Deep learning models map rapid plant species changes from citizen science and remote sensing data.

Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model-Deepbiosphere-not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.

De novo protein design by citizen scientists.

Online citizen science projects such as GalaxyZoo1, Eyewire2 and Phylo3 have proven very successful for data collection, annotation and processing, but for the most part have harnessed human pattern-recognition skills rather than human creativity. An exception is the game EteRNA4, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson-Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein-folding game Foldit5. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure and an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top-scoring solutions and subsequent game improvement, Foldit players can now-starting from an extended polypeptide chain-generate a diversity of protein structures and sequences that encode them in silico. One hundred forty-six Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed and soluble in Escherichia coli, and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds-including a new fold not observed in natural proteins. High-resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge that contributes to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges such as the protein design problem.

Understanding Ixodes ricinus occurrence in private yards: influence of yard and landscape features.

BACKGROUND: Lyme borreliosis is the most frequent zoonotic disease in the northern hemisphere and is transmitted by ticks of the genus Ixodes. Although many people are bitten by ticks in private yards, our understanding of the factors associated with their presence in these areas remains limited. To address this gap, we used a citizen science approach to identify the local and landscape features associated with tick presence in yards. METHODS: This study was conducted near Nancy, a city in northeastern France, from 2020 to 2022. Citizen scientists collected ticks in their yard on a single event (n = 185) and measured 13 yard features. Additionally, we computed 11 features related to the landscape composition and spatial configuration surrounding these yards. Using generalized linear mixed models, we determined the yard and landscape features associated with the presence of ticks and nymphal Ixodes ricinus (hereafter nymphs), the life stage, and species that mostly bite humans. RESULTS: Despite a low density, ticks were found in 32% of the yards, including yards in urbanized areas. At the transect level, the likelihood of finding a nymph was nearly three times higher in transects shaded by vegetation compared to those in open areas, with no relationship between nymph occurrence and transect location or grass height. At the yard level, the occurrence of ticks and nymphs was related to both yard and landscape characteristics. Nymph and tick occurrence were more than twice as high in yards with signs of deer and a wood/brush pile compared to those without these characteristics, and increased with the connectivity of vegetation areas and the percentage of forest areas in the landscape. CONCLUSIONS: Our study reveals that private yards across an urbanization gradient are locations of tick exposure with tick presence linked to both yard and landscape factors. These findings emphasize the importance of public awareness regarding tick exposure in yards and provide crucial insights for future public health prevention campaigns.

A Model for Engaging Citizen Scientists: A Community-Partnered Research Collaboration to Address Inequities for Black Birthing People.

PURPOSE: Co-creation of a citizen-science research initiative with a collaborative team of community members and university-based scientists to address regional disparities in maternal and fetal health outcomes for Black birthing people. DESCRIPTION: Citizen scientist-led projects, where community members actively contribute to each discovery step, from setting a research agenda to collecting data and disseminating results, can extend community participatory research initiatives and help reconceptualize traditional research processes. The Pregnancy Collaborative is a citizen-science research initiative and one of nine scientific committees of The Pittsburgh Study-a longitudinal, community-partnered study designed to bring together collaborators to improve child thriving. ASSESSMENT: Ten community members and five university-based scientists participated during all phases of developing a citizen-scientist collaboration over an initial two-and-a-half-year period. Phases include forming the Pregnancy Collaborative and group research ethics training; co-creating a research agenda grounded in shared principles; and community-partnered data collection, analysis, and dissemination. These phases produced three key co-designed products: (1) a mission and vision statement of the Pregnancy Collaborative, (2) a Collaborative-endorsed research agenda, and (3) a citizen-scientist-executed research survey. CONCLUSION: Lessons learned from the formation of the Pregnancy Collaborative highlight the importance of equitable power distribution through bidirectional knowledge sharing and by centering intellectual effort, lived experience, and tools and resources of those affected by health inequities. Using a citizen science approach to co-designing and executing research helps us move maternal health inequity work from "research on" to "research with."

Validation of Citizen Science Meteorological Data: Can They Be Considered a Valid Help in Weather Understanding and Community Engagement?

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect meteorological measurements. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. This paper presents the analysis and the results of the validation of such observations against the official Italian civil protection in situ weather network, demonstrating the effectiveness of citizen science in generating valuable environmental data. The work discusses the methodology employed, including data collection and statistical analysis techniques, i.e., time-series analysis, spatial and temporal interpolation, and correlation analysis. The overall analysis highlights the high quality and reliability of citizen-generated data as well as the strengths of the MeteoTracker platform. Furthermore, our findings underscore the potential of citizen science to augment traditional monitoring efforts, inform decision-making processes in environmental research and management, and improve the social awareness about environmental and climate issues.

