AndreaBozzo/ceres-open-data-index huggingface dataset:
Caloosahatchee River, Florida, Nutrient-Enrichment Mesocosms: USGS Phytoplankton Taxonomic Quantification February 2021, May 2021, July 2021

Quantitative phytoplankton data was collected as part of an interdisciplinary and multiagency  experimental study investigating harmful algal bloom (HAB) dynamics in the Caloosahatchee River located in southwestern Florida. Algae and cyanobacteria play crucial roles in aquatic ecosystems, but under favorable environmental conditions such as nutrient enrichment certain taxa may experience population growth booms resulting in HABs. HABs are becoming increasing common in the Caloosahatchee and the Lake Okeechobee system, yet the drivers and processes controlling these events is still under investigation. Here, three independent mesocosm experiments were conducted on the Caloosahatchee River at the W. P. Franklin Lock and Dam, Florida, in February 2021, May 2021, and July 2021 to test the effects of nutrient enrichment of phytoplankton assemblage structure at different times in the year to capture natural temporal variability in water quality and phytoplankton assemblages. The experimental installation consisted of three floating metal cradles each containing four fiberglass chambers filled with native river water. The twelve chambers were assigned to one of five treatments: phosphate (P), nitrate (N), urea (U), ammonium (A), or control (C). Only four of the five treatments were selected for a given experiment: February 2021 treatments included P, N, A, and C, May 2021 treatments included P, U, A, and C, and July 2021 treatments included U, N, A, and C. The P, N, U, and A treatment chambers were enriched in the appropriate nutrient by applying a concentrated dosing solution of either dibasic dodecahydrate sodium phosphate, anhydrous sodium nitrate, solid crystal urea, or liquid ammonium hydroxide respectively to elevate concentrations above ambient levels. The mesocosms were treated and sampled   approximately every 24 hours over a 72-hour period (Time 0, Time 24, Time 48, Time 72). May and July 2021 mesocosm were also sampled a week from the initial dosing (T240). Water quality, phytoplankton, cyanotoxin, and environmental DNA (eDNA) samples were collected before and after dosing each day. The current dataset contains only the phytoplankton data including absolute and relative abundance measures at the genus to species level as natural units (NU/mL), cell densities (cells/mL), and biovolumes (um3/mL).

Tags: algal-blooms, eutrophication, field-experiments, fort-myers-florida, phytoplankton, usgs-63b6edb2d34e92aad3caf2b6, w-p-franklin-lock-and-dam
Organization: Department of the Interior
Major and trace element geochemistry of ferromanganese crusts from seamounts within the Tuvalu Exclusive Economic Zone

Ferromanganese crusts were collected via dredge from seamounts within the Tuvalu Exclusive Economic Zone in the Pacific Ocean during cruise RR1310 funded by the National Science Foundation aboard the R/V Roger Revelle in 2013. USGS scientists requested these ferromanganese crust samples from the Oregon State University Marine and Geology Repository where they had been archived. Ferromanganese crust samples were sent to USGS for subsampling and major and trace element geochemical analyses. Major and trace element data and thickness for the ferromanganese crusts as well as location information (latitude, longitude, depth) for each sample are provided here.

Tags: assessments, chemical-analysis, cmhrp, coastal-and-marine-hazards-and-resources-program, dredge-sampling, geochemistry, geoscientificinformation, global-ocean-mineral-resources, mass-spectroscopy, material-resources, metallic-mineral-resources, mineral-resources, nonliving-resources, oceans, pacific-coastal-and-marine-science-center, pacific-ocean, pcmsc, physical-chemical-features, sediment-geochemistry, tuvalu, u-s-geological-survey, usgs, usgs-63b6512ed34e92aad3cacaf8
Organization: Department of the Interior
X-ray diffraction data for heat treated samples of hydrothermal minerals from seamounts within the Tuvalu Exclusive Economic Zone

A sample of hydrothermally mineralized foraminifera ooze (D28A) was collected via dredge from a seamount within the Tuvalu Exclusive Economic Zone in the Pacific Ocean during cruise RR1310 funded by the National Science Foundation aboard the R/V Roger Revelle in 2013. USGS scientists requested these ferromanganese crust samples from the Oregon State University Marine and Geology Repository where they had been archived. Ferromanganese crust samples were sent to USGS for subsampling and x-ray diffraction analyses. Powder x-ray diffraction data for three layer-fractions of the hydrothermal sample are provided here. Measurements were made after heating to 115 C for 48 hours to test the stability of the phyllomanganate mineral phase. Location information for the sample is included in each Attribute Definition of this metadata file, as well as in the geochemical data table (Tuvalu_RR1310_FeMn_geochem_data.csv) of this multi-table data release.

