Online Citizen Science Projects Research Articles

Quantifying online citizen science: Dynamics and demographics of public participation in science.

Citizen scientists around the world are collecting data with their smartphones, performing scientific calculations on their home computers, and analyzing images on online platforms. These online citizen science projects are frequently lauded for their potential to revolutionize the scope and scale of data collection and analysis, improve scientific literacy, and democratize science. Yet, despite the attention online citizen science has attracted, it remains unclear how widespread public participation is, how it has changed over time, and how it is geographically distributed. Importantly, the demographic profile of citizen science participants remains uncertain, and thus to what extent their contributions are helping to democratize science. Here, we present the largest quantitative study of participation in citizen science based on online accounts of more than 14 million participants over two decades. We find that the trend of broad rapid growth in online citizen science participation observed in the early 2000s has since diverged by mode of participation, with consistent growth observed in nature sensing, but a decline seen in crowdsourcing and distributed computing. Most citizen science projects, except for nature sensing, are heavily dominated by men, and the vast majority of participants, male and female, have a background in science. The analysis we present here provides, for the first time, a robust 'baseline' to describe global trends in online citizen science participation. These results highlight current challenges and the future potential of citizen science. Beyond presenting our analysis of the collated data, our work identifies multiple metrics for robust examination of public participation in science and, more generally, online crowds. It also points to the limits of quantitative studies in capturing the personal, societal, and historical significance of citizen science.

Training an AI rover for Mars: Why and how?

This article reports on a case study from a larger project exploring ways in which online citizen science projects can enhance students’ learning in science and in digital technology. In this case, two teachers integrated the digital technology curriculum with the science capability “interpreting representations” using the engaging context of Mars. As part of the unit, the students contributed to the online citizen science project AI4Mars, did some unplugged computational thinking, and then transferred this learning to learning to program mBots. Our aim is to inspire others to plan curricula that integrate opportunities for developing students’ science capabilities and their digital technology skills.

‘Help us better understand our changing climate’: Exploring the discourse of Citizen Science

In this article I claim that online Citizen Science projects are exemplars of a digital genre that acts as text and medium. To support this claim I apply a previously proposed two-dimensional genre analytical model and develop empirical procedures to identify how ‘communicative purpose’ is realised by functional units/links, which in turn are realised by rhetorical strategies (verbal and visual) in two dimensions, the reading mode and the navigation mode. Empirical data show that this genre fulfils a set of distinct communicative purposes, namely to build credibility and trust in scientific research, to make specialised contents accessible to audiences with different levels of scientific literacy, to convey emotion and to build and maintain citizen-volunteers’ engagement. Such multifunctionality fulfils the social exigence of the genre, that is, supporting participatory science. The study contributes to the empirical characterisation of non-linear, multimodal genres taking into account the roles of text producer and text receiver.

An evidence-based approach to secondary school science: Online citizen science and the science capabilities

As part of a multiyear research project investigating the affordances of online citizen science (OCS) projects for enhancing school students’ learning in relation to science and digital technology, teacher–researchers have designed and implemented classroom interventions incorporating one or more OCS projects. The project is situated in New Zealand, and each intervention has embedded an OCS project within a wider unit of learning focusing on one or more “science capabilities” (Ministry of Education, n.d.). This article presents one of the case studies generated in the wider project. It is of a Year 9 class that engaged with the OCS project Planet Four as part of a wider inquiry unit emphasising the science capability Use evidence: “Can humans live on Mars?” The findings demonstrate that a deliberate focus on using evidence throughout the unit gave students multiple opportunities to practise and develop this science capability within the engaging context of space travel.

Planet Four: A Neural Network’s search for polar spring-time fans on Mars

Dark deposits visible from orbit appear in the Martian south polar region during the springtime. These are thought to form from explosive jets of carbon dioxide gas breaking through the thawing seasonal ice cap, carrying dust and dirt which is then deposited onto the ice as dark ‘blotches’, or blown by the surface winds into streaks or ‘fans’. We investigate machine learning (ML) methods for automatically identifying these seasonal features in High Resolution Imaging Science Experiment (HiRISE) satellite imagery. We designed deep Convolutional Neural Networks (CNNs) that were trained and tested using the catalog generated by Planet Four, an online citizen science project mapping the south polar seasonal deposits. We validated the CNNs by comparing their results with those of ISODATA (Iterative Self-Organizing Data Analysis Technique) clustering and as expected, the CNNs were significantly better at predicting the results found by Planet Four, in both the area of predicted seasonal deposits and in delineating their boundaries. We found neither the CNNs or ISODATA were suited to predicting the source point and directions of seasonal fans, which is a strength of the citizen science approach. The CNNs showed good agreement with Planet Four in cross-validation metrics and detected some seasonal deposits in the HiRISE images missed in the Planet Four catalog; the total area of seasonal deposits predicted by the CNNs was 27% larger than that of the Planet Four catalog, but this aspect varied considerably on a per-image basis.

