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

Cailean Osborne
Oxford Internet Institute
University of Oxford
Oxford, UK
<EMAIL_ADDRESS>
###### 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 emergent
governmental funding for OSS. This paper explores this question through a case
study on scikit-learn, a popular Python library for machine learning, which
has been funded by public research grants, commercial sponsorship, micro-
donations, and a €32 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 to research and practice.
First, it contributes novel empirical findings on the effective design and
implementation of a public-private funding model in an OSS project, as well as
how the maintainers of scikit-learn have designed and employed governance
protocols to balance the diverse interests of their funders and to safeguard
their community ethos. Second, it offers practical lessons on funding in
community-led OSS projects and makes recommendations to practitioners. The
paper concludes with a discussion of the key recommendations.

_K_ eywords Open source software, OSS funding models, machine learning,
scikit-learn, OSS sustainability

##  1 Introduction

Open source software (OSS) has moved up the priority list of governments in
light of concerns about digital sovereignty [1], software security [2], and
national competitiveness in science and innovation [3], amongst others. The
disclosure of the log4j vulnerability in November 2021 is widely seen as a
turning point that mobilised national governments, from the United States of
America (USA) to Germany, to mobilise public funds for the development and
maintenance of OSS [4]. Increasing governmental involvement in funding OSS has
been generally well-received by OSS developers. However, little is known about
how they view the relative benefits and drawbacks of governmental 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 (ML),
which is described as “the Swiss army knife of ML” due to its widespread use
[5]. Its sustainability as a community-led project in the industry-dominated
field of artificial intelligence (AI) [6] is partly attributed to its mixed
funding model, including public research grants, commercial sponsorship,
community donations, and the institutional backing from the French Institute
for Research in Computer Science and Automation (Inria). In November 2021, in
its national AI strategy, la “Stratégie IA”, the French government announced a
€32 million grant to support the expansion of scikit-learn [7], marking a
significant expansion of public funding for scikit-learn.

Through 25 interviews with scikit-learn’s maintainers and funders, this paper
investigates the historical role of public and private funding in supporting
the community-led OSS project. It explores the diverse interests of scikit-
learn’s funders, and how they have aligned or conflicted with those of the
maintainers (RQ1). The study also examines how the maintainers view the
relative benefits and drawbacks of public and private funding (RQ2). Finally,
it discusses practical lessons from scikit-learn’s funding model for OSS
practitioners (RQ3). This paper makes two key contributions to research and
practice. First, it provides novel empirical findings on the design and
implementation of a public-private funding model in an OSS project.
Furthermore, it sheds light on how the maintainers employed governance
protocols to balance the diverse interests of their funders and to safeguard
their community ethos. Second, it offers practical lessons on funding in
community-led OSS projects. The paper concludes with a discussion of key
recommendations to OSS developer communities, companies, and governments.

The paper has the following structure. First, it discusses prior work on the
role of funding in supporting the sustainability of OSS projects. Second, it
presents the study design and the case of scikit-learn. Then, it presents the
results and discusses their implications for research and practice. The paper
concludes with a discussion of the key recommendations to practitioners and
suggestions for future research directions on OSS funding models.

##  2 Related Work

###  2.1 The Role of Funding in OSS Sustainability

The adoption of OSS is ubiquitous, estimated to be used in 96% of code bases
[8] and constituting up to 90% of software stacks [9]. As a result, OSS is
considered a critical component of the digital infrastructure that underpins
modern society and the digital economy [10, 11, 12]. Given its importance, the
health and sustainability of OSS projects is a concern for a diverse range of
stakeholders. However, the health and sustainability of OSS projects is a
complex and multi-faceted issue [13, 14, 15]. A range of factors are relevant,
including but not limited to open governance and strong leadership [16, 17,
18, 19]; maintainer best practices, such as timely responses [20], mentoring
contributors [21, 22], and recommending tasks [23]; a steady inflow of new
contributors [24, 25, 22]; and diversity within the community [26]. Amongst
these factors, the financial health of a project plays a crucial, but hitherto
understudied, role in the sustainability of OSS projects, determining the
extent to which maintenance is funded or reliant on volunteers [27].

However, money has long been a contentious subject amongst OSS developers. On
the one hand, many developers take pride in the origins of the OSS movement as
a social movement [28], describing it as a “programmers’ paradise” for
“geeks”, “hackers”, and “hobbyists”, who do not contribute to OSS for money
[29, 30]. Developers cite their political ideals [31], altruism [32], or their
passion for spotting and quashing bugs in software [33] as intrinsic
incentives for contributing to OSS. Furthermore, many developers complain that
“money ruins everything” [34]. On the other hand, a plethora of OSS projects
are unfunded and maintained by overstretched volunteers, who often struggle to
keep up with the workload required to keep projects running and communities
thriving [27, 35, 20].

The consequences of systemic issues of free-riding and under-investment in OSS
are highlighted when developers spot major vulnerabilities that pose
significant threats to digital systems in governments, critical
infrastructure, and companies’ products and services. In particular, the
discovery of vulnerabilities, such as the heartbleed bug in April 2014 or
log4j vulnerability in December 2021, which was described as the “worst
security problem in a generation” [36], shocked industry and governments
alike. It highlighted the need for cross-sector stakeholders, including the
public and private sectors, to fund the developer communities who build and
maintain our open source digital infrastructure [4]. As Benjamin Bikinbine has
argued, there is an urgent need “not just [for] investment in institutions,
organisations, technologies, or innovations, but long-term and sustainable
investment in the true source of their value: people” [37].

###  2.2 Private Funding for OSS Development

The private sector has been the largest funder of OSS to date, funding OSS
developers and projects in both direct and indirect ways. The impact of
commercial investments in OSS are gauged to be significant. According to one
estimate, companies in the European Union (EU) invested around €1 billion in
OSS in 2018, which generated between €65-95 billion for the EU’s GDP that year
[38]. Direct funding methods include sponsorship of OSS foundations and
consortia, which operate on a fee-paying membership model [39]. Foundations
and consortia are known to play a critical role as “boundary organisations”,
which through their open governance protocols facilitate collaboration between
diverse volunteers and companies [16]. However, according to a survey of 369
OSS developers, only 12% of respondents reported that organisational funding
for OSS via a foundation, a consortium, or an independent legal entity was
either extremely or very useful to their development activity, while 5%
reported it was ineffective [40].

Recently, companies have begun to establish FOSS Contributor Funds, which
allow employees to nominate and vote on OSS projects that should receive
funding [41], and represent a more “democratic” way for companies to fund the
OSS projects that they use [42]. A shortcoming is that these funds typically
award up to $10,000 over a one-year time frame. Alyssa Wright from Bloomberg’s
FOSS Contributor Fund [43] has acknowledged this shortcoming, arguing that
“true sustainability comes from a multi-sided commitment” and “while financial
support is important, real engagement through contribution and dialogue is as
crucial to the ecosystem’s survival as writing a check”.

Other ways companies support OSS projects by sponsoring OSS developers or by
allowing their employees to contribute to OSS projects at work [44, 45].
Sponsoring developers is a recognised strategy that companies employ to
influence projects [46, 47] and to improve their reputation as OSS patrons,
which in turn is considered to aid the recruitment of developers [48, 49, 50].
In the mid-2000s, it was estimated that 40% of OSS contributors were paid
[51]. We should expect this number to be much higher today, with companies
like Google, Microsoft, and Amazon accounting for a rapidly growing number of
OSS developers and contributions to OSS projects [52]. In addition, companies
invest in projects through donations and by joining project steering
committees [53].