Tags: assessments, chemical-analysis, cmhrp, coastal-and-marine-hazards-and-resources-program, dredge-sampling, geoscientificinformation, global-ocean-mineral-resources, marine-geology, material-resources, metallic-mineral-resources, mineral-resources, mineralogical-analysis, nonliving-resources, oceans, pacific-coastal-and-marine-science-center, pacific-ocean, pcmsc, physical-chemical-features, sediment-geochemistry, tuvalu, u-s-geological-survey, usgs, usgs-63b65086d34e92aad3cacaf0, x-ray-diffraction
Organization: Department of the Interior
Prairie Fire Assessment of Burned Areas Boundaries for Flint Hills Region

This product ("Prairie fires") presents burned area boundaries for The Flint Hills Ecoregion (KS and OK), one of the most fire prone ecosystems in the United States where hundreds of thousands of acres burn annually as prescribed fire and wildfire. The prairie fire products provide the extent of larger prairie fires in the Flint Hills to record the occurrence of fire and can be used to identify individual burned areas within the perimeters. This product is published to provide fire information of the most fire prone ecosystems to individuals and land management communities for assessing burn extent and impacts on a time sensitive basis. 
The methods used to produce the prairie fire products from 2009 to present are different than Monitoring Trends in Burn Severity Program (MTBS) methods. The product is developed by running a classification tree model on Landsat and Sentinel imagery for all available image dates with visible fires and without greater than 80 percent cloud cover in the spring of each year. The model takes each image, uses all Landsat bands 2-7 or Sentinel 2b bands 2-4, 8, 11, and 12, and finds thresholds between burnt and unburnt areas to create perimeters. Fire perimeters are created by the model and no manual editing is performed. Thus, these data are 100 percent (model based) auto-generated, however, analysts do review and remove small polygons less than 3 acres. The Prairie Fire dataset will include multi-part polygons and have one record for each source image date.
These new methods are optimized to efficiently map and characterize the large number of fires that occur in this region on an annual basis. Prior to 2009, the standard MTBS fire mapping methods were used. Because of the unique frequency and extent of fire in this prairie biome, these fire products are now delivered through the Burn Severity Portal and are no longer included as part of the MTBS products unless a fire is identified in IRWIN, NFPORS or a legacy federal fire occurrence database. The provided data products will vary slightly based on the mapping methodology applied at the time of fire occurrence (pre-2019 or 2019 and later). This map layer is a vector polygon shapefile of fires occurring three acres and greater in size between calendar year 1986 and 2024 for the Flint Hills Ecoregion.

Tags: burned-area, continental-u-s, conus, fire-location, fire-occurrence, imagerybasemapsearthcover, landsat, location, normalized-burn-ratio, prescribed-fire, sentinel, united-states, us, usgs-63b62140d34e92aad3cacaaf, wildfire, wildland-fire
Organization: Department of the Interior
Isotopic Data for Fishes in Wyoming, USA

Trophic studies can provide valuable information on the risk an invader poses to native species. Brook Stickleback Culaea inconstans is an invasive fish species in Wyoming whose effect on native fish assemblages is poorly understood. Our goal was to understand the potential for competitive interactions between Brook Stickleback and native fishes. We used stable isotopes of carbon (ẟ13C) and nitrogen (ẟ15N) to evaluate the feeding ecology of Brook Stickleback relative to four native fishes. The data are appropriate for exploring whether native fish isotopic niches changed in sympatry with Brook Stickleback and to evaluate niche overlap between native fishes and Brook Stickleback.
The data are associated with the manuscript: Ruthven, J.S. and A.W. Walters. In review. Invasive Brook Stickleback Culaea inconstans minimally alters the trophic ecology of four native fishes in Wyoming, USA. Food Webs.

