The loss of major health surveys once backed by the United States Agency for International Development and proposed cuts to environmental programs threaten the tracking of sustainable development. Citizen science can and should be central to building stronger, more resilient data systems. The loss of major health surveys once backed by the United States Agency for International Development and proposed cuts to environmental programs threaten the tracking of sustainable development. Citizen science can and should be central to building stronger, more resilient data systems.

In today's interconnected world, data surged in volume. This exponential increase in data availability has sparked the rise of data science and artificial intelligence (AI), changing how we handle information (Aldoseri et al. 2023). In a world undergoing rapid change across various dimensions, it is important to involve data-driven methods to be able to assess and to follow, and assist citizens in their sustainable actions and projects. These methods help analyze, support, and motivate citizens in actions and projects, addressing complex issues like biodiversity loss, urban liveability, and local activism. With a focus on inclusivity and adaptability, the session aimed to discuss transformative potential approaches that integrate citizen science, data science, and human-computer interaction (HCI). Of particular importance was the focus on the ethical considerations of data science and AI, emphasizing fairness and equity in decision-making, and showcase real-world impacts. Additionally, we explored the conditions for cross-disciplinary collaborations among data scientists, citizen scientists, researchers, practitioners, within various citizen science projects. The current paper presents the session report, highlighting the main discussion points and conclusions.

Measuring the progress towards the Sustainable Development Goals (SDGs) requires the collection of relevant and reliable data. To do so, Citizen Science can provide an essential source of non-traditional data for tracking progress towards the SDGs, as well as generate social innovations that enable such progress. At its core, citizen science relies on participatory processes involving the collaboration of stakeholders with diverse standpoints, skills, and backgrounds. The ability to measure these participatory processes is therefore key for the monitoring and evaluation of citizen science projects and to support the decisions of their coordinators. Here, we show that the monitoring of social interaction networks provides unique insights on the participatory processes and outcomes of citizen science projects. We studied fourteen early-stage citizen science projects that participated in an innovation cycle focused on SDG 13, Climate Action, as part of the Crowd4SDG project. We implemented a monitoring strategy to measure the collaborative profiles of citizen science teams. This allowed us to generate dynamic interaction networks across complementary dimensions, making visible both formal and informal interactions associated with the division of labor, collaborations, advice seeking, and communication processes of the projects during their development. Leveraging jury evaluation data, we showed that while team composition and communication are associated with project quality, measures of collaboration and activity are associated with engagement quality. Overall, monitoring social interaction dynamics helps build a more comprehensive picture of participatory processes, which is of importance for guiding citizen science projects and for designing initiatives leveraging citizen science to address the SDGs.

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.

In this article, we introduce the Open17 Challenge, an online coaching programme, inspired by the 17 United Nations (UN) Sustainable Development Goals (SDGs). This challenge has occurred roughly once a year since 2015, when the UN launched the SDGs. It lasts five weeks and involves five two-hour online coaching sessions as well as homework for the participants between sessions. The objective of the challenge is to coach a team of students about how to apply citizen science tools and methodologies to generate open data relevant to the SDGs. The goal of the coaching is to help each team develop their idea to the stage where they can make a compelling pitch that involves crowdsourcing of citizen-generated data. The format of the challenge has evolved as the organizing institutions have learned from each edition and improved iteratively. The purpose of this article is to describe the evolving methodology of the Open17 Challenge in the context of challenge-based learning (CBL) and more specifically discuss its relevance to e-learning. In particular, we analyse the potential of this methodology to generate new citizen science projects on issues relevant to the SDGs, with a view to enabling other organizations to adapt and apply this approach to specific SDG-related challenges.

Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss and categorize the potential of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluable testbed for the development of human-centered AI including HI, while also benefiting CS. In order to investigate this potential, we first relate CS to adjacent computational disciplines. Then, we demonstrate that CS projects can be grouped according to their potential for HI-enhancement by examining two key dimensions: the level of digitization and the amount of knowledge or experience required for participation. Finally, we propose a framework for types of human-AI interaction in CS based on established criteria of HI. This “HI lens” provides the CS community with an overview of ways to utilize the combination of AI and human intelligence in their projects. For AI researchers, this work highlights the opportunity CS presents to engage with real-world data sets and explore new AI methods and applications.

