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Selecting meaningful features is central in the analysis of scientific data. Today's multivariate scientific datasets are often large and complex making it difficult to define general features of interest significant to scientific applications. To address this problem, we propose three general, spatiotemporal metrics to quantify the significant properties of data features-concentration, continuity and co-occurrence, named collectively as CO 3 . We implemented an interactive visualization systemdoi:10.1109/pacificvis.2013.6596139 dblp:conf/apvis/WangSH13 fatcat:kszkamkbcnhc3jjpfz7ohopxpe