While OSS projects benefit from commercial participation, maintainers seek to
manage the influence or the dominance of individual companies [54]. A popular
strategy is to establish or transfer projects to foundations [39, 55], which
through open governance protocols limit the dominance of single companies [56]
and facilitate collaboration between diverse contributors [16, 57]. Crucially,
these organisations enable OSS developers to “negotiate their relationship
with companies and, when necessary, to defend their commons-based resources
from unwanted influence” [37].

###  2.3 Micro-Donations for OSS Development

Individuals or organisations support OSS developers and project by making
micro-donations to individual developers and OSS projects. For instance, the
GitHub Sponsors scheme, launched in 2019, allows users to make one-off or
recurring payments to individuals or organisations with sponsored profiles
[58]. According to prior work, common incentives for donations via GitHub
Sponsors include expressing appreciation, recognising a developer’s work, and
encouraging future contributions [30]. By comparison, common incentives for
maintainers to accept donations are to receive recognition for their work or
to provide a side-income; while those who do not participate in GitHub
Sponsors state that they do not need to be sponsored or that they do not
contribute to OSS for money [30]. Furthermore, tther work shows that the
amount that individuals donate is associated with the length of their use of
an OSS [59]. The impact of micro-donations is still contested. On the one
hand, they have been shown to shorten response times to issues [60, 61] and to
increase maintenance-related activities [62]. On the other hand, overall the
impact appears trivial [63] and donations tend to be ad-hoc, small, or legally
complicated for individuals to receive [64].

###  2.4 Public Funding for OSS Development

The public sector has indirectly or directly funded OSS since the origins of
the World Wide Web [65] through research grants for (scientific) OSS [66, 67,
68] as well as bespoke OSS funding bodies, such as the Open Technology Fund in
the USA, the Next Generation Initiative at the European Commission, or the
Sovereign Tech Fund (STF) in Germany [4]. Furthermore, OSS has recently moved
up the priority list of governments in light of concerns about the security of
OSS [69, 2]; its role as critical digital infrastructure [10, 11, 12]; and its
importance for digital sovereignty [1, 70] and national competitiveness in
science and innovation [3]. In Europe, the concern for digital sovereignty is
particularly salient. For example, the European Commission’s OSS strategy
argues that OSS can give “Europe a chance to create and maintain its own,
independent digital approach and stay in control of its processes” [71].

Public sector support for OSS is also a recognised strategy to stimulate
competition in domestic software markets, which might otherwise be monopolised
by industry giants [72]. For example, it has been estimated that the
Circulaire 5608, a French law requiring government agencies to favour OSS when
procuring software, led to an increase of nearly 600,000 OSS contributions
from French developers per year as well as yearly increases in the national
competitiveness of the French IT market [3]. Beyond governmental adoption of
OSS, government investments in OSS are often targeted at stimulating national
competitiveness. For example, in June 2023, President Emmanuel Macron of
France announced a €40 million fund to create a “digital commons” to support
the development of open source large language models to support domestic
startups and to challenge the dominance of Big Technology companies in the AI
industry [73].

The discovery of the log4j vulnerability in November 2021 marked a turning
point for governmental interest and involvement in funding OSS development. In
light of the magnitude of the security concerns [69], governments rapidly
mobilised to address the threats posed by OSS vulnerabilities to their
critical infrastructure and digital economies. For example, in January 2022,
the White House convened cross-sector stakeholders, including government
agencies, Big Technology companies, and OSS foundations, to discuss the urgent
need to improve the security of OSS, recognising its indispensable role as
digital infrastructure whose maintenance largely depends on volunteers [74].
Across the pond, in June 2022, the newly formed European Working Team on the
Digital Commons, comprising representatives from 19 member states of the EU
and the European Commission, published its inaugural report, highlighting the
importance of funding OSS for bolstering the digital sovereignty of Europe
[75]. In October 2022, Germany established the STF, allocating a budget of
€11.5 million for its first year alone to fund OSS maintenance in the interest
of digital sovereignty, software security, innovation, and digital democracy
[76]. These developments underscore the growing recognition amongst
policymakers of the public value of funding OSS development and maintenance
[4].

While there is a growing recognition of the need for sustainable funding for
OSS by governments, little is known about how OSS developers evaluate the
relative merits and drawbacks of governmental involvement in funding OSS
development compared to other types of funding that have primarily supported
OSS development so far, or the impact thereof on OSS development and
maintenance. This study addresses this gap by examining the case of scikit-
learn and how its maintainers view the benefits and drawbacks of its public
and private funders. By focusing on the perspectives of OSS developers, this
study contributes practical insights on this timely question in the debate
about OSS sustainability.

##  3 Study Design

###  3.1 Research aims

This paper investigates the emerging role that governments are playing in
funding OSS development and maintenance, and how OSS developers view the
relative merits and drawbacks of governmental funding through a case study on
the scikit-learn project. In software engineering research, a case study “is
an empirical enquiry that draws on multiple sources of evidence to investigate
one instance (or a small number of instances) of a contemporary software
engineering phenomenon within its real-life context” [77]. Single case studies
are suitable when one seeks to provide in-depth insights, generate new
hypotheses, or develop theories about a critical, extreme, unusual, or
revelatory phenomenon [78]. The scikit-learn project was selected as an
exploratory case that could provide in-depth insights into the role of the
French government in supporting this OSS project and how its maintainers view
the relative merits and drawbacks of the government’s funding compared to the
other funding sources that have supported the project to date.

To this end, the study focuses on the following research questions (RQ):

  * •

RQ1: What are the interests of the public and private funders of scikit-learn,
and how have they aligned or conflicted with the interests of the scikit-learn
maintainers?

  * •

RQ2: How do the maintainers of scikit-learn view the relative benefits and
drawbacks of public and private funding for their OSS project?

  * •

RQ3: What practical lessons can be drawn from scikit-learn’s funding model for
OSS practitioners?

###  3.2 Case presentation

scikit-learn is a Python library that implements ML algorithms for
classification, regression, and clustering, as well as tools for data pre-
processing, model evaluations, and data visualisation. Initially it was
started as scikits.learn by David Courapeau during a Google Summer of Code
project in 2007. After a dormant period, it was relaunched as scikit-learn by
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, and Vincent Michel with
the backing of INRIA, with the first public release in February 2010. It has
since become one of the most popular OSS libraries for ML [INRIA_2019_2020],
described as “the Swiss army knife of ML” due to its widespread use
[INRIA_2019_2020]. For example, in November 2023, it had 2,752 contributors
and 61,085 GitHub stars [79]. The success and sustainability of this
community-led OSS project is noteworthy, considering the extent of industry
dominance in ML/AI research [6] and OSS ecosystems [80, 81].

scikit-learn’s sustainability is partly credited to its funding model, which
has combined research grants, private sponsorships via the scikit-learn
consortium, and community donations [82, 83]. The consortium was established
under the Inria Foundation in 2018 in order to secure employment for
maintainers, to sustain the quality of software, and to develop new features
[83]. It comprises companies that pay annual membership fees. Since its
founding, it has included Dataiku, Microsoft, Nvidia, Intel, AXA, Boston
Consulting Group, BNP Paribas Cardif, Hugging Face, Nvidia, Fujitsu, and
Chanel. Other companies, such as Quansight Labs, have indirectly funded the
project by sponsoring maintainers. The project also benefits from donations
from community members via NUMFOCUS and student projects have been sponsored
by the Google Summer of Code programme. In November 2021, the French AI
strategy, la “Stratégie IA”, announced a €32 million grant to support scikit-
learn’s development [7]. This significant public investment alongside scikit-
learn’s existing funding sources makes it a suitable case for the research
aims of this exploratory case study.

