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
DATE:
TEAM MEMBERS:
Mmachi God’sglory ObiorahJames K.L. HammermanWill GrangerHaley Margaret WestLaura TrouilleBecky RotherMichael Horn
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.