INTEROP: A Community-driven Scientific Observations Network to achieve Interoperability of Environmental and Ecological Data PI: Mark Schildhauer, National Center for Ecological Analysis and Synthesis, UC Santa

from multiple disciplines to tackle broader and more complex questions about the natural world. Such advances, however, are hindered by data heterogeneity, which impedes the ability of researchers to discover, interpret, and integrate relevant data that have been collected by oth-ers. A recent NSF-funded workshop on multi-disciplinary data management concluded that interoperability can be significantly improved by better describing data at the level of observa-tion and measurement, rather than the traditional focus at the level of the data set. That is, for systems to interoperate effectively, the scientific community must unify the various existing ap-proaches for representing and describing observational data. A community-sanctioned, unified data model for observational data is thus needed to enable interoperability among existing data resources, which will in turn provide the necessary foundation to support cross-disciplinary synthetic research in the environmental sciences. The investigators propose the Scientific Obser-vations Network to initiate a multi-disciplinary, community-driven effort to define and develop the necessary specifications and technologies to facilitate semantic interpretation and integra-tion of observational data. The technological approaches will derive from recent advances in knowledge representation that have demonstrated practical utility in enhancing scientific com