###  3.3 Data & analysis

####  3.3.1 Data collection

To answer the research questions, this study employs a mixed-methods approach,
comprising document analysis and 25 semi-structured interviews with scikit-
learn maintainers, commercial sponsors, and government officials between
November 2021 and April 2023. Most interviews took place during on-site visits
to the scikit-learn team’s office in Paris. The sampling approach sought to
ensure complete coverage of the various stakeholder groups in the project—that
is, the OSS developers (i.e. the scikit-learn maintainers), commercial
sponsors (i.e. the consortium members), and public funders (i.e. Inria and the
French government)—as well as diversity within each group. Interviews were
conducted with maintainers with various tenures in the project, including some
who had been involved for over 10 years and recent university graduates. One
maintainer worked for a company that was a consortium member but spoke on his
own behalf. From the consortium, interviews were conducted with three
representatives from three companies with different sponsorship tiers,
organisational sizes, and different sectors. In addition, interviews were
conducted with two government officials who were responsible for the
coordination of the AI strategy. The interviews were semi-structured,
combining general questions about scikit-learn’s funding model and tailored
questions for each interviewee [84]. To inform tailored questions for each
maintainer, their contribution histories on GitHub’s Contributor Insights Page
were reviewed; and for the funders, public information about their involvement
in funding scikit-learn was reviewed. The author conducted and recorded every
interview to aid the analysis and enhance the validity of the findings [78].

The interview data was supplemented and triangulated through detailed field
notes from on-site visits and secondary documents to strengthen the validity
of the findings [78]. The field notes stemmed from two on-site visits to the
scikit-learn office in Paris-Saclay in March 2022 and 2023; two on-site visits
to the offices of sponsors in September and December 2022; and attendance at
the Global Partnership on AI Summit in November 2021, which was hosted by the
French government and attended by a co-founder of scikit-learn. The secondary
documents included both publicly available and non-disclosed documents, which
interviewees shared concerning the French government’s AI Strategy and its
funding package for scikit-learn. Additional information about scikit-learn
and its funding model was collected from internet searches, included the
scikit-learn website and company blogs, or provided by interviewees.

####  3.3.2 Qualitative data analysis

Throughout the 17 months of data collection, an iterative, integrated approach
was employed to identify common themes in the interview data, field notes, and
secondary documents [85]. Given the exploratory aims of this study, the
integrated approach combined the application of grounded theory analysis [86]
and a deductive, organising framework informed by prior literature about the
different interests and involvement models of individuals, companies, and
governments in the development of OSS [37, 48, 72, 16]. Thematic codes were
continuously developed as data was collected, and in turn translated into
themes until the point of saturation [87]. The resulting themes were member-
checked to ensure their accuracy and resonance with research participants
[88]. Throughout the research process, a social identity map was maintained by
the author to exercise reflexivity regarding the potential influence of
personal biases stemming from the author’s views and relationships built with
the scikit-learn team during the 17 months of data collection [89].

It should be noted that each stakeholder group was not assumed to be
homogenous; instead, it was accepted that there could be divergent or even
conflicting interests within each stakeholder group. For instance, the scikit-
learn consortium comprises a range of companies, including startups and
multinational corporations from different sectors. Prior work indicates that
companies primarily pursue strategic interests by participating in OSS [90],
and it was not assumed that the consortium members shared the same strategies
in funding scikit-learn. Similarly, public funders, including Inria and
government officials who were responsible for France’s AI strategy, were
grouped together solely by their belonging to the French public sector,
despite obvious differences between these stakeholders. Even the maintainers
were a heterogeneous group, combining employees of Inria and external
companies, each with their unique paths to and histories in the scikit-learn
project. These theoretical groupings were used solely for analytical purposes
and were iteratively refined during the 17 months of data collection to ensure
their coherence and suitability for the analysis.

##  4 Results

The results are organised into three parts, sequentially responding to RQ1 on
how the interests of scikit-learn have aligned with or differed from those of
the maintainers, and RQ2 on the relative benefits and drawbacks of public and
private funding from the perspective of the scikit-learn maintainers. Finally,
practical lessons and recommendations for practitioners based on the funding
model of scikit-learn RQ3 are discussed in the subsequent Discussion section.

###  4.1 Stewards of the Community: The Roles & Interests of the scikit-learn
Maintainers

scikit-learn’s maintainers are a diverse group employed by INRIA, public
universities, and software companies. While some members of the core team work
on scikit-learn full-time, others balance their roles as maintainers with
their full-time jobs. Several maintainers also held academic positions at
universities and research labs. The maintainers explained they perform a range
of technical and social tasks, including reviewing code, mentoring
contributors, and community building. François Goupil, the community manager,
explained, “Of course, software development itself is the major part of the
workload—going through issues, pull requests, and responding to community
feedback—but lots of other work goes into the project. Community animation is
a huge part. After all, it’s a community project”. In addition, sponsored
developers from companies make focused contributions, such as the development
of new features, which are agreed upon by the maintainers and the sponsoring
companies. For example, at the time of this research, Nvidia sponsord a full-
time maintainer who was building a plug-in to enable alternative computational
backends for scikit-learn to enable compatibility with Nvidia’s GPU AI
hardware accelerators.

Several maintainers expressed a strong sense of stewardship for the project,
whose strength they viewed as rooted in its diverse community, comprising
contributors from across the world (see Table 1) and companies (see Table 2).
Julien Jerphanion, a maintainer, highlighted this community ethos, noting
that, “the major part of the labour [in the project] is based on benevolence
of people working in their free time and not asking to get paid”. Furthermore,
Gaël Varoquaux, a co-founder and maintainer, explained that the community was
at the heart of the project’s DNA and that major decisions about the future of
the project remained community-driven despite their diverse funders.
Similarly, Adrin Jalali, a maintainer, commented, “Even as a [maintainer], I
don’t have enough credit to just be like, ‘Hey, we should do this.’ It’s a
slow project with hundreds of 1000s of users. You can’t just change things”.

Table 1: Most active countries in the scikit-learn community [79] |  Stargazer |  Issue Creator |  Pull Request Creator
---|---|---|---
1 |  China (23.7%) |  USA (33.7%) |  USA (34.0%)
2 |  USA (21.8%) |  Germany (7.5%) |  India (8.9%)
3 |  India (7.9%) |  UK (7.0%) |  Germany (8.5%)
4 |  Germany (4.4%) |  India (6.8%) |  France (6.9%)
5 |  Brazil (3.8%) |  France (5.5%) |  UK (6.4%)
6 |  UK (3.7%) |  China (5.4%) |  Canada (4.5%)
7 |  Canada (3.3%) |  Canada (4.5%) |  China (2.6%)
8 |  France (3.2%) |  Netherlands (2.5%) |  Japan (2.5%)
9 |  Japan (2.5%) |  Switzerland (2.2%) |  Switzerland (2.3%)
10 |  Russia (1.3%) |  Australia (2.0%) |  Netherlands (2.2%)
Table 2: Most active countries in the scikit-learn community [79] |  Stargazer |  Issue Creator |  Pull Request Creator
---|---|---|---
1 |  Google (1.5%) |  Google (2.5%) |  Google (3.7%)
2 |  Microsoft (1.5%) |  Microsoft (2.0%) |  Microsoft (2.5%)
3 |  Tencent (0.9%) |  Amazon (0.7%) |  INRIA (0.9%)
4 |  Alibaba (0.8%) |  DeepMind (0.7%) |  Meta (0.8%)
5 |  Amazon (0.8%) |  IBM (0.6%) |  Amazon (0.8%)
6 |  ByteDance (0.8%) |  Johns Hopkins (0.6%) |  Dataiku (0.7%)
7 |  Baidu (0.6%) |  Tencent (0.5%) |  UC Berkeley (0.6%)
8 |  Meta (0.5%) |  ETH Zurich (0.5%) |  Shopify (0.6%)
9 |  AWS (0.4%) |  Uni. of Washington (0.4%) |  MIT (0.6%)
10 |  Tsinghua University (0.4%) |  NeuroData (0.4%) |  AWS (0.6%)

In addition, the maintainers expressed their personal interests in maintaining
scikit-learn. Several maintainers explained they became maintainers because of
their scientific interest in ML, and they liked the scientific culture of the
project. Many of the maintainers had academic backgrounds, some still worked
in research labs, and some still taught at universities. For example, Julien
Jerphanion said that he enjoyed working at the intersection of statistical
theory and software engineering, which was lacking in his previous job in
industry. In addition, an anonymous maintainer explained that while he may not
become a millionaire, he had a comfortable life and enjoyed the challenge of
the job. For many, being a maintainer of scikit-learn was also a source of
pride, given the project’s reputation.

