Core Science Analytics, Synthesis, and Libraries (CSAS&L)
Core Science Analytics, Synthesis, and Libraries (CSAS&L) is formerly the Biological Informatics Program (BIP) and Core Science Informatics (CSI) Organization under the U.S. Geological Survey Core Science Systems (CSS) Mission Area. The two groups merged on October 24, 2011.
|Goal 1:||Develop and implement standard business practices across CSAS&L to improve efficiency and effectiveness in accomplishing the Program's Mission.|
|Goal 2:||Provide leadership, guidance, and expertise to assist USGS scientists in fully implementing the data lifecycle for the long-term management of earth science data and information products.|
|Goal 3:||Enable data-driven science by continuing to develop and integrate core species and habitat datasets, scalable to the national level.|
|Goal 4:||Enable earth science data discovery, integration, and analysis in USGS by developing and supporting suites of tools, data transformation capabilities, and technological capacity.|
|Goal 5:||Conduct research, in coordination with other USGS teams, which advances the science of data discovery, integration and analysis, and improves understanding of our key stakeholders, their data needs, and workflow processes.|
CSAS&L broadens its focus to incorporate all of USGS by maintaining key expertise in biological informatics and by leveraging current expertise and capacity to strengthen the USGS leadership role in scientific data management and preservation. These efforts advance the capacity of CSAS&L to conduct synthesis, analysis, and visualization of scientific data and reduce duplication of effort.
Key Functions Include:
Data management is increasingly recognized as a vital element of the science lifecycle. More and more science organizations, including the National Science Foundation, require a data management plan as part of the proposal process, consistent with recommended good practice. The Science Data Management component of CSAS&L provides expertise in data lifecycle best practices and community and interdisciplinary partnership facilitation.
The Scientific Data Integration and Visualization Team is responsible for scientific project applications, geospatial data visualization and research, application development, and scientific computing. The team is an integral component of the CSAS&L organization within CSS.
The goals of the Applied Earth Systems Informatics Research group are to:
• develop and maintain partnerships across the USGS Mission Areas and with academic, industry, and other government institutions to focus and apply computer and information science knowledge to USGS science questions;
• conduct an applied research agenda to answer the most compelling computer and information science questions facing USGS Mission Areas through USGS researchers working in concert with partners; and
• develop and conduct an educational program in partnership with academic and other institutions to attract new scientists with advanced scientific programming skills and to train scientists from across all disciplines represented in the USGS in the best uses of scientific computing technologies.
The Biogeographic Characterization Branch is responsible for developing scientific data, products, and analyses associated with the following programs and projects: Gap Analysis Program (GAP), Vegetation Characterization, Ocean Biogeographic Information System for the United States, National Vegetation Classification Standard, Aquatic GAP, and the Library of Images from the Environment. The team works both independently and cooperatively with all other components of CSAS&L to create, manage, and distribute results from BCB activities.
The Eco-Science Synthesis Branch is focused on basic and applied scientific research, especially involving biological data synthesis. The branch manages the major CSAS&L national products: Biodiversity Information Serving Our Nation (BISON), and the Integrated Taxonomic Information System (ITIS). The branch also leads and participates in inter-agency coordination of efforts to foster broad mobilization and integration of biological data.