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

TL;DR

Public-private funding models in OSS are increasingly prominent as governments seek to secure digital infrastructure. This paper uses a case study of scikit-learn to examine how public grants, private sponsorship, micro-donations, and large national programs interact with community governance, based on 25 interviews with maintainers and funders. It reports empirical insights into the relative benefits and drawbacks of public and private funding and describes governance practices that balance diverse stakeholder interests. The findings offer practical recommendations for OSS developers, companies, and governments aiming to sustain critical OSS projects while safeguarding community ethos and independence.

Abstract

Governments are increasingly funding open source software (OSS) development to support software security, digital sovereignty, and national competitiveness in science and innovation, amongst others. However, little is known about how OSS developers evaluate the relative benefits and drawbacks of governmental funding for OSS. This study explores this question through a case study on scikit-learn, a Python library for machine learning, funded by public research grants, commercial sponsorship, micro-donations, and a 32 euro million grant announced in France's artificial intelligence strategy. Through 25 interviews with scikit-learn's maintainers and funders, this study makes two key contributions. First, it contributes empirical findings about the benefits and drawbacks of public and private funding in an impactful OSS project, and the governance protocols employed by the maintainers to balance the diverse interests of their community and funders. Second, it offers practical lessons on funding for OSS developers, governments, and companies based on the experience of scikit-learn. The paper concludes with key recommendations for practitioners and future research directions.