AndreaBozzo/ceres-open-data-index huggingface dataset:
Waterfalls and Rapids in the Conterminous United States Linked to the National Hydrography Datasets V2.0

This GeoJSON dataset contains information about 10780 waterfall and 1080 rapid locations (referred to as falls throughout the metadata) and characteristics (e.g. type and height) for the conterminous United States. This dataset centralizes known information about falls while providing basic quality control (i.e. resolving duplicate records and spatial accuracy checks) and linkages to stream networks intended to facilitate stream network analyses. Locations of falls were sourced from the World Waterfall Database (WWD, www.worldwaterfalldatabase.com), the US Forest Service Center for Aquatic Technology Transfer (acquired from Southeast Aquatic Barrier Inventory), and Geographic Names Information System (GNIS, https://geonames.usgs.gov). The coordinates and spatial attributes from source data were used to verify locations using The HydroLink Tool (https://maps.usgs.gov/hydrolink).  The HydroLink Tool was also used to address locations (similar to the concept of street addresses) to both the National Hydrography Dataset Plus Medium Resolution Version 2.1 (1:100,000 scale)(NHDPlusV2.1) and National Hydrography Dataset High Resolution (1:24,000 scale)(NHD HR) geospatial stream networks.  The development of this dataset did not impose strict fall definitions but instead compiled qualified falls as defined by sources while capturing characteristics when available to help users identify falls of interest for any given use case. More specifically there is a gradation of waterfalls represented in the dataset where general fall types are defined by source datasets but are not standardized by USGS staff.

Tags: conterminous-united-states, contiguous-united-states, conus, habitat-connectivity, habitat-fragmentation, hydrography-linked-data, hydrolinked-data, linear-referenced, linear-referencing, lower-48, national-hydrography-dataset-plus, national-hydrography-datasets, national-hydrography-datasets-high-resolution, point-event, rapids, rivers, spatial-waterfall-datasets, streams, usgs-5e8d2b5982cee42d13466001, waterfalls
Organization: Department of the Interior
Soil sample locations for chronic wasting disease prion surveillance in Grand Teton National Park and National Elk Refuge 2019

A total of 200 soil samples were collected in the Greater Yellowstone Ecosystem in Grand Teton National Park and the National Elk Refuge in July 2019 for chronic wasting disease (CWD) monitoring purposes. To collect samples from locations where ungulates are most likely to be shedding or encountering CWD, we targeted elk collar data locations, known migration routes of mule deer and elk, and areas where yearly elk supplemental feeding generally occurs on the National Elk Refuge. We sampled 10 transects in Grand Teton National Park and 10 transects in the National Elk Refuge, each 1,000 meters in length. We collected soil samples every 100 meters along the transect for a total of 10 samples per transect. Terra Core Samplers were used to collect 10 grams of surface soil at each sample location. Here we provide the transect identifier, transect name, sample point name, GPS location where each soil sample was collected, and the date when each sample was collected. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Tags: biota, chronic-wasting-disease, ecology, elk, grand-teton-national-park, mule-deer, national-elk-refuge, soil-samples, teton, usgs-5e8ccbfe82cee42d13465e6b, wildlife-biology, wildlife-disease, wyoming
Organization: Department of the Interior
Predictive soil property map: Electrical conductivity (salinity)

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 &gt; 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building.  More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

Tags: accuracy-and-error-estimated, arizona, available-water-capacity, bulk-density, calcium-carbonate, colorado, colorado-river, colorado-river-basin, colorado-river-basin-above-hoover-dam, digital-soil-mapping, electrical-conductivity, environmental-conditions, environmental-raster-layers, erodibility, geoscientificinformation, gypsum, hoover-dam, interpolate, machine-learning, maps-and-atlases, nevada, new-mexico, organic-matter, predicitve-modeling, predictive-mapping, predictive-maps, random-forest-models, random-forests, restrictive-layer, rock, sodium-adsorption-ratio, soil-conductivity, soil-density, soil-forming-factors, soil-ph, soil-properties, soil-property-maps, soil-sciences, soil-texture, soils, surface-rock-cover, surface-rock-size, texture-fractions, uncertainty, usgs-5e90b25782ce172707ed6af7, utah, wyoming
Organization: Department of the Interior
Assessing Exposure, Physiological Effects, and Geographic Scope of Anticoagulant Rodenticides in the Critically Endangered California Condor

