Who Do You Think You Are? Creating RSE Personas From GitHub Interactions
Anderson, Felicity, Sindt, Julien, Chue Hong, Neil · Edinburgh Research Explorer (University of Edinburgh) · 2025
We describe data-driven RSE personas: an approach combining software repository mining and data-driven personas applied to research software (RS). We evaluate the method on different patterns of collaborative interaction behaviours by contributors to mid-sized public RS repositories (those with 10-300 committers) on GitHub. We demonstrate how the RSE personas method successfully characterises a sample of 115,174 repository contributors across 1,284 RS repositories on GitHub, sampled from 42,284 candidate software repository records queried from Zenodo. We identify, name and summarise seven distinct personas from low to high interactivity: Ephemeral Contributor; Occasional Contributor; Project Organiser; Moderate Contributor; Low-Process Closer; Low-Coding Closer; and Active Contributor. This demonstrates that large datasets can be analysed despite difficulties of comparing software projects with different project management factors, research domains and contributor backgrounds.