Computational Reproducibility: Best Practices for Writing and Documenting Research Code

Are you a researcher who writes code? An intimidated grad student who's scrambling to learn R or Python? Or maybe a software developer who works with researchers? Whether you're just starting out or have been doing this for decades, this workshop is for you. Let's come together to share our experiences – the horror stories, a-ha moments and imposter-syndrome-induced doubts – with writing, documenting, publishing, and reusing research code. Modern research often depends on patchworks of custom code, organically grown over the lifespan of a project. Scripts written on the fly, evolving as questions change and deadlines loom. Many, if not most, of us never received any formal training to write code that others (or even just our future self) could actually understand and use. Instead, we learn by trial and error – and, most importantly, from each other. Take this workshop as an opportunity to share what you know, help out others and learn a few new tricks. Together, we'll: - Define what "computational reproducibility" means to us - Discuss our experiences producing and reusing research code - Reflect on how the daily work of researchers has changed and what constraints we face - Share what strategies and tools have worked for us in the past The goal is to develop a set of community guidelines or best practices for working with research code that are applicable and accessible across disciplines and backgrounds. --- Note: - This workshop aims to center perspectives from marginalized folks whose voices frequently go unheard in academia. If that includes you, your participation is especially valued. - This session can also be held in German if all participants speak German.