SPECIALTY GRAND CHALLENGE article Front. Environ. Sci., 20 September 2022Sec. Environmental Citizen Science Volume 10 - 2022 | https://doi.org/10.3389/fenvs.2022.1019628

The secretive behavior and life history of snakes makes studying their biology, distribution, and the epidemiology of venomous snakebite challenging. One of the most useful, most versatile, and easiest to collect types of biological data are photographs, particularly those that are connected with geographic location and date-time metadata. Photos verify occurrence records, provide data on phenotypes and ecology, and are often used to illustrate new species descriptions, field guides and identification keys, as well as in training humans and computer vision algorithms to identify snakes. We scoured eleven online and two offline sources of snake photos in an attempt to collect as many photos of as many snake species as possible, and attempt to explain some of the inter-species variation in photograph quantity among global regions and taxonomic groups, and with regard to medical importance, human population density, and range size. We collected a total of 725,565 photos—between 1 and 48,696 photos of 3098 of the world's 3879 snake species (79.9%), leaving 781 “most wanted” species with no photos (20.1% of all currently-described species as of the December 2020 release of The Reptile Database). We provide a list of most wanted species sortable by family, continent, authority, and medical importance, and encourage snake photographers worldwide to submit photos and associated metadata, particularly of “missing” species, to the most permanent and useful online archives: The Reptile Database, iNaturalist, and HerpMapper.

Species identification can be challenging for biologists, healthcare practitioners and members of the general public. Snakes are no exception, and the potential medical consequences of venomous snake misidentification can be significant. Here, we collected data on identification of 100 snake species by building a week-long online citizen science challenge which attracted more than 1000 participants from around the world. We show that a large community including both professional herpetologists and skilled avocational snake enthusiasts with the potential to quickly (less than 2 min) and accurately (69–90%; see text) identify snakes is active online around the clock, but that only a small fraction of community members are proficient at identifying snakes to the species level, even when provided with the snake's geographical origin. Nevertheless, participants showed great enthusiasm and engagement, and our study provides evidence that innovative citizen science/crowdsourcing approaches can play significant roles in training and building capacity. Although identification by an expert familiar with the local snake fauna will always be the gold standard, we suggest that healthcare workers, clinicians, epidemiologists and other parties interested in snakebite could become more connected to these communities, and that professional herpetologists and skilled avocational snake enthusiasts could organize ways to help connect medical professionals to crowdsourcing platforms. Involving skilled avocational snake enthusiasts in decision making could build the capacity of healthcare workers to identify snakes more quickly, specifically and accurately, and ultimately improve snakebite treatment data and outcomes.

Artificial Intelligence (AI) can augment and sometimes even replace human cognition. Inspired by efforts to value human agency alongside productivity, we discuss the benefits of solving Citizen Science (CS) tasks with Hybrid Intelligence (HI), a synergetic mixture of human and artificial intelligence. Currently there is no clear framework or methodology on how to create such an effective mixture. Due to the unique participant-centered set of values and the abundance of tasks drawing upon both human common sense and complex 21st century skills, we believe that the field of CS offers an invaluable testbed for the development of HI and human-centered AI of the 21st century, while benefiting CS as well. In order to investigate this potential, we first relate CS to adjacent computational disciplines. Then, we demonstrate that CS projects can be grouped according to their potential for HI-enhancement by examining two key dimensions: the level of digitization and the amount of knowledge or experience required for participation. Finally, we propose a framework for types of human-AI interaction in CS based on established criteria of HI. This "HI lens" provides the CS community with an overview of several ways to utilize the combination of AI and human intelligence in their projects. It also allows the AI community to gain ideas on how developing AI in CS projects can further their own field.

