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4,276 results for “Estimation”
iEEG Data for "Functional Group Bridge for Simultaneous Regression and Support Estimation"
<p>The repository contains analysis scripts and data used in Wang Z, Magnotti J, Beauchamp MS, Li M. Functional Group Bridge for Simultaneous Regression and Support Estimation, 2020. The data contains high-gamma brain responses across 8 subjects from "congruency" audio-visual experiment.</p>
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
Earliest snowmelt estimation dates for Arctic sea ice (2003)
<p>Earliest snowmelt estimation dates calculated for the year 2003 are provided using sea ice brightness temperatures from AMSR-E (Cavalieri et al., 2014) and DMSP SSM/I-SSMIS (Meier et al., 2019), as well as simulated sea ice brightness temperatures from the CESM2 JRA-55 (Danabasoglu et al., 2020; Kobayashi et al., 2015; Tsujino et al., 2018), which were created using the Arctic Ocean Observation Operator (ARC3O; Burgard et al, 2020a,b). Scripts and README files are provided for preparing the model data to act as input to ARC3O. </p>
Temperature and Climate Attribution estimates supporting "Human Fingerprints on Daily Temperatures in 2022" (2x2 degrees, 2022)
<p>These data support the publication of "Human Fingerprints on Daily Temperatures in 2022" published in the <a href="https://www.ametsoc.org/index.cfm/ams/publications/bulletin-of-the-american-meteorological-society-bams/explaining-extreme-events-from-a-climate-perspective/">BAMS-EEE special issue</a> in 2024 (DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0264.1">10.1175/BAMS-D-23-0264.1</a>). Included are:</p> <ul> <li>Temperatures: <strong>Gilfordetal2024_BAMS-EEE_T2022.nc</strong></li> <li>Attributions estimates (Climate Shift Index and Change in Information due to Perspective): <strong>Gilfordetal2024_BAMS-EEE_ChIP2022.nc</strong></li> </ul> <p>And an accompanying land-sea mask from ERA5 (<strong>Gilfordetal2024_BAMS-EEE_LandSeaMask.nc</strong>). All data values valid for the 2022 calendar year and interpolated to a 2x2 degrees spatial grid to support the study's analysis.</p> <p>For more information on this dataset or to follow up, please contact Daniel Gilford (<a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a>).<br><br><em>Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, High Meadows Foundation, and the William and Flora Hewlett Foundation.</em></p>
Decadal BIOCLIM estimates based on ISIMIP3b climatic forcing data for the European continent
<p>This dataset contains BIOCLIM variables (plus huss, sfcwind, rsds) which have been prepared and calculated from the original ISIMIP3b bias-adjusted climate forcing data from 5 GCM models (obtained on 2023-08-07). <br><br>For more information on the original data and its properties, please see the ISIMIP3b modelling protocol and here specifically the climate forcing section <a href="https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data" target="_blank" rel="noopener">https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data</a> and <a href="https://doi.org/10.5194/gmd-17-1-2024">Frieler et al. (2024)</a>.</p> <p>The original climate forcing data (global extent, daily temporal grain) were cropped to the European extent and spatial-temporally aggregated. Here 10 year (decadal) steps were chosen as target climatology.<br><br>For each time slot (e.g. 10 years) and scenario (historical or ssps) the following 22 variables were calculated:</p> <p>bioclim01 = Annual Mean Temperature<br>bioclim02 = Mean Diurnal Range (Mean of monthly (max temp - min temp))<br>bioclim03 = Isothermality (BIO2/BIO7) (×100)<br>bioclim04 = Temperature Seasonality (standard deviation ×100)<br>bioclim05 = Max Temperature of Warmest Month<br>bioclim06 = Min Temperature of Coldest Month<br>bioclim07 = Temperature Annual Range (BIO5-BIO6)<br>bioclim08 = Mean Temperature of Wettest Quarter<br>bioclim09 = Mean Temperature of Driest Quarter<br>bioclim10 = Mean Temperature of Warmest Quarter<br>bioclim11 = Mean Temperature of Coldest Quarter<br>bioclim12 = Annual Precipitation<br>bioclim13 = Precipitation of Wettest Month<br>bioclim14 = Precipitation of Driest Month<br>bioclim15 = Precipitation Seasonality (Coefficient of Variation)<br>bioclim16 = Precipitation of Wettest Quarter<br>bioclim17 = Precipitation of Driest Quarter<br>bioclim18 = Precipitation of Warmest Quarter<br>bioclim19 = Precipitation of Coldest Quarter<br>huss = Average (arithmetric