Community‐based citizen science projects can support the distributional monitoring of fishes

Abstract Effective conservation and fisheries management requires data to capture demographic processes and range limits for each species to maximize population health and productivity. This need is constrained by limited funding and resources, particularly for countries with large land areas and coastlines as well as expansive exclusive economic zones. This imbalance means that monitoring efforts are often focused on targets of commercial and recreational fishing, which results in incomplete distributional records for non‐target, small‐bodied, and/or cryptic species. Community‐based citizen science projects offer one potential alternative for scientists and fisheries managers needing this type of information but lacking sufficient resources to gather it. This study investigated whether data sourced from an online citizen science project (iNaturalist: Australasian Fishes) can assist in the distributional monitoring of a subset of fish species. Given the regional focus of this citizen science project, distributional data in the form of occurrence records for abundant, protected, and threatened fish species as assessed by the International Union for Conservation of Nature in Australia and New Zealand were explored. Data for important commercial and recreational fishery targets in New South Wales were also explored, as a case study of a large jurisdiction with extensive monitoring requirements. The occurrence records for some of these categories of fishes were well represented in the quality‐filtered citizen science data set, particularly endemic fishes whose threat status had not yet been assessed and species not currently under any form of management. Despite gaps in coverage between major urban centres, citizen science data for the best represented endemic fishes were qualitatively comparable to the available geographic distributions for these species. We suggest that quality‐filtered citizen science data can in fact be used to improve taxonomic representation and the geographic breadth of species monitoring with increased participation.

An open innovation approach to co-produce scientific knowledge: an examination of citizen science in the healthcare ecosystem

PurposeThe article applies the citizen science phenomenon – i.e. lay people involvement in research endeavours aimed at pushing forward scientific knowledge – to healthcare. Attention is paid to initiatives intended to tackle the COVID-19 pandemic as an illustrative case to exemplify the contribution of citizen science to system-wide innovation in healthcare.Design/methodology/approachA mixed methodology consisting of three sequential steps was developed. Firstly, a realist literature review was carried out to contextualize citizen science to healthcare. Then, an account of successfully completed large-scale, online citizen science projects dealing with healthcare and medicine has been conducted in order to obtain preliminary information about distinguishing features of citizen science in healthcare. Thirdly, a broad search of citizen science initiatives targeted to tackling the COVID-19 pandemic has been performed. A comparative case study approach has been undertaken to examine the attributes of such projects and to unravel their peculiarities.FindingsCitizen science enacts the development of a lively healthcare ecosystem, which takes its nourishment from the voluntary contribution of lay people. Citizen scientists play different roles in accomplishing citizen science initiatives, ranging from data collectors to data analysts. Alongside enabling big data management, citizen science contributes to lay people's education and empowerment, soliciting their active involvement in service co-production and value co-creation.Practical implicationsCitizen science is still underexplored in healthcare. Even though further evidence is needed to emphasize the value of lay people's involvement in scientific research applied to healthcare, citizen science is expected to revolutionize the way innovation is pursued and achieved in the healthcare ecosystem. Engaging lay people in a co-creating partnership with expert scientist can help us to address unprecedented health-related challenges and to shape the future of healthcare. Tailored health policy and management interventions are required to empower lay people and to stimulate their active engagement in value co-creation.Originality/valueCitizen science relies on the wisdom of the crowd to address major issues faced by healthcare organizations. The article comes up with a state of the art investigation of citizen science in healthcare, shedding light on its attributes and envisioning avenues for further development.

Digital Citizen Science for Responding to COVID-19 Crisis: Experiences from Iran.

The Coronavirus Disease 2019 (COVID-19) pandemic has so far been the most severe global public health emergency in this century. Generally, citizen science can provide a complement to authoritative scientific practices for responding to this highly complex biological threat and its adverse consequences. Several citizen science projects have been designed and operationalized for responding to COVID-19 in Iran since the infection began. However, these projects have mostly been overlooked in the existing literature on citizen science. This research sheds light on the most significant online citizen science projects to respond to the COVID-19 crisis in Iran. Furthermore, it highlights some of the opportunities and challenges associated with the strengths and weaknesses of these projects. Moreover, this study captures and discusses some considerable insights and lessons learned from the failures and successes of these projects and provides solutions to overcome some recognized challenges and weaknesses of these projects. The outcomes of this synthesis provide potentially helpful directions for current and future citizen science projects—particularly those aiming to respond to biological disasters such as the COVID-19 pandemic.

