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National Cross-Section Database (NXSDB), Water Year 2022 (schema version 1.2.0, September 2025) National Cross-Section Database (NXSDB), Water Year 2022 (schema version 1.2.0, September 2025)
This data release presents a national cross-section (XS) database (NXSDB) of bathymetric cross-sections collected during water year (WY) 2022 (October 1, 2021, to September 30, 2022). The WY 2022 NXSDB has been approved by the U.S. Geological Survey (USGS) for publication and supersedes the provisional versions. The data were collected by hydrographers during routine site visits to...
National Cross-Section Database (NXSDB), Water Year 2023 (schema version 1.2.0, September 2025) National Cross-Section Database (NXSDB), Water Year 2023 (schema version 1.2.0, September 2025)
This data release presents a national cross-section (XS) database (NXSDB) of bathymetric cross-sections collected during water year (WY) 2023 (October 1, 2022, to September 30, 2023). The WY 2023 NXSDB has been approved by the U.S. Geological Survey (USGS) for publication and supersedes the provisional version. The data were collected by hydrographers during routine site visits to...
Data release for Remotely Sensed Surface Water Storage Shows Distinct Patterns from SWAT-Simulated Data (ver. 2.0, February 2026) Data release for Remotely Sensed Surface Water Storage Shows Distinct Patterns from SWAT-Simulated Data (ver. 2.0, February 2026)
Understanding and projecting the downstream benefits of terrestrial surface water storage, i.e., volumetric water stored in lakes and wetlands (SWstorage) requires watershed hydrologic models. Use of external datasets to calibrate and validate modeled SWstorage dynamics remains uncommon, particularly across major river basins. Here, we: (1) develop and assess the utility of a novel...
Machine Learning Model: Estimates of Metal Abundance in Global Seafloor Massive Sulfide Deposits Machine Learning Model: Estimates of Metal Abundance in Global Seafloor Massive Sulfide Deposits
A multi-stage ensembled machine learning model was developed to estimate metal abundances in seafloor massive sulfide deposits worldwide. The modeling framework integrates (1) KMeans++ clustering to identify geochemical groupings based on enrichment controls, (2) Random Forest classification to assign geochemical labels to vent fields with incomplete or absent geochemical data, and (3)...
Organic content and carbon dating of soil samples collected from a fen in Big Smoky Valley, Nye County, Nevada Organic content and carbon dating of soil samples collected from a fen in Big Smoky Valley, Nye County, Nevada
This U.S. Geological Survey data release consists of two comma-separated values (CSV) tables containing the results of organic content and radiocarbon dating analyses performed on soil samples collected from a fen in Big Smoky Valley, Nye County, Nevada. Samples were collected from a single soil boring referred to as F1-D on September 11, 2019, using a hand auger. A shapefile of the site...
USGS National and Global Oil and Gas Assessment Project—Permian Basin Province, Texas and New Mexico—Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables USGS National and Global Oil and Gas Assessment Project—Permian Basin Province, Texas and New Mexico—Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables
This data release contains the boundaries of assessment units and input data for the assessment of undiscovered oil and gas resources in the Woodford and Barnett Shales of the Permian Basin Province, Texas and New Mexico. The assessment unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The assessment unit is...