Beyond maintaining scikit-learn, several maintainers mentioned the importance
for them to contribute to the wider scientific Python ecosystem and to support
their dependencies, such as numpy and scipy. During my on-site visit in
September 2022, the maintainers had just returned from EuroSciPy, an annual
gathering of the European scientific Python community. One anonymous
maintainer spoke fondly of “putting faces to GitHub IDs”. François Goupil
explained that beyond collaborating on the code base, they collaborate with
other projects in other ways, such as by applying for grants together. In
addition, some of the maintainers had initiated “satellite projects”, such as
joblib and skrub. However, their ability to support the ecosystem is
constrained by their limited capacity. Julien Jerphanion explained that while
projects like scikit-learn, numpy, and scipy are very important in ML/AI,
“There are many which are not visible to the public, for example OpenBLAS
which is fundamental and only maintained by two people, who have very specific
expertise. This is tremendous software work, but it is invisible to the
public. Projects like this are cornerstones for many projects. They are the
foundations; they are, like, infrastructure — you do not see them if there is
no problem in your daily life”.

###  4.2 Public Funding: From Research Grants at INRIA to “la Stratégie IA”

Public funding has been a cornerstone of scikit-learn’s development since its
beginning. Starting as a Google Summer of Code project in 2007, the library
was later revived by researchers at INRIA. Over the past 13 years, INRIA had
provided an estimated €1.5 million in support through staff salaries, research
grants, office space, computing resources, and event sponsorship. As Olivier
Grisel, a maintainer, noted, “[Public funding] is not new with the AI
strategy”. However, the announcement of a €32 million grant in France’s AI
strategy, la “Stratégie IA”, supporting the expansion of scikit-learn and OSS
tools for data science, marked a significant expansion of public funding for
scikit-learn. A government official explained that the grant seeks to
facilitate AI adoption across the economy and to enhance French
competitiveness in AI R&D, while also supporting the “digital sovereignty” of
France and Europe.

The maintainers were delighted to receive the funding and recognition from the
government. Adrin Jalali highlighted the stability it provided for the
project, enabling longer-term planning around hiring and the technical
roadmap. Julien Jerphanion characterised it as “a good signal that the French
state is getting involved in funding projects like this”. François Goupil
particularly commended the requirement to seek matching funds from both public
and private sources across the European Union, viewing this as an important
safeguard for the project’s independence: “I think it would be dangerous for
us to be exclusively funded by the private sector or to be exclusively funded
by the French government, because we have many good contributors who are not
French”.

At the same time, challenges have emerged in aligning broad policy goals with
the day-to-day realities and expertise of the maintainers. Initial requests
from officials to expand scikit-learn into deep learning to provide a
“sovereign” alternative to PyTorch and TensorFlow were resisted, with
maintainers insisting on focusing on the project’s core strength in classical
ML and the need to allocate sufficient funding and capacity to maintenance.
The government officials ultimately deferred to their expertise, with one
official acknowledging, “We understand that scikit-learn is specialised in ML,
and it should not compete against TensorFlow and PyTorch”.

However, other tensions have proven thornier. Requirements to simultaneously
develop OSS tools for data science whilst not competing with domestic
companies have left maintainers in a bind, as the resulting tools inherently
provide open source alternatives to the proprietary offerings of companies.
Maintainers have also expressed frustration with the slow pace of
disbursement, with funds still not arriving 17 months after the announcement
by the time of the last interview. While scikit-learn’s diverse funding model
provided a buffer, they worried that such sluggishness could threaten the
sustainability of more financially precarious OSS projects.

Another official explained that the funding decision was informed by a multi-
stakeholder process involving experts from research and industry. “It was not
just the government. It was a dialogue between several actors—with the AI
strategy team, the ‘Confiance IA’ project,111“Confiance IA” is a government-
sponsored programme that aims to develop and industrialise trustworthy AI
technologies in France. INRIA, with scikit-learn, and so on”. He confessed
they had faced hurdles within the government: “Financing open source means
financing a product that cannot be valued on the market and a priori it is not
clear what its success will be. So, if [we want] the government to finance it
in a substantial way—here we are talking about €32 million—the government
wants to know where the money is going and to achieve a measurable return on
investment”. Ultimately, it took months of coordination and negotiation to get
the grant approved by the finance ministry.

Two consortium members raised concerns that such a substantial government
grant could politicise scikit-learn, potentially influencing it to prioritise
French policy objectives over the interests of the community. One anonymous
representative underlined the need to balance the French government’s concern
about digital sovereignty with the norms of the global community, while
another argued that imposing French policy priorities on the project would not
be received well by the community. These concerns were acknowledged by the
maintainers, who insisted that the community ethos of the project remained at
the heart of their discussions with the government.

###  4.3 Private Sponsorship: The Role of the scikit-learn Consortium

scikit-learn has received significant support from the private sector through
the scikit-learn consortium and sponsorship of maintainers by companies like
Nvidia, HuggingFace, and QuantSight. By creating the consortium, François
Goupil explained they had sought to provide a more stable source of funding
beyond research grants and to hire full-time maintainers, which was not
possible previously.

Companies in the consortium have pursued a range of social, economic, and
technological goals. By joining the consortium, companies are able to support
a software tool that they use, whilst building goodwill and a reputation as
OSS advocates, which an important signal for hiring in a competitive labour
market. François Goupil explained, “A lot of people come from this open source
background and if you see that a company is promoting such an open source
project, you might say, ’Okay, maybe, I would be happy working with them. They
could be okay with me contributing to OSS at work”. He viewed this incentive
as more important for companies that did not already have a strong reputation
in ML/AI field, since it would make them more credible and attractive to job-
seekers.

For their part, consortium members cited a mix of pragmatic and principled
reasons for funding scikit-learn. On a basic level, it allows them to support
the maintenance of a tool they use in their products and services. Mike
McCarty from Nvidia shared that his concern about unmaintained dependencies
“keeps [him] up at night”, citing numpy as a critical library that had been
under-maintained for some time. Leo Dreyfus-Schmidt from Dataiku expressed a
similar sentiment, who stated, “We are very much aware that we rely on open
tools like scikit-learn” and highlighted the importance of “giv[ing] back to
the community”. An anonymous consortium member was even more direct:
“Companies pay millions in commercial licenses, so why not support the OSS
projects we use¿‘

However, discussions with maintainers and consortium members underlined that
consortium membership is not simply a charitable contribution. In exchange for
their sponsorship, companies gain a voice in scikit-learn’s development
through bodies like the Technical Committee and Advisory Board, which are
venues for surfacing industry use cases and needs. Leo Dreyfus-Schmidt
characterised this as a “win-win”, allowing scikit-learn to benefit from “the
perspective of people in industry, which…is a very different type of feedback
from the one they get from the open source community”, whilst giving
commercial members direct input into the project.