-Test subject identification information (sample collection site and date, test subject identification number, and sample number) 
-Clotting time parameters and comments (prothrombin time, Russell’s viper venom time, fibrinogen concentration, and coefficient of variation for replicate analyses)

Tags: anticoagulant-rodenticide, california, california-condor, clotting-time, hazard, non-target-effects, oregon, secondary-poisoning, united-states, usgs-5e90b1f482ce172707ed66c4
Organization: Department of the Interior
Sonar surveys of water depth from the Colorado River near Lees Ferry Arizona, September 23, 2019

Field-based multibeam sonar surveys were collected along the Colorado River, near Lees Ferry, Arizona from a motorized cataraft.  These data were used to assess the accuracy of river bathymetry inferred from the ASTRALiTe bathymetric lidar, acquired contemporaneously from the same survey vessel.  These data sets were collected to support research focused on developing innovative methods for non-contact measurement of river discharge based on various forms of remotely sensed data. The sonar survey data were exported to a comma-separated text file and the resulting *.csv file contain for each point the spatial coordinates, and depth (expressed as a negative number), all in meters

Tags: arizona, bathymetry, colorado-river, depth, glen-canyon, lees-ferry, multibeam-echosounder, sonar, usgs-5e8cb28182cee42d13465d9e
Organization: Department of the Interior
Bathymetric lidar data from the Colorado River, near Lees Ferry, Arizona, September 23, 2019

The U.S. Geological Survey contracted with Juniper Unmanned to conduct field tests of the ASTRALiTe bathymetric lidar system on the Colorado River near Lees Ferry, Arizona, on September 23, 2019. The objective of this project was to assess the potential to map river bathymetry (i.e., channel bed topography) using lidar data. The ASTRALiTe lidar instrument was mounted on a cataraft owned and operated by USGS Grand Canyon Monitoring and Research Center. This data release includes data delivered to the USGS by ASTRALite on November 1, 2019.  The data are in *txt format and include bare earth (i.e., river bed) and water surface returns and have not been filtered or modified in any other way.

Tags: arizona, bathymetric-lidar, bathymetry, co, colorado-river, depth, lees-ferry, remote-sensing, river-discharge, suas, uas, unmanned-aircraft-system, usa, usgs-5e8ca7bd82cee42d13465d58
Organization: Department of the Interior
Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains)

Image library of (1) tile-drained landscapes and (2) tile-drain types used for training a machine-learning model that identifies (1) tile-drained landscapes and (2) differentiates two types of tile-drained areas visible in satellite imagery. These images were sourced from WorldView, Quickbird, and GeoEye satellite imagery (copyright DigitalGlobe) and cropped to features of interest. Imagery has a ground resolution of 0.34 - 0.65 m.

Tags: agriculture, anthropocene, great-lakes, holocene, usgs-5e8c855e82cee42d13465d00, western-lake-erie
Organization: Department of the Interior
Specific conductance: Yellowstone River at Corwin Springs, USGS Gage 06191500

Measuring the thermal output of Yellowstone’s large magmatic system is not straightforward, as there are thousands of thermal features spread across 3470 square miles. One way to capture and integrate the contributions from this broad area is to monitor river chemistry, because thermal water discharge eventually enters a nearby river, which acts as a collection and delivery system. Nearly all the chloride in rivers that drain Yellowstone comes from emerging hot spring water heated underground by underlying magma. Monitoring river chemistry is therefore an important way to track the behavior and overall changes in Yellowstone’s hydrothermal system. By monitoring the chloride flux, the hydrothermal discharge and heat flux from Yellowstone can be estimated and variations (both short- and long-term) can be used to identify changes in the deep hydrothermal system, earthquake activity, geyser eruptions, and other natural events (like floods and the impacts of wildfire). The U.S. Geological Survey (USGS) and the National Park Service (NPS) have collaborated on Cl flux monitoring of the major rivers since the 1970s. In the past, researchers collected water samples from the major rivers in YNP, but funding restrictions, winter conditions, and the great distances between sites limited the number of samples collected annually. Beginning in 2010, specific conductance, which is relatively easy to measure and can be automated, has been used as a proxy for Cl. The use of specific conductance probes at the various monitoring sites enables a more consistent estimation of Cl flux.  
First posted - January 28, 2019 (available from author)
Revised - May 6, 2020 (version 2.0; available from author)
Revised - May 1, 2025 (version 3.0)
NOTE: While previous versions are available from the author, all the records in previous versions can be found in version 3.0.

