AndreaBozzo/ceres-open-data-index huggingface dataset:
Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species in the Sagebrush Biome, USA - 2023

This dataset release provides historical (2016 - 2023) estimates of fractional cover for Exotic Annual Grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four (five for 2023) fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, Cheatgrass (Bromus tectorum); Medusahead (Taeniatherum caput-medusae); and Sandberg Bluegrass (Poa secunda). Field Brome (Bromus arvensis) is added as individual map species in 2023. The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. 
It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Database (NLCD).  2021 onward maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD for 2021 and 2022 and 2021 NLCD for 2023 were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and later maps. Additionally, HLS NDWI were not used after 2021 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.

Tags: annual-grass, annual-herbaceous, arizona, california, cheatgrass, colorado, exotic, great-basin, harmonized-landsat-sentinel, idaho, invasive-species, kansas, medusahead, montana, ndvi, nebraska, nevada, new-mexico, nonindigenous-species, north-dakota, noxious, oklahoma, oregon, perennial-bunch-grass, poa-secunda, red-brome, remote-sensing, rye-brome, sagebrush, soft-brome, south-dakota, texas, texas-brome, united-states, usgs-66a167dad34ec831f2c2c352, utah, washington, western-united-states, wyoming
Organization: Department of the Interior
Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species in the Sagebrush Biome, USA - 2021

This dataset release provides historical (2016 - 2023) estimates of fractional cover for Exotic Annual Grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four (five for 2023) fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, Cheatgrass (Bromus tectorum); Medusahead (Taeniatherum caput-medusae); and Sandberg Bluegrass (Poa secunda). Field Brome (Bromus arvensis) is added as individual map species in 2023. The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. 
It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Database (NLCD).  2021 onward maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD for 2021 and 2022 and 2021 NLCD for 2023 were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and later maps. Additionally, HLS NDWI were not used after 2021 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.

Tags: annual-grass, annual-herbaceous, arizona, california, cheatgrass, colorado, exotic, great-basin, harmonized-landsat-sentinel, idaho, invasive-species, kansas, medusahead, montana, ndvi, nebraska, nevada, new-mexico, nonindigenous-species, north-dakota, noxious, oklahoma, oregon, perennial-bunch-grass, poa-secunda, red-brome, remote-sensing, rye-brome, sagebrush, soft-brome, south-dakota, texas, texas-brome, united-states, usgs-66a167bad34ec831f2c2c34e, utah, washington, western-united-states, wyoming
Organization: Department of the Interior
Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species in the Sagebrush Biome, USA - 2020

This dataset release provides historical (2016 - 2023) estimates of fractional cover for Exotic Annual Grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four (five for 2023) fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, Cheatgrass (Bromus tectorum); Medusahead (Taeniatherum caput-medusae); and Sandberg Bluegrass (Poa secunda). Field Brome (Bromus arvensis) is added as individual map species in 2023. The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. 
It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Database (NLCD).  2021 onward maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD for 2021 and 2022 and 2021 NLCD for 2023 were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and later maps. Additionally, HLS NDWI were not used after 2021 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.

Tags: annual-grass, annual-herbaceous, arizona, california, cheatgrass, colorado, exotic, great-basin, harmonized-landsat-sentinel, idaho, invasive-species, kansas, medusahead, montana, ndvi, nebraska, nevada, new-mexico, nonindigenous-species, north-dakota, noxious, oklahoma, oregon, perennial-bunch-grass, poa-secunda, red-brome, remote-sensing, rye-brome, sagebrush, soft-brome, south-dakota, texas, texas-brome, united-states, usgs-66a16794d34ec831f2c2c34c, utah, washington, western-united-states, wyoming
Organization: Department of the Interior
Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species in the Sagebrush Biome, USA - 2019

This dataset release provides historical (2016 - 2023) estimates of fractional cover for Exotic Annual Grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four (five for 2023) fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, Cheatgrass (Bromus tectorum); Medusahead (Taeniatherum caput-medusae); and Sandberg Bluegrass (Poa secunda). Field Brome (Bromus arvensis) is added as individual map species in 2023. The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. 
It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Database (NLCD).  2021 onward maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD for 2021 and 2022 and 2021 NLCD for 2023 were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and later maps. Additionally, HLS NDWI were not used after 2021 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.

Tags: annual-grass, annual-herbaceous, arizona, california, cheatgrass, colorado, exotic, great-basin, harmonized-landsat-sentinel, idaho, invasive-species, kansas, medusahead, montana, ndvi, nebraska, nevada, new-mexico, nonindigenous-species, north-dakota, noxious, oklahoma, oregon, perennial-bunch-grass, poa-secunda, red-brome, remote-sensing, rye-brome, sagebrush, soft-brome, south-dakota, texas, texas-brome, united-states, usgs-66a166b5d34ec831f2c2c344, utah, washington, western-united-states, wyoming
Organization: Department of the Interior
Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species in the Sagebrush Biome, USA - 2018

This dataset release provides historical (2016 - 2023) estimates of fractional cover for Exotic Annual Grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four (five for 2023) fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, Cheatgrass (Bromus tectorum); Medusahead (Taeniatherum caput-medusae); and Sandberg Bluegrass (Poa secunda). Field Brome (Bromus arvensis) is added as individual map species in 2023. The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. 
It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Database (NLCD).  2021 onward maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD for 2021 and 2022 and 2021 NLCD for 2023 were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and later maps. Additionally, HLS NDWI were not used after 2021 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.

