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38 results for “contiguous united states”
Bankfull and Mean-flow Channel Geometry Dataset across the CONtiguous United States (CONUS)
<p>This dataset includes estimated river channel geometry attributes, specifically width and depth, under bankfull and mean-flow conditions across the CONtiguous United States (CONUS). The method utilized for providing these estimations are based on eXtreme Gradient Boosting Regression (XGBR). This dataset can be linked to the National Hydrograohy Dataset Plus (NHDPlusV2.1) through Common identifier of the NHD feature, known as COMID.</p>
Data and code for: Degradation of floodplain integrity within the contiguous United States
<p class="MsoNormal">Despite the numerous hydrological, geological, and ecological benefits produced by floodplain landscapes, floodplains continue to be degraded by human activities at a much higher rate than other landscape types. This large-scale landscape modification has been widely recognized, yet a comprehensive, national dataset quantifying the degree to which human activities are responsible for this degradation has not previously been evaluated. In this research, we analyze floodplain integrity for the contiguous United States by spatially quantifying the impact of anthropogenic stressors on almost 80,000 floodplain units. This dataset demonstrates the prevalence of human modifications through widely available geospatial datasets, which we use to quantify indicators of floodplain integrity for five essential floodplain functions of flood attenuation, groundwater storage, habitat provision, sediment regulation, and organics and solute regulation. Our results show that floodplain degradation is spatially heterogeneous and that the integrity of nearly 70% of floodplains in the United States is poor. We highlight that quantifying the integrity of spatially explicit floodplain elements can allow for restoration efforts to be targeted to the areas in most desperate need of preservation.</p>
River metabolism in the contiguous United States: Random forest model code, inputs and outputs
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Nitrogen deposition weakens soil carbon control of nitrogen dynamics across the contiguous United States
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Data and code for: Degradation of floodplain integrity within the contiguous United States
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Raw isotope values from fossil herbivore enamel from sites located below 37° latitude within the contiguous United States
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Sea level rise, groundwater rise, and contaminated sites in the San Francisco Bay Area, and Superfund Sites in the contiguous United States
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Skillful Seasonal Prediction of Wind Energy Resources in the contiguous United States
<p>This dataset contains data files used to replicate figures in a paper published in communications earth & environment. These datasets are in .mat format, which can be readable by Matlab software.</p>
Data from: Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States
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Supplementary material 1 from: Gladstone NS, Bordeau TA, Leppanen C, McKinney ML (2020) Spatiotemporal patterns of non-native terrestrial gastropods in the contiguous United States. NeoBiota 57: 133-152. https://doi.org/10.3897/neobiota.57.52195
All records with associated georeferences used in analyses
Data accompanying Jonko et al. "How will future climate change impact prescribed fire across the contiguous United States?"
<p>This CSV file contains prescription information for 83 location across the United States which was analyzed in the publication Jonko, A., J. Oliveto, T. Beaty, A. Atchley, M.A. Battaglia, M.B. Dickinson, M.R. Gallagher, A. Gilbert, D. Godwin, J.A. Kupfer, J.K. Hiers, C. Hoffman, M. North, J. Restaino, C. Sieg, and N. Skowronski: "How will future climate change impact prescribed fire across the contiguous Unites States?", submitted to npj Climate and Atmospheric Science. Please contact the corresponding author at ajonko@lanl.gov if you are interested in using this data in your own research.</p>
Aboveground Biomass Estimates for Salt Marsh for the Contiguous United States, 2020
This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost, random forests, and support vector machines, trained with salt marsh location identified with the National Wetland Inventory (NWI). The data are organized by Hydrologic Unit Code (HUC) 6-digit basin. Within each HUC, the spatial extent of salt marsh and its uncertainty were estimated by machine learning and input data from NWI maps, the National Elevation Dataset, along with Sentinel-1 and Sentinel-2 imagery. Estimates were compared to in situ biomass data from salt marshes in Georgia and Massachusetts. The data are provided in cloud-optimized GeoTIFF format.
Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016)
The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set includes predictions of PM2.5 concentrations in grid cells at a resolution of 1 km for the years 2000 to 2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, as well as other predictors. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions.
Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016)
The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set contains estimates of ozone concentrations at a high resolution spatially (1-km grid cells) and temporally (daily) for the years 2000 to 2016. These predictions incorporated various predictor variables such as Ozone (O3) ground measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitoring data, land-use variables, meteorological variables, chemical transport models and remote sensing data, along with other data sources. After imputing missing data with machine learning algorithms, a geographically-weighted ensemble model was applied that combined estimates from three types of machine learners (neural network, random forest, and gradient boosting). The annual predictions were computed by averaging the daily 8-hour maximum predictions in each year for each grid cell. The results demonstrate high overall model performance with a cross-validated R-squared value against daily observations of 0.90 and 0.86 for annual averages. In version 1.10, we have enhanced the completeness of daily O3 predictions by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, we used inverse distance weighting interpolation to fill the missing grid cells. Other missing daily O3 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily 8-hour maximum and annual O3 predictions allow public health researchers to respectively estimate the short- and long-term effects of O3 exposures on human health, supporting the U.S. EPA for the revision of the National Ambient Air Quality Standards for O3. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.
Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016)
The Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016) data set contains estimates of ozone concentrations at a high resolution in space (1 km x 1 km grid cells) and time (daily) for the years 2000 to 2016. These predictions incorporated various predictor variables such as Ozone (O3) ground measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitoring data, land-use variables, meteorological variables, chemical transport models and remote sensing data, along with other data sources. After imputing missing data with machine learning algorithms, a geographically weighted ensemble model was applied that combined estimates from three types of machine learners (neural network, random forest, and gradient boosting). The annual predictions were computed by averaging the daily 8-hour maximum predictions in each year for each grid cell. The results demonstrate high overall model performance with a cross-validated R-squared value against daily observations of 0.90 and 0.86 for annual averages.
Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016)
The Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000-2016) data set contains daily predictions of Nitrogen Dioxide (NO2) concentrations at a high resolution (1 km x 1 km grid cells) for the years 2000 to 2016. An ensemble modeling framework was used to assess NO2 levels with high accuracy, which combined estimates from three machine learning models (neural network, random forest, and gradient boosting), with a generalized additive model. Predictor variables included NO2 column concentrations from satellites, land-use variables, meteorological variables, predictions from two chemical transport models, GEOS-Chem and the U.S. Environmental Protection Agency (EPA) CommUnity Multiscale Air Quality Modeling System (CMAQ), along with other ancillary variables. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensemble produced a cross-validated R-squared value of 0.79 overall, a spatial R-squared value of 0.84, and a temporal R-squared value of 0.73.
Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016)
The Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set includes predictions of PM2.5 concentration in grid cells at a resolution of 1-km for the years 2000-2016. A generalized additive model was used that accounted for geographic difference to ensemble daily predictions of three machine learning models: neural network, random forest, and gradient boosting. The three machine learners incorporated multiple predictors, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis data sets, and others. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensembled model demonstrated better predictive performance than the individual machine learners with 10-fold cross-validated R-squared values of 0.86 for daily predictions and 0.89 for annual predictions. In version 1.10, the completeness of daily PM2.5 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily PM2.5 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual PM2.5 predictions allow public health researchers to respectively estimate the short- and long-term effects of PM2.5 exposures on human health, supporting the U.S. Environmental Protection Agency (EPA) for the revision of the National Ambient Air Quality Standards for 24-hour average and annual average concentrations of PM2.5. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.
Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016)
The Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set contains daily predictions of Nitrogen Dioxide (NO2) concentrations at a high resolution (1-km grid cells) for the years 2000 to 2016. An ensemble modeling framework was used to assess NO2 levels with high accuracy, which combined estimates from three machine learning models (neural network, random forest, and gradient boosting), with a generalized additive model. Predictor variables included NO2 column concentrations from satellites, land-use variables, meteorological variables, predictions from two chemical transport models, GEOS-Chem and the U.S. Environmental Protection Agency (EPA) CommUnity Multiscale Air Quality Modeling System (CMAQ), along with other ancillary variables. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensemble produced a cross-validated R-squared value of 0.79 overall, a spatial R-squared value of 0.84, and a temporal R-squared value of 0.73. In version 1.10, the completeness of daily NO2 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily NO2 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual NO2 predictions allow public health researchers to respectively estimate the short- and long-term effects of NO2 exposures on human health, supporting the U.S. EPA for the revision of the National Ambient Air Quality Standards for daily average and annual average concentrations of NO2. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.