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Scientific research is not value-neutral but builds on the stated and unstated values of those leading the research, influencing the choice of study topics; decisions about methods, judgments, or inferences with data; and considerations of the consequences of errors. In some fields, researchers create a positionality statement to disclose bias as a way to manage or neutralize the influence of values. Positionality refers to the way in which an individual’s worldview, and thus perceptions and research activities, is shaped by the frameworks, social identities, lived experiences, and sociopolitical context within which they live. Thinking about positionality is a valuable, yet missing, element for practitioners of participatory sciences. In this essay, we suggest that those leading participatory science projects explore their positionality, irrespective of whether or not they choose to disclose it, in order to manage values for several goals: research integrity, ethical data practices, and equity and inclusion. By reviewing and synthesizing literature, we created a tool to help leaders of participatory science projects think reflectively (for awareness of their identities and characteristics) and reflexively (from an external position for critical observation of themselves) to recognize their influence on project design and implementation. We view examining positionality as a precursor to anticipating and taking actions to minimize epistemic injustices and ultimately enhance the unique capacity of each project to advance equity, inclusion, and scientific productivity. 

In citizen science, data stewards and data producers are often not the same people. When those who have labored on data collection are not in control of the data, ethical problems could arise from this basic structural feature. In this Perspective, we advance the proposition that stewarding data sets generated by volunteers involves the typical technical decisions in conventional research plus a suite of ethical decisions stemming from the relationship between professionals and volunteers. Differences in power, priorities, values, and vulnerabilities are features of the relationship between professionals and volunteers. Thus, ethical decisions about open data practices in citizen science include, but are not limited to, questions grounded in respect for volunteers: who decides data governance structures, who receives attribution for a data set, which data are accessible and to whom, and whose interests are served by the data use/re-use. We highlight ethical issues that citizen science practitioners should consider when making data governance decisions, particularly with respect to open data. 

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 forexecutingthe 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.