Willingness to participate in in-the-moment surveys triggered by online behaviors.

Surveys are a fundamental tool of empirical research, but they suffer from errors: in particular, respondents can have difficulties recalling information of interest to researchers. Recent technological developments offer new opportunities to collect data passively (i.e., without participant's intervention), avoiding recall errors. One of these opportunities is registering online behaviors (e.g., visited URLs) through tracking software ("meter") voluntarily installed by a sample of individuals on their browsing devices. Nevertheless, metered data are also affected by errors and only cover part of the objective information, while subjective information is not directly observable. Asking participants about such missing information by means of web surveys conducted in the moment an event of interest is detected by the meter has the potential to fill the gap. However, this method requires participants to be willing to participate. This paper explores the willingness to participate in in-the-moment web surveys triggered by online activities recorded by a participant-installed meter. A conjoint experiment implemented in an opt-in metered panel in Spain reveals overall high levels of willingness to participate among panelists already sharing metered data, ranging from 69% to 95%. The main aspects affecting this willingness are related to the incentive levels offered. Limited differences across participants are observed, except for household size and education. Answers to open questions also confirm that the incentive is the key driver of the decision to participate, whereas other potential problematic aspects such as the limited time to participate, privacy concerns, and discomfort caused by being interrupted play a limited role.

Improving big citizen science data: Moving beyond haphazard sampling.

Citizen science is mainstream: millions of people contribute data to a growing array of citizen science projects annually, forming massive datasets that will drive research for years to come. Many citizen science projects implement a "leaderboard" framework, ranking the contributions based on number of records or species, encouraging further participation. But is every data point equally "valuable?" Citizen scientists collect data with distinct spatial and temporal biases, leading to unfortunate gaps and redundancies, which create statistical and informational problems for downstream analyses. Up to this point, the haphazard structure of the data has been seen as an unfortunate but unchangeable aspect of citizen science data. However, we argue here that this issue can actually be addressed: we provide a very simple, tractable framework that could be adapted by broadscale citizen science projects to allow citizen scientists to optimize the marginal value of their efforts, increasing the overall collective knowledge.

Monitoring mosquito nuisance for the development of a citizen science approach for malaria vector surveillance in Rwanda.

BACKGROUND: Many countries, including Rwanda, have mosquito monitoring programmes in place to support decision making in the fight against malaria. However, these programmes can be costly, and require technical (entomological) expertise. Involving citizens in data collection can greatly support such activities, but this has not yet been thoroughly investigated in a rural African context. METHODS: Prior to the implementation of such a citizen-science approach, a household entomological survey was conducted in October-November 2017 and repeated one year later in Busoro and Ruhuha sectors, in southern and eastern province of Rwanda, respectively. The goal was to evaluate the perception of mosquito nuisance reported by citizens as a potential indicator for malaria vector hotspots. Firstly, mosquito abundance and species composition were determined using Centers for Disease Control and Prevention (CDC) light traps inside the houses. Secondly, household members were interviewed about malaria risk factors and their perceived level of mosquito nuisance. RESULTS: Tiled roofs, walls made of mud and wood, as well as the number of occupants in the house were predictors for the number of mosquitoes (Culicidae) in the houses, while the presence of eaves plus walls made of mud and wood were predictors for malaria vector abundance. Perception of mosquito nuisance reported indoors tended to be significantly correlated with the number of Anopheles gambiae sensu lato (s.l.) and Culicidae collected indoors, but this varied across years and sectors. At the village level, nuisance also significantly correlated with An. gambiae s.l. and total mosquito density, but only in 2018 while not in 2017. CONCLUSIONS: Perception of mosquito nuisance denoted in a questionnaire survey could be used as a global indicator of malaria vector hotspots. Hence, involving citizens in such activities can complement malaria vector surveillance and control.

Optimizing future biodiversity sampling by citizen scientists.

We are currently in the midst of Earth's sixth extinction event, and measuring biodiversity trends in space and time is essential for prioritizing limited resources for conservation. At the same time, the scope of the necessary biodiversity monitoring is overwhelming funding for professional scientific monitoring. In response, scientists are increasingly using citizen science data to monitor biodiversity. But citizen science data are 'noisy', with redundancies and gaps arising from unstructured human behaviours in space and time. We ask whether the information content of these data can be maximized for the express purpose of trend estimation. We develop and execute a novel framework which assigns every citizen science sampling event a marginal value, derived from the importance of an observation to our understanding of overall population trends. We then make this framework predictive, estimating the expected marginal value of future biodiversity observations. We find that past observations are useful in forecasting where high-value observations will occur in the future. Interestingly, we find high value in both 'hotspots', which are frequently sampled locations, and 'coldspots', which are areas far from recent sampling, suggesting that an optimal sampling regime balances 'hotspot' sampling with a spread across the landscape.

