Who speaks for “citizen science” on Twitter? Which territory of citizen science have they made visible so far? This paper offers the first description of the community of users who dedicate their online social media identity to citizen science. It shows that Twitter users who identify with the term “citizen science” are mostly U.S. science professionals in environmental sciences, and rarely projects' participants. In contrast to the original concept of “citizen science”, defined as a direct relationship between scientists and lay participants, this paper makes visible a third category of
Identifying private gardens in the U.K. as key sites of environmental engagement, we look at how a longer-term online citizen science programme facilitated the development of new and personal attachments of nature. These were visible through new or renewed interest in wildlife-friendly gardening practices and attitudinal shifts in a large proportion of its participants. Qualitative and quantitative data, collected via interviews, focus groups, surveys and logging of user behaviours, revealed that cultivating a fascination with species identification was key to both ‘helping nature’ and wider
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TEAM MEMBERS:
Nirwan SharmaSam GreavesAdvaith SiddharthanHelen AndersonAnnie RobinsonLaura Colucci-GrayAgung Toto WibowoHelen BostockAndrew SalisburyStuart RobertsDavid SlawsonRené van der Wal
Online citizen science platforms increasingly provide types of infrastructural support previously only available to organisationally-based professional scientists. Other practices, such as creative arts, also exploit the freedom and accessibility afforded by the World Wide Web to shift the professional-amateur relationship. This paper compares communities from these two areas to show that disparate practices can learn from each other to better understand their users and their technology needs. Three major areas are discussed: mutual acknowledgement, infrastructural support, and platform
We developed a multi-touch interface for the citizen science video game Foldit, in which players manipulate 3D protein structures, and compared multi-touch and mouse interfaces in a 41-subject user study. We found that participants performed similarly in both interfaces and did not have an overall preference for either interface. However, results indicate that for tasks involving guided movement to dock protein parts, subjects using the multi-touch interface completed tasks more accurately with fewer moves, and reported higher attention and spatial presence. For tasks involving direct
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TEAM MEMBERS:
Thomas MuenderSadaab Ali GulaniLauren WestendorfClarissa VerishRainer MalakaOrit ShaerSeth Cooper
Effective classification of large datasets is a ubiquitous challenge across multiple knowledge domains. One solution gaining in popularity is to perform distributed data analysis via online citizen science platforms, such as the Zooniverse. The resulting growth in project numbers is increasing the need to improve understanding of the volunteer experience; as the sustainability of citizen science is dependent on our ability to design for engagement and usability. Here, we examine volunteer interaction with 63 projects, representing the most comprehensive collection of online citizen science
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TEAM MEMBERS:
Helen SpiersAlexandra SwansonLucy FortsonBrooke SimmonsLaura TrouilleSamantha BlickhanChris Lintott
In citizen science, user-centred development is often emphasised for its potential to involve participants in the development of technology. We describe the development process of the mobile app “Naturblick” as an example of a user-centred design in citizen science and discuss digital user feedback with regard to the users' involvement. We have identified three types of digital user feedback using qualitative content analysis: general user feedback, contributory user feedback and co-creational user feedback. The results indicate that digital user feedback can link UCD techniques with more
The growing interest in citizen science has resulted in a new range of digital tools that facilitate the interaction and communications between citizens and scientists. Considering the ever increasing number of applications that currently exist, it is surprising how little we know about how volunteers interact with these technologies, what they expect from them, and why these technologies succeed or fail. Aiming to address this gap, JCOM organized this special issue on the role of User Experience (UX) of digital technologies in citizen science which is the first to focus on the qualities and
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TEAM MEMBERS:
Artemis SkarlatidouMarisa PontiJames SprinksChristian NoldMuki HaklayEiman Kanjo
This INSPIRE award is partially funded by the Cyber-Human Systems Program in the Division of Information and Intelligent Systems in the Directorate for Computer Science and Engineering, the Gravitational Physics Program in the Division of Physics in the Directorate for Mathematical and Physical Sciences, and the Office of Integrative Activities.
