AndreaBozzo/ceres-open-data-index huggingface dataset:
Serologic Survey of Selected Arthropod Borne Pathogens in Free-ranging Snowshoe Hares (Lepus americanus) Captured in Northern Michigan, USA

We conducted a serosurvey of snowshoe hares (Lepus americanus) for vector-borne pathogens in 2016-2017 that were captured in the Hiawatha National Forest in the eastern Upper Peninsula of Michigan, USA. At capture, in addition to age, sex, body weight of the hare and a blood sample data was collected on the ecological land type (USDA Forest Service. 2006. Hiawatha National Forest 2006 Forest Plan. https://www.fs.usda.gov/detail/hiawatha/landmanagement/planning/?cid=STELPRDB5106336) at the point of capture. Serology was conducted for antibodies to Snowshoe hare virus, Jamestown Canyon virus, La Crosse virus, West Nile virus, tick-transmitted Powassan virus, Silverwater virus, Borrelia burgdorferi, and Francisella tularensis.

Tags: biota, borrelia-burgdorferi, climate-change, francisella-tularensis, jamestown-canyon-virus, lacrosse-encephalitis-virus, powassan-virus, silverwater-virus, snowshoe-hare-virus, usgs-649dde1ed34ef77fcb0402e9, west-nile-virus
Organization: Department of the Interior
Capsaicin-treated bait trials for Argentine Black and White Tegu lizards in Georgia and Florida, USA 2020-2021

Invasive Argentine Black and White Tegu (Salvator merianae) lizards are trapped with chicken eggs when populations are discovered in the United States. Unfortunately, non-targeted mammals are trapped in tegu lizard traps which reduces trap efficacy. We evaluated if non-target mammal captures could be reduced by coating the chicken eggs with a known irritant to mammals (capsaicin) while maintaining a highly desired bait for tegu lizards. The data set quantifies trap disturbance by non-targets species and efficacy of capsaicin coated eggs as bait in live traps set for Argentine Black and White Tegu lizards (Salvatore merianae). The dataset consists of three live trapping experiments to test if 1) trap disturbance by mammals habituated to eggs without capsaicin from previous trapping efforts decreased when capsaicin-coated eggs were deployed (experiment 1), 2) mammals not habituated to eggs as bait (either treated with capsaicin or not treated with capsaicin) disturbed live traps at the same rate as those habituated to eggs in experiment 1 when presented with either capsiacin-treated or non-treated eggs (experiment 2), and 3) tegu capture rates were different when capsaicin-treated eggs were deployed in Florida (experiment 3).

Tags: biota, capturing-animals, florida-usa, georgia-usa, greater-everglades-ecosystem, piedmont-se, reptiles, usgs-649f02c8d34ef77fcb041d64
Organization: Department of the Interior
Ground control points Marconi Beach, Wellfleet, MA on March 22, 2023

The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the beach. In March 2023, U.S. Geological Survey and Woods Hole Oceanographic Institute (WHOI) scientists conducted field surveys to collect topographic and bathymetric data. Images of the beach for use in structure-from-motion were taken with a camera (SONY a6000) and a post-processed kinematic (PPK) system attached to a helium powered balloon-kite (Helikite). High-precision GPS targets (AeroPoints) and numbered black and white tarps were used as ground control points. Bathymetry was collected in the nearshore using a single-beam echosounder mounted on a surf capable self-righting electric autonomous surface vehicle. Agisoft Metashape (v. 1.8.1) was used to create a digital surface model and orthomosaic with the collected imagery.

Tags: aeropoint, cape-cod, cmhrp, coastal-and-marine-hazards-and-resources-program, elevation, geolocation-measurement, geoscientificinformation, geospatial-datasets, global-positioning-system-gps-observations, gps-measurement, ground-control-point, helikite, location, marconi-beach, massachusetts, oceans, u-s-geological-survey, united-states, usgs, usgs-648b767cd34ef77fcafe6957, wellfleet, whcmsc, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior
Historical simulated snowpack and other hydrometeorology data at 30 m for the Crown of the Continent and vicinity, United States and Canada, water years 1981-2020

This data release contains historical SnowModel (Liston and Elder, 2006) output for the Crown of the Continent and surrounding areas in Montana, USA; and Alberta and British Columbia, Canada from September 1, 1981 through August 31, 2020. Fifteen daily variables were simulated or derived for this release: (1) snow water equivalent (swed), (2) liquid precipitation (rpre), (3) solid precipitation (spre), (4) albedo (albd), (5) glacial ice melt (glmt), (6) total precipitation (prec), (7) runoff (roff), (8) snow covered area (sca), (9) snow density (sden), (10) snowmelt (smlt), (11) snow depth (snod), (12) snow sublimation (ssub), (13) air temperature (tair), (14) wind speed (wspd), and (15) wind direction (wdir). The simulation used to produce these outputs was conducted on a 30 m geospatial grid and was forced using meteorology from a recently completed (2023) 4 kilometer reanalysis product using the Weather Research and Forecasting (WRF) model covering the conterminous United States (CONUS404, Rasmussen and others, 2023a; 2023b). Land cover information for the simulation was provided by the 2016 National Land Cover Database (Jin and others, 2019) and 30 m elevation information was provided by the National Elevation Dataset (Gesch and others, 2018).