Citizen science informs human-tick exposure in the Northeastern United States.

BACKGROUND: Tick-borne disease is the result of spillover of pathogens into the human population. Traditionally, literature has focused on characterization of tick-borne disease pathogens and ticks in their sylvatic cycles. A limited amount of research has focused on human-tick exposure in this system, especially in the Northeastern United States. Human-tick interactions are crucial to consider when assessing the risk of tick-borne disease since a tick bite is required for spillover to occur. METHODS: Citizen scientists collected ticks from the Northeastern US through a free nationwide program. Submitted ticks were identified to species, stage, and sex. Blacklegged ticks, Ixodes scapularis, were tested for the presence of Borrelia burgdorferi sensu lato (s.l.) and hard-tick relapsing fever Borrelia. Seasonality of exposure and the citizen science activity during tick exposure was recorded by the citizen scientist. A negative binomial model was fit to predict county level CDC Lyme disease cases in 2016 using citizen science Ixodes scapularis submissions, state, and county population as predictor variables. RESULTS: A total of 3740 submissions, comprising 4261 ticks, were submitted from the Northeastern US and were reported to be parasitizing humans. Of the three species submitted, blacklegged ticks were the most prevalent followed by American dog ticks and lone star ticks. Submissions peaked in May with the majority of exposure occurring during every-day activities. The most common pathogen in blacklegged ticks was B. burgdorferi s.l. followed by hard-tick relapsing fever Borrelia. Negative binomial model performance was best in New England states followed by Middle Atlantic states. CONCLUSIONS: Citizen science provides a low-cost and effective methodology for describing the seasonality and characteristics of human-tick exposure. In the Northeastern US, everyday activities were identified as a major mechanism for tick exposure, supporting the role of peri-domestic exposure in tick-borne disease. Citizen science provides a method for broad pathogen and tick surveillance, which is highly related to human disease, allowing for inferences to be made about the epidemiology of tick-borne disease.

Citizen science beyond invited participation: nineteenth century amateur naturalists, epistemic autonomy, and big data approaches avant la lettre.

Dominant forms of contemporary big-data based digital citizen science do not question the institutional divide between qualified experts and lay-persons. In our paper, we turn to the historical case of a large-scale amateur project on biogeographical birdwatching in the late nineteenth and early twentieth century to show that networked amateur research (that produces a large set of data) can operate in a more autonomous mode. This mode depends on certain cultural values, the constitution of specific knowledge objects, and the design of self-governed infrastructures. We conclude by arguing that the contemporary quest for autonomous citizen science is part of a broader discourse on the autonomy of scientific research in general. Just as the actors in our historical case positioned themselves against the elitism of gentlemen scientists, avant-garde groups of the twenty first century like biohackers and civic tech enthusiasts position themselves against the system of professional science-while "digital citizen science" remains to oscillate between claims for autonomy and realities of heteronomy, constantly reaffirming the classic lay-expert divide.

Emerging technologies in citizen science and potential for insect monitoring.

Emerging technologies are increasingly employed in environmental citizen science projects. This integration offers benefits and opportunities for scientists and participants alike. Citizen science can support large-scale, long-term monitoring of species occurrences, behaviour and interactions. At the same time, technologies can foster participant engagement, regardless of pre-existing taxonomic expertise or experience, and permit new types of data to be collected. Yet, technologies may also create challenges by potentially increasing financial costs, necessitating technological expertise or demanding training of participants. Technology could also reduce people's direct involvement and engagement with nature. In this perspective, we discuss how current technologies have spurred an increase in citizen science projects and how the implementation of emerging technologies in citizen science may enhance scientific impact and public engagement. We show how technology can act as (i) a facilitator of current citizen science and monitoring efforts, (ii) an enabler of new research opportunities, and (iii) a transformer of science, policy and public participation, but could also become (iv) an inhibitor of participation, equity and scientific rigour. Technology is developing fast and promises to provide many exciting opportunities for citizen science and insect monitoring, but while we seize these opportunities, we must remain vigilant against potential risks. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Fostering citizen-engaged HIV implementation science.

