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
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 Growing Beyond Earth Project (GBE) is a STEM education program designed to have middle and high school students conduct botany experiments, designed in partnership with NASA researchers at Kennedy Space Center, that support NASA research on growing plants in space. GBE was initiated by Fairchild Tropical Botanic Garden in collaboration with NASA's Exploration Research and Technology Programs and Miami-Dade County Public School District. Project goals are to: (1) improve STEM instruction in schools by providing authentic research experiments that have real world implications through curricular activities that meet STEM education needs, comprehensive teacher training, summer-long internships and the development of replicable training modules; (2) increase and sustain youth and public engagement in STEM related fields; (3) better serve groups historically underrepresented in STEM fields; and (4) support current and future NASA research by identifying and testing new plant varieties for future growth in space. During the 2016-17 academic year, 131 school classrooms participated in the program. To date, students have tested 91 varieties of edible plants and produced more than 100,000 data points that have been shared with the researchers at KSC.