AndreaBozzo/ceres-open-data-index huggingface dataset:
Predictive soil property map: Bulk density (oven dry)

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-5e90b15e82ce172707ed60a9, utah, wyoming
Organization: Department of the Interior
Concentration of inorganic chemical elements associated with suspended sediment at Muddy Creek below Paonia Reservoir, Gunnison County, Colorado, U.S. Geological Survey site number 385626107212000

Dataset includes the analysis results of 49 inorganic chemical elements bound to suspended-sediment particles below Paonia Reservoir during a sediment release in September 2017. Suspended-sediment samples were collected at U.S. Geological Survey (USGS) station number 385626107212000 by following data collection procedures and protocols in Edwards and Glysson (1999). The samples were freeze-dried after collection and then decomposed using a mixture of nitric, hydrofluoric, and perchloric acids. The dried residue after acid digestion was analyzed by atomic absorption spectrometry and inductively coupled plasma-mass spectrometry following procedures and protocols in Taggart (2002). Reference samples of flue gas desulfurization gypsum were also analyzed to test for contamination during the freeze-drying process.

Tags: chemical-elements, chemistry, colorado, gunnison-county, inlandwaters, muddy-creek, paonia-reservoir, sediment-transport, somerset, stream-discharge, surface-water, suspended-material-water, suspended-sediment, usgs-5e90ac5482ce172707ed2ecb
Organization: Department of the Interior
Predictive soil property map: Available water holding capacity

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-5e90a97982ce172707ed0ef9, utah, wyoming
Organization: Department of the Interior
Mineralogy, chemistry and isotope composition of silica sinter deposits from the Upper Geyser Basin, Yellowstone National Park: inductively coupled plasma optical emission spectrometry (ICP-OES) data

Sample Analysis: Cation concentration data was obtained as a part of the beryllium isotope analyses. Samples collected from the Upper Geyser Basin were first treated in clean laboratory facilities at the University of New Hampshire following the methods laid out in the “Beryllium Isotope Data” portion of this data release. Following the hydrofluoric acid (HF) etches, and prior to Accelerated Mass Spectroscopy (AMS) analyses of 10Be, opal purity was assessed by measurement of major cation concentrations via inductively coupled plasma-optical emission spectrometry (ICP-OES) using a Perkin Elmer Optima 3300DV at the Analytical and Technical Services lab at SUNY College of Environmental Science and Forestry in Syracuse, New York. The cations were run on a ~0.2 g dry aliquot of the purified opal, which was removed from a homogenized sample (the same material analyzed for 10Be with AMS). The opal aliquot was digested in a few mL concentrated HF, dried down, and then redissolved in a solution of 5% nitric acid (HNO3)/1% sulfuric acid (H2SO4) prior to ICP-OES measurements. The first etch ran for 24 hours, and all subsequent etches ran for 48 hours. Sample YGT18-30D dissolved completely after 6 etches, hence no ICP-OES measurements were possible.
Sample cation concentrations were determined with reference to in-house standard solutions prepared from commercially purchased certified single-element ICP standards and diluted to concentration ranges customized for each measured element. Several blanks were run in each ICP-OES measurement batch, and average blank values for each batch were subtracted from measured sample concentrations to derive blank-corrected cation values. ICP-OES analyte wavelengths for each element are as follows: Al (396.153, 308.215), Be (313.107, 313.042, 234.861), Ca (317.933, 315.887), Fe (238.204, 259.939), K (766.490), Ti (334.940, 336.121).  All reported cation concentrations are corrected for blanks run concurrently with samples. Maximum blank corrections were negligible for all elements, as follows: Al (0.04%), Be (0.1%), Ca (0.05%), Fe (0.3%), K (0.01%), Ti (0.5%).
Database Contents: The data file (ICPOES Data.csv) contains the inductively coupled plasma-optical emission spectrometry measured cation concentrations for samples collected from Castle and Solitary Geyser.
A. Sample ID
B. Location
C. # Etches
D. Al (ppm)
E. Be (ppm)
F. Ca (ppm)
G. Fe (ppm)
H. K (ppm)
I. Ti (ppm)

Tags: cation, geysers, upper-geyser-basin, usgs-5e909bda82ce172707ec7b89, yellowstone-national-park
Organization: Department of the Interior
Supply of and demand for water purification of nonpoint source pollutants in the Southeast United States

Natural land cover can remove pollutants from runoff water by slowing water flow and physically trapping suspended particles.  We identified
natural land cover in the Southeast US potentially contributing to water purification due to its location in the flowpath between sources of nonpoint-source pollution
and waterways.

Tags: alabama, arkansas, contamination-and-pollution, florida, freshwater-ecosystems, georgia, louisiana, mississippi, missouri, natural-resource-management, north-carolina, runoff, south-carolina, tennessee, usgs-5e90937c82ce172707ec2a4b, water-resources
Organization: Department of the Interior
Supply of and demand for wild pollinator habitat in the Southeast United States

Wild insect pollination has significant positive effects on pollinator-dependent crop production.  To assess the spatial 
distribution of potential wild insect pollination, we mapped the supply of potential wild pollinator habitat (forest, 
grassland, wetland, and shrubland land cover types) and the demand for pollination (directly pollinator-dependent crops).  
A foraging travel distance for temperate native bees (1308 meters) was used to identify wild pollinator habitat that is within
foraging range of pollinator-dependent crops, and pollinator-dependent crops that are within foraging range of pollinator
habitat.

