Engaging Faith-based Communities in Citizen Science through Zooniverse was an 18-month pilot initiative funded by the Alfred P. Sloan Foundation. Any opinions, findings, or recommendations expressed are those of the authors and do not necessarily reflect the views of the Sloan Foundation. The goals of this initiative were to broaden participation in citizen science (aka people-powered research) among religious and interfaith communities by establishing pathways for them to engage with science using the online Zooniverse platform, and to build positive, long-term relationships with these
This poster was presented at the 2021 NSF AISL Awardee Meeting.
The project created a multi-person, collaborative touchtable museum exhibit experience engaging guests in Zooniverse’s Galaxy Zoo citizen science project.
Scientists have long sought to engage public audiences in research through citizen science projects such as biological surveys or distributed data collection. Recent online platforms have expanded the scope of what people-powered research can mean. Science museums are unique cultural institutions that translate scientific discovery for public audiences, often conducting research of their own. This makes museums compelling sites for engaging audiences directly in scientific research, but there are associated challenges as well. This project engages public audiences in contributing to real
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TEAM MEMBERS:
Mmachi God’sglory ObiorahJames K.L. HammermanWill GrangerHaley Margaret WestLaura TrouilleBecky RotherMichael Horn
Peer production projects involve people in many tasks, from editing articles to analyzing datasets. To facilitate mastery of these practices, projects offer a number of learning resources, ranging from project-defined FAQsto individually-oriented search tools and communal discussion boards. However, it is not clear which project resources best support participant learning, overall and at different stages of engagement. We draw on Sørensen's framework of forms of presence to distinguish three types of engagement with learning resources: authoritative, agent-centered and communal. We assigned
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TEAM MEMBERS:
Corey Brian JacksonCarsten OsterlundKevin CrowstonMahboobeh HarandiLaura Trouille
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
This study examines the relative efficacy of citizen science recruitment messages appealing to four motivations that were derived from previous research on motives for participation in citizen-science projects. We report on an experiment (N=36,513) that compared the response to email messages designed to appeal to these four motives for participation. We found that the messages appealing to the possibility of contributing to science and learning about science attracted more attention than did one about helping scientists but that one about helping scientists generated more initial
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TEAM MEMBERS:
Tae Kyoung LeeKevin CrowstonMahboobeh HarandiCarsten ØsterlundGrant Miller
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.
The Adler Planetarium, Johns Hopkins University, and Southern Illinois University-Edwardsville are investigating the potential of online citizen science projects to broaden the pool of volunteers who participate in analysis and investigation of digital data and to deepen volunteers' engagement in scientific inquiry. The Investigating Audience Engagement with Citizen Science project is administering surveys and conducting case studies to identify factors that lead volunteers to engage in the astronomy-focused Galaxy Zoo project and its Zooniverse extensions. The project is (1) identifying volunteers' motivations for joining and staying involved, (2) determining factors that influence volunteers' movement from lower to higher levels of involvement, and (3) designing features that influence volunteer involvement. The project's research findings will help informal science educators and scientists refine existing citizen science programs and develop new ones that maximize volunteer engagement, improve the user experience, and build a more scientifically literate public.
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 discovery of a class of galaxies called Green Peas provides an example of scientific work done by volunteers. This unique situation arose out of a science crowdsourcing website called Galaxy Zoo. It gave the ability to investigate the research process used by the volunteers. The volunteers’ process was analyzed in terms of three models of scientific research and an iterative work model to show the path to this discovery. As has been illustrated in these models of science, the path was iterative, not predetermined, and driven by empirical data. This paper gives a narrative of the 11-month
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Scientists and researchers from fields as diverse as oceanography and ecology, astronomy and classical studies face a common challenge. As computer power and technology improve, the sizes of data sets available to us increase rapidly. The goal of this project is to develop a new methodology for using citizen science to unlock the knowledge discovery potential of modern, large data sets. For example, in a previous project Galaxy Zoo, citizen scientists have already made major contributions, lending their eyes, their pattern recognition skills and their brains to address research questions that need human input, and in so doing, have become part of the computing process. The current Galaxy Zoo project has recruited more than 200,000 participants who have provided more than 100 million classifications of galaxies from the Sloan Digital Sky Survey. This project builds upon early successes to develop a mode of citizen science participation which involves not only simple "clickwork" tasks, but also involves participants in more advanced modes of scientific thought. As part of the project, a symbiotic relationship with machine learning tools and algorithms will be developed, so that results from citizen scientists provide a rich training set for improving algorithms that in turn inform citizen science modes of participation. The first phase of the project will be to develop a portfolio of pilot projects from astrophysics, planetary science, zoology, and classical studies. The second phase of the project will be to develop a framework - called the Zooniverse - to facilitate citizen scientists. In particular, research and machine-learning communities will be engaged to identify suitable projects and data sets to integrate into Zooniverse.
The ultimate goal with the Zooniverse is to create a sustainable future for large-scale, internet-based citizen science as part of every researcher?s toolkit, exemplifying a new paradigm in computational thinking, tapping the mental resources of a community of lay people in an innovative and complex manner that promises a profound impact on our ability to generate new knowledge. The project will engage thousands of citizens in authentic science tasks leading to a better public understanding of science and also, by the engagement of students, leading to interest in scientific careers.
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TEAM MEMBERS:
Geza GyukPamela GayChristopher LintottMichael RaddickLucy FortsonJohn Wallin
We investigate the development of scientific content knowledge of volunteers participating in online citizen science projects in the Zooniverse (http://www.zooniverse.org). We use econometric methods to test how measures of project participation relate to success in a science quiz, controlling for factors known to correlate with scientific knowledge. Citizen scientists believe they are learning about both the content and processes of science through their participation. We don't directly test the latter, but we find evidence to support the former — that more actively engaged participants
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TEAM MEMBERS:
Karen MastersEun Young OhJoe CoxBrooke SimmonsChris LintottGary GrahamAnita GreenhillKate Holmes