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4,276 results for “Estimation”
Input Dataset for Estimating Continuous Soil Water Retention Curves Using Physics-Informed Neural Networks
<p>This dataset was used as input to a physics-informed neural network (PINN) model developed to estimate continuous soil water retention curves (SWRCs). It includes basic soil properties such as particle-size distribution (sand, silt, clay), organic carbon content (OC), bulk density (BD), and measurements of soil water retention at various matric potentials. These inputs allow the model to learn the relationship between soil properties and water retention, via both data and embedded physical constraints. This data set consists of 4,200 Danish soil samples with measurements spanning the wet and dry ends of the SWRC. </p>
Meteor observations and wind estimates from the northern Germany SIMONe radar network on November 5, 2018
<p>This dataset includes meteor observations and wind estimates taken as part of the SIMONe 2018 campaign in northern Germany on November 5, 2018. The files are in netCDF-4 format and follow CF conventions (https://cfconventions.org/). We recommend loading the data using the xarray Python package.</p> <p>The SIMONe 2018 campaign ran from November 2, 2018 through November 9, 2018 in northern Germany. The radar network consisted of two pulsed transmitters in Juliusruh and Collm and a five-element interferometric MIMO-CW transmitter located in Kühlungsborn. Monostatic receiver stations co-located with the pulsed transmitters and six additional receiver stations located in Mechelsdorf, Breege, Neustrelitz, Guderup, Salzwedel, and Bornim were used to form a total of two monostatic and ten bistatic links. The data from these individual links were then processed to detect specular meteor echoes and estimate their parameters, including Doppler shift. The Doppler shifts, imposed by movement of the meteor trail due to the neutral winds, were then used to estimate the 4-D wind field. More details about the campaign can be found in Vierinen et al. (2019). Details for the wind field estimation can be found in Volz et al. (submitted).</p>
Dataset with Risk estimates of major currency pairs on the Forex market
<p>This dataset includes Value at Risk (VaR) and Expected Shortfall (ES) estimations of the major currency pairs on the Forex market. Notably, it provides daily VaR and ES estimates for the AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, and USDJPY FX assets for January 2021 to September 2022. The reported risk estimates were calculated by various parametric and non-parametric models, including Variance-Covariance (VS), Historical Simulation (HS), Monte Carlo (MC), and Garch(1,1) at both 95% and 99% confidence levels. To enable model evaluation, the last column of each CSV file, named pnl, provides the actual daily returns of the FX asset.</p> <p>The data and code used to create this dataset are available at <a href="https://doi.org/10.5281/zenodo.7411148">Zenodo</a> and <a href="https://marketplace.infinitech-h2020.eu/assets/portfolio-value-at-risk-estimation">INFINITECH Marketplace</a>, respectively.</p>
Novel estimates of the leaf relative uptake rate of carbonyl sulfide from optimality theory
<p>Data and Matlab scripts for repeating the analysis presented in the paper. In addition, global monthly climatological LRUs are provided at 0.05° resolution for the period 2001-2010 as nc-files. </p>
Global groundwater recharge estimates
<p>Groundwater recharge estimates have been presented in Berghuijs et al. (2022). The estimates exclude regions with mean temperatures below −2°C and regions with aridity below 1. The estimates consist of total groundwater recharge (units mm per year) and the fraction of precipitation becoming groundwater recharge (dimensionless).</p> <p> </p> <p> </p>
Pan-European exposure maps and uncertainty estimates from HANZE v2.0 model, 1870-2020
<p>This dataset provides all output data generated in the standard settings of HANZE v2.0 model. The 100-m pan-European maps (GeoTIFF) provide gridded totals of five variables for years 1870-2020 for 42 countries. The rasters are group in five ZIP files:</p> <p>- CLC: land cover/use (Corine Land Cover classification; legend files are included in a separate ZIP)</p> <p>- Pop: population</p> <p>- GDP: gross domestic product (2020 euros)</p> <p>- FA: fixed asset value (2020 euros)</p> <p>- imp: imperviousness density (%)</p> <p>Two additional CSV files contain uncertainty estimates of population, GDP and fixed asset value per NUTS3 region and flood hazard zone. The files provide 5th, 20th, 50th, 80th and 95th percentile for all timesteps, separately for coastal and riverine floods.</p> <p>Two further Excel files contain subnational and national-level statistical data on population, land use and economic variables.</p> <p>For detailed description of the files, see the documentation provided with the code.</p> <p>This version replaces the airport list, which was previously incorrectly taken from HANZE v1, and adds land cover/use legend files for ArcGIS and QGIS.</p>
Towards Parameter Estimation in Global Hydrological Models
