Data Releases
The data collected and the techniques used by USGS scientists should conform to or reference national and international standards and protocols if they exist and when they are relevant and appropriate. For datasets of a given type, and if national or international metadata standards exist, the data are indexed with metadata that facilitates access and integration.
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SAS: Software Application for SMASH (Spectral Mixture Analysis for Surveillance of Harmful Algal Blooms) SAS: Software Application for SMASH (Spectral Mixture Analysis for Surveillance of Harmful Algal Blooms)
The Software Application for SMASH (Spectral Mixture Analysis for Surveillance of Harmful Algal Blooms), or SAS for short, is an application to facilitate mapping of potentially harmful algal blooms in reservoirs, rivers, and lakes from remotely sensed data. More specifically, SAS is designed to exploit the detailed observations of reflectance available within a hyperspectral image to...
Airborne magnetic and radiometric survey, western Arkansas, 2022 Airborne magnetic and radiometric survey, western Arkansas, 2022
This publication provides digital flight line data for a high-resolution magnetic and radiometric survey over part of western Arkansas. The airborne geophysical survey was funded by the USGS National Cooperative Geologic Mapping Program. The survey covers a rectangular area 91.55 by 92.15 kilometers (km) over the transition between the Ouachita Mountains and Ozark Plateau physiographic...
Mofete and San Vito geothermal field ore mineralization data Mofete and San Vito geothermal field ore mineralization data
The Mofete and San Vito geothermal fields, located west of Naples, Italy, are part of the Campi Flegrei volcanic complex. A joint venture in the 1970s between AGIP and ENEL, the Italian National electric and petroleum utilities, respectively, drilled exploratory wells to a depth of ~3000 m in an attempt to locate high-enthalpy fluids for potential power production. Drill core samples...
Data for Machine Learning Predictions of Nitrate in Shallow Groundwater in the Conterminous United States Data for Machine Learning Predictions of Nitrate in Shallow Groundwater in the Conterminous United States
An extreme gradient boosting (XGB) machine learning model was developed to predict the distribution of nitrate in shallow groundwater across the conterminous United States (CONUS). Nitrate was predicted at a 1-square-kilometer (km) resolution at a depth below the water table of 10 m. The model builds off a previous XGB machine learning model developed to predict nitrate at domestic and...
Urban Waters Federal Partnership: Novel bacteria monitoring technology in support of recreational water quality monitoring in the Lower Delaware River Urban Waters Federal Partnership: Novel bacteria monitoring technology in support of recreational water quality monitoring in the Lower Delaware River
The United States Geological Survey’s (USGS) New Jersey Water Science Center, in coordination with the Delaware River Basin Commission (DRBC) deployed a novel bacterial water-quality monitor, the Fluidion Alert V2 (Fluidion), in the Delaware River at Pyne Poynt Park in Camden County, New Jersey. Following United States Environmental Protection Agency (EPA) recreational water quality...
HOPS: Hyperparameter optimization and predictor selection HOPS: Hyperparameter optimization and predictor selection
We developed the hyperparameter optimization and predictor selection (HOPS) software to optimize hyperparameters and predictor selection while limiting correlation among the selected predictors for machine learning models. Including correlated predictors in machine learning models can distort model estimation and prediction and introduce bias in predictor importance estimates. The HOPS...