Everyone Needs a Little Python | Open Science Toolbox Part 3

Make Reproducible Research Using Git, Python, Zenodo, and More! Join the DUTC Discord Server: https://discord.gg/ZhJPKYSfNp Sign up for DUTC Weekly: https://mailchi.mp/dutc/ytweekly LinkedIn: https://www.linkedin.com/company/do-n-t-use-this-code/ Website: https://www.dontusethiscode.com/index.html Upcoming events: learning.dutc.io Looking for training? Contact [email protected] This is part of the Open Science Toolbox series. Reproducibility in research starts with using the right tools. This series offers a practical introduction to essential technologies that help researchers write clean, trackable, and shareable code. We will begin by reviewing how to find and evaluate public datasets. Participants will learn how to assess licensing, source quality, and fitness for use, using examples from popular repositories. We will then look at how to set up a clean, reproducible computational environment using Pixi. This makes it easy to install Python and the packages needed for analysis without running into version conflicts. From there, we will introduce version control using Git. Participants will see how Git can be used to track changes to code in tandem with GitHub to support collaboration, and prepare research outputs for sharing. We will also demonstrate how to archive a project using Zenodo, making it easy for others to cite and reuse. Throughout the session, we will use Python to explore and visualize datasets. Examples will focus on pandas for data processing and Matplotlib for creating clear, publication-ready figures. The emphasis will be on writing transparent code that others can inspect and reuse.