AndreaBozzo/ceres-open-data-index huggingface dataset:
Process-guided deep learning water temperature predictions: 6a Lake Mendota detailed evaluation data

This dataset includes "test data" compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North Temperate Lakes Long-TERM Ecological Research Program (NTL-LTER; https://lter.limnology.wisc.edu/). The buoy is supported by both the Global Lake Ecological Observatory Network (gleon.org) and the NTL-LTER. The dataset also includes Lake Mendota model erformance as measured as root-mean squared errors relative to temperature observations during the test period. This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

Tags: biota, climate-change, deep-learning, environment, hybrid-modeling, inlandwaters, machine-learning, modeling, reservoirs, temperate-lakes, temperature, thermal-profiles, united-states, us, usgs-5d925066e4b0c4f70d0d0599, water, wi, wisconsin
Organization: Department of the Interior
Process-guided deep learning water temperature predictions: 6c All lakes historical evaluation data

This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations. Performance was measured as root-mean squared errors relative to temperature observations during the test period. Test data include compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM Ecological Research Program (https://lter.limnology.wisc.edu/), the Minnesota department of Natural Resources, and the Global Lake Ecological Observatory Network (gleon.org). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

Tags: biota, climate-change, deep-learning, environment, hybrid-modeling, inlandwaters, machine-learning, minnesota, mn, modeling, reservoirs, temperate-lakes, temperature, thermal-profiles, united-states, us, usgs-5d925048e4b0c4f70d0d0596, water, wi, wisconsin
Organization: Department of the Interior
Bathymetric Survey of the Ichilo and Sajta Rivers, near Puerto Villarroel, Bolivia, May 23-24, 2019

A bathymetric survey was conducted at the confluence of the Ichilo and Sajta Rivers near Puerto Villarroel, Bolivia. The surveyed reach extends approximately 12 kilometers upstream of the confluence along the Ichilo River, approximately 2 kilometers upstream of the confluence along the Sajta River, and 1.5 kilometers downstream of the confluence along the Ichilo River. The bathymetric data include survey point data provided as a text file of horizontal coordinates in Universal Transverse Mercator Zone 20 South in meters. The river bed elevation is expressed as height above the WGS-84 ellipsoid, in meters. The survey was collected by USGS personnel from May 23, 2019, through May 24, 2019 using an interferometric multibeam echosounder and an inertial navigation system (INS). The INS measured the attitude (pitch, roll, and yaw) and determined the position of the echosounder during the survey using a differential Global Navigation Satellite Systems (GNSS). The INS files were also stored during the survey and later post-processed to determine a more precise (centimeter-level) position of the echosounder.

Tags: bathymetry, bolivia, depth, ichilo, interferometric-sonar, usgs-5d961f59e4b0c4f70d110e39
Organization: Department of the Interior
Tree mortality in blue oak woodlands in Sequoia National Park during extreme drought

These data were collected to quantify oak mortality during the 2014-2017 drought. 30 tenth hectare drought plots were established throughout the blue oak woodland of Sequoia National Park. Information on species, size, and mortality were collected for all standing trees in each plot. In addition, two long-term 2.25 ha plots were established, in which the same data were collected.

Tags: biota, blue-oak, droughts, tree-mortality, usgs-5d924075e4b0c4f70d0d052f, vegetation
Organization: Department of the Interior
Petroleum geology data from hydrous and anhydrous pyrolysis residues (ver. 2.0, January 2023)

This data release contains programmed pyrolysis, organic petrographic (reflectance), and semiquantitative X-ray diffraction mineralogy data for subsurface coal and shale samples from around the world. Samples were subjected to hydrous or anhydrous pyrolysis experiments at varying temperatures and the resulting residues were analyzed via programmed pyrolysis and reflectance to document changes in thermal maturity.