In practice, this dynamic has yielded a mix of synergies and tensions. In some
cases, companies’ business interests are compatible with scikit-learn’s
technical roadmap. For example, in addition to its consortium membership,
Nvidia has sponsored a maintainer to build a plug-in that enables alternative
computational backends for scikit-learn. Mike McCarty explained, “One thing
that Nvidia is very interested in is scikit-learn’s effort to accelerate with
GPUs. I would love to see at the end of the day scikit-learn to be accelerated
natively with a very nice user experience, kind of like what we have with
PyTorch, which is the canonical example of a library that you can install and
if you happen to have a GPU, you can accelerate it without changing your
code”. Guillaume Lemaitre, a maintainer, described Nvidia’s simultaneous
sponsorship of a maintainer and consortium membership as “a win-win
situation”.

In other cases, however, sponsor requests to shape the project’s direction or
burnish their own reputations have run afoul of project norms. Two anonymous
maintainers explained that in the past, a sponsor had requested modifications
to the technical documentation that amounted, in their view, to free
advertising for the company. This self-promotional request, they explained,
was not acceptable, especially not in the technical documentation of the
project. While disputes of this kind have been rare, one anonymous maintainer
mentioned that disputes had led to the termination of sponsorship by one
company in the past. To manage the interests and demands of consortium
members, the scikit-learn maintainers employ carefully designed governance
protocols that limit funders’ influence and prioritise community control. The
Technical Committee, for instance, is barred from overriding decisions that
achieve rough consensus amongst the maintainers. Adrin Jalali explained, “We
don’t want the technical committee to have power over the core contributor
team as long as [the maintainers] have a consensus. If there’s two thirds
majority in cast vote, for whatever vote we do, then the technical committee
doesn’t step in…because the power is given to the [maintainers] and that’s by
design”.

These governance measures are important to maintain the trust of the community
and to mitigate fears that the project had been bought out. François Goupil
explained that as a community-led OSS project, they communicate to consortium
members that their feedback is valued but they cannot guarantee that they will
implement it. For example, the maintainers organised a workshop on MLOps
because this was a topic that the consortium members had expressed an interest
in. “MLOps will probably end up in the technical roadmap in some way…but it’s
a discussion and in the end the team will decide if it’s worth it or not”, he
explained. Through these governance protocols, the maintainers ensure that the
project remains community-driven while still considering and incorporating
feedback from the consortium. As François Goupil put it, “We are a community
with different interests”, and preserving that pluralism is key to the
project’s long-term sustainability and independence.

Finally, even if relationships with their sponsors can be troublesome at
times, the maintainers were grateful for their support and for not free-riding
on scikit-learn, which many companies do. During an on-site visit, François
Goupil showed me a dashboard, comprising world maps of contributors and charts
of contributions from companies. Upon observing that the most active
organisations were Chinese and American companies (see Table 2), I asked if
these insights had influenced his opinion about whether they contributed
sufficiently. He responded frankly: while such companies contribute useful
issues and pull requests, their contributions ultimately create more work for
the maintainers. He explained that it would be more helpful if these companies
sponsored a maintainer or joined the consortium in order to provide more
capacity to the maintainers to process the extra workload created by these
contributions.

##  5 Discussion

###  5.1 Implications for Research

####  5.1.1 The design and role of public-private OSS funding models

This study contributes novel findings on the roles of public and private
funding in OSS development and maintenance. It reveals the diverse interests
of funders, from the economic and technological goals of companies [48] to the
policy objectives like digital sovereignty [1], national competitive in
science and innovation [3], and economic growth [72] for governments. While
the various stakeholders pursue different goals, they are not necessarily in
conflict, as they share the goal of sustaining scikit-learn. For example,
while the maintainers and the companies do not necessarily share the
government’s policy goals or digital sovereignty concerns, its long-term
investment in scikit-learn aligns with the maintainers’ and companies’
interest in its sustainability for research and innovation purposes.
Similarly, while the maintainers and the government officials do not
necessarily share the companies’ business interests in scikit-learn, the
project benefits from companies’ feedback and funding for development and
maintenance. The study also highlights the role of the governance mechanisms
to balance the interests of the community and the companies in the consortium,
extending prior work on the role of foundations as “boundary organisations”
that mitigate the dominance of companies and enable collaboration between OSS
developers and companies [16, 54].

####  5.1.2 Research design for future research on OSS funding

There is still a dearth of research on OSS funding, relative to other topics
on the economics of OSS [91]. With the emergence of new funding models, from
FOSS Contributor Funds to governmental funds like Germany’s STF, it is timely
to empirically investigate OSS funding from multiple angles, including
funders, incentives, funding models, and impact(s), amongst others. To this
end, this paper contributes novel insights and a research design that can
inform future studies on OSS funding. As discussed below, while the uniqueness
of scikit-learn limits the generalisability of the case-specific findings,
there are aspects of the scikit-learn experience that will resonate with OSS
developers in general, such as the processes of balancing the varied interests
of funders while preserving community values.

###  5.2 Implications for Practice

####  5.2.1 Implications for OSS Developer Communities

The case of scikit-learn shows that a diversified funding model can be an
effective strategy for safeguarding a project’s independence and community-
driven ethos whilst benefiting from both public and private support.
Furthermore, public and private funding come with their own challenges. On the
one hand, commercial sponsors often focus on specific technical issues related
to maintenance and feature development, and their demands may sometimes
conflict with project goals or norms. On the other hand, government funders
may prioritise high-level policy objectives over the activities or expertise
of the project. Navigating these tensions requires ongoing dialogue and
negotiation to align goals and ensure a sustainable balance of funding
sources. Ultimately, maintainers must be prepared to advocate for and defend
their project’s needs and values. As the scikit-learn case demonstrates, a
diverse funding portfolio can provide both stability and leverage in these
discussions.

####  5.2.2 Implications for Companies

For companies, the findings serve as a timely reminder that sponsoring OSS
maintainers or projects can make a substantial difference to maintainers, who
often struggle with limited capacity and towering workloads [27]. As shown in
Table 2, several major technology companies, such as Google, Meta, and AWS,
have benefited significantly from scikit-learn but have not yet directly
funded the project or its maintainers. While these companies may contribute
useful issues and pull requests, their engagement can also create additional
work for already overstretched maintainers. Sponsoring a maintainer or joining
a project’s funding consortium, even at a relatively low level, could be a
high-impact way for well-resourced companies to support the OSS projects that
they use and depend on. By stepping up to provide more direct financial
support, companies can help ensure the sustainability and health of critical
OSS infrastructure.

####  5.2.3 Implications for Governments

For governments, the findings highlight the importance of funding the
maintenance of existing, widely-used OSS infrastructure and not just the
development of new libraries or features, extending prior recommendations for
the funding of scientific OSS [68]. To ensure that funding is aligned with
project needs, governments should engage in multi-stakeholder dialogue with
OSS developers. This consultation is crucial for designing grants that balance
policy goals with the expertise of maintainers. In the case of scikit-learn,
initial government requests around expanding into deep learning were
misaligned with the project’s scope and capabilities. Ongoing conversations
were necessary to bridge this gap and ensure that funding supported both
overarching policy objectives and essential project functions. Governments
should proactively seek input from the OSS community to avoid such
misalignments and to craft funding programs that effectively support the
sustainability of critical (scientific) OSS projects.

###  5.3 Threats to Validity

The validity of the study is evaluated by following guidance for case studies
and qualitative research methods in the field of software engineering research
[92, 78, 93].