mean) specific humidity<br>rsds = Average (arithmetric mean) Surface downwelling shortwave radiation<br>sfcwind = Average near-surface wind speed (arithmetric mean)<br><br>---<br><strong>Data properties:</strong></p> <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL</td> </tr> <tr> <td>Spatial grain</td> <td>0.5 degree (~50km²)</td> </tr> <tr> <td>Geographic projection</td> <td>WGS 84</td> </tr> <tr> <td>Temporal grain</td> <td>10 year steps</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1850 to 2010 (Historical), 2010 - 2100 (Future)</td> </tr> <tr> <td>Number of variables</td> <td>22</td> </tr> </tbody> </table> <p><br>All files are provided in netCDF (nc) format. The preprocessed datasets are provided as it and the author takes no responsibility for errors or misuse. </p>
Sparse observations induce large biases in estimates of the global ocean CO2 sink: an ocean model subsampling experiment
<p>Dataset underlying the analysis in Hauck et al., 2023: Sparse observations induce large biases in estimates of the global ocean CO<sub>2</sub> sink - an ocean model subsampling experiment, Philosophical Transactions A</p> <p>Surface ocean partial pressure of CO<sub>2 </sub>(pCO<sub>2</sub>) and air-sea CO<sub>2</sub> flux reconstructions, using two mapping methods (MPI-SOM-FFN, CarboScope) three different sampling masks: SOCAT, SOCAT+SOCCOM, IDEAL (based on bgcArgo, Roemmich et al., 2019).</p> <p>Also, all FESOM-REcoM output fields that were used in the reconstructions are provided.</p> <p>We further provide the three masks that were used for subsampling: SOCAT, SOCAT+SOCCOM, IDEAL (bgcArgo).</p> <p> </p>
30 m Normalized Difference Vegetation Index Maps of Pure Pixels over China for Estimation of Fractional Vegetation Cover (2014, 2018, 2022)
<p>Using multi-angle remote sensing data, we generated 30-m maps for the normalized difference vegetation index (NDVI) of fully-covered vegetation (<em>Vv</em>) and bare soils (<em>Vs</em>) across China in 2014, 2018 and 2022. These pixel-wise <em>Vv</em> and <em>Vs</em> maps can be integrated with the vegetation index (VI)-based model to facilitate the accurate and rapid estimation of fractional vegetation cover (FVC) across various spatial resolutions and large scales. The products were produced using a multi-angle algorithm (MultiVI), which effectively addressed the spatial variability inherent in <em>Vv</em> and <em>Vs</em> and enhanced the accuracy of FVC estimations in comparison to traditional statistical methods. The estimated FVC demonstrated a root mean square deviation (RMSD) of approximately 0.1 when evaluated against field-measured FVC across different experimental sites.</p>
Species-level estimated abundances and zero counts of nighttime collected female mosquitoes 2014 - 2022 (Derived from NEON Mosquitoes sampled from CO2 traps (DP1.10043.001, RELEASE-2024))
This Level 2 data package contains species level estimated abundances, including zero counts, and estimated mean number of female mosquitoes per trap derived from the NEON Mosquitoes sampled from CO2 traps (DP1.10043.001), RELEASE-2024 Level 0 data (https://doi.org/10.48443/3cyq-6v47). The data set includes mosquito records of traps collecting mosquito samples at night, for up to 24 trap hours, across a total of 20 terrestrial core and 27 terrestrial gradient sites from 2014 to 2022. To ensure high confidence in abundance estimates, records were only included when at least 90% of collected individuals were identified to sex, and 90% of female specimens were identified to species. Information across multiple QC/QA fields within the NEON mosquito data was evaluated to identify and exclude records where confidence in estimated abundances may have been compromised. Species level zero counts were added for all species collected at least once within the sampling year and trap location. Additionally, species level zero counts were included for trap events where only male mosquitoes had been collected or where QC/QA remarks indicated traps were inactive due to cold temperatures. The data set provides an analysis ready time series of estimated abundances across NEON sites and plots. An R Markdown file that contains descriptions of the QC/QA and data filtering steps along with annotated code, as well as data tables used to filter active and inactive trap events based on QC/QA fields, are published with the data package. Any questions about this data package should be directed to Amely Bauer listed under contacts.