With increased complexity in various global health challenges comes a need for increased precision and the adoption of more tailored health interventions. Building on precision public health, we propose precision global health (PGH), an approach that leverages life sciences, social sciences, and data sciences, augmented with artificial intelligence (AI), in order to identify transnational problems and deliver targeted and impactful interventions through integrated and participatory approaches. With more than four billion Internet users across the globe and the accelerating power of AI, PGH taps on our current augmented capacity to collect, integrate, analyse and visualise large volumes of data, both non-specific and specific to health. With the support of governments and donors, and together with international and non-governmental organisations, universities and research institutions can generate innovative solutions to improve health and wellbeing of the most vulnerable populations around the world. In line with the Sustainable Development Goals, we propose here a road map for the development and implementation of PGH.

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• Digital social innovation shares the basic ideas of citizen science, as well as the common challenge of motivating and structuring citizen engagement. However, it is different in scope, focus, forms of participation and impact. • Digital social innovation explores new models where researchers, social innovators and citizen participants collaborate in co-creating knowledge and solutions for societal challenges. • There are critical issues and effective practices in engaging citizens as knowledge brokers and co-designers of solutions to societal challenges, which should inform the design and implementation of new projects and approaches.

Volunteer computing (VC) or distributed computing projects are common in the citizen cyberscience (CCS) community and present extensive opportunities for scientists to make use of computing power donated by volunteers to undertake large-scale scientific computing tasks. Volunteer computing is generally a non-interactive process for those contributing computing resources to a project whereas volunteer thinking (VT) or distributed thinking, which allows volunteers to participate interactively in citizen cyberscience projects to solve human computation tasks. In this paper we describe the integration of three tools, the Virtual Atom Smasher (VAS) game developed by CERN, LiveQ, a job distribution middleware, and CitizenGrid, an online platform for hosting and providing computation to CCS projects. This integration demonstrates the combining of volunteer computing and volunteer thinking to help address the scientific and educational goals of games like VAS. The paper introduces the three tools and provides details of the integration process along with further potential usage scenarios for the resulting platform.

March 1988 Risø National Laboratory, DK-4000 Roskilde, Denmark

Air pollutants have become the major problem of many cities, causing millions of human deaths worldwide every year. Among all the noxious pollutants in air, particles with a diameter of 2.5 micrometers or less (PM2.5) are the most hazardous because they are small enough to penetrate to the lungs and invade the smallest airways. Since the presence of dangerous levels of PM2.5, commonly reported in newspapers and on TV, is intertwined with the global pattern of production and consumption, there is a need for citizen science projects that engage the young generations in efforts toward reducing air pollution as they will become the future leaders of society. With this goal, and to enable the geo-temporal characterization of PM2.5, we present a crowdsourcing-based air pollution measurement system that uses affordable DIY atomic force microscopes to measure and characterize PM2.5, exploiting the power of human computation through an online crowdsourcing platform to study how PM2.5 varies over time and across geographical locations. Our system is intended as both a scientific platform and a teaching tool for children to engage in environmental policy.

The journal Human Computation provides an international and interdisciplinary forum for the electronic publication of high-quality scholarly articles in all areas of human computation. There are no author fees and all published papers are freely available online.

Analytics tools have been widely used over the last years for the development of web-based application and services. Analytics data allows improving user interfaces through planning, executing, and evaluating actions intended to increase user engagement. Measuring and improving user engagement in citizen science projects is not different from other web applications such as on-line shopping, newspapers, or sites for recommending music or movies. However, citizen science projects also aim to produce learning outcomes on the participants. Current analytics tools do not present sufficient information regarding user behaviour with the application, thus making measuring engagement and learning outcomes difficult. This paper presents the CCLTracker analytics framework that is intended to overcome current limitations in analytics tools, by providing an API for monitoring user activities such as time spent watching a video, time to complete a task, or how far down a page is scrolled. CCLTracker has been integrated in 3 different citizen science projects which have proved its value for measuring user engagement and learning.