####  5.3.1 Credibility

Credibility refers to the believability of qualitative research findings [93].
A number of steps were taken to enhance the credibility of the findings.
First, multiple data sources—interviews, fieldwork, and secondary
documents—were combined to triangulate findings and minimise the bias of
individual sources [78]. Second, the prolonged engagement with the project’s
maintainers over 17 months provided in-depth insights into the project’s
evolving funding model, witnessing the ongoing negotiation with the
government, as well as the arrival and departure of different consortium
members. This longitudinal approach minimised the biases of snapshots at one
moment in time. Third, findings were member-checked with research participants
to enhance their accuracy [88]. Fourth, the community manager was invited to
review a draft of the case study, further enhancing the accuracy and resonance
of the findings [78]. However, data collection was constrained by data access
challenges. A major obstacle was gaining access to interviewees, especially
high-ranking stakeholders in the government and at sponsor companies, which is
a common problem in “elite interviewing” [94]. For example, despite the
generous support of the community manager and the offer of gift vouchers, only
three representatives of commercial sponsors could be recruited for
interviews.

####  5.3.2 Robustness

Robustness concerns the strength, reliability, and soundness of the study’s
design, methods, and findings. A number of steps were taken to enhance the
robustness. First, before the analysis, the theoretically-informed stakeholder
groups were tested with interviewees to ensure their relevance, whilst
accounting for nuances within and between stakeholder groups. This ensured
that theoretical constructs were not imposed on the study that did not
resonate with the research participants. Second, since the research data was
collected over a prolonged time span of 17 months, the author took an
iterative approach to the data analysis, combining an inductive, grounded
theory analysis with a deductive approach based on the study’s theoretical
framework throughout the period of data collection [87]. Third, the author
maintained a social identity map throughout the research process to minimise
the influence of personal biases on the study [89]

####  5.3.3 Transferability

Transferability concerns the generalisability of the findings. The single case
study on scikit-learn threatens the generalisability of the findings.
Certainly, it is easier to generalise from multiple case studies, which by
design test direct replication [95] and mitigate the risk of case uniqueness
[96]. For example, there are a number of unique characteristics of the scikit-
learn project, such as the longstanding institutional support of Inria, the
design of the consortium, and the specific support from the French government.
However, these threats to the validity of this study can be tempered by
considering that, contrary to statistical generalisation, case studies aim for
analytical generalisation [78]. For example, there are aspects of the scikit-
learn project that will resonate across OSS projects, including the
maintainers’ experience of weaving the diverse interests of stakeholders into
the project whilst seeking to safeguard the project’s community ethos, as well
as the effective design and implementation of funding governance protocols.
Furthermore, this study provides insights into the merits and challenges
involved in the implementation of a public-private funding model in a
community-led OSS project that may be useful for other projects considering
similar approaches.

####  5.3.4 Dependability

Dependability refers to the consistency of the research process. To maximise
the dependability of the research process, a systematic study protocol was
employed for the collection, analysis, and storage of research data. Where
consent was given, interviews were recorded and transcribed to aid analysis.
When interview responses were ambiguous, follow-up discussions with the
interviewees were initiated to obtain clarifications. In addition to interview
data, detailed field notes from on-site visits and supplementary secondary
documents facilitated data triangulation. Finally, the case study underwent
thorough review, benefiting from the feedback from domain experts as well as
peer review. The combination of these steps enhanced the dependability of the
study.

##  6 Conclusion

This study on the funding model of scikit-learn makes two key contributions to
research and practice. First, it contributes novel findings on emerging OSS
funding models, illustrating how the maintainers of an impactful community-led
OSS project have combined public and private funding to sustain the
development and maintenance of the project, whilst safeguarding its community
ethos. Second, it offers practical lessons for the funding of community-led
OSS projects, and it makes recommendations to various OSS practitioners. In
particular, it highlights effectiveness of diversified funding models as a
method to sustain community-led OSS projects in industry-dominated R&D fields,
such as AI.

## Acknowledgements

This study was funded by the UK’s Economic and Social Research Council [Grant
number: ES/P000649/1]. The author thanks the scikit-learn maintainers and
funders for participating in this study.