Tags: biota, usgs-63bc48f6d34e92aad3cd2b94
Organization: Department of the Interior
Prairie Fire Assessment (ver. 12.0, September 2025)

This product ("Prairie fires") presents burned area boundaries for The Flint Hills Ecoregion (KS and OK), one of the most fire prone ecosystems in the United States where hundreds of thousands of acres burn annually as prescribed fire and wildfire. The prairie fire products provide the extent of larger prairie fires in the Flint Hills to record the occurrence of fire and can be used to identify individual burned areas within the perimeters. This product is published to provide fire information of the most fire prone ecosystems to individuals and land management communities for assessing burn extent and impacts on a time sensitive basis. 
The methods used to produce the prairie fire products from 2009 to present are different than Monitoring Trends in Burn Severity Program (MTBS) methods. The product is developed by running a classification tree model on Landsat and Sentinel imagery for all available image dates with visible fires and without greater than 80 percent cloud cover in the spring of each year. The model takes each image, uses all Landsat bands 2-7 or Sentinel 2b bands 2-4, 8, 11, and 12, and finds thresholds between burnt and unburnt areas to create perimeters. Fire perimeters are created by the model and no manual editing is performed. Thus, these data are 100 percent (model based) auto-generated, however, analysts do review and remove small polygons less than 3 acres. The Prairie Fire dataset will include multi-part polygons and have one record for each source image date.
      
These new methods are optimized to efficiently map and characterize the large number of fires that occur in this region on an annual basis. Prior to 2009, the standard MTBS fire mapping methods were used. Because of the unique frequency and extent of fire in this prairie biome, these fire products are now delivered through the Burn Severity Portal and are no longer included as part of the MTBS products unless a fire is identified in IRWIN, NFPORS or a legacy federal fire occurrence database. The provided data products will vary slightly based on the mapping methodology applied at the time of fire occurrence (pre-2009 or 2009 and later). This map layer is a vector polygon shapefile of fires occurring three acres and greater in size between calendar year 1986 and 2025 for the Flint Hills Ecoregion. 

Tags: burned-area, continental-u-s, conus, fire-location, fire-occurrence, imagerybasemapsearthcover, landsat, location, normalized-burn-ratio, prescribed-fire, sentinel, united-states, us, usgs-63b62090d34e92aad3cacaad, wildfire, wildland-fire
Organization: Department of the Interior
Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Thematic Burn Severity Mosaic for CONUS in 2022

The RAVG (Rapid Assessment of Vegetation Condition after Wildfire) program provides assessments of vegetation conditions following large fires on forested lands. Fire effects are represented by three metrics: percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized Composite Burn Index (CBI). These data are derived from moderate resolution multi-spectral imagery (e.g., Landsat 8 Operational Land Imager or Sentinel-2 Multispectral Instrument). The Relative Differenced Normalized Burn Ratio (RdNBR), which is correlated to the variation of burn severity within a fire, is calculated from a pair of images (pre- and postfire), judiciously selected to capture fire effects. The three-severity metrics are in turn calculated from RdNBR using regression equations developed from and calibrated with historical field data. This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in CONUS during calendar year 2022. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires were not discernable from available imagery.

Tags: ak, alaska, burn-severity, burned-area, continental-u-s, conus, differenced-normalized-burn-ratio, fire-location, fire-occurrence, imagerybasemapsearthcover, landsat, location, normalized-burn-ratio, prescribed-fire, ravg, sentinel, united-states, us, usgs-63b61dd2d34e92aad3cacaa7, wildfire, wildland-fire
Organization: Department of the Interior
National Park Service Thematic Burn Severity Mosaic for CONUS in 2022

The National Park Service (NPS) requests burn severity assessments through an agreement with the U.S. Geological Survey (USGS) to be completed by analysts with the Monitoring Trends in Burn Severity (MTBS) Program. The MTBS Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a thematic raster image of burn severity classes for all NPS-requested burn severity fires, occurring in CONUS during calendar year 2022. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires which were not discernable from available imagery.

Tags: ak, alaska, burn-severity, burned-area, continental-u-s, conus, differenced-normalized-burn-ratio, fire-location, fire-occurrence, hawaii, hi, imagerybasemapsearthcover, landsat, location, normalized-burn-ratio, nps, pr, prescribed-fire, puerto-rico, sentinel, united-states, us, usgs-63b600bad34e92aad3caaa29, wildfire, wildland-fire
Organization: Department of the Interior
Burned Area Reflectance Classification Thematic Burn Severity Mosaic for CONUS in 2022

These data products are preliminary burn severity assessments derived from data obtained from suitable imagery (including Landsat TM, Landsat ETM+, Landsat OLI, Sentinel 2A, and Sentinel 2B). The pre-fire and post-fire subsets included were used to create a differenced Normalized Burn Ratio (dNBR) image. The dNBR image attempts to portray the variation of burn severity within a fire. The severity ratings are influenced by the effects to the canopy.  The severity rating is based upon a composite of the severity to the understory (grass, shrub layers), midstory trees and overstory trees. Because there is often a strong correlation between canopy consumption and soil effects, this algorithm works in many cases for Burned Area Emergency Response (BAER) teams whose objective is a soil burn severity assessment. It is not, however, appropriate in all ecosystems or fires. It is expected that BAER teams will adjust the thresholds to match field observations to produce a soil burn severity. This map layer is a thematic raster image of burn severity classes for all inventoried fires occurring in CONUS during calendar year 2022. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires which were not discernable from available imagery.

