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

多尺度、多模式、多地点的科学，如陆地观测站和其他基于传感器的动态和适应性应用、高分辨率亚细胞成像或生物群落基因组分析，建立了可作为硅学探索和分析的基础的大型数据集。通常，数据收集代表了广泛的学科，但往往只有有限数量的解释，由单个科学家--S的专业知识--指导。超越学科边界和专业知识的直观探索可以加强跨学科合作的进程，并推动发现进程朝着新的方向发展。这项提议旨在开发一种基于动态元数据的方法，用于直观、交互和身临其境地对各种数据集及其关联元数据进行多尺度、多模式的数据探索应用。最初的重点将是全球海洋调查的元基因组数据。环境的发展将实现数据的不同动态分类和筛选，而不是仅仅依赖已知或预期的特征。这种开放式的探索不仅出现在观测科学中，也出现在从网络入侵检测在内的一系列任务中。学生将参与研究和合作，在公共文化活动中传播，以及通过更传统的学术场所。

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.