By Evan Josselin
About the author: Evan Josselin is a PhD student in philosophy of science at the Institut Jean Nicod (ENS–PSL, CNRS). Their research explores the intersection of philosophy of science and marine biology, with a particular focus on marine citizen science. Evan works closely with the marine citizen science project Plankton Planet project and the CNRS research group OMER to investigate how collaborative scientific practices involving non-professionals contribute to marine knowledge production.

This quote is drawn from the Data Ethics Toolkit developed by Cooper, Rasmussen, and Jones (2022), which provides an ethical framework for data governance in citizen science¹. Data governance refers to the decision-making processes that occur throughout the data life cycle to ensure the integrity, availability, usability, and security of data. All scientific endeavours that generate data must grapple with these questions, which encompass both technical and ethical dimensions.

In this blog post, I focus specifically on the ethical challenges that can emerge in the context of data governance within environmental citizen science projects. There are two main reasons for this focus. Firstly, much of the scholarly literature on citizen science has concentrated on the issue of data quality—evaluating the reliability and accuracy of data produced by non-professionals—while paying comparatively little attention to the ethical issues involved in the production of these data by participants. Yet involving individuals who are not professional scientists in research raises a range of ethical concerns that deserve closer examination. As Kasperowski et al. (2021) point out, while there is broad agreement that traditionalethics are insufficient for guiding citizen science practices, there is no consensus on how ethical responsibility should be redefined or redistributed in this context. Secondly, I chose to focus on environmental citizen science projects in particular because they are often perceived as ethically less problematic. Unlike in biomedical citizen science—where participants may be asked to share personal health data or report on symptoms—environmental data collection seems, at first glance, more detached from the individuals involved. Scientists often view the data as neutral observations about the world, unrelated to the people who gathered them. However, this perception overlooks the fact that the production of environmental data can also raise important ethical issues, including risks to individuals, communities, and ecosystems.

To better conceptualize these issues, I propose using the term ethical risk—inspired by Biddle and Kukla’s (2017) notion of epistemic risk, which refers to the potential for epistemic errors at any stage of scientific inquiry. Similarly, I define ethical risk as the potential for harm to individuals or the environment that may arise throughout the research process. In the remainder of this post, I examine four interconnected forms of ethical risk that are especially relevant in the context of environmental citizen science: (1) the erasure of contributors’ roles in data production, (2) the loss of participant agency through restricted data ownership and access, (3) unequal distribution of benefits that can lead to data extractivism, and (4) the potential misuse of personal data.

1. Erasure of contributions

In a 2014 article, Cooper et al. showed that many scientific publications focused on long-term bird monitoring rely heavily on data collected by volunteers. Yet, such articles rarely use the term “citizen science,” effectively erasing the role of citizen contributors. Although citizen participants play an active role in data collection, their contributions often go unrecognized—especially when the data are reused by researchers who were not involved in the original project. This erasure constitutes a significant ethical risk in environmental citizen science.

At a minimum, scientists and institutions using citizen-generated data should be transparent about their origins and give proper credit to the people who collected them. Recognition can take different forms, such as acknowledgments, citations, or, in some cases, authorship. While authorship may not always be appropriate or feasible for volunteers (Ward-Fear et al., 2020), some form of recognition is essential to uphold ethical standards and respect contributors’ efforts.

• Loss of agency: Data ownership and accessibility 

A second ethical risk inherent in environmental citizen science projects is the potential loss of agency for participants when they are denied ownership of and access to the data they help generate.

When different stakeholders in a project have conflicting interests regarding data governance, participants often find themselves in a structurally inferior position, with limited ability to influence decisions about which data should—or should not—be shared. Those who primarily hold ownership of the data ultimately control its use and dissemination. Without formal data ownership, participants are effectively stripped of their agency over the data they helped produce.

This issue becomes particularly acute when citizens contribute culturally embedded information—such as traditional knowledge about local species, ecological conditions, or medicinal practices. In such cases, there is a serious ethical risk that scientists or institutions may claim ownership over this knowledge, potentially commercializing it without consent (Shiva, 2007). While with traditional knowledge this risk is most obvious, similar tensions can arise more broadly: participants may have diverging interests regarding data use, not only compared to scientists, but also among themselves. For instance, mushroom foragers often prefer to keep the locations of rare species private to avoid overharvesting or poaching, while birdwatchers are generally more open to sharing locations and may even seek recognition for spotting rare species (Quinn, 2025).

