SciPy 2025

Open-source software thrives on community contributions, but for users without formal software development training, stepping into development can be intimidating. The perception that core contributors come from well-established software backgrounds and mentorship lineages can create an implicit barrier, leaving eager users without clear pathways to contribute. However, napari—a fast, interactive, multi-dimensional scientific data viewer—breaks down these barriers and empowers contributors through a strong presence in the bioimage analysis community (e.g. forums, video tutorials, and popular learning materials), fantastic human-centered documentation, engagement with community through accessible channels (e.g. Zulip, Github, and community meetings) and opportunity for anyone to contribute to its rich plugin ecosystem.

I stumbled into bioimage analysis eight years ago as a bench scientist automating the quantification of microscopy data, a manual task amongst my peers. At the time, ImageJ/FIJI, written in Java, was the most well established open source tool for this undertaking. While its macro-recorder provided an accessible entry point into scripting and sharing my workflows, transitioning beyond it—especially towards GUI development—was incredibly challenging without programming experience. During my postdoc, I sought an ecosystem that integrated data processing, quantification, and analysis without constant tool-switching. The scientific Python stack stood out, and I was particularly drawn to the rapid development and excitement surrounding napari.

Beyond its powerful core features, napari’s plugin ecosystem brings a diverse set of features, from advanced visualization and image reading and writing, to fully featured interfaces to develop workflows, to making machine and deep learning analyses accessible. Napari’s ecosystem is great as both a general user and as a bioimage analyst looking to share their work with the community. Thus, I began to work on developing my own plugin, which was made accessible by great tutorials and understandable tools to make contributing easier, like the napari-plugin-template and magicgui (a project that grew out of the napari community’s efforts).

The plugin model allowed me to explore, try things out, and make mistakes while improving my own plugin, without having to work directly on core code. I will discuss the great direct and indirect mentorship that empowered me to achieve my goals, all the while gaining confidence and the satisfaction that I contributed meaningfully to the community. I gained the confidence to contribute directly to napari and have found the process inspiring and fulfilling. I will highlight ways in which napari maintainers and contributors help guide and encourage users through issues and first pull requests, including accessibility through discussion at frequent open community meetings. I will walk through the challenges that I and other contributors have faced and the ways in which the napari team works to resolve these gaps; I’ll particularly highlight how the napari team formats the documentation to provide clear tutorials on functionality, architecture, and how-to develop and test.

Finally, I will share how napari’s emphasis on clear goals, transparent decision-making, and inviting culture has fostered a community where scientists can not only use, but also shape the tools they rely on. By sharing strategies that have worked in napari and the bioimage analysis community, this talk aims to provide inspiration and practical takeaways for building and sustaining welcoming communities, bridging the gap between users and contributors and between scientists and software developers.