Tags: alberta, british-columbia, climatologymeteorologyatmosphere, crown-of-the-continent, glacier-national-park, idaho, montana, northern-rocky-mountains, precipitation-atmospheric, runoff, snow, snow-and-ice-cover, snowmelt, snowpack, usgs-649f17d9d34ef77fcb04217c, waterton-lakes-national-park
Organization: Department of the Interior
Spreadsheet of areal reduction factors by region in Florida (Areal_reduction_factors.xlsx)

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. 
An areal reduction factor (ARF) is computed to convert rainfall statistics of a point, such as at a weather station, to an area, such as a watershed or model grid cell.  Regions considered for the development of change factors as part of this study study are taken from NOAA National Center for Environmental Information (NCEI) U.S. Climate Divisions for the state of Florida with some modifications in south Florida. A Microsoft Excel workbook is provided which tabulates areal reduction factors (ARF) by ARF region, event duration, and model grid-cell area. The ARF were developed for each ARF region based on the PRISM gridded precipitation dataset for Florida. The PRISM dataset is based on the Parameter-elevation Relationships on Independent Slopes Model (Daly and others, 2008).

Tags: climatologymeteorologyatmosphere, depth-duration-frequency, extremes, florida, florida-flood-hub-for-applied-research-and-innovation, precipitation-atmospheric, precipitation-extremes, usgs-649f2a0fd34ef77fcb0421c9
Organization: Department of the Interior
Spreadsheet of best models for each downscaled climate dataset and for all downscaled climate datasets considered together (Best_model_lists_FL_CMIP6.xlsx)

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in 2040) or to the period 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period.  
A Microsoft Excel workbook is provided that tabulates best models for each CMIP6 downscaled climate dataset. Best models were identified based on how well the models capture the climatology and interannual variability of four climate extreme indices using the Model Climatology Index (MCI) and the Model Variability Index (MVI) of Srivastava and others (2020). The four indices consist of annual maxima consecutive precipitation for durations of 1, 3, 5, and 7 days compared against the same indices computed based on the PRISM and SFWMD gridded precipitation datasets for five climate regions: climate region 1 in Northwest Florida, 2 in North Florida, 3 in North Central Florida, 4 in South Central Florida, and climate region 5 in South Florida. The PRISM dataset is based on the Parameter-elevation Relationships on Independent Slopes Model interpolation method of Daly and others (2008). The South Florida Water Management District’s (SFWMD) precipitation super-grid is a gridded precipitation dataset developed by modelers at the agency for use in hydrologic modeling (SFWMD, 2005). This dataset is considered by the SFWMD as the best available gridded rainfall dataset for south Florida and was used in addition to PRISM to identify best models in the South Central and South Florida climate regions. Best models were selected based on MCI and MVI evaluated within each individual downscaled dataset. Models were not compared across CMIP6 downscaled climate datasets due to all the best models belonging to a single downscaled climate dataset (LOCA2).

Tags: climatologymeteorologyatmosphere, depth-duration-frequency, extremes, florida, florida-flood-hub-for-applied-research-and-innovation, precipitation-atmospheric, precipitation-extremes, usgs-649f2ac3d34ef77fcb0421d2
Organization: Department of the Interior
Spreadsheet with information about downscaled climate datasets and National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations used in this project (Datasets_station_information.xlsx)

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2020-59 (centered in 2040) or to the period 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period.  
A Microsoft Excel workbook is provided which tabulates information about the downscaled climate datasets and the NOAA Atlas 14 stations used in this project.

Tags: climatologymeteorologyatmosphere, depth-duration-frequency, extremes, florida, florida-flood-hub-for-applied-research-and-innovation, precipitation-atmospheric, precipitation-extremes, usgs-649f2b07d34ef77fcb0421d5
Organization: Department of the Interior
Spreadsheet of change factors at 170 NOAA Atlas 14 stations in Florida derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) downscaled climate dataset (CF_JupiterWRF_2040_to_historical.xlsx)

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2038-42 (centered in the year 2040) as compared to the 1966-2005 historical period.  
A Microsoft Excel workbook is provided which tabulates change factors derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) at grid cells closest to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical precipitation depths fitted to extreme-precipitation data. The change factors are tabulated by duration (1 day) and return period (5, 10, 25, 50, 100, 200, and 500 years).

Tags: climatologymeteorologyatmosphere, depth-duration-frequency, extremes, florida, florida-flood-hub-for-applied-research-and-innovation, precipitation-atmospheric, precipitation-extremes, usgs-649f2c45d34ef77fcb0421e7
Organization: Department of the Interior
Spreadsheet of change factors at 170 NOAA Atlas 14 stations in Florida derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) downscaled climate dataset (CF_JupiterWRF_2070_to_historical.xlsx)

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2068-72 (centered in the year 2070) as compared to the 1966-2005 historical period.  
A Microsoft Excel workbook is provided which tabulates change factors derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) at grid cells closest to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical precipitation depths fitted to extreme-precipitation data. The change factors are tabulated by duration (1 day) and return period (5, 10, 25, 50, 100, 200, and 500 years).