Tags: annual-grass, annual-herbaceous, arizona, california, cheatgrass, colorado, exotic, great-basin, harmonized-landsat-sentinel, idaho, invasive-species, kansas, medusahead, montana, ndvi, nebraska, nevada, new-mexico, nonindigenous-species, north-dakota, noxious, oklahoma, oregon, perennial-bunch-grass, poa-secunda, red-brome, remote-sensing, rye-brome, sagebrush, soft-brome, south-dakota, texas, texas-brome, united-states, usgs-66a16038d34ec831f2c2c2e3, utah, washington, western-united-states, wyoming
Organization: Department of the Interior
Water quality and toxicity data to assess the potential effect of nitrogen on juvenile freshwater mussels (Bivalvia: Unionidae) in the Buffalo National River, Arkansas from September 16 2019 to November 20, 2020

The Buffalo National River in Arkansas was established as the first national river in 1972 and later designated as an Extraordinary Resource Water with a Wild and Scenic section in the upper portion. The Buffalo National River's water quality database indicates nitrogen compounds have increased since the 1980s. This study aimed to investigate the potential threat of elevated nutrients and its association with freshwater mussel declines in the Buffalo National River through laboratory and in situ exposures. We evaluated (1) the toxicity of receiving water and sediment samples containing elevated ammonia and nitrate to native juvenile Lampsilis reeveiana (Arkansas Brokenray) and three common aquatic test species (Ceriodaphnia dubia, Pimephales promelas, and Hyallela azteca) and (2) in situ bioassays with juvenile L. reeveiana to investigate the effects of nitrogen contaminants and other water quality factors on survival and growth.

Tags: aquatic-habitat, biological-assessment, biota, contamination-and-pollution, freshwater-mussels, mollusks, pollution-load, usgs-66a16002d34ec831f2c2c2de, water-quality
Organization: Department of the Interior
Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species in the Sagebrush Biome, USA - 2017

This dataset release provides historical (2016 - 2023) estimates of fractional cover for Exotic Annual Grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four (five for 2023) fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, Cheatgrass (Bromus tectorum); Medusahead (Taeniatherum caput-medusae); and Sandberg Bluegrass (Poa secunda). Field Brome (Bromus arvensis) is added as individual map species in 2023. The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and various environmental, vegetation, remotely sensed, and geophysical drivers. Additionally, artificial intelligence and machine learning techniques were employed in the data generation process. 
It should be noted that the training of regression-tree models and the development of historical maps (2016-2020) utilized a total of 17,536 AIM plots from years 2016 – 2019. For the creation of 2021 maps, 19,415 AIM plots from years 2016 - 2021 were employed, and 2022 maps, 28,901 AIM plots from 2016-2022 were used. In the case of 2016 – 2020 maps, areas above 2250-m elevation and pixels classified other than grassland/herbaceous or shrub (likely rangelands) were masked based on the 2016 National Land Cover Database (NLCD).  2021 onward maps, areas above 2350-m elevation and pixels classified as other than grassland/herbaceous by the 2019 NLCD for 2021 and 2022 and 2021 NLCD for 2023 were masked. The seed source variable from the Rangeland Analysis Platform (RAP) [Jones et al., 2018]) was used as one of the drivers for modeling of 2016 – 2020 maps but was not utilized for modeling of 2021 and later maps. Additionally, HLS NDWI were not used after 2021 maps. All other predictor variables are identical for all sets of maps. For details, please check data quality information section.

Tags: annual-grass, annual-herbaceous, arizona, california, cheatgrass, colorado, exotic, great-basin, harmonized-landsat-sentinel, idaho, invasive-species, kansas, medusahead, montana, ndvi, nebraska, nevada, new-mexico, nonindigenous-species, north-dakota, noxious, oklahoma, oregon, perennial-bunch-grass, poa-secunda, red-brome, remote-sensing, rye-brome, sagebrush, soft-brome, south-dakota, texas, texas-brome, united-states, usgs-66a153abd34ec831f2c2b9a2, utah, washington, western-united-states, wyoming
Organization: Department of the Interior
Ground-Based Bathymetric Data Collected within the Wilderness Breach, Fire Island, New York, (2014) in XYZ ASCII Text File Format

Scientists from the U.S. Geological Survey St. Petersburg Coastal
and Marine Science Center in St. Petersburg, Florida, conducted a
bathymetric survey of Fire Island, New York, from October 5 to 10,
2014. The U.S. Geological Survey is involved in a post-Hurricane
Sandy effort to map and monitor the morphologic evolution of the
wilderness breach, which formed in October 2012 during Hurricane
Sandy, as part of the Hurricane Sandy Supplemental Project GS2-2B.
During this study, bathymetry data were collected, using single-beam
echo sounders and global positioning systems mounted to personal
watercraft, along the Fire Island shoreface and within the
wilderness breach, Fire Island Inlet, Narrow Bay, and Great South
Bay east of Nicoll Bay. Additional bathymetry and elevation data
were collected using backpack and wheel-mounted global positioning
systems along the subaerial beach (foreshore and backshore), and
flood shoals and shallow channels within the wilderness breach and
adjacent shoreface.

