This project will build a "Scientific Observations Network"--as a multi-disciplinary, community-driven effort to define and develop a unified model for observational data, to enhance data sharing, merging and reuse in the earth and life sciences. This effort will coordinate work of a community of experts drawn from numerous disciplines, including ecology, hydrology, oceanography, geo-sciences, the geospatial community, and life sciences, working closely with computer scientists and information managers, to develop necessary specifications and technologies to facilitate intelligent interpretation and seamless integration of observational data. Advances in environmental science and ecology increasingly depend on information from multiple disciplines to address broad, complex questions about the natural world. Researchers are extremely challenged, however, in effectively locating, interpreting, and integrating data that might be relevant for these investigations. This is due to extreme variability in the structure and contents of the data that scientists collect. This project will support the growing interest in the earth and life sciences in the possibilities of describing data at the level of observation and measurement, rather than the traditional focus at the level of the data set, in order to achieve stronger data discovery and interoperability.
The Scientific Observations Network will work to develop compatible, open-source, standards-based approaches to the semantic modeling of observational data. A key goal will be the development of a core conceptual data model for representing scientific observations. This core observations model will provide a common basis for developing, extending, and applying highly specialized scientific terminologies required for detailed descriptions of data relevant for environmental research. Subgroups of experts will engage in extending the core data model to include a broad range of specific measurements collected by the representative disciplines, and a series of demonstration projects will illustrate the capabilities of these approaches to confederate data for reuse in broader and unanticipated contexts. The scientific Observations Network will help to insure that scientific data, once collected, is put to the greatest possible use by the broadest group of users.
