URSSI Fall 2025 Fellowship: Building Sustainable Plugin Ecosystems in Scientific Software
Tim Monko • November 24, 2025
Foundational scientific software tools deliver innovative, yet reliable methods for research across multiple domains. These tools often rely on community expansion by “downstream” libraries to meet domain-specific needs. napari, an open-source, multidimensional imagee viewer for Python, provides core infrastructure for visualizing and annotating scientific imaging data. Its extendable GUI has empowered a diverse ecosystem of over 550 community-developed plugins (i.e., downstream libraries). Since the public announcement of napari in 2019 and the addition of plugin support soon after, collaboration among core contributors, plugin authors, and users has enabled research workflows that no single library could accomplish alone.
URSSI Fall Fellowship: Preserving and Expanding Access to County-level Public Health Data
Hannah Olson-Williams • November 20, 2025
For over fifteen years, County Health Rankings & Roadmaps (CHR&R) has provided data, evidence, and tools to help communities better understand health and how they can take action to improve health and equity. With our current funding ending, we are exploring ways to innovate under a different funding model to sustain and evolve CHR&R resources.
This project aims to expand access to county-level public health data by moving CHR&R calculations, documentation, and data onto open-source platforms and tools. In doing so, we hope to build a community of data scientists and public health professionals who can help make aspects of CHR&R self-sustaining, while also supporting ongoing efforts to secure new funding.
Winter 2025 URSSI Fellowship Report: Reproducible Machine Learning Workflows for Scientists
Matthew Feickert • November 17, 2025
During my Winter 2025 URSSI Early-Career Fellowship project, I researched how modern technologies and tools can assist researchers in easily creating fully reproducible hardware accelerated software environments for scientific and machine learning workflows. I compiled the techniques and best practices I had learned into a short course, which I contributed to The Carpentries Incubator, and then taught this open source educational material to the broader scientific community at workshops.
The material focused on using Pixi — a modern multi-platform software environment manager that builds on the conda and Python package ecosystems — and CUDA conda packages distributed on conda-forge. Pixi is a tool with high-level semantics designed to let users declaratively specify project software requirements and then record the fully resolved (“locked”) dependencies in a “lock file”. Written in Rust, Pixi exploits the language’s speed and technologies to efficiently resolve complex dependency trees and update the project lock file for every Pixi operation that could affect the software environment. This means that that if a Pixi project is version controlled, any state of that project is fully reproducible, byte for byte, indefinitely into the future.
Applications now open for the 2025 URSSI Winter School in Research Software Engineering
Kyle Niemeyer • November 5, 2025
Do you develop software for your research? Do you have some basic skills but desire more?
If so, you might be interested in the upcoming URSSI Winter School in Research Software Engineering. Building off our prior winter and summer schools, we are hosting a three-day workshop on research software engineering skills over 15–17 December 2025 in Portland, OR, at Oregon State University’s Portland Center.
This is aimed at early-career researchers, particularly graduate students and postdocs, who are familiar with basic skills such as interacting with the Unix shell, version control using Git, and Python programming, and would like to learn more about best practices for developing research software.
Check out our upcoming community calls, events, and updates.