## References

  * [1] Cailean Osborne, Mirko Boehm, and Ana Jimenez Santamaria.  The European Public Sector Open Source Opportunity: Challenges and Recommendations for Europe’s Open Source Future.  Technical report, The Linux Foundation, Brussels, Belgium, September 2023.
  * [2] Sven Herpig.  Fostering Open Source Software Security – Blueprint for a Government Cybersecurity Open Source Program Office.  Technical report, Stiftung Neue Verantwortung, Berlin, Germany, May 2023.
  * [3] Frank Nagle.  Government Technology Policy, Social Value, and National Competitiveness, March 2019.
  * [4] Paul Keller.  European Public Digital Infrastructure Fund White Paper.  Open Future, December 2022.  Publisher: Open Future Foundation.
  * [5] Inria.  The 2019 Inria French Academy of Sciences Dassault Systèmes Innovation Prize: scikit-learn, a success story for machine learning free software, January 2020.
  * [6] Nur Ahmed, Muntasir Wahed, and Neil C. Thompson.  The growing influence of industry in AI research.  Science, 379(6635):884–886, March 2023.  Publisher: American Association for the Advancement of Science.
  * [7] SGPI.  France 2030: Stratégie National pour L’Intelligence Artificielle, November 2021.
  * [8] Synopsys.  Open Source Security and Analysis Report.  Technical report, Mountain View, CA, USA, 2023.
  * [9] Open Source Security Foundation.  OSS Mobilization Plan.  Technical report, Open Source Security Foundation, Linux Foundation, Washington D.C., USA, May 2022.
  * [10] Nadia Eghbal.  Roads and Bridges: The Unseen Labor Behind Our Digital Infrastructure.  Technical report, Ford Foundation, June 2016.
  * [11] Argyri Panezi, Jessica Feldman, and Lucy Bernholz.  Open Source Projects as Critical Digital Infrastructure, August 2020.
  * [12] Stewart Scott, Sarah Ann Brackett, Trey Herr, and Maia Hamin.  Avoiding the Success Trap: Toward Policy for Open-Source Software as Infrastructure.  Technical report, Atlantic Council, Washington D.C., USA, February 2023.
  * [13] Sean Goggins, Kevin Lumbard, and Matt Germonprez.  Open Source Community Health: Analytical Metrics and Their Corresponding Narratives.  In 2021 IEEE/ACM 4th International Workshop on Software Health in Projects, Ecosystems and Communities (SoHeal), pages 25–33, May 2021.
  * [14] Kevin Lumbard, Matt Germonprez, and Sean Goggins.  An empirical investigation of social comparison and open source community health.  Information Systems Journal, 34(2):499–532, 2024.  Publisher: Wiley Online Library.
  * [15] Wenxin Xiao, Hao He, Weiwei Xu, Yuxia Zhang, and Minghui Zhou.  How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?, September 2023.  arXiv:2308.06005 [cs].
  * [16] Siobhán O’Mahony and Beth A. Bechky.  Boundary Organizations: Enabling Collaboration among Unexpected Allies.  Administrative Science Quarterly, 53(3):422–459, September 2008.  Publisher: SAGE Publications Inc.
  * [17] Bianca Trinkenreich, Mariam Guizani, Igor Wiese, Anita Sarma, and Igor Steinmacher.  Hidden Figures: Roles and Pathways of Successful OSS Contributors.  Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2):180:1–180:22, October 2020.
  * [18] Yan Li, Chuan-Hoo Tan, and Hock-Hai Teo.  Leadership characteristics and developers’ motivation in open source software development.  Information & Management, 49(5):257–267, July 2012.
  * [19] Yuekai Huang, Ye Yang, Junjie Wang, Wei Zheng, and Qing Wang.  Identifying Emergent Leadership in OSS Projects Based on Communication Styles, January 2022.
  * [20] Alex Salkever.  Open Source Maintainers: Exploring the people, practices, and constraints facing the world’s most critical open source software projects.  Technical report, Linux Foundation, July 2023.
  * [21] Xin Tan, Yiran Chen, Haohua Wu, Minghui Zhou, and Li Zhang.  Is It Enough to Recommend Tasks to Newcomers? Understanding Mentoring on Good First Issues, February 2023.  arXiv:2302.05058 [cs].
  * [22] Minghui Zhou and Audris Mockus.  Who Will Stay in the FLOSS Community? Modeling Participant’s Initial Behavior.  IEEE transactions on software engineering, 41(1):82–99, 2015.  Place: New York Publisher: IEEE.
  * [23] Wenxin Xiao, Hao He, Weiwei Xu, Xin Tan, Jinhao Dong, and Migahui Zhou.  Recommending Good First Issues in GitHub OSS Projects.  In 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE), pages 1830–1842. ACM, 2022.  ISSN: 1558-1225.
  * [24] Guilherme Avelino, Eleni Constantinou, Marco Tulio Valente, and Alexander Serebrenik.  On the abandonment and survival of open source projects: An empirical investigation.  In 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), pages 1–12. IEEE, 2019.  ISSN: 1949-3789.
  * [25] Ann Barcomb, Andreas Kaufmann, Dirk Riehle, Klaas-Jan Stol, and Brian Fitzgerald.  Uncovering the Periphery: A Qualitative Survey of Episodic Volunteering in Free/Libre and Open Source Software Communities.  IEEE transactions on software engineering, 46(9):962–980, 2020.  Place: LOS ALAMITOS Publisher: IEEE.
  * [26] Amiangshu Bosu and Kazi Zakia Sultana.  Diversity and Inclusion in Open Source Software (OSS) Projects: Where Do We Stand?  In 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), pages 1–11, September 2019.  ISSN: 1949-3789.
  * [27] Nadia Eghbal.  Working in Public: The Making and Maintenance of Open Source Software.  Stripe Press, San Francisco, August 2020.
  * [28] Sébastien Broca.  Communs et capitalisme numérique : histoire d’un antagonisme et de quelques affinités électives.  Terminal. Technologie de l’information, culture & société, (130), June 2021.  Number: 130 Publisher: Creis-Terminal.
  * [29] Eric S. Raymond.  The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary.  O’Reilly Media, Incorporated, Sebastopol, 2001.
  * [30] Xunhui Zhang, Tao Wang, Yue Yu, Qiubing Zeng, Zhixing Li, and Huaimin Wang.  Who, What, Why and How? Towards the Monetary Incentive in Crowd Collaboration: A Case Study of Github’s Sponsor Mechanism.  arXiv:2111.13323 [cs], November 2021.  arXiv: 2111.13323.
  * [31] Christopher M. Kelty.  Two bits: the cultural significance of free software.  Experimental futures. Duke University Press, Durham, N.C. ; London, 2008.
  * [32] Lynne Markus, Brook Manville, and Carole Agres.  What Makes a Virtual Organization Work: Lessons From the Open-Source World.  MIT Sloan Management Review, October 2000.
  * [33] Claudia Loebbecke and Albert A Angehrn.  Open Source Platforms Under Co-opetition: A Comparative Analysis of SourceForge and ’CodeX’ (Xerox) as Two ’Co-opetitive Learning and Knowledge Exchange Networks’ (CoLKENs).  2003\.
  * [34] Nadia Eghbal.  Where money meets open source, 2017.
  * [35] R. Stuart Geiger, Dorothy Howard, and Lilly Irani.  The Labor of Maintaining and Scaling Free and Open-Source Software Projects.  Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1):175:1–175:28, April 2021.
  * [36] Steven Vaughan-Nichols.  Hard work and poor pay stresses out open-source maintainers.  ZDNET, June 2021.
  * [37] Benjamin Birkinbine.  Incorporating the Digital Commons: Corporate Involvement in Free and Open Source Software.  University of Westminster Press, February 2020.
  * [38] K Blind, S Pätsch, S Muto, M Böhm, T Schubert, P Grzegorzewska, and A Katz.  The impact of open source software and hardware on technological independence, competitiveness and innovation in the EU economy.  Technical report, European Commission, Brussels, Belgium, 2021.
  * [39] Siobhán O’Mahony and Fabrizio Ferraro.  The Emergence of Governance in an Open Source Community.  Academy of Management journal, 50(5):1079–1106, 2007.  Place: Briarcliff Manor Publisher: Academy of Management.
  * [40] Tidelift.  2020: The managed open source survey.  Technical report, Tidelift, 2020.
  * [41] Duane O’Brien.  Sustaining FOSS Projects By Democratizing The Sponsorship Process, 2019.
  * [42] Duane O’Brien.  The FOSS Contributor Fund: Forming a Community of Adopters, November 2019.
  * [43] Alyssa Wright.  Bloomberg Launches FOSS Fund to Support Free and Open Source Projects.  Bloomberg L.P., March 2023.
  * [44] Simon Butler, Jonas Gamalielsson, Bjorn Lundell, Christoffer Brax, Johan Sjoberg, Anders Mattsson, Tomas Gustavsson, Jonas Feist, and Erik Lonroth.  On Company Contributions to Community Open Source Software Projects.  IEEE transactions on software engineering, 47(7):1381–1401, 2021.  Place: LOS ALAMITOS Publisher: IEEE.
  * [45] Xiaoya Xia, Wei Wang, Shengyu Zhao, Sikang Bian, and Rong Wang.  Lessons Learned From the Ant Group Open Source Program Office.  Computer, 56(4):92–97, April 2023.  Conference Name: Computer.
  * [46] Linus Dahlander and Mats Magnusson.  How do Firms Make Use of Open Source Communities?  Long Range Planning, 41(6):629–649, December 2008.
  * [47] Linus Dahlander and Martin W. Wallin.  A man on the inside: Unlocking communities as complementary assets.  Research Policy, 35(8):1243–1259, October 2006.
