SGER: Metadata-Driven Approach to Discovery-Oriented Exploration of Massive Data Sets

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Multi-scale, multi-modal, multi-site science, such as terrestrial observatories and other dynamic and adaptive sensor-based application, high resolution sub-cellular imaging, or genomic analyses of communities of organisms, builds large data sets that can serve as the basis for in silico exploration and analysis. Often the data collections represent a wide range of disciplines, yet there are often a limited number of interpretations, guided by an individual scientist?s expertise. Intuitive exploration that transcends disciplinary boundaries and expertise could enhance the process of interdisciplinary collaboration, as well as drive the process of discovery in new directions. This proposal seeks to develop a dynamic metadata-based approach for intuitive, interactive, and immersive multi-scale, multi-modal data exploration application to a wide range of data sets and their associated metadata. The original focus will be on metagenomics data from the Global Ocean Survey. The development of the environment will enable a different dynamic sorting and sifting of data rather than relying solely on known or expected features. Such open-ended exploration occurs not only in observational science, but also in tasks ranging from network intrusion detection. Students will be engaged in the research and collaboration, with dissemination in public cultural activities, as well as through more traditional academic venues.