American Journal of Law and Medicine

Despite concerns that sociocultural connotations of the term citizen science may discourage engagement with such projects among certain groups, little empirical evidence is available about the behavioral effects of this terminology. One specific area of concern is the persistent gender gap in citizen science participation. A two-week field experiment (N = 699) with users of an online platform framed as either a citizen science or an environmental stewardship project examined framing and gender effects on engagement, sense of community (SoC), and indicators of pro-environmental interest. Results revealed no direct effects of the frame. Rather, framing interacted with participants’ perceptions of the extent to which the project was about citizen science or environmental stewardship. Perceiving the project as environmental stewardship predicted higher engagement and environmental interest among women than among men, and greater SoC only among men assigned to the environmental stewardship frame. A key implication is that the congruence between a project’s label and people’s experiences in the project may be more important than how the project is labeled.

Shifting baselines, whereby people’s perceptions of what was the “natural” state of the environment changes with each generation, hinders conservation, restoration, and management. Formal and informal historical animal records can be used to inform past biological, ecological, and environmental patterns and processes. Trophy specimens are cultural and social objects but also are examples of informal historical records that may supply biological tissue and supplement formal natural history collections. The use of social media to gather information from citizen scientists has great potential for data collection of such specimens. The aim of this study was to evaluate the potential utility of Facebook and traditional media to collect data on taxidermal Murray cod (Maccullochella peelii), a large, long-lived freshwater fish endemic to the Murray-Darling Basin, Australia. A Facebook group, “Cod Spot,” was established as the location for information dissemination to potential citizen scientists, and where data on Murray cod mounts could be uploaded. This was complemented with social and mainstream media promotion, a research website, and an e-survey. Cod Spot received >7,000 interactions and approximately 400 participants. A total of 189 verified locations of Murray cod head and whole mounts were found. The e-survey provided verification of the potential to turn these cultural and social objects into ones with scientific value. Participants included interested persons, collectors, taxidermists, stewards, or owners of mounts. Most participants were males aged 35+, although women comprised almost a third of website users. This research has shown that low-cost marketing, combined with a widely dispersed, relatively common and well-known object of interest, can be effective at gaining participation in citizen science collaborations.

Citizen science (CS) projects focused on microplastics (MP), small plastics that cause widespread ocean pollution, have gained interest from international research communities. However, research is lacking on designing citizen science educational programmes that promote microplastic problem-solving and enhance local understanding. These programmes could use STEAM (Science, Technology, Engineering, Art, and Math) education and creative activities for children, supported by local adults such as parents and teachers. We, therefore, created a nine-week STEAM education CS after-school programme for primary school children. In addition to MP sampling, we have also incorporated elements of STEAM education, combining creative projects and motivational activities, in this creative-motivated after-school programme. Our goals are to encourage long-term community cooperation in research, learning about MP issues, and thinking about local solutions through this community participatory CS programme. As a result, our qualitative results showed that five primary school children and five community adults were actively involved in the programme. Three creative project outputs were produced, and four MP data sampling sessions were conducted. Three pairs of children and their mother participants remained engaged in this ongoing problem-solving activity 10 months after its conclusion. During our programme progression, we observed familial engagement between local children and parents, which has not commonly been studied in the context of CS programmes. We believe that designing action-motivating long-term programmes to raise participants’ awareness of issues and interest in research is important. This CS programme has the potential to encourage long-term community interaction with research and enhance community involvement in environmental issues.

Freshwater ecosystems are increasingly facing major global and local stressors, while monitoring surface water status by regulatory agencies is often limited by financial and political constraints. Citizen science–based approaches with robust quality control and training can support regulatory monitoring and decision-making. Herein, we outline the criteria used to develop a citizen science monitoring program for water quality, based on a standardized methodology designed to support the Agenda 2030 indicator 6.3.2 and the EU’s Water Framework Directive. We explore the evolution of protocols used to ensure data robustness and transferability and examine the utility of contextual information registered by citizen scientists. We present laboratory and field experiments conducted to validate chemical and optical methods. Using the data from more than 80 projects across 4 biogeographical regions, we explore consistencies and differences in seasonal and spatial trends in macronutrient concentrations between regions. Our results indicate that nitrate and phosphate concentrations tend to increase in areas with agricultural intensification and industrial land use. Seasonally, nitrate concentrations reach a maximum in spring and autumn in temperate regions, while phosphate levels are highest in summer and autumn, reaching a minimum in winter. We also found that observations of algal blooms coincided with periods of lower nitrate concentrations. Importantly, data of ecological, chemical, and optical conditions recorded by citizen scientists are being used by local and regional stakeholders in managing freshwater ecosystems. This study reveals the potential for scaling citizen science–based monitoring programs to contribute towards a global assessment of water quality.