This innovative project will develop a citizen science system to support the Advanced Laser Interferometer Gravitational wave Observatory (aLIGO), the most complicated experiment ever undertaken in gravitational physics. Before the end of this decade it will open up the window of gravitational wave observations on the Universe. However, the high detector sensitivity needed for astrophysical discoveries makes aLIGO very susceptible to noncosmic artifacts and noise that must be identified and separated from cosmic signals. Teaching computers to identify and morphologically classify these artifacts in detector data is exceedingly difficult. Human eyesight is a proven tool for classification, but the aLIGO data streams from approximately 30,000 sensors and monitors easily overwhelm a single human. This research will address these problems by coupling human classification with a machine learning model that learns from the citizen scientists and also guides how information is provided to participants. A novel feature of this system will be its reliance on volunteers to discover new glitch classes, not just use existing ones. The project includes research on the human-centered computing aspects of this sociocomputational system, and thus can inspire future citizen science projects that do not merely exploit the labor of volunteers but engage them as partners in scientific discovery. Therefore, the project will have substantial educational benefits for the volunteers, who will gain a good understanding on how science works, and will be a part of the excitement of opening up a new window on the universe.
This is an innovative, interdisciplinary collaboration between the existing LIGO, at the time it is being technically enhanced, and Zooniverse, which has fielded a workable crowdsourcing model, currently involving over a million people on 30 projects. The work will help aLIGO to quickly identify noise and artifacts in the science data stream, separating out legitimate astrophysical events, and allowing those events to be distributed to other observatories for more detailed source identification and study. This project will also build and evaluate an interface between machine learning and human learning that will itself be an advance on current methods. It can be depicted as a loop: (1) By sifting through enormous amounts of aLIGO data, the citizen scientists will produce a robust "gold standard" glitch dataset that can be used to seed and train machine learning algorithms that will aid in the identification task. (2) The machine learning protocols that select and classify glitch events will be developed to maximize the potential of the citizen scientists by organizing and passing the data to them in more effective ways. The project will experiment with the task design and workflow organization (leveraging previous Zooniverse experience) to build a system that takes advantage of the distinctive strengths of the machines (ability to process large amounts of data systematically) and the humans (ability to identify patterns and spot discrepancies), and then using the model to enable high quality aLIGO detector characterization and gravitational wave searches
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TEAM MEMBERS:
Vassiliki KalogeraAggelos KatsaggelosKevin CrowstonLaura TrouilleJoshua SmithShane LarsonLaura Whyte
A report following the 2016 Environmental Health Summit recommended engaging citizens in creating their own knowledge and solutions, thus ensuring that their concerns are adequately addressed and promoting sustainability of community projects. Indeed, citizen science has the potential to initiate a cascade of events with a positive ripple effect that includes a more diverse future STEM and biomedical workforce. This SEPA proposal involves the establishment of WE ENGAGE – an informal, citizen science-based, environmental health experiential learning program designed in partnership with and for under resourced communities struggling with health and environmental health challenges. Its purpose is to actively engage and build the citizen science capacity of citizens living in a single cluster of three contiguous under resourced, minority Cincinnati neighborhoods where generational challenges continue to plague residents despite the presence of established academic-community partnerships. Our hypothesis is that community-informed, experiential learning opportunities outside of the classroom that are structured, multi-generational, and story-based will encourage a) the active asking, discussion about, and answering of relevant complex health and environmental questions so that individuals and communities can plan action steps to make better health choices and pursue healthier environments, and b) greater interest and confidence in pursuing formal biomedical/STEM education and STEM careers. Our program has three specific aims: 1) We will co-create tailored story- based (graphic novel style) STEM education materials with a community advisory board and offer informal STEM education and research training to our target communities; 2) we will facilitate the application of scientific inquiry skills to improve health via community-led health fairs that use an innovative electronic health passport platform to collect data and through facilitated community discussions of health fair data to generate motivating stories to share; and 3) we will facilitate the application of scientific inquiry skills to foster community pride and activism in promoting healthier/safer built environments via walking environmental assessments. As in aim 2, facilitated discussions will be held to spur future community based participatory research studies and interventions. Critical to our success is the concept of storytelling. Storytelling is a foundation of the human experience. A key purpose of storytelling is not just understanding the world, but positively transforming it. It is a common language. Bringing together STEM concepts in the form of a story increases their appeal and meaning. Later, the very process of community data collection gives individuals a voice. In a data story, hundreds to millions of voices can be distilled into a single narrative that can help community members probe important underlying associations and get to the root causes of complicated health issues relevant to their communities. Through place based, understandable, motivating data stories, the community’s collective voice is clearer—leading to relevant and viable actions that can be decided and taken together. From preventing chronic disease, to nurturing healthier environments, to encouraging STEM education — stories have unlimited potential.
