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

Governments are increasingly funding open source software (OSS) development to address concerns regarding software security, digital sovereignty, and national competitiveness in science and innovation. While announcements of governmental funding are generally well-received by OSS developers, we still have a limited understanding of how they 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 combines research grants, commercial sponsorship, community donations, and a 32 million Euro grant from 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 contributes novel findings about the design and implementation of a public-private funding model in an OSS project. It sheds light on the respective roles that public and private funders have played in supporting scikit-learn, and the processes and governance mechanisms employed by the maintainers to balance their funders' diverse interests and to safeguard community interests. Second, it offers practical recommendations. For OSS developer communities, it illustrates the benefits of a diversified funding model for balancing the merits and drawbacks of different funding sources and mitigating dependence on single funders. For companies, it serves as a reminder that sponsoring developers or OSS projects can significantly help 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 funding the development of new software or features. The paper concludes with suggestions for future research.