Tags: geoscientificinformation, inlandwaters, usgs-5e8be68682cee42d13444358, yellowstone-specific-conductance
Organization: Department of the Interior
Specific conductance: Snake River above Jackson Lake, approximately 200 meters downstream from USGS Gage 13010065

Measuring the thermal output of Yellowstone’s large magmatic system is not straightforward, as there are thousands of thermal features spread across 3470 square miles. One way to capture and integrate the contributions from this broad area is to monitor river chemistry, because thermal water discharge eventually enters a nearby river, which acts as a collection and delivery system. Nearly all the chloride in rivers that drain Yellowstone comes from emerging hot spring water heated underground by underlying magma. Monitoring river chemistry is therefore an important way to track the behavior and overall changes in Yellowstone’s hydrothermal system. By monitoring the chloride flux, the hydrothermal discharge and heat flux from Yellowstone can be estimated and variations (both short- and long-term) can be used to identify changes in the deep hydrothermal system, earthquake activity, geyser eruptions, and other natural events (like floods and the impacts of wildfire). The U.S. Geological Survey (USGS) and the National Park Service (NPS) have collaborated on Cl flux monitoring of the major rivers since the 1970s. In the past, researchers collected water samples from the major rivers in YNP, but funding restrictions, winter conditions, and the great distances between sites limited the number of samples collected annually. Beginning in 2010, specific conductance, which is relatively easy to measure and can be automated, has been used as a proxy for Cl. The use of specific conductance probes at the various monitoring sites enables a more consistent estimation of Cl flux.  
First posted - January 28, 2019 (available from author)
Revised - May 6, 2020 (version 2.0; available from author)
Revised - May 1, 2025 (version 3.0)
NOTE: While previous versions are available from the author, all the records in previous versions can be found in version 3.0.

Tags: geoscientificinformation, inlandwaters, usgs-5e8be63782cee42d13444354, yellowstone-specific-conductance
Organization: Department of the Interior
Predictive soil property map: Depth to top of first restrictive layer

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 &gt; 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building.  More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

Tags: accuracy-and-error-estimated, arizona, available-water-capacity, bulk-density, calcium-carbonate, colorado, colorado-river, colorado-river-basin, colorado-river-basin-above-hoover-dam, digital-soil-mapping, electrical-conductivity, environmental-conditions, environmental-raster-layers, erodibility, geoscientificinformation, gypsum, hoover-dam, interpolate, machine-learning, maps-and-atlases, nevada, new-mexico, organic-matter, predicitve-modeling, predictive-mapping, predictive-maps, random-forest-models, random-forests, restrictive-layer, rock, sodium-adsorption-ratio, soil-conductivity, soil-density, soil-forming-factors, soil-ph, soil-properties, soil-property-maps, soil-sciences, soil-texture, soils, surface-rock-cover, surface-rock-size, texture-fractions, uncertainty, usgs-5e90b1d982ce172707ed65ba, utah, wyoming
Organization: Department of the Interior
Specific conductance: Madison River near West Yellowstone, USGS Gage 06037500

Measuring the thermal output of Yellowstone’s large magmatic system is not straightforward, as there are thousands of thermal features spread across 3470 square miles. One way to capture and integrate the contributions from this broad area is to monitor river chemistry, because thermal water discharge eventually enters a nearby river, which acts as a collection and delivery system. Nearly all the chloride in rivers that drain Yellowstone comes from emerging hot spring water heated underground by underlying magma. Monitoring river chemistry is therefore an important way to track the behavior and overall changes in Yellowstone’s hydrothermal system. By monitoring the chloride flux, the hydrothermal discharge and heat flux from Yellowstone can be estimated and variations (both short- and long-term) can be used to identify changes in the deep hydrothermal system, earthquake activity, geyser eruptions, and other natural events (like floods and the impacts of wildfire). The U.S. Geological Survey (USGS) and the National Park Service (NPS) have collaborated on Cl flux monitoring of the major rivers since the 1970s. In the past, researchers collected water samples from the major rivers in YNP, but funding restrictions, winter conditions, and the great distances between sites limited the number of samples collected annually. Beginning in 2010, specific conductance, which is relatively easy to measure and can be automated, has been used as a proxy for Cl. The use of specific conductance probes at the various monitoring sites enables a more consistent estimation of Cl flux.  
First posted - January 28, 2019 (available from author)
Revised - May 6, 2020 (version 2.0; available from author)
Revised - March 1, 2025 (version 3.0)
NOTE: While previous versions are available from the author, all the records in previous versions can be found in version 3.0.

