Equitable Data Governance Models for the Participatory Sciences

Abstract

When participants share data to a central entity, those who have taken on the responsibility of accepting the data and handling its management may also have control of decisions about the data, including its use, re‐use, accessibility, and more. Such concentrated control of data is often a default practice across many forms of participatory sciences, which can be extractive in some contexts and a way to protect participants in other contexts. To avoid extractive practices and related harms, projects can adopt structures so that those who make decisions about the data set and/or each datum are different from those responsible for executing the subsequent decisions about data management. We propose two alternative models for improving equity in data governance, each model representing a spectrum of options. With an individualized control model, each participant can place their data in a central repository while still retaining control of it, such as through simple opt‐in or opt‐out features or through blockchain technology. With a shared control model, representatives of salient participant groups, such as through participant advisory boards, collectively make decisions on behalf of their constituents. These equitable models are relevant to all participatory science systems, and particularly necessary in contexts where dominant‐culture institutions engage marginalized peoples.

Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic

“…While participants acknowledged the progress of these steps, some felt the language were too idealistic, lacked specificity, and were challenging to operationalize. Commentary on these issues includes calls for more participatory approaches, inclusion of equity and human rights, and representation from groups especially impacted, such as marginalized communities ( 60 , 61 ). Additionally, several conceptual frameworks have been proposed, including ones rooted in data solidarity and data justice ( 62 ), principles of equity, individuals rights ( 59 ), and antiracism ( 63 ).…”

A framework for contextualizing social‐ecological biases in contributory science data

“…Inclusion: Intentionally created power structures, designs and processes where individuals with identities that have been historically and systematically excluded feel safe, welcomed, supported, valued and respected to participate in a project as their authentic selves, that is, present with all identities they hold (Cooper et al, 2023).…”

“…Hence, professional scientists in nations in the Global North (specifically the United States, Canada, Australia and Western Europe) should be keen to empower scientists in the Global South to collect and store data in ways that they see fit rather than through methods that are dictated by the Global North (Economou‐Garcia, 2022; Pettorelli et al., 2021; Rodrigues, 2021; Trisos et al., 2021). Contributory projects—like eBird and iNaturalist—can increase equity by creating coalitions that include shared governance of projects by people from the Global North and South (Cooper et al., 2023). This is crucial not only because scientists in the Global North have historically colonized and profited from the biodiversity in the Global South (Bernstein, 2000; Dados & Connell, 2012; McMichael & Weber, 2020; Quijano, 2008) but because these data likely come with nuances best understood and interpreted by local scientists (Ocampo‐Ariza et al., 2023; Trisos et al., 2021).…”