Research Infrastructure project (676541) that aims to advance the open source computational mathematics ecosystem.
This work is licensed under a Creative Commons Attribution 4.0 International License.
