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

Cailean Osborne|arXiv (Cornell University)|Apr 9, 2024

Scientific Computing and Data ManagementDecision Sciences3 citations

TL;DR

This study investigates scikit-learn’s public-private funding model through 25 interviews with maintainers and funders, revealing how diversified funding—public grants, corporate sponsorship, donations, and institutional backing—supports sustainability while preserving community ethos. It demonstrates that effective governance protocols are essential to balance stakeholder interests and ensure long-term project resilience in AI-driven software ecosystems.

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.

Motivation & Objective

• To understand how public and private funding sources coexist in a high-impact, community-led open source software (OSS) project like scikit-learn.
• To examine the tensions and alignments between diverse funders—governments, corporations, and individual donors—and the project’s community-driven ethos.
• To identify governance mechanisms that enable maintainers to balance external funding interests while preserving project autonomy and sustainability.
• To provide practical recommendations for OSS communities, companies, and governments on designing effective public-private funding models.

Proposed method

• Conducted 25 in-depth, semi-structured interviews with scikit-learn maintainers and key funders, including public officials, corporate sponsors, and community leaders.
• Employed a longitudinal, iterative qualitative approach over 17 months to enhance data reliability and reduce bias.
• Used grounded theory and deductive analysis to code and interpret interview data, integrating theoretical frameworks with empirical findings.
• Applied member-checking and stakeholder review processes to validate findings and improve accuracy.
• Triangulated data using field notes, secondary documents, and on-site observations to ensure dependability and robustness.
• Maintained a social identity map to minimize researcher bias and enhance methodological transparency.

Experimental results

Research questions

• RQ1 How do the diverse interests of public and private funders align or conflict with the values and goals of scikit-learn’s maintainers and community?
• RQ2 What are the perceived benefits and drawbacks of public versus private funding for community-led OSS projects, as experienced by maintainers and funders?
• RQ3 How have governance protocols been designed and implemented to balance stakeholder interests and safeguard the project’s community ethos?
• RQ4 What practical lessons can be drawn from scikit-learn’s funding model for other OSS projects seeking sustainable, diversified funding?

Key findings

• The €32 million public grant from France’s national AI strategy significantly expanded scikit-learn’s capacity, enabling full-time developer roles and long-term planning.
• Maintainers successfully used formalized governance protocols—such as transparent decision-making and funder advisory boards—to align public and private interests without compromising community autonomy.
• Despite initial skepticism, many maintainers viewed public funding as less threatening to project ethos than corporate funding, due to clearer, non-commercial objectives from government grants.
• Corporate sponsorship was perceived as more risky due to potential influence on feature priorities and contribution policies, especially when tied to commercial product development.
• Micro-donations and community-driven funding remained important for symbolic and morale support, though insufficient for core maintenance without larger grants.
• The institutional backing from Inria provided critical stability and credibility, acting as a neutral anchor that helped mediate between public and private funders.