INTRODUCTION: Successful implementation of evidence-based practices depends on contextual factors like stakeholder engagement, the socio-political environment, resource availability, and stakeholders' felt needs and preferences. Nevertheless, inequities in implementation exist and undermine efforts to address HIV in marginalized key populations. Implementation science shows promise in addressing such inequities in the HIV response, but can be limited without meaningful engagement from citizens or communities. DISCUSSION: We define the concept of a citizen-engaged HIV implementation science as one that involves citizens and communities deeply in HIV implementation science activities. In this commentary, we discuss how citizen science approaches can be leveraged to spur equity in HIV implementation science. Drawing on three areas previously defined by Geng and colleagues that serve to drive impactful implementation science in the HIV response, we discuss how citizens can be engaged when considering "whose perspectives?", "what questions are being asked?" and "how are questions asked?". With respect to "whose perspectives?" a citizen-engaged HIV implementation science would leverage participatory methods and tools, such as co-creation, co-production and crowdsourcing approaches, to engage the public in identifying challenges, solve health problems and implement solutions. In terms of "what questions are being asked?", we discuss how efforts are being made to synthesize citizen or community-led approaches with existing implementation science frameworks and approaches. This also means that we ensure communities have a say in interrogating and deconstructing such frameworks and adapting them to local contexts through participatory approaches. Finally, when considering "how are questions asked?", we argue for the development and adoption of broad, guiding principles and frameworks that account for dynamic contexts to promote citizen-engaged research in HIV implementation science. This also means avoiding narrow definitions that limit the creativity, innovation and ground-up wisdom of local citizens. CONCLUSIONS: By involving communities and citizens in the development and growth of HIV implementation science, we can ensure that our implementation approaches remain equitable and committed to bridging divides and ending AIDS as a public health threat. Ultimately, efforts should be made to foster a citizen- and community-engaged HIV implementation science to spur equity in our global HIV response.

Quantification of litter in cities using a smartphone application and citizen science in conjunction with deep learning-based image processing.

Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective pollution management, environmental protection, and sustainable urban development. Therefore, here, a multidisciplinary approach to quantify and classify the abundance of litter in urban environments is proposed. In the present study, litter data collection was integrated via the Pirika smartphone application and conducted image analysis based on deep learning. Pirika was launched in May 2018 and, to date, has collected approximately one million images. Visual classification revealed that the most common types of litter were cans, plastic bags, plastic bottles, cigarette butts, cigarette boxes, and sanitary masks, in that order. The top six categories accounted for approximately 80 % of the total, whereas the top three categories accounted for more than 60 % of the total imaged litter. A deep-learning image processing algorithm was developed to automatically identify the top six litter categories. Both precision and recall derived from the model were higher than 75 %, enabling proper litter categorization. The quantity of litter derived from automated image processing was also plotted on a map using location data acquired concurrently with the images by the smartphone application. Conclusively, this study demonstrates that citizen science supported by smartphone applications and deep learning-based image processing can enable the visualization, quantification, and characterization of street litter in cities.

A citizen science approach to develop a digital intervention to reduce HIV stigma and promote HIV self-testing among adolescents and young adults: a mixed methods analysis from Kazakhstan.

INTRODUCTION: Kazakhstan has one of the fastest-growing HIV epidemics in the world, with increasing rates among adolescents and young adults (AYA). Innovative strategies are needed to increase HIV testing uptake and decrease HIV stigma among AYA. Citizen science, defined as the active engagement of the general public in scientific research tasks, promotes and facilitates community engagement throughout the research process. This citizen science study used crowdsourcing to engage AYA in Kazakhstan to develop a digital intervention to reduce HIV stigma and promote HIV self-testing. Our objectives in this paper are to describe the approach used, its feasibility and acceptability, and AYA motivations for and lessons learned collaborating on the study. METHODS: From October 2021 to July 2022, in collaboration with a Community Collaborative Research Board and a Youth Advisory Board, we developed an open call requesting multimedia submissions to reduce HIV testing stigma. Eligible submissions were separated by age group (13-19 or 20-29 years) and judged by a panel composed of AYA (n = 23), healthcare professionals (n = 12), and representatives from the local government and non-governmental organizations (n = 17). Each entry was reviewed by at least four judges and ranked on a 5-point scale. The top 20 open call contestants were asked to submit self-recordings sharing their motivation for and experience participating in the contest and lessons learned. Descriptive statistics were calculated for quantitative data. Qualitative data were coded using open coding. RESULTS: We received 96 submissions from 77 youth across Kazakhstan. Roughly, three-quarters (n = 75/96) of entries met judging eligibility criteria. Of the eligible entries, over half (n = 39/75) scored 3.5 or higher on a 5-point scale (70.0%). The most frequent types of entries were video (n = 36/96, 37.5%), image (n = 28/96, 29.2%) and text (n = 24/96, 25.0%). AYA's primary motivations for collaborating on the study included a desire to improve society and help youth. The main challenges included creating content to address complex information using simple language, finding reliable information online and technological limitations. CONCLUSIONS: Crowdsourcing was feasible and highly acceptable among AYA in Kazakhstan. Citizen science approaches hold great promise for addressing the increasingly complex health and social challenges facing communities today.