Platforms like iNaturalist respond to these differing needs by offering flexible options: participants can choose to obscure location details or to track their own activity via public leaderboards (Agrin et al., 2008). Such practices reflect a more inclusive model of data governance that respects contributors’ preferences. In contrast, the risk of disempowerment increases when data ownership is centralized within a single authority—such as GBIF, a major repository of biodiversity data.

Loss of agency is not limited to ownership alone; it is also tied to data accessibility. When participants are cut off from accessing the data they contributed, they also lose the ability to manage or reuse that information. One of the core values of citizen science is the opportunity for participants to build communities around shared data—collaborating to identify species, question uncertain observations, or explore taxonomic issues. Without ongoing access to their data, participants cannot engage in these exchanges or form the relationships and knowledge networks that are essential to citizen science. More broadly, they are prevented from using the data to respond to new questions or to pursue goals related to local environmental governance or public policy. Removing access to contributed data thus undermines participants’ ability to take action—not only within the project, but also within their communities and ecosystems. It blocks the possibility of meaningful data sharing, a foundational principle of ethical research (Shamoo & Resnik, 2015; Soranno et al., 2015).

• Unequal benefits: Data extractivism 

In many citizen science projects, participants voluntarily contribute their time and effort without expecting financial compensation. While this spirit of collaboration is central to citizen science, it also opens the door to data extractivism—a form of exploitation that arises when the benefits of the research are unequally distributed (Riesch & Potter, 2014). Exploitation, as defined by Wertheimer (1999), occurs when one party takes unfair advantage of another in a relationship or transaction.

Participants’ contributions warrant fair recognition and reciprocity, especially because they are essential to the success of citizen science projects. Placing reciprocity at the heart of project design is a key way to reduce the risk of data extractivism. As emphasized in the Data Ethics Toolkit (Cooper, Rasmussen, & Jones, 2022, p. 7), reciprocity is understood as a “principle of fairness in exchanges of mutual advantage.” It encourages a “two-way street” approach, where both professional scientists and participants benefit from the collaboration. There are many ways to foster reciprocity in citizen science. Two particularly important practices are:

1. Giving appropriate credit to participants.
2. Providing accessible and meaningful “report-backs”—that is, sharing results with volunteers in ways that are understandable and relevant. This might include personalized data summaries (e.g., showing how an individual’s contributions fit into the broader project) or well-designed visualizations of collective findings (Cooper, Rasmussen, & Jones, 2022).

In addition, scientists can offer formal recognition, through certificates of participation or letters of appreciation, for example. Educational resources or feedback on participants’ contributions can also serve as meaningful ways to return value to those who supported the research.

• Misuse of personal data

When dealing with environmental data, it is easy to overlook the fact that such data often carry embedded personal metadata about the participants. As the Data Ethics Toolkit explains, personal data in citizen science projects typically fall into two categories:

1. Enrollment or administrative data—including information such as names, usernames, email addresses, IP addresses, gender, race, age, and geolocation, which are collected when participants register on a platform or submit data.
2. Incidental data—personally identifiable information that can be inferred from the primary data or the enrollment data. For instance, geotagged insect observations can inadvertently reveal a participant’s location and patterns of movement.

These types of data—although not the primary focus of environmental science—pose significant privacy risks if they are not adequately protected. Without appropriate safeguards, there is a real risk that personal data could be accessed or misused by third parties with malicious intent. Therefore, ethical data governance in environmental citizen science must go beyond protecting environmental datasets alone. It must also include robust strategies for protecting participants’ personal information—both direct and inferred—to ensure their privacy and safety.