Weaving a web of connections through online citizen science

This article explores how the funding process of New Zealand’s Teaching and Learning Research Initiative (TLRI) has catalysed the coming together of an interdisciplinary research team of education researchers, information systems researchers, and teacher practitioners. Through two funded research projects, a large and growing web of connections is being woven, benefiting the research partnership and outcomes. Our collective aim is to investigate the affordances of online citizen science projects to enhance science teaching and learning. Using examples, we trace the development of some key lines of inquiry that have been made possible because of the interdisciplinary foundation of the projects.

Gender Imbalance and Spatiotemporal Patterns of Contributions to Citizen Science Projects: The Case of Zooniverse

Citizen Science is research undertaken by professional scientists and members of the public collaboratively. Despite numerous benefits of citizen science for both the advancement of science and the community of the citizen scientists, there is still no comprehensive knowledge of patterns of contributions, and the demography of contributors to citizen science projects. In this paper we provide a first overview of spatiotemporal and gender distribution of citizen science workforce by analyzing 54 million classifications contributed by more than 340 thousand citizen science volunteers from 198 countries to one of the largest online citizen science platforms, Zooniverse. First we report on the uneven geographical distribution of the citizen scientist and model the variations among countries based on the socio-economic conditions as well as the level of research investment in each country. Analyzing the temporal features of contributions, we report on high “burstiness” of participation instances as well as the leisurely nature of participation suggested by the time of the day that the citizen scientists were the most active. Finally, we discuss the gender imbalance among online citizen scientists (about 30% female) and compare it with other collaborative projects as well as the gender distribution in more formal scientific activities. Online citizen science projects need further attention from outside of the academic community, and our findings can help attract the attention of public and private stakeholders, as well as to inform the design of the platforms and science policy making processes.

Affordances and tensions in recording bird observations: how coordinators and volunteers perceive and experience citizen science in birding

Digital citizen science projects differ greatly in their goals and design. Tensions arise when coordinators' design choices and conceptions of citizen science conflict with users' motivations and expectations. In this paper, we use a combination of qualitative methods to gain new insights into the ways citizen science is understood and implemented digitally. This includes a study into the affordances of two citizen science portals for bird observations, and qualitative interviews with users and coordinators of the portals. This reveals tensions related to data sharing, community hierarchies, and communicated expectations. Awareness of these tensions can benefit the future design of online citizen science projects.

Maintaining User Engagement in an Infectious Disease Surveillance-Related Citizen Science Project

Over the past fifteen years, infectious disease surveillance has evolved to include online citizen science projects that implement active digital data collection. Flu Near You (FNY) is an online participatory surveillance system in the United States that collects weekly health reports from its volunteers. Because high engagement levels and consistent participation of users are essential to accurately track disease and estimate burden, FNY implemented three measures: (1) adding a new feature in that allows website visitors to submit a health report without registering, (2) administering user surveys at the end of each influenza season, and (3) partnering with Science Friday (SciFri) (a weekly public radio program that discusses topics relevant to science, nature, and technology) with the goals of increasing the number of weekly participant reports, improving the frequency of user reporting, and understanding user motivation. In this paper, we evaluate the impact of these three measures. The number of registered users who submitted only one report decreased after allowing users to submit reports without registering. The survey indicated that respondents were primarily motivated by the importance of disease tracking and by a desire to participate in a citizen science project. Finally, users who registered from SciFri were highly engaged and also motivated by a desire to participate in a citizen science project.

Exploring the participation of young citizen scientists in scientific research: The case of iNaturalist.

Online citizen science projects have broadened options for accessing science and enabled different forms of participation in scientific research for adult and young volunteers. Yet, little is known regarding participation patterns among youth participants. Quantitative approaches were used to investigate the contribution of 183 young volunteers to citizen science on the iNaturalist platform and the participation behaviour that relates to their contribution. The participants accessed and used iNaturalist as part of one-day field-based events (bioblitzes) facilitated by museums. Compared to the observation behaviour of all iNaturalist users, as documented on the platform, the young volunteers observe fewer plants and birds, and more molluscs, arachnids and insects. The average daily contributions of young volunteers were found to be positively associated with a large proportion of active days on iNaturalist and a systematic contribution behaviour, yet negatively related to a long duration on the platform. This study enhances our understanding of young volunteers’ contributions to citizen science and provides insights for research on participation in online citizen science. Our findings have implications on how museums design the field-based events to encourage follow-up systematic participation and maintain active contribution.

A Knowledge Perspective on Quality in Complex Citizen Science

This article examines how project leaders in complex citizen science projects ensure the quality of project outcomes given the challenges of involving citizens, whose knowledge is diverse and unknown beforehand. To this aim, a qualitative multiple-case study was carried out to compare the practices of five collaborative online citizen science projects in the humanities in which citizens transcribe, translate, and annotate handwritten manuscripts from the 17th, 18th, and 19th centuries. The choices made to access knowledge, thus to recruit participants either through open or targeted calls, seem to be fundamental for the different configurations of knowledge management activities that project leaders apply to ensure quality outcomes. Other factors influencing these knowledge management configurations are citizens’ proximity, knowledge characteristics, technology affordances, and the extent to which project leaders are aware of citizens’ backgrounds and skills. This study adds to earlier frameworks proposed to advise the design and management of citizen science projects. By taking a knowledge perspective, this article provides practical directions for project leaders involved in citizen science and highlights the need to put time and effort in managing knowledge processes.