Tags: 2014-325-fa, atlantic-ocean, backpack-gps, barrier-island, bathymetry, bellport-bay, breaches, cmgp, coastal-and-marine-geology-program, elevation, fire-island, fire-island-inlet, fire-island-national-seashore, geophysical, geoscientificinformation, great-south-bay, hurricane-sandy, hurricane-sandy-supplemental-fire-island, imagerybasemapsearthcover, mapping, marine-geology, narrow-bay, new-york, ny, oceans, otis-pike-fire-island-high-dune-wilderness, shoals, spcmsc, st-petersburg-coastal-and-marine-science-center, the-wilderness-breach, u-s-geological-survey, united-states, us, usgs, usgs-66a14793-412b-454d-86bc-2fe46db7be2b
Organization: Department of the Interior
Data Release for Replicate amphibian swab and standard dilution of artificial DNA data to assess qPCR and sampling detection error for Batrachochytrium dendrobatidis.

We generated a data set from by collecting duplicate swabs from all amphibian individuals sampled at 5 sites (96 individuals) and analyzed these duplicate swabs running 3 subsamples of the extracted DNA in separate qPCR reactions. The resulting data set included 2 swabs per individual with 1 qPCR result and a 2nd swab with 3 qPCR results for detection of Batrachochytrium dendrobatidis (Bd) DNA. We generated data to assess the ideal laboratory functional relationship between quantity of target Bd DNA and detection, as well as limits of detection (LOD) and quantification (LOQ) for the assay, 5 µL of synthetic double-stranded Bd DNA template (gBlocks™ Gene Fragments, Integrated DNA Technologies, Coralville, IA) ranging from 1.46 x 10-3 fg to 3 fg (approximately 6.6 – 13,000 copies) per reaction at 2-fold dilutions were each run 80 times on 10 separate plates (12 dilution series per plate).

Tags: amphibian-batrachochytrium-dendrobatidis-detection-disease-occupancy-prevalence-sensitivity, biota, usgs-66a02911d34eef99d5abc7a9
Organization: Department of the Interior
Phytoplankton Identification and Enumeration Data Collected from Seven Reservoirs in the United States (July to November 2019)

This dataset contains algal identification and enumeration data for phytoplankton samples collected by the U.S. Geological Survey (USGS) between July 2019 and November 2019 at seven reservoirs across the United States. Reservoirs sampled included Lake Koshkonong, Wisconsin, Pelican Lake, Minnesota, Lake Ida, Minnesota, Pomme de Terre, Minnesota, Lake Emily, Minnesota, Milford Lake, Kansas, and Jordan Lake, North Carolina. The samples were analyzed at the Caribbean - Florida Water Science Center (CFWSC) Phycology Laboratory using morphology-based microscopy methods. This data is part of a larger multi-agency project between the U.S. Environmental Protection Agency, the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and USGS called the Cyanobacteria Assessment Network (CyAN). The goal of the CyAN project is to develop a satellite-based, early warning system to detect harmful algal blooms (HABs) in freshwater systems.

Tags: biota, cyanobacteria-blue-green-algae, datasets, jordan-lake, kansas, lake-emily, lake-ida, lake-koshkonong, milford-lake, minnesota, north-carolina, pelican-lake, pomme-de-terre-lake, usgs-669fb8eed34eef99d5abc57b, water-quality, wisconsin
Organization: Department of the Interior
Water Level Data from October 30, 2020 through May 8, 2023 at Three Sites of the Bad River (Mashkiiziibii) Estuary

Mean daily water level NAVD88 elevations from U.S. Geological Survey (USGS) gaging stations 04027655, Bad River at Mouth; 463836090423701, Kakagon River Slough; and 463741090521301, Chequamegon Bay; Ashland Lighthouse Duluth, MN. Water level NAVD88 elevations encompass mean daily values ranging from October 30, 2020 through September 30, 2022 at site 04027655; October 30, 2020 through September 30, 2022, as well as April 8, 2023 through May 8, 2023 at site 463741090521301; and April 16, 2021 through September 30, 2022 at site 463836090423701.

Tags: ashland-county, elevation, estuary, floods, gage-height, lake-superior, odanah-wisconsin, usgs-664f5e3fd34e702fe8747eb3
Organization: Department of the Interior