CO2 Flux and Temperature Data for Estimating Thermal Acclimation in Ecosystem Respiration
We have compiled an extensive dataset of long-term, hourly CO2 flux measurements from 93 global eddy covariance sites to estimate the thermal response strength in nighttime ecosystem respiration. Flux data was sourced from AmeriFlux (https://ameriflux.lbl.gov/), FluxNet (https://fluxnet.org/), and ICOS (https://www.icos-cp.eu/). The processed data product encompasses five categories. (1) Long-term, directly measured, Ustar-filtered, hourly or subhourly nighttime ecosystem respiration, along with corresponding air temperature, soil temperature, and soil water content at the 93 sites. (2) Long-term gap-filled data, including hourly or subhourly net ecosystem exchange, air temperature, and soil temperature at these sites. (3) Annual topsoil (< 0.1 m) temperature and nighttime ecosystem respiration curves during the growing season, designed for calculating thermal response strength. (4) Estimated thermal response strength across these sites, detailed with geographic, climatic, soil, and vegetation conditions for each site. (5) An R script to calculate thermal response strength using the data product (3).
Metabolism estimates from dissolved oxygen and inorganic carbon in the Judith River Watershed, MT, USA, 2019-2021.
This dataset provides necessary supporting data and models for the manuscript titled "Stream corridor structure drives patterns in lateral inorganic carbon inputs and CO2 emissions in agricultural headwaters". The entire dataset consists of sensor data, alkalinity and metabolism estimates from oxygen and carbon models, collected in three reaches located in the Judith River Watershed, MT. The sensor data include partial pressure of carbon dioxide in water, dissolved oxygen and temperature. At each reach, we established a two station approach, meaning two pairs of sensor suits were distributed upstream and downstream. In addition to data products, we also provide R packages for metabolism models.
Stream metabolism estimates and surface water chemistry in Glenbrook Creek (NV) and Blackwood Creek (CA) in the Lake Tahoe Basin, 2021-2024
The goal of this project was to develop a process-based understanding of how in-stream productivity and nitrogen dynamics respond to hydroclimatic volatility. We used a combined approach of high-frequency sensor deployment and maintenance, ecosystem metabolism modeling, and routine monitoring of water chemistry and other parameters. The data we collected as part of this project demonstrate how variable ecosystem productivity is in time and space in within and among mountain streams. Although maintenance of the sensor arrays during the exceptionally wet winter of 2023 was challenging, we were able to estimate a time series of stream metabolism within the upper and lower reaches of two streams with different basin characteristics and through hydroclimatic conditions (2021 to 2024). Throughout this project we: 1. We generated four years of daily estimates of ecosystem metabolism (gross primary productivity, ecosystem respiration, and net ecosystem productivity) from upper and lower reach stations on both the east and west shores of the Lake Tahoe Basin. 2. We measured ammonium (NH4+) and nitrate (NO3-) concentrations in surface water and sediment samples from both Glenbrook and Blackwood creeks. 3. We modeled nitrogen supply and demand dynamics at each reach location. See this git code repository for project analysis: https://github.com/kellyloria/Mountain-stream-biogeochem-and-productivity.
Survival, growth and biomass estimates of two dominant palmetto species of south-central Florida from 1981 - 2022, ongoing at 5-year intervals
This data package is comprised of three datasets all pertaining to two dominant palmetto species, Serenoa repens and Sabal etonia, at Archbold Biological Station in south-central Florida. The first dataset, palmetto_data, contains survival and growth data across multiple years, habitats and experimental treatments. The second dataset, seedlings_data, follows the fate of marked putative palmetto seedlings in the field to assess survivorship and growth. The final dataset, harvested_palmetto_data, contains size data and estimated dry mass (biomass in grams) of 33 destructively harvested palmetto plants (17 S. repens and 16 S. etonia) of varying sizes and across habitats. Thirty-two