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We examine the intersection of participatory science, social justice, and higher education in the United States to investigate how instructors can teach about social justice and enhance collaborations to work toward enacting social justice. 

Abstract Contributory science—including citizen and community science—allows scientists to leverage participant‐generated data while providing an opportunity for engaging with local community members. Data yielded by participant‐generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts.The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases.We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation,detectability,samplingandpreference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data.Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the freePlain Language Summaryfor this article on the Journal blog. 

Citizen science harnesses the power of nonscientist observations, often resulting in a vast network of data. Such projects have potential to democratize science by involving the public. Yet participants are mostly white, affluent, and well-educated, participants that contribute data from their residence or places they frequent. The geography of the United States is heavily segregated along lines of race and class. Using a Census Tract-level hurdle model, we test the relationship between the locations of the rain gauges from the citizen science project Community Collaborative Rain, Hail, and Snow Network (CoCoRaHS) with continuous variables for percent non-Hispanic white and median household income. We find whiter and more affluent Census Tracts are significantly more likely to have a rain gauge. The highly localized nature of precipitation combined with the uneven geography of storm-water infrastructure make data missing from citizen science projects like CoCoRaHS of vital importance to the project’s goals. We warn that scientific knowledge created from citizen science projects may produce scientific knowledge in service of wealthy, whiter communities at the expense of both communities of color and low-income communities. 

Abstract The bulk of research on citizen science participants is project centric, based on an assumption that volunteers experience a single project. Contrary to this assumption, survey responses (n = 3894) and digital trace data (n = 3649) from volunteers, who collectively engaged in 1126 unique projects, revealed that multiproject participation was the norm. Only 23% of volunteers were singletons (who participated in only one project). The remaining multiproject participants were split evenly between discipline specialists (39%) and discipline spanners (38% joined projects with different disciplinary topics) and unevenly between mode specialists (52%) and mode spanners (25% participated in online and offline projects). Public engagement was narrow: The multiproject participants were eight times more likely to be White and five times more likely to hold advanced degrees than the general population. We propose a volunteer-centric framework that explores how the dynamic accumulation of experiences in a project ecosystem can support broad learning objectives and inclusive citizen science. 

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.