Tags: baer, burn-severity, burned-area, burned-area-emergency-response, differenced-normalized-burn-ratio, fire-location, imagerybasemapsearthcover, landsat, location, normalized-burn-ratio, sentinel, united-states, us, usgs-63b5e619d34e92aad3caa53d, wildfire, wildland-fire
Organization: Department of the Interior
LANID: Landsat-based irrigation dataset for CONUS 2018-20

This data release provides a monthly irrigation water use reanalysis for the period 2000-20 for all U.S. Geological Survey (USGS) Watershed Boundary Dataset of Subwatersheds (Hydrologic Unit Code 12 [HUC12]) in the conterminous United States (CONUS). Results include reference evapotranspiration (ETo), actual evapotranspiration (ETa), irrigated areas, consumptive use, and effective precipitation for each HUC12. ETo and ETa were estimated using the operational Simplified Surface Energy Balance (SSEBop, Senay and others, 2013; Senay and others, 2020) model executed in the OpenET (Melton and others, 2021) web-based application implemented in Google Earth Engine. Results provided by OpenET/SSEBop were summarized to hydrologic response units (HRUs) in the National Hydrologic Model (NHM; Regan and others, 2019) to estimate consumptive use and effective precipitation on irrigated lands. Irrigated lands for the CONUS were provided by the Landsat-based Irrigation Dataset (LANID; Xie and others, 2019) for each year of the reanalysis period. Consumptive use estimates provided by the NHM were disaggregated to HUC12s using area weighted intersections with HRUs and the relative proportion of irrigated lands in each intersected area.
The Landsat-based Irrigation Dataset (LANID) uses a random-forest machine-learning model with greenness and vegetative indices, climate data, and crop masks to identify irrigated crops (Xie and others, 2021, Xie and Lark, 2021). Separate western US and eastern US methods are used to train and validate the model. Annual LANID maps for 2018 -20 were created using the same techniques in Xie and others, 2021, and Xie and Lark, 2021.

Tags: inlandwaters, irrigation, land-use, usgs-63b5dbd9d34e92aad3caa517, water-use
Organization: Department of the Interior
Monitoring Trends in Burn Severity Thematic Burn Severity Mosaic for CONUS in 2021

The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in CONUS during calendar year 2021. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires were not discernable from available imagery.

Tags: ak, alaska, burn-severity, burned-area, continental-u-s, conus, differenced-normalized-burn-ratio, fire-location, fire-occurrence, hawaii, hi, imagerybasemapsearthcover, landsat, location, mtbs, normalized-burn-ratio, pr, prescribed-fire, puerto-rico, sentinel, united-states, us, usgs-63b5d7d7d34e92aad3caa504, wildfire, wildland-fire
Organization: Department of the Interior
Building locations identified before and after the Camp, Tubbs, and Woolsey wildfires

Wildland-urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated due to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex making it challenging for end-users, such as those who use or create WUI maps, to apply. We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs, and Woolsey.  We used a CNN model from Esri to detect buildings from high-resolution imagery. This dataset represents the state-of-the-art of what is readily available for potential WUI mapping.

Tags: biota, building-footprint, convolutional-neural-network, usgs-63b5c2d2d34e92aad3caa32b, wildland-fire, wildland-urban-interface
Organization: Department of the Interior
Upper Midwest Water Science Center Groundwater Model Archive Index v1.0 2023

This vector geospatial dataset contains bounding polygons that visualize the domain of archived groundwater flow models published by the Upper Midwest Water Science Center and attributes pertaining to the associated publication (citation, year of publication, authors, report ID, publication URL), model construction details, and a URL for publicly available model archives when available or a directory pathway for internal model archives. The index currently depicts 115 groundwater model domains across Michigan, Minnesota, and Wisconsin from publications dating back to 1985. Separately published groundwater transport models are not included in this dataset.

Tags: geoscientificinformation, groundwater, michigan, minnesota, usgs-63b59ae5d34e92aad3caa2ad, water-resources, wisconsin
Organization: Department of the Interior