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Citizen science has become a valuable and reliable method for interpreting and processing big datasets, and is vital in the era of ever-growing data volumes. However, there are inherent difficulties in the generating labels from citizen scientists, due to the inherent variability between the members of the crowd, leading to variability in the results. Sometimes, this is useful — such as with serendipitous discoveries, which corresponds to rare/unknown classes in the data — but it might also be due to ambiguity between classes. The primary issue is then to distinguish between the intrinsic variability in the dataset and the uncertainty in the citizen scientists’ responses, and leveraging that to extract scientifically useful relationships. In this paper, we explore using a neural network to interpret volunteer confusion across the dataset, to increase the purity of the downstream analysis. We focus on the use of learned features from the network to disentangle feature similarity across the classes, and the ability of the machines’ “attention” in identifying features that lead to confusion. We use data from Jovian Vortex Hunter, a citizen science project to study vortices in Jupiter’s atmosphere, and find that the latent space from the model helps effectively identify different sources of image-level features that lead to low volunteer consensus. Furthermore, the machine’s attention highlights features corresponding to specific classes. This provides meaningful image-level feature-class relationships, which is useful in our analysis for identifying vortex-specific features to better understand vortex evolution mechanisms. Finally, we discuss the applicability of this method to other citizen science projects.

Many bioimaging research projects require objects of interest to be identified, located, and then traced to allow quantitative measurement. Depending on the complexity of the system and imaging, instance segmentation is often done manually, and automated approaches still require weeks to months of an individual’s time to acquire the necessary training data for AI models. As such, there is a strong need to develop approaches for instance segmentation that minimize the use of expert annotation while maintaining quality on challenging image analysis problems. Herein, we present our work on a citizen science project we ran called Science Scribbler: Virus Factory on the Zooniverse platform, in which citizen scientists annotated a cryo-electron tomography volume by locating and categorising viruses using point-based annotations instead of manually drawing outlines. One crowdsourcing workflow produced a database of virus locations, and the other workflow produced a set of classifications of those locations. Together, this allowed mask annotation to be generated for training a deep learning–based segmentation model. From this model, segmentations were produced that allowed for measurements such as counts of the viruses by virus class. The application of citizen science–driven crowdsourcing to the generation of instance segmentations of volumetric bioimages is a step towards developing annotation-efficient segmentation workflows for bioimaging data. This approach aligns with the growing interest in citizen science initiatives that combine the collective intelligence of volunteers with AI to tackle complex problems while involving the public with research that is being undertaken in these important areas of science.

In the era of rapidly growing astronomical data, the gap between data collection and analysis is a significant barrier, especially for teams searching for rare scientific objects. Although machine learning (ML) can quickly parse large data sets, it struggles to robustly identify scientifically interesting objects, a task at which humans excel. Human-in-the-loop (HITL) strategies that combine the strengths of citizen science (CS) and ML offer a promising solution, but first, we need to better understand the relationship between human- and machine-identified samples. In this work, we present a case study from the Galaxy Zoo: Weird & Wonderful project, where volunteers inspected ~200,000 astronomical images—processed by an ML-based anomaly detection model—to identify those with unusual or interesting characteristics. Volunteer-selected images with common astrophysical characteristics had higher consensus, while rarer or more complex ones had lower consensus. This suggests low-consensus choices shouldn’t be dismissed in further explorations. Additionally, volunteers were better at filtering out uninteresting anomalies, such as image artifacts, which the machine struggled with. We also found that a higher ML-generated anomaly score that indicates images’ low-level feature anomalousness was a better predictor of the volunteers’ consensus choice. Combining a locus of high volunteer-consensus images within the ML learnt feature space and anomaly score, we demonstrated a decision boundary that can effectively isolate images with unusual and potentially scientifically interesting characteristics. Using this case study, we lay important guidelines for future research studies looking to adapt and operationalize human-machine collaborative frameworks for efficient anomaly detection in big data.