Tags: alabama, alaska, alberta, alum-shale-formation, australia, bakken-formation, barakar-formation, barnett-formation, bihar, blackstone-oil-shale-layer, boquillas-formation, caldwell-parish, california, cambrian, canada, carbonate, carbondale-formation, carter-county, cenozoic, china, chlorite, choctaw-county, clark-county, clegg-creek-member, colorado, cretaceous, desmoinesian, devonian, duvernay-formation, england, eocene, excello-shale-member, fairfax-county, feldspar, fort-union-formation, freestone-county, garfield-county, garo-hills, geochemistry, gibson-county, greeley-county, green-river-formation, harrison-county, heibei, herrin-coal-member, hi, huron-member, hydrogen-enrichment-index, illinois, illite, india, indiana, jaintia-hills, jefferson-county, joadja-formation, jurassic, kalmar-county, kansas, kaolinite, kentucky, khasi-hills, khewra-gorge-eastern-salt-range, kimmeridge-clay-formation, knox-county, lakadong-sandstone-member, leduc-county, loi, loss-on-ignition, louisiana, mahogany-oil-shale-zone-informal-in-parachute-creek-member, marion-county, mary-lee-cycle, mclean-county, meghalaya, mesoproterozoic, mesozoic, mineralogy, miocene, mississippi, mississippian, montana, monterey-formation, naborton-formation, nanafalia-formation, neogene, neoproterozoic, new-albany-shale, niobrara-formation, north-dakota, nowata-county, ohio, ohio-shale, oi, oklahoma, oland, ordovician, organic-content, oxygen-enrichment-index, pakistan, paleocene, paleogene, paleozoic, pennsylvanian, permian, phanerozoic, pi, pottsville-formation, powell-county, precambrian, production-index, proterozoic, pyrite, pyrolysis, quartz, red-river-parish, richland-county, rock-eval, s1, s2, s3, salt-range-formation, san-saba-county, santa-barbara-county, senora-formation, sentinel-butte-member, sheridan-county, shublik-formation, smectite, sohan-nala-eastern-salt-range, solid-bitumen-reflectance, sweden, sylhet-limestone-formation, texas, thermal-maturity, tmax, tongue-river-member, total-organic-carbon, triassic, tuscaloosa-county, united-states, usgs-5d921851e4b0c4f70d0ce85e, utica-shale, val-verde-county, virginia, vitrinite-reflectance, ward-county, wilcox-group, woodford-shale, wyodak-anderson-coal-zone-informal, xiamaling-formation
Organization: Department of the Interior
Process-guided deep learning water temperature predictions: 5a Lake Mendota detailed prediction data

Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations.

Tags: 007, 012, climate-change, deep-learning, environment, hybrid-modeling, inlandwaters, machine-learning, modeling, reservoirs, temperate-lakes, temperature, thermal-profiles, united-states, us, usgs-5d915cb2e4b0c4f70d0ce523, water, wi, wisconsin
Organization: Department of the Interior
Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data

Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations. Zip files for each lake contain four files, one for each of PB, PB0, DL, and PGDL.

Tags: biota, climate-change, deep-learning, environment, hybrid-modeling, inlandwaters, machine-learning, minnesota, mn, modeling, reservoirs, temperate-lakes, temperature, thermal-profiles, united-states, us, usgs-5d915c8ee4b0c4f70d0ce520, water, wi, wisconsin
Organization: Department of the Interior
Cascades frog (Rana cascadae) historical site surveys in the Oregon Cascade Range, 2018-2019

We used visual surveys to document the presence of all life stages of Cascades frog (Rana cascadae) at historically occupied sites. We surveyed 67 sites 1-2 times between May and August of 2018 and 2019. This effort was a continuation of 2001-2004 surveys conducted at the same site pool. This dataset includes counts of amphibians, reptiles, and fish observed during each site survey, as well as habitat covariates.

Tags: aquatic, aquatic-biology, biota, clackamas-county, day, deschutes-county, douglas-county, environment, habitats, hood-river-county, inlandwaters, jackson-county, jefferson-county, klamath-county, land-surface, lane-county, linn-county, marion-county, modern, multnomah-county, native-species, oregon, oxbow, river, slough, spring, summer, usgs-5d9e03e7e4b036616292584d, wasco-county, wetland
Organization: Department of the Interior
Process-guided deep learning water temperature predictions: 5 Model prediction data

Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations.

Tags: 007, 012, climate-change, deep-learning, environment, hybrid-modeling, inlandwaters, machine-learning, minnesota, mn, modeling, reservoirs, temperate-lakes, temperature, thermal-profiles, united-states, us, usgs-5d915c5de4b0c4f70d0ce51e, water, wi, wisconsin
Organization: Department of the Interior
Data to accompany U.S. Geological Survey Scientific Investigations Report 2019-5077: Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona

The dataset comprises analyses of core and surface samples collected in and around the Red Mountain porphyry Cu-Mo deposit, Santa Cruz County, Arizona. The dataset includes: analyses for 13 minerals in 245 core samples (Appendix 1); analyses for 44 elements in 818 core samples (Appendix 2); analyses for 54 elements in 122 rock samples (Appendix 3); analyses for 55 elements in 119 soil samples (Appendix 4); and analyses for percent ash and 66 elements in 57 mesquite ash (Appendix 5), 68 oak ash (Appendix 6), and 68 juniper ash (Appendix 7) samples, respectively. The samples were collected and analyzed between 1980 and 2000.