of these were used to calculate estimated biomass, using regression equations, for palmettos sampled in the palmetto_data. Below we summarize experimental setup and data collected for each dataset. Palmetto data Demographic data were collected as three separate components. The first component compared growth among habitats. Starting in 1981, equal numbers of both palmetto species were marked across scrubby flatwoods (oak scrub) and flatwoods habitats (3 sites per habitat) for a total of 240 marked plants. These habitats had not burned within the last decade, but historically had experienced a natural fire return interval of 5 - 20 years prior to this studies initiation. The second component added an additional 400 palmettos (200 of each species), which were marked in sand pine scrub (n = 200) in 1985 and sandhill habitat (n = 200) in 1989 on Archbold's Red Hill. At the time of this project's initiation, all Red Hill management units were last burned in 1927 and were considered long unburned. Part of Archbold's management plan included restoring fire into some management units while leaving others long unburned to serve as reference units. Therefore, for our second component, we were able to create a 2x2 factorial design using habitat types on Red Hill and fire management as factors, with 100
Model estimates of runoff, dissolved organic carbon, soil temperature and moisture for Elson Lagoon watershed, Alaska, 1981-2020
This dataset contains model estimates of dissolved organic carbon (DOC) yield (mg C/m^2) and runoff (mm), for surface and subsurface flows, soil temperature (degree C), and soil moisture (% of soil volume) for grid cells spanning the Elson Lagoon watershed in northwest Alaska. Daily air temperature, precipitation, and wind speed data from Utqiagvik airport were used for meteorological forcings for the daily simulation by the Permafrost Water Balance Model (PWBM) from 1981 to 2020. The DOC and runoff data files are organized by grid cell and month. The soil temperature and soil moisture files are organized by grid cell and day of year (DOY), and contain values for the first eight model soil layers, with centers of the layers at 1, 3, 8, 13, 23, 33, 45, 55 cm depth. The estimates are most useful for analyses of the dynamics of the watershed’s surface and subsurface runoff and DOC yield. Leachate DOC concentrations can be obtained using the gridded runoff and yield values. A manuscript describing the data and associated analysis has been accepted for publication in Environmental Research Letters (Rawlins et al., 2021).
Satellite-derived estimate of artificial lakes in the Phoenix, Arizona, USA Metropolitan area (2000)
Artificial lakes are a prominent example of a local hydrogeomorphic modification in the Phoenix, Arizona metropolitan area. Although lakes are not a natural feature of Sonoran desert ecosystems, numerous artificial lakes are evident in the region. Here we attempt to quantify the heretofore unknown number and extent of artificial lakes. As official GIS layers from local and state agencies had significant misclassifications and omissions, we used two published land cover datasets to estimate the number and areal extent of artificial lakes. We discovered that there are 908-1,390 lakes in the Phoenix area, with the number varying according to level of aggregation. Accurate data on the extent and distribution of these designed ecosystems is vital for water-resources planning and stormwater management. Results, discussion, and additional details are available in document DOI 10.1007/s11252-011-0208-1.
Evaporation Estimates for Long Key C-MAN Weather Station, Florida Bay (FCE) from July 1998 to May 2004
This file contains data from the National Data Buoy Office Coastal-Meteorological Automated Network (C-MAN) weather station near Long Key, in Florida Bay (LONF1, 24deg 50min 36sec N, 80deg 51min 42sec W). The time period is 1600 EST February 6, 2004 through 0700 EST May 28, 2004. The record is a combination of NDBO data (wind speed, air pressure, air temperature water temperature) and Harbor Branch data (relative humidity) needed to calculate evaporative water loss. The file is one of several from this location. Together, they can be used to estimate of hourly, daily, monthly, seasonal and annual evaporation rates. Calculations include hourly water loss and cumulative water loss. Times are Eastern Standard.
