Scientific Data Sovereignty in the tension between global openness and local autonomy

By Fabiano Couto Corrêa da Silva

Introduction

Imagem: jonakoh _ by Unsplash.

At the heart of contemporary debates on open science lies a fundamental tension that is rarely made explicit: how can we reconcile the ideal of global scientific knowledge,accessible to all, with the need for data sovereignty, that is, the right of communities, institutions and nations to control the data they produce? This dilemma is not merely technical or administrative; it strikes at the core of power relations structuring global science. There is a “trilemma” among global openness, digital sovereignty and the risk of data colonialism. For researchers in the Global South, understanding and navigating this trilemma is essential to building a truly equitable science.

The Open Science Trilemma: openness, sovereignty and colonialism

The ideal of open science is compelling: a global public good in which knowledge circulates freely across borders, accelerating discovery and democratizing access. The scientific response to the COVID-19 pandemic demonstrated the power of this vision. The rapid, open sharing of SARS-CoV-2 genomic data among laboratories worldwide was decisive for the accelerated development of vaccines and treatments. This is the transformative potential of borderless collaboration.

However, this optimistic vision must be contrasted with the geopolitical reality of the digital infrastructure that sustains it. Cloud-computing platforms, large-scale data repositories and state-of-the-art AI algorithms are dominated by a small number of multinational corporations (Big Tech), concentrated mostly in the Global North. This concentration of power creates a problematic structural dependence.

When researchers in the Global South deposit their data in repositories controlled by foreign companies, use proprietary analytical platforms, or depend on cloud infrastructures located in other countries, they are, to some extent, ceding sovereignty over a strategic resource. This dependence is not only technical; it is also political and epistemic. Whoever controls the infrastructure ultimately controls the rules of access, the possibilities for analysis and even the forms of knowledge that can be produced.

Data Extractivism: a new face of colonialism

The concept of data extractivism, inspired by post-colonial critiques of economic extractivism, describes practices whereby researchers and institutions with greater economic and symbolic power extract data, samples and knowledge from local contexts without genuinely reciprocal relationships with the communities and researchers who make such extraction possible.

This phenomenon is especially problematic in areas such as global health, environmental studies and social research, where local knowledge and access to specific contexts are essential to research quality. Data on tropical diseases, Amazonian biodiversity, traditional agricultural practices or social dynamics in vulnerable communities are collected by Northern researchers, analyzed with sophisticated tools, published in high-impact journals and, frequently, the benefits, both in academic recognition and practical applications, do not return to the communities of origin.

Data extractivism is not limited to appropriating material resources. It also includes the appropriation of traditional knowledge, local ways of knowing and cultural perspectives that are subsequently recontextualized and reinterpreted according to dominant theoretical and methodological frameworks, often erasing the intellectual contributions of local communities.

Data sovereignty: principles and practices

Scientific data sovereignty does not mean isolation or closure. It is about ensuring that the communities who generate data retain control over how those data are used, by whom and for what purposes. This implies:

1. Local and regional infrastructures

Investment in local and regional data repositories, analytical platforms and computational capacity. Initiatives such as SciELO Data, national research-data repositories and regional scientific-computing networks are efforts to reduce dependence on foreign infrastructures.

2. Participatory governance

Developing governance models in which researchers, institutions and, where appropriate, communities affected by research participate in decisions about data access, use and sharing. This is particularly important in research involving Indigenous peoples, traditional communities or vulnerable groups.

3. Equitable sharing protocols

Creating protocols that ensure data sharing is accompanied by benefit-sharing, including co-authorship, capacity building, technology transfer and returning results to communities of origin.

4. Legal and policy frameworks

Developing national and regional legislation and policies that protect data sovereignty, such as Brazil’s LGPD and similar regulations across Latin America.

CARE Principles: an alternative to FAIR

Whereas the FAIR principles (Findable, Accessible, Interoperable, Reusable) emphasize technical data management, the CARE principles (Collective Benefit, Authority to Control, Responsibility, Ethics)¹, developed by Indigenous communities, center data governance from a justice- and equity-oriented perspective.

