Public-private funding models in open source software development: A case study on scikit-learn

Governments are increasingly allocating funding for open source software (OSS) development in order to address concerns related to software security, digital sovereignty, and the competitiveness of domestic software markets, amongst others. While such funding is generally welcomed by OSS practitioners, how OSS developers perceive the relative benefits and drawbacks of governmental funding remains an open question. This paper explores this question through a case study on scikit-learn, a Python library for machine learning, whose funding model combines research grants, commercial sponsorship, community donations, and a 32 million EUR grant from the French government's artificial intelligence strategy. Through 25 interviews with scikit-learn maintainers and funders, this study makes two key contributions with implications for research and practice. First, it provides novel insights into the role of a public-private funding model in a successful, community-led OSS project and how maintainers evaluate their funding model. Furthermore, it highlights the governance mechanisms employed by maintainers to safeguard the community ethos of the project. Second, it offers practical implications for OSS developer communities, companies, and governments. For OSS communities, the study illustrates the benefits of a diversified funding model in balancing the merits and drawbacks of different funding sources. For companies, it serves as a reminder that sponsoring developers or directly funding OSS projects can significantly support OSS maintainers, who often struggle with limited resources and towering workloads. For governments, the findings emphasise the importance of funding the maintenance of existing OSS projects in addition to or exclusively funding new innovations. The paper concludes with suggestions for future research on OSS funding models.