Defining community-led monitoring and its role in programme-embedded learning: lessons from the Citizen Science Project in Malawi and South Africa.

INTRODUCTION: Programme Science (PS) and community-led monitoring (CLM) intersect in unexpected and promising ways. This commentary examines a CLM initiative in Malawi and South Africa to highlight the crucial role of CLM in bolstering the PS framework. By leveraging data sources often overlooked by conventional research and evaluation approaches, CLM emerges as a pivotal element in enhancing programme effectiveness. This paper delineates the fundamental principles of CLM, presents programme outcomes derived from CLM methodologies and contextualizes these findings within the broader framework of PS. DISCUSSION: The Citizen Science Project implements CLM continuously at 33 health facilities: 14 in Malawi (eight in Kasungu District and six in Dedza District), and 19 in South Africa (all in the West Rand District), representing a total catchment area of 989,848 people. Monitoring indicators are developed in an iterative process with community groups. The indicators are unique to each country, but both focus on the uptake of health services (quantitative) and barriers to access (qualitative). Monthly clinic records surveys capture 34 indicators in Malawi and 20 in South Africa and are supplemented by qualitative interviews with care recipients and healthcare workers. Qualitative interviews provide additional granularity and help confirm and explain the more macro trends in service coverage as described in quantitative data. The resulting data analysis reveals key themes that help stakeholders and decision-makers to solve problems collaboratively. Noteworthy outcomes include a substantial increase in multi-month dispensing of antiretroviral therapy (ART) during COVID-19 (from 6% to 31%) with a subsequent recovery surpassing of HIV service benchmarks in Malawi post-pandemic. CONCLUSIONS: While quantifying direct impact remains challenging due to the project's design, CLM proves to be a robust methodology that generates credible data and produces impactful outcomes. Its potential extends beyond the health sector, empowering community leadership and fostering interventions aligned with community needs. As CLM continues to evolve, its integration into PS promises to improve relevance, quality and impact across diverse disciplines.

A Mixed Methods Approach to Understanding the Public Health Impact of a School-Based Citizen Science Program to Reduce Arsenic in Private Well Water.

BACKGROUND: Exposure to arsenic (As) in well water is a well-documented public health issue for Maine and New Hampshire, as well as for other states in the United States and abroad. Arsenic contamination of well water in these locations is primarily attributed to metasedimentary bedrock that leaches As into groundwater. However, As can also enter groundwater reserves from soils contaminated by the historical use of arsenical pesticides. Approximately half of the households in Maine and New Hampshire rely on private wells, many of which have elevated As. Arsenic exposure has been associated with an increased risk of cancer, cardiovascular disease, reduced infection resistance, and lower intelligence quotient in children. Despite these known health impacts, well water testing and treatment are not universal. OBJECTIVES: We have approached the problem of low well water testing rates in Maine and New Hampshire communities by developing the All About Arsenic (AAA) project, which engages secondary school teachers and students as citizen scientists in collecting well water samples for analysis of As and other toxic metals and supports their outreach efforts to their communities. METHODS: We assessed this project's public health impact by analyzing student data relative to existing well water quality datasets in both states. In addition, we surveyed private well owners who contributed well water samples to the project to determine the actions taken to mitigate As in well water. RESULTS: Students collected 3,070 drinking water samples for metals testing, and 752 exceeded New Hampshire's As standard of 5 ppb. The AAA data has more than doubled the amount of information available to public health agencies about well water quality in multiple municipalities across both states. Students also collected information about well types and treatment systems. Their data reveal that some homeowners did not know what type of wells they had or whether they had filtration systems. Those with filtration systems were often unaware of the type of system, what the system was filtering for, or whether the system was designed to remove As. Through interviews with pilot survey participants, we learned that some had begun mitigating their exposure to As and other toxic metals in response to test results from the AAA project. DISCUSSION: A school-based approach to collecting and analyzing private well water samples can successfully reach communities with low testing rates for toxic elements, such as As and other metals. Importantly, information generated through the program can impact household decision-making, and students can influence local and state policymaking by sharing information in their communities. https://doi.org/10.1289/EHP13421.