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Science Analytics and Synthesis

Science Analytics and Synthesis
(Previously named: Core Science Analytics, Synthesis and Libraries Program. Updated 11/2018)

SAS Vision

Providing a reliable, comprehensive view of USGS data that accelerates scientific discovery and reveals new connections to increase our understanding of Earth's natural systems.

SAS Mission

With expertise in technology, informatics, and science, SAS leads the management and delivery of scientific data and information for the USGS. SAS implements and promotes standards and best practices to enable efficient, data-driven science for decision making at multiple scales. We build critical relationships to identify the data, and to develop and deploy appropriate technological solutions that support rapid response to emerging natural resource issues.
Parent Organization: Core Science Systems
This ScienceBase space provides an array of USGS Series publications, journal articles, and other published references for the Gap Analysis Project (GAP). Information on GAP-related publications can also be found at:
This USGS data release is intended to provide a baselayer of information on likely stream crossings throughout the United States. The geopackage provides likely crossings of infrastructure and streams and provides observed information that helps validate modeled crossings and build knowledge about associated conditions through time (e.g. crossing type, crossing condition). Stream crossings were developed by intersecting the 2020 United States Census Bureau Topologically Integrated Geographic Encoding and Referencing (TIGER) U.S. road lines with the National Hydrography Dataset High Resolution flowlines. The current version of this data release specifically focuses on road stream crossings (i.e. TIGER2020 Roads)...
This USGS data release documents species distribution models for 271 fluvial fish species in their native ranges of the conterminous United States. Source data, supporting code and model results are documented in this data package. Boosted Regression Tree (BRT) models were used to develop presence/absence predictions for each of the National Hydrography Dataset Plus Version 2.1 stream segments within a species' native range. The predictions provided can be linked to the NHDPlusV2.1 geospatial dataset through the COMID to create a spatial depiction of the models. The primary results are stored in the file "BRT Predictions" and are provided in comma separated value (CSV) and Parquet file formats. Parquet file format...
The Gap Analysis Project (GAP) Analytical Database represents a synthesis of three core datasets for the conterminous U.S. Specifically 1) the GAP/LANDFIRE National Terrestrial Ecosystems_2011; 2) the Protected Areas Database of the United States (PAD-US) 1.4; and 3) the Species Ranges and Habitat Distribution Models for all terrestrial vertebrates. This database provides a mechanism to effiiently obtain summary statistics of those for a variety of spatial extents, including US states, US counties, Landscape Conservation Cooperation Network Areas, EPA's Level III-IV Ecoregions of the United States, and Level I-III Ecoregions of North America and 12-digit (6th level) hydrologic units. Disclaimer for Approved Database...
Tags: Alabama, Alaska, Arizona, Arkansas, California, All tags...
This shapefile contains fish habitat condition index (HCI) scores as well as specific disturbance indices for 6th level Hydrologic Unit Code (HUC12) watersheds of the Watershed Boundary Dataset. The source datasets compiled and attributed to spatial units were identified as being: (1) meaningful for assessing fluvial fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) broadly representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among HUC12 units. In this data set, variable summaries are linked to HUC12 watersheds developed for the Watershed Boundary Dataset downloaded on March 18,...
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ScienceBase brings together the best information it can find about USGS researchers and offices to show connections to publications, projects, and data. We are still working to improve this process and information is by no means complete. If you don't see everything you know is associated with you, a colleague, or your office, please be patient while we work to connect the dots. Feel free to contact