Variation in Landsat 8-estimated land surface temperature with elevation from Spartina alterniflora marsh cross sections in the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site and Virginia Coast Reserve (VCR) LTER sites for winter and summer observations spanning 2013-2018
We estimated land surface temperature from top of atmosphere brightness temperature provided by Landsat 8's band 10 (a thermal band). We collected these measurements first for Spartina alterniflora dominated marsh near the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) eddy covariance flux tower. Measurements were collected from pixels along three east-west cross sections that spanned a marsh edge to interior gradient. We extracted Landsat 8 data for all available cloud-free low tide dates during August, September, January and February during the years 2013 to 2018 and associated these with marsh elevation information from a 1 m^2 Digital Elevation Model (DEM), created by Haldik et al 2013, also available from the GCE data catalog (http://dx.doi.org/10.6073/pasta/4c5187ef603f70cd0a77ece24ef0fed9). We rescaled the DEM to the coarser spatial resolution of Landsat 8 (30 x 30 m) where the rescaled elevation was the mean of the constituent DEM values. Ultimately, we used generalized additive models to relate land surface temperature to elevation, while accounting for variation from spatial proximity, transect and sample date. These models revealed that land surface temperature was negatively related to marsh elevation on the marsh platform. We then confirmed the generality of this pattern by rederiving these same relationships for three cross sections of Spartina alterniflora marsh at Virginia Coast Reserve (VCR) LTER for winter sampling dates only (data also included here). DEM data for VCR LTER are available at https://www.vcrlter.virginia.edu/gisdata/LIDAR/USGS2015/. We used custom R functions that can convert Landsat 8 top of atmosphere brightness temperature or top of atmosphere radiance from band 10 data to land surface temperature, which are available at https://github.com/jloconnell/convert_top_of_atmosphere_thermal_to_land_surface_temperature. Currently, a provisional land surface temperature product is available on earthexplorer.usgs.gov, w
Estimation of Abundance and Distribution of Salt Marsh Plants from Images Using Deep Learning
Recent advances in computer vision and machine learning, most notably deep convolutional neural networks (CNNs), are exploited to identify and localize various plant species in salt marsh images. Three different approaches are explored that provide estimations of abundance and spatial distribution at varying levels of granularity in terms of spatial resolution. In the coarsest-grained approach, CNNs are tasked with identifying which of six plant species are present/absent in large patches within the salt marsh images. CNNs with diverse topological properties and attention mechanisms are shown capable of providing accurate estimations with > 90% precision and recall in the case of the more abundant plant species whereas the performance of the CNNs is observed to decline in the case of less common plant species. Estimation of percent cover of each plant species is performed at a finer spatial resolution, where smaller image patches are extracted and the CNNs tasked with identifying the plant species or substrate at the center of the image patch. In an ecological setting, several image patches (~100) are extracted and classified using this approach to estimate the percent cover of the various plant species in the image. For the percent cover estimation task, the CNNs are observed to exhibit a performance profile similar to that for the presence/absence estimation task, but with an ~ 5–10% reduction in precision and recall. Finally, estimation of the spatial distribution of the various plant species is performed via semantic segmentation of the input images at the finest level of granularity in terms of spatial resolution. The Deeplab-V3 semantic segmentation architecture is observed to provide very accurate estimations for abundant plant species; however, a significant degradation in performance is observed in the case of less abundant plant species and, in extreme cases, rare plant classes are seen to be ignored entirely. Overall, a clear trade-off is observed between
Size-fractionated net primary productivity (NPP) estimates based on 13C uptake during cruises along the Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect, ongoing since 2019
This dataset consists of primary production measurements based on uptake of carbon-13 added as 13C-bicarbonate during 24-h deckboard incubations of seawater. Sampling occurred on cruises along the Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect during summer, fall, and winter, starting in summer 2019. Net primary production (NPP) was determined from particulate organic carbon (POC) content and associated stable isotope enrichment. Three data tables are included: 1. Depth-specific primary productivity based on fractional light levels of the surface irradiance with reference to the profile of photosynthetically active radiation (PAR). 2. Integrated primary productivity. 3. Natural abundance POC. The tables derive from the raw data included as other entities.
Annual estimates of the water budget for the Ipswich River watershed, 1931 to 2018
Annual estimates of the water budget for the Ipswich River watershed, 1931 to 2018. The water budget includes precipitation, evapotranspiration, stream flows, water withdrawals, sewer export and public water supply imports.
Estimates of nitrogen and phosphorus excretion rates in individual marine and estuarine animals
This dataset contains nitrogen and phosphorus excretion rate, as well as dry biomass, estimates for individual vertebrate and invertebrate animals in marine and estuarine environments. This dataset is a product of an LTER Synthesis Working Group aimed at evaluating the spatiotemporal variability in consumer nutrient dynamics in the wake of global change across eight long-term ecological research projects. These projects include seven long-term ecological research programs (LTER) funded by the National Science Foundation: (1) California Current Ecosystem, (2) Florida Coastal Everglades, (3) Moorea Coral Reef, (4) Northern Gulf of Alaska, (5) Plum Island Ecosystems, (6) Santa Barbara Coastal, and (7) Virginia Coast Reserve LTER projects. Additionally, the dataset includes data from (8) The Partnership for Interdisciplinary Science of Coastal Oceans (PISCO) research program. The temporal coverage of each time series data varies among projects, with the earliest record in 1997 and the most recent in 2023. This data package also includes two folders of R scripts used for data harmonization, identical to those in the LTER Synthesis Working Group: Consumer-Mediated Nutrient Dynamics Project, v2.0.0. You can find the release in GitHub here: https://github.com/lter/lterwg-marine-cnd/releases/tag/v2.0.0
ScienceDex guides
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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.