Tags: agricultural-sites, alabama, arkansas, environment, farming, florida, georgia, habitats, louisiana, mississippi, missouri, natural-resource-management, north-carolina, pollination, pollinators, remediation, south-carolina, tennessee, usgs-5e90934682ce172707ec2934
Organization: Department of the Interior
Modeling bird species richness for the southeastern United States (2001, 2006, and 2011)

These datasets are the inputs, script, and output of bird species richness modeling for the southeastern United States in the years 2001, 2006, and 2011 using general joint attribute modeling (GJAM).  Inputs include environmental predictor variables for Breeding Bird Survey routes, bird counts from Breeding Bird Survey observations, and environmental predictor variables on a half-degree grid for the study area.  The R script uses these inputs to create a general joint attribute model to predict bird abundance by species on a half-degree grid in for the study area in 2001, 2006, and 2011.  The output file summarizes the results as bird species richness by state, land cover class, and year.

Tags: alabama, arkansas, biota, birds, florida, georgia, louisiana, mississippi, missouri, north-carolina, population-and-community-ecology, south-carolina, tennessee, usgs-5e90930f82ce172707ec271e
Organization: Department of the Interior
Data used to evaluate arsenic and uranium occurrence in Connecticut groundwater through spatially weighted and bedrock geology assessments

This data release contains two spatial datasets and a data table in support of an evaluation of arsenic and uranium occurrence in Connecticut groundwater through spatially weighted and bedrock geology assessments. Spatial datasets include 1) a shapefile of 130 equal-area grid cells with associated arsenic attribute data, and 2) a shapefile of 110 equal-area grid cells with associated uranium attribute data. The State of Connecticut was divided based on a set of randomized equal-area grid cells based on the method of Scott (1990); one grid was created for arsenic, with 130 grid cells, and one was created for uranium, with 110 grid cells. Arsenic and uranium attribute data associated with the equal-area grid cells include the number of wells in each grid cell, the number of wells with constituent concentrations above three selected thresholds, the fraction of wells with constituent concentrations above three selected thresholds, and the percentage of wells with constituent concentrations above three selected thresholds. The three selected thresholds for arsenic include 3, 5, and 10 micrograms per liter (ug/L), with 10 ug/L representing the maximum contaminant level (MCL) established by the U.S. Environmental Protection Agency (EPA) for human health for arsenic. The three selected thresholds for uranium include 1, 10, and 30 ug/L, with 30 ug/L representing the EPA MCL for human health for uranium. The bedrock geology data table is table 4 from Gross and others (2020) formatted so that it can easily be joined with Connecticut's bedrock geology dataset (Connecticut Department of Environmental Protection, 2000) using the geologic unit abbreviation (UNIT attribute) in order to recreate figure 3 from Gross and others (2020). The data table includes counts and percentages of arsenic and uranium concentrations that exceed maximum contaminant levels from private wells in Connecticut, by geologic unit and major bedrock category, 2013-18.

Tags: groundwater, usgs-5e90900a82ce172707ec0aa1, water-quality
Organization: Department of the Interior
Field blank and field replicate datasets for inorganic and organic compounds collected for the National Water Quality Network, water years 2013-17

The U.S. Geological Survey (USGS) National Water Quality Network - Rivers and Streams (NWQN) comprises 117 surface-water monitoring sites designed to track ambient water-quality conditions across the nation. This dataset includes field quality-control results (field blank and field replicate concentrations), along with the water-quality result of each associated surface-water sample, of water samples collected from October 2012 through September 2017 at NWQN sites. 
This dataset includes 2 tables and 6 files of plots of the data. Tables are in Comma Separated Value, CSV, format and plotfiles are in Portable Document Format, PDF, format. The plotfiles are intended to provide a succinct view of the data. 
Table1.NWQNFieldBlanksC3.csv
Table2.NWQNFieldReplicatesC3.csv
PlotFile 1. Time-series plots showing concentrations of nutrients, carbon, UV absorbance, and suspended sediments in surface-water samples and field blanks in the National Water Quality Network, water years 2013–17.
PlotFile 2. Scatterplots showing concentrations of pairs of surface-water samples and field replicates for nutrients, carbon, and suspended sediment in samples in the National Water Quality Network, water years 2013–17 .
PlotFile 3. Time-series plots showing concentrations of major ions and trace elements in surface-water samples and field blanks in the National Water Quality Network, water years 2013–17. 
PlotFile 4. Scatterplots showing concentrations of pairs of surface-water samples and field replicates for major ions and trace elements in the National Water Quality Network, water years 2013–17.
PlotFile 5. Time-series plots showing concentrations of pesticides in surface-water samples and field blanks in the National Water Quality Network, water years 2013–17.
PlotFile 6. Scatterplots showing concentrations of pairs of surface-water samples and field replicates for pesticides in the National Water Quality Network, water years 2013–17.

