Definition 7: Sovereignty

Definition

Data Sovereignty refers to the management of information in a manner that is consistent with a group or nation’s protocol and laws. Questions of data sovereignty look at “from whom data are collected, the content of these data, the purposes for which these data are to be used and who will ultimately control access to these data” (Snip, 2016, pg. 40). To add nuance to this definition, the Indigenous data sovereignty and data justice movements work to democratize data by providing decision making powers to communities and research participants.

In Context

The needs and aspirations for how data is used is unique to each community. In all cases, researchers should respect community partners and research participants’ self-determination, which can manifest through co-creation and consent. This decreases harm and emotional labour in research processes that might be an unfamiliar or even violent experience for marginalized communities. Here are some ways to implement co-creation in research:

Co-Creation

Researchers conducting Community-Based Research should make space for co-creation. This means handing over decision-making to research participants (something that has not been done in traditional research).

An example of good data practice: create a Data/Information Sharing Agreement that is a living document. Co-write how the researcher and community will navigate data together. Plan to review this document, and update it regularly according to the changing needs of the community.

Barriers to Data Participation

Challenges like language, digital access, and cultural differences can limit community members’ ability to participate fully in data gathering. Recognizing and addressing these barriers is key to fostering inclusive research practices.

Literacy

Varying literacy levels, including digital and scientific literacy, may affect how participants engage with and understand research. It is the research team’s responsibility to ensure research participants have the necessary literacy to make informed decisions regarding how their data is collected, used, and analyzed. Simplifying language and using accessible formats can help bridge these gaps.

Citational Justice is also a key principle of data sovereignty. This principle promotes equitable recognition of marginalized voices and knowledge sources in research citations, challenging traditional hierarchies and ensuring representation of diverse perspectives. In the words of Amed in Living a Feminist Life (2017): “...citation is how we acknowledge our debt to those who came before; those who helped us find our way when the way was obscured because we deviated from the paths we were told to follow…”

Reflection Questions

Consider your capacity: Are resources and time sufficient to conduct this research ethically? If not, scaling down may allow for more thorough and respectful engagement.
What steps are we taking to provide data literacy and skills training for community members?
When collecting data, ask critically, who will this data be obtained from, and how can you honour their role as knowledge keepers?
Who holds decision-making power in the research process? How are we ensuring that the community retains control over their data?

Additional Resources

Indigenous Data Sovereignty: Toward an Agenda

Engagement, Governance, Access, and Protection (EGAP): A Data Governance Framework for Health Data Collected from Black Communities in Ontario

The First Nations Principles of OCAP (ownership, control, access, and possession)

The Mutually Reinforcing Cycle Of Poor Data Quality And Racialized Stereotypes That Shapes Asian American Health | Health Affairs JournalThe Asian American health narrative reflects a long history of structural racism in the US and the complex interplay of racialized history, immigrant patterns, and policies regarding Asians in the US. Yet owing to systematic issues in data collection including missing or misclassified data for Asian Americans and practices that lead to indiscriminate grouping of unlike individuals (for example, Chinese, Vietnamese, and Bangladeshi) together in data systems and pervasive stereotypes of Asian Americans, the drivers and experiences of health disparities experienced by these diverse groups remain unclear. The perpetual exclusion and misrepresentation of Asian American experiences in health research is exacerbated by three racialized stereotypes—the model minority, healthy immigrant effect, and perpetual foreigner—that fuel scientific and societal perceptions that Asian Americans do not experience health disparities. This codifies racist biases against the Asian American population in a mutually reinforcing cycle. In this article we describe the poor-quality data infrastructure and biases on the part of researchers and public health professionals, and we highlight examples from the health disparities literature. We provide recommendations on how to implement systems-level change and educational reform to infuse racial equity in future policy and practice for Asian American communities.

The Mutually Reinforcing Cycle Of Poor Data Quality And Racialized Stereotypes That Shapes Asian American Health

Data Use Agreements

Living a Feminist Life

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