We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the zone of proximal development (ZPD) concept, which allows us to describe AI augmentation of human learning, human augmentation of machine learning, and how tasks can be designed to facilitate co-learning. The study takes a design-science approach to explore the design, deployment, and evaluations of the Gravity Spy citizen science project. The findings highlight the challenges and opportunities of co-learning, where both humans and machines contribute to each other’s learning and capabilities. The study takes its point of departure in the literature on co-learning and develops a framework for designing projects where humans and machines mutually enhance each other’s learning. The research contributes to the existing literature by developing a dynamic approach to human-AI augmentation, by emphasizing that the ZPD supports ongoing learning for volunteers and keeps machine learning aligned with evolving data. The approach offers potential benefits for project scalability, participant engagement, and automation considerations while acknowledging the importance of tutorials, community access, and expert involvement in supporting learning.

One of the oldest and largest biodiversity-related citizen science (CS) projects is eBird (https://ebird.org/home), developed by the Cornell Lab of Ornithology. It provides a mobile application for birdwatchers to record checklists of when, where, and how they have seen or heard birds. The Cornell Lab has also developed a mobile application, Merlin, which uses a deep convolutional neural network to help users automatically identify bird species from photos, sounds (converted to spectrograms), or descriptions. This research investigates how the use of machine learning (ML) classification models affects the learning of novice birders. Our participants (computer science students with no previous background in ornithology) were randomly divided into three groups: one using the eBird application and identifying bird species themselves; one using the Merlin application, which uses ML to automatically identify birds from photos or sounds; and a control group. Participants were tested on their knowledge of birds before and after participating in the project to see how using the ML classification model affected their learning. We also interviewed selected participants after the post-test to understand what they had done and what might explain the results. Our results show that novice participants who participate in a CS project for even a short time significantly improve their content knowledge of familiar birds in their neighbourhood, and that eBird users outperform Merlin users on the knowledge post-test. Although AI may improve volunteer productivity and retention, there is a risk that it may reduce their learning. Further research with different participant profiles and project designs is needed to understand how to optimise volunteer productivity, retention, and learning in AI-assisted CS projects.

There is a duality of trust in participatory science (citizen science) projects in which the data produced by volunteers must be trusted by the scientific community and participants must trust the scientists who lead projects. Facilitator organizations are third-party organizations that engage their members in participatory science to enrich their members’ experience at their organization. In Crowd the Tap, we engaged participants through facilitator organizations including high schools, faith communities, universities, and a corporate volunteer program. We used Kruskal Wallis tests and chi-square tests with Bonferroni post hoc tests to assess how data quality and privacy preferences differed across facilitator groups and amongst those who participated in the project independently (unfacilitated). Faith communities provided higher data quality while students provided lower data quality. Data quality in education settings differed based on the level of investment of the project in terms of both time and money as well as student age. We also found that demographic and household characteristics seemed more important in predicting privacy preferences than facilitation. Our results suggest that project leaders can support diverse participation by extending protection of participant privacy and investing in needed resources to support facilitators. They also suggest that education-oriented facilitators may need to prioritize data quality to ensure authentic learning opportunities. Ultimately our results reveal several tradeoffs that project leaders can weigh when deciding to work with facilitators.

Nature conservation–related citizen science (NCCS) has grown rapidly worldwide in previous years. In Hungary, a few citizen science (CS) projects have been operating for years and some have only recently launched. Our aim herein is to assess the performance of eight Hungarian NCCS projects in three dimensions: a) science, b) nature conservation, and c) participants’ development. An evaluation framework was developed for the assessment. Our results show that the Common Bird Monitoring Program performed the best overall. This is also the oldest NCCS project in the country. When comparing the performance per dimension, the majority of the projects tended to have good performances in the science dimension. Most of the projects ensure data quality using different strategies. However, the need for open data and processing the project results for generating scientific publications still needs to be tackled by some NCCS initiatives. Regarding the nature conservation dimension, data generated have been mostly used in monitoring species/ecosystems, whereas data is less commonly used for conservation policy-making. It was identified that the participants’ development dimension has not received sufficient attention, and neither learning outcomes nor behavioral and attitude change has been evaluated by any projects. Our results of evaluating Hungarian NCCS initiatives in a complex way may offer insights for project managers and coordinators to identify which dimension are performing well and which areas need improvement. Also, our framework serves as a model that can be applied to current and future NCCS initiatives.