Tags: major-ions, national-water-quality-network, nutrients, pesticides, quality-control, surface-water-quality, trace-elements, usgs-5e9088f182ce172707ebdf20
Organization: Department of the Interior
Flow direction raster for Pennsylvania StreamStats

The U.S. Geological Survey (USGS), in cooperation with the Federal Emergency Management Agency, Pennsylvania Department of Environmental Protection, Pennsylvania Department of Transportation, and Susquehanna River Basin Commission, prepared hydro-conditioned geographic information systems (GIS) layers for use in the Pennsylvania StreamStats application. These data were used to update the peak flow and low flow regression equations for Pennsylvania. This dataset consists of flow direction rasters for each 8-digit Hydrologic Unit Code (HUC) area in Pennsylvania, one of the layer types needed to delineate watersheds within the HUC-8 areas, merged into a single dataset. The 59 HUCs represented by this dataset are 02040101, 02040102, 02040103, 02040104, 02040105, 02040106, 02040201, 02040202, 02040203, 02040205, 02050101, 02050102, 02050103, 02050104, 02050105, 02050106, 02050107, 02050201, 02050202, 02050203, 02050204, 02050205, 02050206, 02050301, 02050302, 02050303, 02050304, 02050305, 02050306, 02060002, 02060003, 02070002, 02070003, 02070004, 02070009, 04110003, 04120101, 04130002, 05010001, 05010002, 05010003, 05010004, 05010005, 05010006, 05010007, 05010008, 05010009, 05020001, 05020002, 05020003, 05020004, 05020005, 05020006, 05030101, 05030102, 05030103, 05030104, 05030105, and 05030106.

Tags: environment, geoscientificinformation, geospatial-datasets, hydrology, land-surface-characteristics, pennsylvania, streamflow, topography, united-states, usgs-5e8fe5d682ce172707ebdb4f, watershed-management
Organization: Department of the Interior
Assessment of hydrologic alteration at 12-digit hydrologic unit code (HUC12) pour points in the southeastern United States, 1950 - 2009

Two methods of calculating hydrologic alteration were applied to modeled daily streamflow data for 9,201 12-digit hydrologic unit code (HUC12) pour points draining to the Gulf of Mexico (Robinson and others, 2020). The first method is a new modified method of calculating ecosurplus and ecodeficit called hydro change. For this project, ecosurplus and ecodeficit have been combined to assess overall hydrologic regime change. The second method is the confidence interval hypothesis test (Kroll and others, 2015). The first method is a means of quantifying hydrologic alteration while the second is a hypothesis test to simply determine if statistically significant alteration has occurred. Both methods are employed to determine which is best at analyzing alteration of the hydrologic regime in the Gulf Coast Ecosystem Restoration Council (RESTORE) study area. Statistical analysis was done in RStudio (2020). The data release includes four attached files: (1) metadata .xml file, (2) csv with the p-values for each HUC12, (3) csv with results from the hydrologic change analysis, and (4) the shapefile of the pour point locations for the HUC12s used in the analyses.

Tags: alabama, environmental-flow, florida, gulf-coast, louisiana, mississippi, reference-hydrology, restore, southeast, streamflow, streamflow-alteration, texas, trend-analysis, united-states, usgs-5e8f772b82cee42d134ae086
Organization: Department of the Interior
Mineralogic, grain-size, biologic, and stable isotopic analyses of core TOPGUN-SODA10 2A-K from Big Soda Lake, Nevada, USA.

These data were collected from the TOPGUN-SODA10 2AK core collected in 2010 by LacCore (University of Minnesota), the USGS. and the University of California (U.C.)  Berkeley.  The 8.9 meter length core had stable isotopes of oxygen and carbon sampled every 10 millimeters or so, and diatoms samples at every 50 millimeters to 200 millimeters depending on the depth in the core.  mineralogic data were collected using X-ray diffraction and grain-size analysis were done at various depths.  These data are used to interpret the Holocene and late Pleistocene hydrology and paleoclimate of the lake and the region.  The interpretation can be found in:
Rosen, M.R., Reidy, L., Starratt, S., Zimmerman, S., in review, Middle Holocene hydrologic changes in Big Soda Lake, Nevada, USA, caused by river avulsion. In Rosen, M.R., Finkelstein, D., Park-Boush, L., and Pla Plueyo, S. (eds.) Limnogeology: Progress, challenges and opportunities: A tribute to Beth Gierlowski-Kordesch, Syntheses in Limnogeology No. 2, Springer.

Tags: big-soda-lake, biota, carbonate-isotopes, central-nevada-desert-basins, diatoms, elevation, environment, grain-size-analysis, lake-coring, nevada, piston-and-gravity-coring, soda-lake-east, usgs-5e8f6a3682cee42d134ae02a, x-ray-diffraction
Organization: Department of the Interior
Predictive soil property map: Silt content

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-5e90b32e82ce172707ed72a7, utah, wyoming
Organization: Department of the Interior