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EPA's Office of Research and Development (ORD) has developed a scientific
environmental information management system (EIMS) that stores, manages, and delivers
descriptive information (metadata) for data sets, databases, documents, models,
multimedia, projects, and spatial information. The EIMS user community includes
environmental scientists, resource managers, and other stakeholders -- both within EPA
and from the general public. Partners from ORD's Regional Vulnerability Assessment
(ReVA) project, the National Center for Environmental Assessment (NCEA), EPA
Region 10, and the Surf Your Watershed program are storing their metadata in the
growing EIMS collection.
EIMS is a repository of products and metadata. The descriptive information in metadata
enables users to evaluate and use these products. EIMS stores and maintains descriptive
information in a relational database and refers to the products (data, documents, etc.)
stored either within EIMS or as distributed external files. This architecture supports the
management of remote sensing data, geographical information system (GIS) coverages,
and other types of data for which entry into relational tables is not appropriate. Descriptive
information stored within EIMS is consistent with the Federal Geographic Data Committee
(FGDC) metadata content standards for spatial data. A significant enhancement of these
standards, however, is the addition of a hierarchical metadata framework that organizes
detailed scientific data and documentation, and accommodates customized information at
the catalog level to facilitate a review of the different types of metadata in EIMS.
The EIMS repository of scientific documentation, accessed with standard web browsers,
places a virtual library on the desktop of EPA staff and others with Internet access. Users
can search within EIMS to find information sources of interest based upon topic or defined
criteria related to types of environmental resources, geographical extent, date, or content
origin. These user-defined searches typically are more efficient than currently used web
search engines.
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