<p>The provided elementary effects are used in the publication J. Kupzig, R. Reinecke, F. Pianosi, M.Flörke and T. Wagener: Towards Parameter Estimation in Global Hydrological Models (submitted to Environmental Research Letters in Feb 2023).</p> <p>In a large sample study, the Morris Method (Morris 1991) application produces the provided elementary effects using a new lightweight version of the global hydrological model WaterGAP3: WaterGAPLite.</p> <ul> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/elementary_effects.zip?versionId=e980e961-2334-41db-900a-637b2dcec119">elementary_effects.zip </a>: elementary effects for all 50 trajectories and all basins (each trajectory is the result of 18 model runs; used bounds of parameters can be found in the Supplement of the manuscript)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/results_overview.xlsx?versionId=6f38c60f-9084-4376-907d-579414285506">results_overview.xlsx</a>: parameter ranks for each basin and different evaluation criteria based on the elementary effects.</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/MC_Sample.csv">MC_Sample.csv</a>: normalized parameter samples of the additional Monte-Carlo Simulation (used bounds of parameters are the same as for the Morris method)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/MC_NSE.csv">MC_NSE.csv</a>: resulting NSE values of the Monte-Carlo simulation</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/better_performing_basins.csv">better_performing_basins.csv</a>: list of basins (using GRDC no.) where minimal NSE is greater than -1 within all Monte-Carlo runs</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/standard_calib.csv">standard_calib.csv</a>: calibrated gamma value for each basin and corresponding evaluation criteria, using the standard calibration for WaterGAP3 (fit to mean discharge)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/standard_calib_mod.csv">standard_calib_mod.csv</a>: calibrated gamma value for each basin and corresponding evaluation criteria, using a modified version of the standard calibration for WaterGAP3 (maximizing the NSE)<br> </li> </ul>
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
LAGOS-US LANDSAT: Data module of remotely-sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020
This data package, LAGOS-US LANDSAT, is one of the extension data modules of the LAGOS-US platform that provides six water quality estimates (chlorophyll, Secchi depth, dissolved organic carbon, total suspended solids, turbidity, and true water color) from remote sensing for lakes ≥ 4 ha in the conterminous U.S. (48 states plus the District of Columbia) for the years 1984-2020. These estimates are generated through machine learning models on in-lake water quality matchups from LAGOS-US LIMNO with Landsat 5, 7, and 8 whole lake median reflectance values and pixel-wise band ratios that are subsequently used to make predictions across the U.S. The LANDSAT module contains remotely sensed reflectance values for 136,977 of the 137,465 lakes ≥ 4 ha from the LAGOS-US research platform. Within the module are a total of 45,867,023 sets of reflectance values, a matchup dataset with a window of up to 7 calendar days with in situ data, and associated water quality parameter predictions for each reflectance set. Additional quality control flags are provided for predictions indicating whether reflectance extractions included negative values, the percent of the maximum pixels ever retrieved for that lake that the predictions are based on, and whether there are shared calendar day predictions due to scene overlap.
Gross methane production and consumption estimated for intact soil cores from agricultural plots including environmental covariates and example raw isotope pool dilution data
This study was performed to determine how different soil moistures, soil sources, and agricultural practices affected the gross CH4 fluxes (i.e., rates of methanogenesis) of soils. We extracted intact soil cores from two agricultural sites in the USA in row crop plots under conventional, no-till, and organic management. We then took them to the lab, manipulated their moisture levels, incubated them at room temperature for 22 weeks, and measured gas fluxes at weeks 6 and 21. We developed and utilized a new form of CH4 isotope pool dilution (IPD) to estimate gross CH4 production and consumption fluxes. This new method can measure IPD in a bag headspace that loses volume over time due to sampling. We fit the IPD model to the data and extracted gross CH4 production (P) and consumption (K) constants. These along with calculated fluxes and covariates measured (e.g., moisture, inorganic N) are reported in the main data table.
High resolution stream temperature, pressure, and estimated depth from transducers in streams in the Lake Sunapee watershed, New Hampshire, USA 2010-2018
Seven in-stream HOBO pressure transducers and one reference HOBO pressure transducer have been deployed within the Lake Sunapee, NH, USA watershed. Six of the transducers have been in operation since 2010 and an additional transducer was added in 2016. The transducers record data every 15 minutes, and data are downloaded approximately three times per year (early Spring, mid Summer and late Fall). Shortly after download, the data are processed to estimate stream depth using HOBOware's Barometric Compensation Assistant and converted to a .csv file in HOBOware. The data have been QAQC'd to recode obviously errant data to NA using R programming language. No data transformation has occurred beyond basic QAQC of the data to remove known data issues and obviously errant data. The barometric pressure data from the reference transducer located on land are also included in this data package.
HURRECON Model for Estimating Hurricane Wind Speed, Direction, and Damage (R and Python)
The HURRECON model estimates wind speed, wind direction, enhanced Fujita scale wind damage, and duration of EF0 to EF5 winds as a function of hurricane location and maximum sustained wind speed. Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. HURRECON is available in R and Python. The R version is available on CRAN as HurreconR. The model is an updated version of the original HURRECON model written in Borland Pascal for use with Idrisi (see HF025). New features include support for: (1) estimating wind damage on the enhanced Fujita scale, (2) importing hurricane track and intensity data directly from HURDAT2, (3) creating a land-water file with user-selected geographic coordinates and spatial resolution, and (4) creating plots of site and regional results. The model equations for estimating wind speed and direction, including parameter values for inflow angle, friction factor, and wind gust factor (over land and water), are unchanged from the original HURRECON model. For more details and sample datasets, see the project website on GitHub (https://github.com/hurrecon-model).
