This site describes four research projects at Syracuse University School of Information Studies on the topic of citizen science.

The , now completed, was a two-phase theory-based study of virtual organizations that enable massive virtual collaboration in scientific research. The virtual organizations to be studied have a core of scientists and project leaders coordinating the work of a larger number of volunteer contributors, a format sometimes called citizen science. The project is directed at advancing the understanding of what constitutes effective citizen science virtual organizations and under what conditions citizen science virtual organizations can enable and enhance scientific and education production and innovation. The study was theoretically grounded in small group theory and rooted empirically in a survey of and case studies in citizen science projects. The survey was used to develop a typology of citizen science projects, thus illuminating the important dimensions of this form. The case studies helped identify key lever points in work design for enabling citizen science virtual organizations to involve distributed, diverse volunteers in producing large-scale, high quality, valued scientific research in an organizationally sustainable fashion. Findings from the study were shared and validated with citizen science practitioners in a workshop. The broader importance of the research is that it will indicate opportunities for employing citizen science in scientific research, which could lead to novel implementations of citizen science in other areas of scientific and engineering research and education. Results will aid scientists and project leaders in identifying appropriate project structures and best practices to employ when revising current citizen science projects or launching new ones. This award was funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

The investigated the capabilities and potential of social computational systems (SoCS) in the context of citizen science. Whether it be volunteers playing a role in massive, distributed sensing networks exploring the migration of birds, or applying their unique human perceptual skills to searching the skies, human motivation and performance is fundamental to system performance. However, undertaking science through a social computational system brings unique challenges. To understand and address these challenges, this project was a three-phase study of SoCS to support scientific research, grounded in group theory and rooted empirically in case studies and action research. More specifically, the project included case studies of several citizen science projects to establish the nature of the SoCS currently in use, development of SoCS to support different kinds of citizen science projects and evaluation of the impacts of these systems on the outputs and processes of the projects.

A further examined strategies for dealing with the flood of digital data that confronts researchers. The project developed a next-generation socio-computational citizen science platform that combined the efforts of human classifiers with those of computational systems to maximize the efficiency with which human attention can be used. We recognize that to do so requires a thorough understanding of human motivation and learning in this context, and knowledge of how the proposed system will affect these. The proposed research will be carried out by a partnership between computer and social scientists, addressing research problems both in automated data analysis and social science through systems implementation, alongside field research and experiments with project participants.

Finally, an developed a citizen science system--Gravity Spy ()--to support the Advanced Laser Interferometer Gravitational wave Observatory (aLIGO). The high detector sensitivity needed for astrophysical discoveries makes aLIGO very susceptible to non-cosmic 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. We addressed 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 is its reliance on volunteers to discover new glitch classes, not just use existing ones.