Tags: geoscientificinformation, inlandwaters, usgs-5e8be61782cee42d13444352, yellowstone-specific-conductance
Organization: Department of the Interior
Specific conductance: Gibbon River at Madison Junction, USGS Gage 06037100

Measuring the thermal output of Yellowstone’s large magmatic system is not straightforward, as there are thousands of thermal features spread across 3470 square miles. One way to capture and integrate the contributions from this broad area is to monitor river chemistry, because thermal water discharge eventually enters a nearby river, which acts as a collection and delivery system. Nearly all the chloride in rivers that drain Yellowstone comes from emerging hot spring water heated underground by underlying magma. Monitoring river chemistry is therefore an important way to track the behavior and overall changes in Yellowstone’s hydrothermal system. By monitoring the chloride flux, the hydrothermal discharge and heat flux from Yellowstone can be estimated and variations (both short- and long-term) can be used to identify changes in the deep hydrothermal system, earthquake activity, geyser eruptions, and other natural events (like floods and the impacts of wildfire). The U.S. Geological Survey (USGS) and the National Park Service (NPS) have collaborated on Cl flux monitoring of the major rivers since the 1970s. In the past, researchers collected water samples from the major rivers in YNP, but funding restrictions, winter conditions, and the great distances between sites limited the number of samples collected annually. Beginning in 2010, specific conductance, which is relatively easy to measure and can be automated, has been used as a proxy for Cl. The use of specific conductance probes at the various monitoring sites enables a more consistent estimation of Cl flux.  
First posted - January 28, 2019 (available from author)
Revised - May 6, 2020 (version 2.0; available from author)
Revised - May 1, 2025 (version 3.0)
NOTE: While previous versions are available from the author, all the records in previous versions can be found in version 3.0.

Tags: geoscientificinformation, inlandwaters, usgs-5e8be5fe82cee42d13444350, yellowstone-specific-conductance
Organization: Department of the Interior
Specific conductance: Gardner River near Mammoth, 5 km downstream from USGS Gage 06191000

Measuring the thermal output of Yellowstone’s large magmatic system is not straightforward, as there are thousands of thermal features spread across 3470 square miles. One way to capture and integrate the contributions from this broad area is to monitor river chemistry, because thermal water discharge eventually enters a nearby river, which acts as a collection and delivery system. Nearly all the chloride in rivers that drain Yellowstone comes from emerging hot spring water heated underground by underlying magma. Monitoring river chemistry is therefore an important way to track the behavior and overall changes in Yellowstone’s hydrothermal system. By monitoring the chloride flux, the hydrothermal discharge and heat flux from Yellowstone can be estimated and variations (both short- and long-term) can be used to identify changes in the deep hydrothermal system, earthquake activity, geyser eruptions, and other natural events (like floods and the impacts of wildfire). The U.S. Geological Survey (USGS) and the National Park Service (NPS) have collaborated on Cl flux monitoring of the major rivers since the 1970s. In the past, researchers collected water samples from the major rivers in YNP, but funding restrictions, winter conditions, and the great distances between sites limited the number of samples collected annually. Beginning in 2010, specific conductance, which is relatively easy to measure and can be automated, has been used as a proxy for Cl. The use of specific conductance probes at the various monitoring sites enables a more consistent estimation of Cl flux.  
First posted - January 28, 2019 (available from author)
Revised - May 6, 2020 (version 2.0; available from author)
Revised - May 1, 2025 (version 3.0)
NOTE: While previous versions are available from the author, all the records in previous versions can be found in version 3.0.

Tags: geoscientificinformation, inlandwaters, usgs-5e8be5dc82cee42d1344434e, yellowstone-specific-conductance
Organization: Department of the Interior