Public Health Relevance Statement:
Narrative WE ENGAGE is an informal citizen science-based, experiential learning program designed in partnership with and for middle schoolers to adults living in under resourced minority communities. Using the power of data collection and storytelling, its purpose is to actively engage citizens in STEM/research education and training to encourage a more diverse future workforce and to sustainably build local capacity to ask and answer complex health and environmental questions relevant to their communities. Further, by engaging citizens and giving them a more equitable stake in the research process, they are better able to discover their own solutions.
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TEAM MEMBERS:
Melinda Sue ButschkovacicSusan Ann Hershberger
A team of experts from five institutions (University of Minnesota, Adler Planetarium, University of Wyoming, Colorado State University, and UC San Diego) links field-based and online analysis capabilities to support citizen science, focusing on three research areas (cell biology, ecology, and astronomy). The project builds on Zooniverse and CitSci.org, leverages the NSF Science Gateways Community Institute, and enhances the quality of citizen science and the experience of its participants.
This project creates an integrated Citizen Science Cyberinfrastructure (CSCI) framework that expands the capacity of research communities across several disciplines to use citizen science as a suitable and sustainable research methodology. CSCI produces three improvements to the infrastructure for citizen science already provided by Zooniverse and CitSci.org:
Combining Modes - connecting the process of data collection and analysis;
Smart Assignment - improving the assignment of tasks during analysis; and
New Data Models - exploring the Data-as-Subject model. By treating time series data as data, this model removes the need to create images for classification and facilitates more complex workflows. These improvements are motivated and investigated through three distinct scientific cases:
Biomedicine (3D Morphology of Cell Nucleus). Currently, Zooniverse 'Etch-a-Cell' volunteers provide annotations of cellular components in images from high-resolution microscopy, where a single cell provides a stack containing thousands of sliced images. The Smart Task Assignment capability incorporates this information, so volunteers are not shown each image in a stack where machines or other volunteers have already evaluated some subset of data.
Ecology (Identifying Individual Animals). When monitoring wide-ranging wildlife populations, identification of individual animals is needed for robust estimates of population sizes and trends. This use case combines field collection and data analysis with deep learning to improve results.
Astronomy (Characterizing Lightcurves). Astronomical time series data reveal a variety of behaviors, such as stellar flares or planetary transits. The existing Zooniverse data model requires classification of individual images before aggregation of results and transformation back to refer to the original data. By using the Data-as-Subject model and the Smart Task Assignment capability, volunteers will be able to scan through the entire time series in a machine-aided manner to determine specific light curve characteristics.
The team explores the use of recurrent neural networks (RNNs) to determine automated learning architectures best suited to the projects. Of particular interest is how the degree to which neighboring subjects are coupled affects performance. The integration of existing tools, which is based on application programming interfaces (APIs), also facilitates further tool integration. The effort creates a citizen science framework that directly advances knowledge for three science use cases in biomedicine, ecology, and astronomy, and combines field-collected data with data analysis. This has the ability to solve key problems in the individual applications, as well as benefiting the research of the dozens of projects on the Zooniverse platform. It provides benefits to researchers using citizen scientists, and to the nearly 1.6 million citizen scientists themselves.
This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Research on Learning in Formal and Informal Settings, within the NSF Directorate for Education and Human Resources.
This project is funded by the National Science Foundation's (NSF's) Advancing Informal STEM Learning (AISL) program, which supports innovative research, approaches, and resources for use in a variety of learning settings.
We have created an instrument to measure the prevalance of various motivations in a population of volunteers in an online citizen science project. Our project is Zooniverse (www.zooniverse.org), a collection of citizen science projects that have grown out of the Galaxy Zoo website. The instrument is based on a theoretical model of motivation, which is described in the attached document.
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TEAM MEMBERS:
Jordan RaddickKaren CarneyJason ReedAndrea Lardner
The Year in ISE is a slidedoc designed to track and characterize field growth, change and impact, important publications, and current topics in ISE in 2018. Use it to inform new strategies, find potential collaborators for your projects, and support proposal development. Scope This slidedoc highlights a selection of developments and resources in 2018 that were notable and potentially useful for the informal STEM education field. It is not intended to be comprehensive or exhaustive, nor to provide endorsement. To manage the scope and length, we have focused on meta analyses, consensus reports