Developing science capabilities for citizenship through participation in online citizen science (OCS) projects

Developing science capabilities for citizenship through participation in online citizen science (OCS) projects

Online Citizen Science: A Systematic Review of Effects on Learning and Scientific Literacy

Participation in online citizen science is increasingly popular, yet studies that examine the impact on participants’ learning are limited. The aims of this paper are to identify the learning impact on volunteers who participate in online citizen science projects and to explore the methods used to study the impact. The ten empirical studies, examined in this systematic review, report learning impacts on citizens’ attitudes towards science, on their understanding of the nature of science, on topic-specific knowledge, on science knowledge, and on generic knowledge. These impacts were measured using self-reports, content analysis of contributed data and of forum posts, accuracy checks of contributed data, science and project-specific quizzes, and instruments for measuring scientific attitudes and beliefs. The findings highlight that certain technological affordances in online citizen science projects can cultivate citizens’ knowledge and skills, and they point to unexplored areas, including the lack of experimental and long-term studies, and studies in formal education settings.

What Do We Know about Young Volunteers? An Exploratory Study of Participation in Zooniverse.

Citizen Science (CS) is an increasingly popular activity enacted either in the field or online. Volunteers participate in research activities such as data processing and analysis by, for example, identifying plants and animals. In this paper we examine young people’s participation in online CS projects hosted on the Zooniverse platform. This is an exploratory study, the first of its kind that focuses on young people, mainly 16−19 years old. It uses data analytics and visualisation techniques to capture participation in online CS, and in particular to answer the following questions: (a) What does young people’s participation look like in CS projects? (b) What Zooniverse projects do young people choose to participate in? and (3) What Zooniverse projects do young people choose together? Findings revealed five distinct engagement profiles characterising young people’s participation and identified certain projects as been more popular across participants. Implications for the design of online citizen science projects targeting young people are discussed.

Knowledge Tracing to Model Learning in Online Citizen Science Projects

We present the design of a citizen science system that uses machine learning to guide the presentation of image classification tasks to newcomers to help them more quickly learn how to do the task while still contributing to the work of the project. A Bayesian model for tracking volunteer learning for training with tasks with uncertain outcomes is presented and fit to data from 12,986 volunteer contributors. The model can be used both to estimate the ability of volunteers and to decide the classification of an image. A simulation of the model applied to volunteer promotion and image retirement suggests that the model requires fewer classifications than the current system.

Dynamics of Engagement in Citizen Science: Results from the “Yes, I do!”-Project

Citizen science projects need to attract citizens and motivate them to dedicate their energy and time to science. Recruiting enough participants and keeping them engaged throughout the project is often a big challenge for the scientists involved. In this paper, recruiting and engagement strategies are evaluated for a successful midsize online citizen science project in the field of humanities. Quantitative measures are applied to track the quantity and quality of citizens’ contributions over time, allowing understanding of the dynamics of engagement in citizen science. The study shows that monitoring the level of activity and the quality of contributions provides useful insights about a project’s dynamics. We found that a small core group of volunteers was responsible for most of the input to the project, that their transcriptions were very accurate from the start, and that prompt feedback on their performance was important to keep activity levels high.

Teaching citizen scientists to categorize glitches using machine learning guided training

Existing literature points to scaffolded training as an effective yet resource-intensive approach to help newcomers learn and stay motivated. Experts need to select relevant learning materials and continuously assess learners' progress. Peer production communities such as Wikipedia and Open Source Software Development projects face the additional problem of turning volunteers into productive participants as soon as possible. To address these challenges, we designed and tested a training regime combining scaffolded instruction and machine learning to select learning materials and gradually introduces new materials to individuals as their competences improve. We evaluated the training regime on 386 participants that contribute to Gravity Spy, an online citizen science project where people are asked to categorize glitches to assist scientists in the search for gravitational waves. Volunteers were assigned to one of two conditions; (1) a machine learning guided training (MLGT) system that continuously assesses volunteers skill level and adjusts the learning materials or (2) an unscaffolded training program where all learning materials were administered at once. Our analysis revealed that volunteers in the MLGT condition were more accurate on the categorization task (an average accuracy of 90% vs. 54%), executed more tasks (an average of 228 vs. 121 tasks), and were retained for a longer period (an average of 2.5 vs. 2 sessions) than volunteers in the unscaffolded training. The results suggest that MLGT is an effective pedagogical approach for training volunteers in categorization tasks and increases volunteers’ motivation.