Introduction to Reproducibility with Python Workshop

This repository contains all materials for the Introduction to Reproducibility with Python workshop. This workshop covers practical exercises and examples, including things like automation of data processing, creating modules/packages and publish data using Python.

Workshop Objectives

• Understand the basic concepts of research reproducibility.
• Apply best practices for reproducible workflows.
• Use pyDataverse to connect to DataverseNL API and automate data uploads.

Notebooks

1. Python Building Blocks
2. Reproducible Analysis
3. Working with pyDataverse
4. Python Data Analysis
5. Python Data Transformations

Requirements

• Python: Python 3.6+.
• Jupyter: Jupyter Notebook, JupyterLab or Google Colab.
• pyDataverse: Docs - Release v0.3.1.
• DSRI Account: Register here (Only available to Maastricht University scholars)
• DataverseNL Account: Required for exercises involving data uploads to the DataverseNL sandbox. Create an account at demo.dataverse.nl

Credits

This workshop integrates materials from Valentin Danchev's Reproducible Data Science with Python, which is available under the Creative Commons Attribution-ShareAlike 4.0. We also draw a lot of inspiration from The Turing Way handbook, particularly the section of the Guide for Reproducible Research. 

Many examples were initially developed for the Maastricht University's Global Studies Methods Track programme, and we also got inspiration from the famous Data Analysis and Visualization in Python for Ecologists by Data Carpentry.

This workshop ultimately builds up on the Coding Basics for Researchers series, organized by Carien Hilvering and Erik Jansen during the pandemic in collaboration with the Institute of Data Science, which has been adapted to the current Open Science context.

Recommended Sources

• BITSS Resource Library has a collection of materials on research transparency and reproducibility.
• Awesome Reproducible Research is a curated list of reproducible research case studies, projects, tutorials, and media
• Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks: doi: 10.1371/journal.pcbi.1007007
• For those based in the Netherlands, the eScience Center is continuously hosting training on digital skills.

References

• Valentin Danchev (2021). Reproducible Data Science with Python. valdanchev.github.io/reproducible-data-science-python/.
• The Turing Way Community. (2022). The Turing Way: A handbook for reproducible, ethical and collaborative research. Zenodo. doi: 10.5281/zenodo.3233853.
• Data Carpentry. Data Analysis and Visualization in Python for Ecologists. datacarpentry.org/python-ecology-lesson/.
• Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature, 533, 452–454. doi: 10.1038/533452a.

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MIT License

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