  * [48] Andrea Bonaccorsi and Cristina Rossi.  Comparing motivations of individual programmers and firms to take part in the open source movement: From community to business.  Knowledge, Technology & Policy, page 25, 2006.
  * [49] Henry Chesbrough.  Measuring the Economic Value of Open Source.  Technical report, Linux Foundation, San Francisco, CA, USA, March 2023.
  * [50] Leyland F. Pitt, Richard T. Watson, Pierre Berthon, Donald Wynn, and George Zinkhan.  The Penguin’s Window: Corporate Brands From an Open-Source Perspective.  Journal of the Academy of Marketing Science, 34(2):115–127, April 2006.  Publisher: SAGE Publications Inc.
  * [51] Karim R. Lakhani and Robert G. Wolf.  Why Hackers Do What They Do: Understanding Motivation and Effort in Free/Open Source Software Projects, September 2003.
  * [52] Craig Hale.  Google might actually be the best friend for open-source software right now.  TechRadar, August 2022.
  * [53] Simon Butler, Jonas Gamalielsson, Bjorn Lundell, Per Jonsson, Johan Sjoberg, Anders Mattsson, Niklas Ricko, Tomas Gustavsson, Jonas Feist, Stefan Landemoo, and Erik Lonroth.  An Investigation of Work Practices Used by Companies Making Contributions to Established OSS Projects.  In 2018 IEEE/ACM 40th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP), pages 201–210. ACM, 2018.
  * [54] Yuxia Zhang, Klaas-Jan Stol, Hui Liu, and Minghui Zhou.  Corporate dominance in open source ecosystems: a case study of OpenStack.  In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2022, pages 1048–1060, New York, NY, USA, November 2022. Association for Computing Machinery.
  * [55] Patrick Adam Wagstrom.  Vertical interaction in open software engineering communities.  PhD thesis, Carnegie Insitute of Technology/School of Computer Science, Carnegie Mellon University, 2009.
  * [56] Debora Di Giacomo, Barbora Kudzmanaite, Vivien Deveny, Chloé Dussutour, and Maha Shaikh.  Key success factors of sustainable open source communities.  Technical report, EU, Brussels, Belgium, 2020.
  * [57] Matt Germonprez, J. P. Allen, Brian Warner, Jamie Hill, and Glenn McClements.  Open source communities of competitors.  Interactions, 20(6):54–59, November 2013.
  * [58] GitHub.  Sponsoring an open source contributor, 2023.
  * [59] Sandeep Krishnamurthy and Arvind K. Tripathi.  Monetary donations to an open source software platform.  Research Policy, 38(2):404–414, March 2009.
  * [60] Keitaro Nakasai, Hideaki Hata, Saya Onoue, and Kenichi Matsumoto.  Analysis of Donations in the Eclipse Project.  In 2017 8th International Workshop on Empirical Software Engineering in Practice (IWESEP), pages 18–22, March 2017.
  * [61] Keitaro Nakasai, Hideaki Hata, and Kenichi Matsumoto.  Are Donation Badges Appealing?: A Case Study of Developer Responses to Eclipse Bug Reports.  IEEE Software, 36(3):22–27, May 2019.  Conference Name: IEEE Software.
  * [62] Poonacha K. Medappa, Murat M. Tunc, and Xitong Li.  Sponsorship Funding in Open-Source Software: Effort Reallocation and Spillover Effects in Knowledge-Sharing Ecosystems, June 2023.
  * [63] Cassandra Overney, Jens Meinicke, Christian Kästner, and Bogdan Vasilescu.  How to not get rich: an empirical study of donations in open source.  In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, ICSE ’20, pages 1209–1221, New York, NY, USA, October 2020. Association for Computing Machinery.
  * [64] Nadia Eghbal.  A handy guide to financial support for open source., August 2022.  original-date: 2016-06-16T01:45:55Z.
  * [65] CERN.  The birth of the Web | CERN, 2023.
  * [66] B. During.  Trouble in paradise: the open source project PyPy, EU-funding and agile practices.  In AGILE 2006 (AGILE’06), pages 11 pp.–231, July 2006.
  * [67] James Howison and James D. Herbsleb.  Scientific software production: incentives and collaboration.  In Proceedings of the ACM 2011 conference on Computer supported cooperative work, CSCW ’11, pages 513–522, New York, NY, USA, March 2011. Association for Computing Machinery.
  * [68] Carly Strasser, Kate Hertweck, Josh Greenberg, Dario Taraborelli, and Elizabeth Vu.  Ten simple rules for funding scientific open source software.  PLOS Computational Biology, 18(11):e1010627, November 2022.  Publisher: Public Library of Science.
  * [69] Steven J. Vaughan-Nichols.  Log4Shell: We Are in So Much Trouble, December 2021.
  * [70] Frances Burwell and Kenneth Propp.  Digital sovereignty in practice: The EU’s push to shape the new global economy.  Technical report, November 2022.
  * [71] European Commission.  The European Commission adopts its new Open Source Software Strategy 2020-2023.  Technical report, Directorate-General for Informatics, European Commission, Brussels, Belgium, October 2020.
  * [72] Osden Jokonya.  Investigating Open Source Software Benefits in Public Sector.  In 2015 48th Hawaii International Conference on System Sciences, pages 2242–2251, January 2015.  ISSN: 1530-1605.
  * [73] Mohar Chatterjee and Gian Volpicelli.  France bets big on open-source AI, August 2023.
  * [74] White House.  Readout of White House Meeting on Software Security, January 2022.
  * [75] European Working Team on Digital Commons.  Towards a Sovereign Digital Infrastructure of Commons: Report of the European Working Team on the Digital Commons.  Technical report, OpenFuture, June 2022.
  * [76] STF.  Sovereign Tech Fund, 2022.
  * [77] Per Runeson, Martin Höst, Austen Rainer, and Bjorn Regnell.  Case Study Research in Software Engineering: Guidelines and Examples.  John Wiley & Sons, March 2012.  Google-Books-ID: T7rXoaxqPIAC.
  * [78] Robert K. Yin.  Case study research and applications: design and methods.  Sage, Los Angeles, USA, sixth edition. edition, 2018.
  * [79] OSS Insight.  OSS Insight - scikit-learn, March 2023.
  * [80] Max Langenkamp and Daniel N. Yue.  How Open Source Machine Learning Software Shapes AI.  In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’22, pages 385–395, New York, NY, USA, July 2022. Association for Computing Machinery.
  * [81] Matt White, Ibrahim Haddad, Cailean Osborne, Xiao-Yang, Liu, Ahmed Abdelmonsef, and Sachin Varghese.  The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency and Usability in AI, March 2024.  arXiv:2403.13784 [cs].
  * [82] scikit learn.  scikit-learn, 2023.
  * [83] Gaël Varoquaux.  A foundation for scikit-learn at Inria, September 2018.
  * [84] Steinar Kvale.  InterViews: an introduction to qualitative research interviewing.  Sage, Thousand Oaks ; London, 1996.
  * [85] Virginia Braun and Victoria Clarke.  Using thematic analysis in psychology.  Qualitative research in psychology, 3(2):77–101, 2006.  Place: London Publisher: Taylor & Francis Group.
  * [86] Kathy Charmaz.  Constructing grounded theory: A practical guide through qualitative analysis.  SAGE, 2006.
  * [87] Daniela S. Cruzes and Tore Dyba.  Recommended Steps for Thematic Synthesis in Software Engineering.  In 2011 International Symposium on Empirical Software Engineering and Measurement, pages 275–284, September 2011.  ISSN: 1949-3789.
  * [88] Yvonna S. Lincoln and Egon G. Guba.  Naturalistic inquiry.  Sage, Beverly Hills ; London, 1985.
  * [89] Danielle Jacobson and Nida Mustafa.  Social Identity Map: A Reflexivity Tool for Practicing Explicit Positionality in Critical Qualitative Research.  International journal of qualitative methods, 18:160940691987007–, 2019.  Place: Los Angeles, CA Publisher: SAGE Publications.
  * [90] Andrea Bonaccorsi, Silvia Giannangeli, and Cristina Rossi.  Entry Strategies Under Competing Standards: Hybrid Business Models in the Open Source Software Industry.  Management science, 52(7):1085–1098, 2006.  Place: Linthicum Publisher: INFORMS.
  * [91] X Cao and Henry William Chesbrough.  OSS research in organizational management: A literature review and critical reappraisal, 2022.
  * [92] Per Runeson and Martin Höst.  Guidelines for conducting and reporting case study research in software engineering.  Empirical software engineering : an international journal, 14(2):131–164, 2008.  Place: Boston Publisher: Springer US.
  * [93] Steve Easterbrook, Janice Singer, Margaret-Anne Storey, and Daniela Damian.  Selecting Empirical Methods for Software Engineering Research.  In Forrest Shull, Janice Singer, and Dag I. K. Sjøberg, editors, Guide to Advanced Empirical Software Engineering, pages 285–311. Springer, London, 2008.
  * [94] Robert Mikecz.  Interviewing Elites: Addressing Methodological Issues.  Qualitative Inquiry, 18(6):482–493, 2012.  _eprint: https://doi.org/10.1177/1077800412442818.
  * [95] Kathleen M. Eisenhardt.  Building Theories from Case Study Research.  The Academy of Management review, 14(4):532–550, 1989.  Place: Ada, Ohio, etc Publisher: Academy of Management.
  * [96] Robert E. Herriott and William A. Firestone.  Multisite Qualitative Policy Research: Optimizing Description and Generalizability.  Educational researcher, 12(2):14–19, 1983.  Publisher: American Educational Research Association.