Tags: arizona, ash, chemical-analysis, copper, cu, drilling-and-coring, environmental-assessment, geochemistry, geology-geophysics-and-geochemistry-science-center, geoscientificinformation, gggsc, juniper, mesquite, mineral-deposits, mineral-resources-program, mineralogy, mo, molybdenum, mrp, oak, porphyry, red-mountain, rocks-and-deposits, santa-cruz, soil-chemistry, u-s-geological-survey, usgs, usgs-5d8e59bde4b0c4f70d0ccdbf
Organization: Department of the Interior
Digital subsurface data from USGS Regional Aquifer System Analysis of the San Juan basin and adjacent areas, Utah, Colorado, Arizona, and New Mexico

The San Juan basin is a significant physical and structural element in the southeastern part of the Colorado Plateau physiographic province. The San Juan basin is in New Mexico, Colorado, Arizona, and Utah and has an area of about 21,600 square miles. The basin is about 140 miles wide and about 200 miles long. In the 1980’s and 1990’s, the San Juan basin was the focus of the U.S. Geological Survey's Regional Aquifer-System Analysis (RASA) study. Investigation of the San Juan structural basin began in October 1984 with an objective, among others, to define and evaluate the aquifer system. As part of this analysis, a multi-publication series of reports were produced as Hydrologic Atlas 720 (HA-720) that described on 1:1,000,000-scale maps the subsurface configuration and hydrogeology of the San Jose, Nacimiento, and Animas Formations (Levings and others, 1990; HA-720-A), the Ojo Alamo Sandstone (Thorn and others, 1990; HA-720-B), the Kirtland Shale and Fruitland Formation (Kernodle and others, 1990; HA-720-C), the Pictured Cliffs Sandstone (Dam and others, 1990; HA-720-D), the Cliff House Sandstone (Thorn and others, 1990; HA-720-E), the Menefee Formation (Levings and others, 1990; HA-720-F), the Point Lookout Sandstone (Craigg and others, 1990; HA-720-G), the Gallup Sandstone (Kernodle and others, 1990; HA-720-H), the Dakota Sandstone (Craigg and others, 1990; HA-720-I), and the Morrison Formation (Dam and others, 1990; HA-720-J). 
This digital dataset contains spatial datasets corresponding to the contoured subsurface maps produced by the U.S. Geological Survey's Regional Aquifer-System Analysis (RASA) San Juan basin study. The data define the elevation, thickness, and extent of principal stratigraphic units of the basin. The digital data describe the following stratigraphic units: the San Jose Formation, the Ojo Alamo Sandstone, the Kirtland Shale and Fruitland Formation, the Pictured Cliffs Sandstone, the Cliff House Sandstone, the Menefee Formation, the Point Lookout Sandstone, the Gallup Sandstone, the Dakota Sandstone, and the Morrison Formation. Digital data for each unit are contained in individual features classes within a geodatabase (also saved as individual shapefiles). Feature classes have a single attribute, either elevation or thickness, that represents the contoured value. Contoured values are given in feet, to maintain consistency with the original publication, and in meters.

Tags: animas-formation, cliff-house-sandstone, cretaceous, dakota-sandstone, elevation, fruitland-formation, gallup-sandstone, geoscientificinformation, jurassic, kirtland-shale, lower-san-juan-four-corners, menefee-formation, morrison-formation, nacimiento-formation, ojo-alamo-sandstone, paleogene, pictured-cliffs-sandstone, point-lookout-sandstone, san-jose-formation, san-juan, stratigraphic-thickness, structure-contours, upper-colorado, usgs-5d8e350be4b0c4f70d0cc484
Organization: Department of the Interior
Digital subsurface data from USGS Evolution of Sedimentary Basin studies of the San Juan basin and adjacent areas, Utah, Colorado, Arizona, and New Mexico