EXPOS Model for Estimating Topographic Exposure to Wind (R and Python)
The EXPOS model uses a digital elevation model (DEM) to estimate exposed and protected areas for a given hurricane wind direction and inflection angle. The resulting topograhic exposure maps can be combined with output from the HURRECON model to estimate hurricane wind damage across a region. EXPOS is available in R and Python. The R version is available on CRAN as ExposR. The model is an updated version of the original EXPOS model written in Borland Pascal for use with Idrisi (see HF024). For more details and sample datasets, see the project website on GitHub (https://github.com/expos-model).
Gridded 1-hectare estimates of shrub community structure at the Jornada Basin LTER site derived from NAIP (2011) and LiDAR (2019) data
This dataset contains four raster maps of shrub community structure at the Jornada Basin LTER site in southern New Mexico U.S.A. These shrub structure estimates were created by combining an existing categorical shrub map (Ji et al. 2019) with USGS LiDAR shrub height estimates from 2019. The resulting raster dataset includes four bands of spatially aligned shrub volume, cover, height, and density estimates at one hectare resolution. Data are also included in tabular format, extracted from the 1 hectare grid upon which estimates were created. These shrub structure estimates are intended to facilitate analyses of habitat structure and community dynamics within the northern Chihuahuan Desert.
CBS04 Sweep sample data: prey estimates for Grasshopper Sparrows on Konza Prairie
This data set includes data on the contents of sweep samples. We collected sweeps in select years during May, June, and/or July in 3 locations on each of the focal watersheds. Sweeps were 80m long and centered at veg points. Data consist of information about the sampling events, and sample wet mass, edible mass (combined mass of selected orders listed below). Additionally, the dataset includes the number of individuals in each of a series of size categories, total N, and mass (in grams) of the following groups: Tettigoniidae, Acrididae, other Orthoptera, Gryllidae, Odonata, Ephemeroptera, Coleoptera, Hymenoptera, Lepidoptera, Arachnida, Hemiptera, Neuroptera, Diptera, Phasmatidae, Mantidae, and “other”.
CBS05 Estimates of vegetation structure and composition collected on Konza Prairie watersheds and on the nearby Rannell’s Preserve
Data set includes estimates of vegetation structure and composition collected during ~monthly sampling events on Konza Prairie watersheds and on the nearby Rannell’s Preserve. Vegetation data were collected from three (prior to 2017) or 10 randomly-selected locations on each watershed; two from outside the 10-ha plot (see project abstract) and one inside the plot. We sampled vegetation on each watershed once a month, during May, June, and July. Additional vegetation data were collected from bird nest sites within ~3 days of nests failing. We used 5 sets of Daubenmire frame measures to determine percent cover of major plant functional groups (at the center of the plot and 5 m from center at the 4 cardinal directions). We estimated visual obstruction by placing a Robel Pole in the middle, and 5 m from the middle of the plot in each of the 4 cardinal directions. For each pole placement, we stood 4 m away with eye 1 m above the ground in each of 4 directions, and counting the highest 5-cm segment not completely obscured by vegetation. At nests, we also estimated the slope and aspect in the center of each plot.
PBG03 Disk pasture meter measurements to estimate plant standing biomass in the Patch-Burn Grazing experiment at Konza Prairie
"PBG" datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This dataset includes both the annual calibration data for the disk pasture meter measurements (PBG031) and the actual disk pasture meter measurements in the PBG experiment (PBG032). Measurements were taken at a total of 64 transects (8 watersheds x 4 sites per watershed x 2 pasture meter transects per site). Each cattle-grazed watershed (designated as of May 2011 C3A, C3B, C3C, and C1A) and the four Shane cattle-grazed watersheds (C1B, C3SA, C3SB, and C3SC) includes 4 plant composition sampling transects (A-D). Pasture meter measurements were taken alo
Landslide Revegetation Canopy Measurements & Cover Estimates
The purpose of this data set is to document recovery of vegetation in new landslides in the Luquillo Experimental Forest (LEF) Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Thymidine uptake rates for estimating bacterial production in lakes, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
An important part of the McMurdo Long Term Ecological Research (LTER) is monitoring of spatial and temporal patterns, and processes that control bacterial production in perennial ice-covered lakes. This data set quantifies thymidine (TdR) uptake by bacteria, which can be used to estimate bacterial production.
MCR LTER: Coral Reef: Estimates of component primary production and respiration, 2006-2015
Estimates of primary production and respiration of three representative components of the Moorea coral reef ecosystem were made yearly in a laboratory flume from 2006 through 2015. The components are: algal turf communities, the macroalga Sargassum pacificum, and the common branching coral Pocillopora verrucosa. Metabolism estimates were made using changes in dissolved oxygen over time in a flume in unidirectional flow at saturating irradiances and dark. Rates were normalized to projected (planar) surface area (all components) and biomass (algal turfs, Sargassum). This timeseries completed in 2015.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.