Conclusion 

Environmental citizen science contributes valuable data for research, but it also introduces specific ethical risks that add to—rather than replace—those found in traditional science. To address these challenges, projects must intentionally design governance structures that balance the machinic interoperability of the FAIR (Wilkinson et al., 2016) data principles with the relational accountability of the CARE (Carroll et al., 2020) principles, promote participant recognition and inclusive access to research outputs across all stages of the research process, and foster data literacy to ensure participants possess the interpretive resources necessary to shape how their data are governed and used

The Data Ethics Toolkit (2022) offers practical tools for project leaders engaging in citizen science. It highlights five key pillars:

1. Data governance – choosing governance structures that distribute, rather than concentrate, power and control;
2. Data integrity – implementing mechanisms to ensure data quality;
3. Report-outs – making data available for scientific use and reuse, thereby enhancing the contribution of citizen science to research;
4. Recompense – designing projects to ensure participants and partners benefit fairly;
5. Report-backs – providing participants with clear, meaningful feedback that acknowledges their role as essential contributors to the research.

Embedding these principles from the outset is essential to ensure that citizen science projects not only generate robust data, but also uphold ethical standards.

References

Agrin, N., Kline, J., & Ueda, K.-i. (2008). inaturalist.org: Final project report. https://wwwischoolberke l eyedu/sites/default/ fi les/iNaturalist_Final_Writeuppdf

Biddle, J. B., & Kukla, R. (2017). The Geography of Epistemic Risk. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780190467715.003.0011

Bonney, R. (1996). Citizen Science: a lab tradition. In Living Bird, v. 15, no. 4, p. 7-15

Carroll, S.R., Garba, I., Figueroa-Rodríguez, O.L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J.D., Anderson, J. and Hudson, M. (2020) ‘The CARE Principles for Indigenous Data Governance’, Data Science Journal, 19(1), p. 43. Available at: https://doi.org/10.5334/dsj-2020-043.

Cooper, C. B., Shirk, J., & Zuckerberg, B. (2014). The Invisible Prevalence of Citizen Science in Global Research : Migratory Birds and Climate Change. PLoS ONE, 9(9), Article e106508. https://doi.org/10.1371/journal.pone.0106508

Cooper, C. B., Rasmussen, L. M., and Jones, E. D. (2022). A Toolkit for Data Ethics in the Participatory Sciences. Citizen Science Association.

Irwin, A. (1995). Citizen Science : A Study of People, Expertise and Sustainable Development. Taylor & Francis Group.

Kasperowski, D., Hagen, N., & Rohden, F. (2021). Ethical boundary work in citizen science. Nordic Journal of Science and Technology Studies. https://doi.org/10.5324/njsts.v10i1.4318

Quinn, A. (2025). Community science and the value-free ideal. Synthese, 205(3). https://doi.org/10.1007/s11229-025-04955-2

Riesch, H., Potter, C., (2014). Citizen science as seen by scientists: methodological, ethical, and epistemological dimensions. Public Underst. Sci. 23 (1) 107–120.

Shamoo, A.E., Resnik, D.B., (2015). Responsible Conduct of Research, 3rd ed. Oxford University Press, New York.

Shiva, V. (2007). Bioprospecting as Sophisticated Biopiracy. Signs : Journal of Women in Culture and Society, 32(2), 307–313. https://doi.org/10.1086/508502

Soranno, P., Cheruvelil, K., Elliott, K., Montgomery, G., (2015). It’s good to share: why environmental scientists’ ethics are out of date. Bioscience 65 (1) 69–73.

Ward-Fear, G., Pauly, G. B., Vendetti, J. E., & Shine, R. (2020). Authorship: protocols should include citizen scientists. Nature, 578(7795), 363. https://doi.org/10.1038/d41586-020-00422-9

Wertheimer, A., (1999). Exploitation. Princeton University Press, Princeton.

Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18

1. The term citizen science emerged in the late 1990s through the work of Alan Irwin (1995) and Rick Bonney (1996). Irwin used it in Europe to promote a democratic vision of science—for the people and by the people—while Bonney, in the U.S., emphasized large-scale public data collection in ecology. This dual origin reflects an ongoing ambiguity in the political and scientific aims of citizen science. The term now refers to diverse practices such as distributed computing, amateur biodiversity monitoring, environmental sensing, online data classification, health data sharing, and DIY biology. Participants may engage in all stages of the research process. Related terms like participatory science, community science, and sensor monitoring highlight the field’s growing diversity and commitment to collaboration between scientists and the public. ↩︎