Collaborating on code and software is essential to open science—but it’s not always easy. Join this BoF for an interactive discussion on the real-world challenges of open source collaboration. We’ll explore common hurdles like Python packaging, contributing to existing codebases, and emerging issues around LLM-assisted development and AI-generated software contributions.

We’ll kick off with a brief overview of pyOpenSci—an inclusive community of Pythonistas, from novices to experts—working to make it easier to create, find, share, and contribute to reusable code. We’ll then facilitate small-group discussions and use an interactive Mentimeter survey to help you share your experiences and ideas.

Your feedback will directly shape pyOpenSci’s priorities for the coming year, as we build new programs and resources to support your work in the Python scientific ecosystem. Whether you’re just starting out or a seasoned developer, you’ll leave with clear ways to get involved and make an impact on the broader Python ecosystem in service of advancing scientific discovery.

Python packaging can be overwhelming. However, a trusted, community-vetted workflow can make it easier. In this hands-on workshop, you’ll learn a tested approach developed by the pyOpenSci community and vetted by Python packaging maintainers. You’ll create an installable, maintainable, and citable package using a quickstart template. You’ll also receive step-by-step guidance on publishing to TestPyPI (and resources for conda-forge, and adding a DOI with Zenodo). If you can’t install software on your laptop, you can use GitHub Codespaces to participate in the workshop. Join us to package your Python code confidently and to access ongoing support in our community beyond the workshop.

PyVista is a general purpose 3D visualization library used for over 2000+ open source projects for the visualization of everything from computer aided engineering and geophysics to volcanoes and digital artwork.

PyVista exposes a Pythonic API to the Visualization Toolkit (VTK) to provide tooling that is immediately usable without any prior knowledge of VTK and is being built as the 3D equivalent of Matplotlib, with plugins to Jupyter to enable visualization of 3D data using both server- and client-side rendering.