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
Although hundreds of citizen science applications exist, there is lack of detailed analysis of volunteers' needs and requirements, common usability mistakes and the kinds of user experiences that citizen science applications generate. Due to the limited number of studies that reflect on these issues, it is not always possible to develop interactions that are beneficial and enjoyable. In this paper we perform a systematic literature review to identify relevant articles which discuss user issues in environmental digital citizen science and we develop a set of design guidelines, which we evaluate
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TEAM MEMBERS:
Artemis SkarlatidouAlexandra HamiltonMichalis VitosMuki Haklay
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
As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative research, approaches and resources for use in a variety of settings. In this exploratory Change Makers project, the Concord Consortium will develop, test, and evaluate a citizen science program that leverages innovative technology, such that youth engage directly with energy issues through scientific inquiry. The project will create the Infrared Street View, a citizen science program that aims to produce a thermal version of Google's Street View using an affordable infrared camera attached to a smartphone. The infrared camera serves as a high-throughput data acquisition instrument that collects thousands of temperature data points each time a picture is taken. Youth will collect massive geotagged thermal data that have considerable scientific and educational value for visualizing energy usage and improving energy efficiency at all levels. The Infrared Street View program will provide a Web-based platform for youth and anyone interested in energy efficiency to view and analyze the aggregated data to identify possible energy losses. By sharing their scientific findings with stakeholders, youth will make changes to the way energy is being used. The project will start with school, public, and commercial buildings in selected areas where performing thermal scan of the buildings and publishing their thermal images for educational and research purposes are permitted by school leaders, town officials, and property owners. In collaboration with high schools and out-of-school programs in Massachusetts, this project will conduct pilot-tests with approximately 200 students.
To contribute to advancing learning, the study will probe three research questions: 1) Under what circumstances can technology bridge out-of-school and classroom science learning and improve learning on both sides? 2) To what extent can unobtrusive assessment based on data mining support research and evaluation of student learning in out-of-school settings? and 3) To what extent can instructional intelligence built into the app used in the program help students learn in out-of-school programs and improve the quality of data they contribute to the citizen science project? Data sources for investigating these questions include students' interaction data with the app logged behind the scenes and the images they have taken, as well as results based on traditional assessments from a small number of participants. Throughout the project, staff will widely disseminate project products and findings through the Internet, science fairs, conferences, publications, and partner networks. An eight-member Advisory Board consisting of cleantech experts, science educators, and educational researchers will oversee and evaluate this project.
This INSPIRE project addresses the issue of high volume hydraulic fracturing, also called fracking, and its effects on ground water resources. Fracking allows drillers to extract natural gas from shale deep within the earth. Methane gas sometimes escapes from shale gas wells and can contaminate water resources or leak into the atmosphere where it contributes to greenhouse gas emissions. Monitoring for these potential leaks is difficult because methane is also released into aquifers naturally, and because monitoring is time- and resource-intensive. Such subsurface leakage may also be relatively rare. This project seeks to improve overall understanding of the impacts of natural gas drilling using both advances in computer science and geoscience, and to teach the public about such impacts. The project will elucidate both the effects of human activities such as shale gas development as well as natural processes which release methane into natural waters. Results of the proposed research will lead to a better understanding of water quality in areas of shale-gas development and will highlight problems and potentially problematic management practices. The research will advance both the fields of geoscience and computer science, will train interdisciplinary graduate students, and involve citizen scientists in collecting data and understanding environmental data analysis.
The project combines new hydro-geochemical strategies and data mining approaches to study the release of methane into streams and ground waters. For example, researchers will explore how to analyze the heterogeneous spatial data that describe distributions of methane concentrations in natural waters. The objectives of this project are to i) transform the ability to measure methane in streams; ii) train citizen scientists to work with project scientists to sample streams in an area of shale-gas development and publish large-volume datasets of methane in natural waters and aquifers; iii) innovate data mining and machine learning methods for environmental data to identify anomalous spots with potential leakage; iv) run field campaigns to measure methane concentrations and isotopic signatures of water samples in these spots; v) foster dialogue among nonscientists, consultants, university scientists, members of the gas industry, government agencies, and nonprofit organizations in and beyond the target region. Toward this end, the team will host workshops aimed to build dialogue among stakeholders and will release data analytic software for environmental measurements to benefit a broader research community.
Citizen science engages members of the public in science. It advances the progress of science by involving more people and embracing new ideas. Recent projects use software and apps to do science more efficiently. However, existing citizen science software and databases are ad hoc, non-interoperable, non-standardized, and isolated, resulting in data and software siloes that hamper scientific advancement. This project will develop new software and integrate existing software, apps, and data for citizen science - allowing expanded discovery, appraisal, exploration, visualization, analysis, and reuse of software and data. Over the three phases, the software of two platforms, CitSci.org and CyberTracker, will be integrated and new software will be built to integrate and share additional software and data. The project will: (1) broaden the inclusivity, accessibility, and reach of citizen science; (2) elevate the value and rigor of citizen science data; (3) improve interoperability, usability, scalability and sustainability of citizen science software and data; and (4) mobilize data to allow cross-disciplinary research and meta-analyses. These outcomes benefit society by making citizen science projects such as those that monitor disease outbreaks, collect biodiversity data, monitor street potholes, track climate change, and any number of other possible topics more possible, efficient, and impactful through shared software.
The project will develop a cyber-enabled Framework for Advancing Buildable and Reusable Infrastructures for Citizen Science (Cyber-FABRICS) to elevate the reach and complexity of citizen science while adding value by mobilizing well-documented data to advance scientific research, meta-analyses, and decision support. Over the three phases of the project, the software of two platforms, CitSci.org and CyberTracker, will be integrated by developing APIs and reusable software libraries for these and other platforms to use to integrate and share data and software. Using participatory design and agile methods over four years, the project will: (1) broaden the inclusivity, accessibility, and reach of citizen science; (2) elevate the value and rigor of citizen science software and data; (3) improve interoperability, usability, scalability and sustainability of citizen science software and data; and (4) mobilize data to allow cross-disciplinary research and meta-analyses. These outcomes benefit society by making citizen science projects and any number of other possible topics more possible, efficient, and impactful through shared software and data. Adoption of Cyber-FABRICS infrastructure, software, and services will allow anyone with an Internet or cellular connection, including those in remote, underserved, and international communities, to contribute to research and monitoring, either independently or as a team. This project is also being supported by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments.
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TEAM MEMBERS:
Gregory NewmanLouis LiebenbergStacy LynnMelinda Laituri