The San Juan basin is a significant physical and structural element in the southeastern part of the Colorado Plateau physiographic province. The San Juan basin is in New Mexico, Colorado, Arizona, and Utah and has an area of about 21,600 square miles. The basin is about 140 miles wide and about 200 miles long. In the 1980’s and 1990’s, the U.S. Geological Survey's Evolution of Sedimentary Basins—San Juan basin study produced several reports on aspects of the stratigraphy and sedimentology of the basin. A report of the stratigraphy, structure, and paleogeography of Pennsylvanian and Permian rocks included 18 plates of contoured elevation and thickness data for various units (Huffman and Condon, 1993).
This digital dataset contains spatial datasets corresponding to selected contour maps from the Evolution of Sedimentary Basins—San Juan basin study. The data help define the elevation, thickness, and extent of principal stratigraphic units of the basin. The digital data describe the following stratigraphic units: the Molas Formation, the Rico Formation, the elevation of the top of Permian strata, and the estimated thickness of Permian and Pennsylvanian rocks. Digital data for each unit are contained in individual features classes within a geodatabase (also saved as individual shapefiles). Feature classes have a single attribute, either elevation or thickness, that represents the contoured value. Contoured values are given in feet, to maintain consistency with the original publication, and in meters.

Tags: elevation, geoscientificinformation, lower-san-juan-four-corners, molas-formation, pennsylvanian, permian, rico-formation, san-juan, stratigraphic-thickness, structure-contours, upper-colorado, usgs-5d8e31cce4b0c4f70d0cc479
Organization: Department of the Interior
Adult net-spinning caddisfly (Hydropsyche spp.) catch rates and morphology from large rivers of the southwestern United States, 2015-2016

These data describe the distribution and abundance of Hydropsyche, a widespread and diverse genus of net-spinning caddisflies, in the Colorado River Basin. Abundance data, measured as catch rates, were collected by citizen scientists using light traps. We subsampled Hydropsyche spp. from light trap samples collected throughout the Colorado River Basin and measured the lengths and widths of mesothoracic tibia and first tarsal segments. Samples and measurements were processed at Grand Canyon Monitoring and Research Center in Flagstaff, AZ.

Tags: abundance-data, aquatic-ecosystems, arizona, biogeography, biota, catch-rates, citizen-science, colorado, colorado-river, distribution-of-species, dolores-river, ecological-distribution, ecology, green-river, gunnison-river, hydrology, hydropsyche, insects, light-traps, measurements, morphology-biological, net-spinning-caddisflies, relative-abundance, san-juan-river, species-distribution, united-states, usgs-5d8e2c33e4b0c4f70d0cc441, utah, water-temperature, western-united-states, wyoming, yampa-river
Organization: Department of the Interior
Magnetotelluric data from the Northwest Geysers, 2017, station gz21

This dataset consists of 42 magnetotelluric (MT) stations collected in 2017 in the Northwest Geysers, CA. The U.S. Geological Survey acquired these data as part of a project to image the heat source in the Northwest Geysers.  Support was from the U.S. Geological Survey's Volcano Hazards Program.

Tags: apparent-resistivity, california, cenozoic, earth-magnetic-field, electromagnetic-surveying, geology-minerals-energy-and-geophysics-science-center, geophysics, geoscientificinformation, geothermal, gmegsc, impedance, lake-county, magnetotelluric-surveying, magnetotellurics, mesozoic, mt, napa-county, phase, sonoma-county, the-geysers, time-series, tipper, usgs-5d5f3ae1e4b01d82ce9850f3, vhp, volcano-hazards-program
Organization: Department of the Interior
Fecundity data for midcontinent sandhill cranes, 2003-2006

Midcontinent sandhill cranes (Antigone canadensis) are the most abundant management population of cranes in the world and have a broad breeding range. Four breeding segments of midcontinent sandhill cranes have been designated based on spatial and temporal distributions throughout the year, including Western Alaska–Siberia (WA–S), Northern Canada–Nunavut (NC–N), West-central Canada–Interior Alaska (WC–A) and East-central Canada–Minnesota (EC–M). WA–S and NC–N cranes primarily are composed of the lesser sandhill crane (A. c. canadensis) subspecies that breeds in the arctic, whereas WC–A and EC–M cranes are composed primarily of greater sandhill cranes (A. c. tabida), birds which breeds in northern parts of temperate and subarctic regions. Existing information on annual recruitment rates come primarily from ground surveys conducted on fall staging areas in Saskatchewan during the 1960s and 1970s. It is unclear whether recruitment rates have changed over the past several decades during a period of a marked increase in harvest and changing climate and land use. More recent data described in the metadata file were used to estimate fecundity of sandhill cranes to parameterize a population dynamics model. The model was developed to evaluate effects of varying harvest rates on population dynamics, including consequences of differential harvest of segments. These data also were used to compare estimates of annual recruitment derived from fall age ratio surveys with those from individually marked individuals.

Tags: biota, birds, environment, game-species, migratory-birds, minnesota, north-dakota, saskatchewan, usgs-5d8d0036e4b0c4f70d0c8618
Organization: Department of the Interior