What if the most valuable part of your research isn’t the paper, but the package that made it possible? In this episode, we talk with Leah Wasser, Executive Director and Founder of pyOpenSci, a nonprofit working to make scientific Python more inclusive, reproducible, and discoverable.

We explore what “open science” really means in practice: transparent workflows that others can rerun, review, and extend. Leah explains how pyOpenSci’s peer review process helps turn lab scripts into reliable, citable Python packages with better documentation, testing, and credit through the Journal of Open Source Software (JOSS).

We also unpack how AI is reshaping scientific coding—its potential to speed up work, and the need for careful human oversight to maintain accuracy and trust.

Connect with Leah on the following platforms:

Github: https://github.com/lwasser
LinkedIn: https://www.linkedin.com/in/leahawasser/
Slack: https://www.pyopensci.org/handbook/community/slack.html

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