Tags: climatologymeteorologyatmosphere, depth-duration-frequency, extremes, florida, florida-flood-hub-for-applied-research-and-innovation, precipitation-atmospheric, precipitation-extremes, usgs-649f2c7ad34ef77fcb0421e9
Organization: Department of the Interior
Spreadsheet of projected future precipitation depths at 170 NOAA Atlas 14 stations in Florida fitted to extreme-precipitation events derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) downscaled climate dataset (DDF_JupiterWRF_future_2070.xlsx)

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2068-72 (centered in the year 2070) as compared to the 1966-2005 historical period.  
A Microsoft Excel workbook is provided which tabulates projected future precipitation depths derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) at grid cells closest to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. A maximum likelihood approach is used to fit the projected future extreme-precipitation depths to extreme projected future precipitation data estimated using a statistical scaling approach. The return levels are modified to account for changes in the future frequency of large-scale meteorological factors conducive to precipitation by means of an analog resampling approach. The projected future extreme-precipitation depths are tabulated by duration (1 day) and return period (5, 10, 25, 50, 100, 200, and 500 years).

Tags: climatologymeteorologyatmosphere, depth-duration-frequency, extremes, florida, florida-flood-hub-for-applied-research-and-innovation, precipitation-atmospheric, precipitation-extremes, usgs-64a2e4a8d34ef77fcb056b76
Organization: Department of the Interior
Downscaled western bumble bee predicted occupancy for 2020, western conterminous United States.

This data represents occupancy predictions for western bumble bee in 2020 across the western conterminous United States. This product is a 30-meter resolution downscaled version of previously published occupancy layers for the western bumble bee.  For more information on original publications, see the publication listed in the Larger Works Cited Section.

Tags: arizona, biota, bombus-occidentalis, california, colorado, ecosystems, idaho, montana, nebraska, nevada, new-mexico, north-america, north-dakota, occupancy-probability, oregon, pollinators, south-dakota, united-states, usgs-64ad662fd34e70357a29283d, utah, washington, western-bumble-bee, wildlife-biology, wyoming
Organization: Department of the Interior
Spreadsheet of historical precipitation depths at 170 NOAA Atlas 14 stations in Florida fitted to extreme-precipitation events derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) downscaled climate dataset (DDF_JupiterWRF_historical.xlsx)

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 NOAA Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the period 2038-42 (centered in 2040)  or to the period 2068-72 (centered in the year 2070) as compared to the 1966-2005 historical period.  
A Microsoft Excel workbook is provided which tabulates historical precipitation depths derived from the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF) at grid cells closest to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The historical extreme-precipitation depths are fitted to extreme-precipitation data using a maximum likelihood approach and tabulated by duration (1 day) and return period (5, 10, 25, 50, 100, 200, and 500 years).

Tags: climatologymeteorologyatmosphere, depth-duration-frequency, extremes, florida, florida-flood-hub-for-applied-research-and-innovation, precipitation-atmospheric, precipitation-extremes, usgs-64a2e4e3d34ef77fcb056b7a
Organization: Department of the Interior
R script that creates a wrapper function to automate the generation of boxplots of change factors for all Florida HUC-8 basins (basin_boxplot.R)

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period.  
An R script (basin_boxplot.R) is provided as an example on how to create a wrapper function that will automate the generation of boxplots of change factors for all Florida HUC-8 basins. The wrapper script sources the file create_boxplot.R and calls the function create_boxplot() one Florida basin at a time to create a figure with boxplots of change factors for all durations (1, 3, and 7 days) and return periods (5, 10, 25, 50, 100, 200, and 500 years) evaluated as part of this project. An example is also provided in the code that shows how to generate a figure showing boxplots of change factors for a single duration and return period. A Microsoft Word file documenting code usage is also provided within this data release (Documentation_R_script_create_boxplot.docx). As described in the documentation, the R script relies on some of the Microsoft Excel spreadsheets published as part of this data release.
The script uses HUC-8 basins defined in the "Florida Hydrologic Unit Code (HUC) Basins (areas)" from the Florida Department of Environmental Protection (FDEP; https://geodata.dep.state.fl.us/datasets/FDEP::florida-hydrologic-unit-code-huc-basins-areas/explore) and their names are listed in the file basins_list.txt provided with the script. County names are listed in the file counties_list.txt provided with the script. NOAA Atlas 14 stations located in each Florida basin or county are defined in the Microsoft Excel spreadsheet Datasets_station_information.xlsx which is part of this data release. Instructions are provided in code documentation (see highlighted text on page 7 of Documentation_R_script_create_boxplot.docx) so that users can modify the script to generate boxplots for basins different from the FDEP "Florida Hydrologic Unit Code (HUC) Basins (areas)."

Tags: climatologymeteorologyatmosphere, depth-duration-frequency, extremes, florida, florida-flood-hub-for-applied-research-and-innovation, precipitation-atmospheric, precipitation-extremes, usgs-64a2e69dd34ef77fcb056b96
Organization: Department of the Interior