CARE recognizes that data, especially those related to Indigenous peoples and traditional communities, are not merely technical resources but are intrinsically tied to collective rights, cultural identities and sovereignties. They propose that:

• Collective Benefit: Data should be used in ways that benefit the communities that generated them.
• Authority to Control: Communities have the right to govern the collection, ownership and application of their data.
• Responsibility: Those who work with data have a responsibility to promote community well-being.
• Ethics: People’s rights and well-being must be the primary concern at every stage of the data life cycle.

Integrating CARE with FAIR is a pathway to an open science that is also just and respectful of local sovereignties.

Equitable collaboration models: overcoming extractivism

Overcoming data extractivism requires developing new collaboration models based on reciprocity, transparency and mutual empowerment. These models must go beyond the mere inclusion of Global South researchers in projects conceived and led in the North, to foster genuine co-creation in which research agendas, methodologies and interpretive frameworks are developed collaboratively from the outset.

Mutual capacity building emerges as a central element in any truly equitable model of scientific collaboration. Unlike traditional “technology transfer” models, which presume a one-way flow of knowledge from North to South, mutual capacity building recognizes that all partners have valuable knowledge and competencies to share.

Examples of equitable practices include:

• Co-designing research projects from the conception stage
• Sharing resources (financial, technological, human)
• Joint publication with equitable authorship
• Returning results to communities in accessible, culturally appropriate ways
• Developing local capacities for data analysis and interpretation

Conclusion: building a sovereign open science

Scientific data sovereignty is not a barrier to open science but a condition for its full and fair realization. Truly open science respects the autonomy of knowledge-producing communities, promotes equity in access not only to data but also to the capabilities to process and interpret them, and recognizes the diversity of epistemologies and ways of knowing.

For the Global South and for the scientific community committed to equity, building sovereign infrastructures, developing participatory governance frameworks and promoting equitable collaborations are essential steps to ensure that open science does not become a new vehicle of colonialism but rather a tool for emancipation and cognitive justice. The question “Who controls your data?²  is, in the end, about who has the power to define the future of science and knowledge. The answer should be: all of us, under conditions of equality and mutual respect.

Posts of the series about the Quem controla seus dados? book

• Data Colonialism in Science: A New Form of Epistemic Domination
• Open Science between Promises and Paradoxes, democratization or new dependency?
• Scientific Integrity in the Age of AI: fraud, manipulation, and new transparency challenges
• Scientific Data Sovereignty in the tension between global openness and local autonomy

Notes

1. CARROLL, S.R., et al. The CARE Principles for Indigenous Data Governance. Data Science Journal, 2020, vol. 19, no. 1 [viewed 17 December 2025] https://doi.org/10.5334/dsj-2020-043. Available from: https://datascience.codata.org/articles/dsj-2020-043↩

2. SILVA, F.C.C. Quem controla seus dados? Ciência Aberta, Colonialismo de Dados e Soberania na era da Inteligência Artificial e do Big Data. São Paulo: Pimenta Cultural, 2025 [viewed 17 December 2025]. https://doi.org/10.31560/pimentacultural/978-85-7221-474-2. Available from: https://www.pimentacultural.com/livro/quem-controla-dados/↩

References

CARROLL, S.R., et al. The CARE Principles for Indigenous Data Governance. Data Science Journal, 2020, vol. 19, no. 1 [viewed 17 December 2025] https://doi.org/10.5334/dsj-2020-043. Available from: https://datascience.codata.org/articles/dsj-2020-043

SILVA, F.C.C. Quem controla seus dados? Ciência Aberta, Colonialismo de Dados e Soberania na era da Inteligência Artificial e do Big Data. São Paulo: Pimenta Cultural, 2025 [viewed 17 December 2025]. https://doi.org/10.31560/pimentacultural/978-85-7221-474-2. Available from: https://www.pimentacultural.com/livro/quem-controla-dados/

About the author Fabiano Couto Corrêa da Silva

Fabiano Couto Corrêa da Silva is a researcher in Information Science focusing on open science, data colonialism and informational sovereignty. He leads DataLab – Laboratory for Data, Institutional Metrics and Scientific Reproducibility, with an emphasis on FAIR/CARE.

Translated from the original in Portuguese by Fabiano Couto Corrêa da Silva.