reproduciblepython
Workshop materials for PyCon 2018 workshop on reproducible analysis in Python
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Repository
Workshop materials for PyCon 2018 workshop on reproducible analysis in Python
Basic Info
- Host: GitHub
- Owner: trallard
- License: other
- Language: Jupyter Notebook
- Default Branch: master
- Size: 110 MB
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- Stars: 111
- Watchers: 4
- Forks: 21
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
ReproduciblePython 🐍🐱👤
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
🗒️ The slides for the workshop can be found here: - Online html version: interactive slides - PDF version
💬 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
🗃️ 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
🦄 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
🖥️ 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.
Owner
- Name: Tania Allard
- Login: trallard
- Kind: user
- Location: Manchester, UK
- Company: @Quansight-Labs
- Website: https://trallard.dev
- Twitter: ixek
- Repositories: 144
- Profile: https://github.com/trallard
✨ Director @Quansight-Labs 👩🏻💻Research Software/MLOps ⭐ Passionate about open source and community building
Citation (CITATION.cff)
cff-version: 1.0.3
message: If you use this materials, please cite it as below.
authors:
- family-names: Allard
given-names: Tania
orcid: https://orcid.org/0000-0003-4925-7248
title: 101 on reproducible workflows with Python
version: 1.1
doi: 10.5281/zenodo.1241112
date-released: 2018-05-04
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