Scientific Data Management
The goal of scientific data management is to articulate overarching scientific data models, standards (such as ontologies, schema-naming conventions, metadata-naming practices, collection methods), and scientific computing architectures to help advance scientific data interoperability. PNNL accomplishes this goal with
- Life-cycle data integrity, by providing assurance of data quality and accuracy from generation to end-use
- Data management tools, by providing visual data management tools to enable efficient operations of complex data sets
- End-user tools, by providing visualization tools to facilitate and accelerate scientific discovery
- Value-added products, by providing scientific enhancement to existing data sets
- Archives, by providing a high-performance and highly reliable long-term storage architecture.
Today, numerous scientific databases, archives, information systems, knowledge repositories, and scientific computing environments are constantly emerging and evolving. By partnering with organizations within PNNL as well as across the DOE complex, we are learning how to leverage these investments as we strive to develop new centers for scientific computing excellence.
We have created an integrated environment for scientific data management. Working in multidisciplinary teams, we have developed systems and processes that allow long-term archival storage and retrieval of datasets as well as capture and maintain descriptive metadata sets. In addition, we have developed and deployed tools to facilitate the location and retrieval of these datasets through queries on the metadata and have broken new ground by developing products that extend the traditional file system metaphor to include searchable metadata.
Contact: Eric Stephan