SoCS: Collaborative Research: Focusing Attention to Improve the Performance of Citizen Science Systems: Beautiful Images and Perceptive Observers

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The goal of this project is to develop a next-generation socio-computational citizen science platform that combines the efforts of human classifiers with those of computational systems to maximize the efficiency with which human attention can be used. Dealing with the flood of digital data that confronts researchers is the fundamental challenge of twenty-first century research. New techniques, tools and strategies for dealing with massive data sets, whether they consist of vast numbers of base-pair DNA sequences or terabytes of data from all-sky astronomical surveys, present an opportunity to establish a new paradigm of scientific discovery, but the task is not easy. In many areas of research, the relentless growth of data sets has led to the adoption of increasingly automated and unsupervised methods of classification. In many cases, this has led to degradation in classification quality, with machine learning and computer vision unable to replicate the successes of human pattern recognition. The growth of citizen science on the web has provided a temporary solution to this problem, demonstrating that it is possible to recruit hundreds of thousands of volunteers to make an authentic contribution to results, boosting human analysis through the collective wisdom of a crowd of classifiers. However, human classifiers alone will not be able to cope with expected flood of data from future scientific instruments. This 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. The intellectual merit of this project lies in its contribution to advancing knowledge and understanding in multiple domains of science. First, the work will contribute to developing new methods of computational data analysis, initially with analysis of astronomical images, and later extending to additional fields. Second, the project includes social science research to test and apply theories of human motivation and learning in an online context, which can then be applied to a broad range of social-computational problems. By mixing human and computational elements, the planned system has the potential to transform the application of citizen science and its approach to data analysis. This project will advance science while promoting teaching, training and learning. One of the most significant broader impacts for its citizen science activities is enabling a community of hundreds of thousands of volunteers to participate in research, a powerful and rapidly developing form of informal science education. By choosing the relatively generic topic of image classification, beginning with astronomy but not limited to that field of science, the techniques developed under this grant will be of significant value to future investigations in similar research areas, thus enhancing the infrastructure for research and education.