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 to address concerns related to software security, digital sovereignty, and national competitiveness in science and innovation, amongst others. While announcements of governmental funding are generally well-received by OSS developers, we still have a limited understanding of OSS developers evaluate the relative benefits and drawbacks of such funding compared to other types of funding. 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 euro grant from the France's artificial intelligence strategy. Through 25 interviews with scikit-learn's maintainers and funders, this study makes two key contributions to research and practice. First, the study illustrates how the maintainers have weaved public and private funding into their project to ensure the continued provision of scikit-learn as a digital public good, as well as the importance of diversified funding and governance protocols for funding to safeguard the community ethos of the project. Second, it offers practical recommendations to various stakeholders. For OSS developer communities, it 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 OSS projects can significantly support OSS maintainers, who often struggle with limited resources and towering workloads. For governments, it emphasises the importance of funding the maintenance of existing OSS in addition to or exclusively funding the development of new OSS libraries or features. The paper concludes with suggestions for future research directions.