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

TL;DR

This paper examines how public and private funding sources influence open source software development, using scikit-learn as a case study to reveal benefits, drawbacks, and governance strategies.

Contribution

It provides empirical insights into funding impacts and governance in OSS, and offers practical recommendations based on scikit-learn's funding experience.

Findings

Public and private funding have distinct benefits and drawbacks.

Governance protocols help balance diverse interests of stakeholders.

Practical funding strategies are derived for OSS projects and policymakers.

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.