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10,016 results for “Observation”
Figure 2. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 2. - The Plazi workflow (green) within EU BON.
Emergency Department observational data in The Netherlands
<p>With this prospectively registered data (2011 - 2016) of three emergency departments in the Netherlands research was done to adress the question whether older sepsis patients present at the Emergency Department with a higher disease severity and to adress the question whether older patients receive an equal quality of care. </p>
US Water Network Observed and Inferred Flows
<p>This dataset provides a synthesized national record of river gauges. It includes inferred flows which are based on the structure of the river network and empirical flow relationships. In total, the network contains 22619 gauges (or virtual junction gauges) and over 1 million years of monthly data.</p> <p>The dataset is provide as a saved data file for R, flowdata.RData, containing two variables: nodes and allflow.</p> <p>`nodes` describes each gauges or virtual junction gauge in the water flow network, representing a combination of gauges from multiple sources. The source is specified by the `collection` column, as follows:</p> <ul> <li>`rivdis`: From the RivDIS dataset, at http://iridl.ldeo.columbia.edu/SOURCES/.UNH/.CSRC/.RivDIS/index.html?Set-Language=en</li> <li>`usgs`: GAGES II river gauge</li> <li>`reservoir`: National Inventory of Dams reservoir</li> <li>`usgsres`: USGS gauged reservoir</li> <li>`canal`: Canal cross county boundaries</li> <li>`junction`: Junction node added to capture the structure of rivers from HydroSHEDS</li> </ul> <p>The `colid` specifies the ID of the gauge within its collection, and combined `collection` and `colid` are unique. The location of the gauge is specified by the latitude `lat` and longitude `lon`. The elevation is provided in meters in column `elev`.</p> <p>The flow data is contained in the `allflow` matrix. This matrix has a column for each node (as many columns as there are rows in `nodes)`, and a row for each month starting from Jan. 1915. All the flows are in units of m^3 / s.</p>
Flash propagation and inferred charge structure relative to radar-observed ice alignment signatures in a small Florida Mesoscale Convective System
<p>Data for paper of above title, submitted to <em>Geophysical Research Letters</em>, June 2017. Manuscript number: 2017GL072767</p>
A dataset of atmospheric ozone above the Mexico City basin retrieved from FTIR remote sensing observations made at two different ground altitudes
<p>This dataset of atmospheric ozone (O<sub>3</sub>) has been generated from solar absorption spectra measured in central Mexico using ground-based Fourier-Transform Infrared (FTIR) spectrometers. The FTIR experiments have been operated by the “Spectroscopy and Remote Sensing” Research Group of the Centro de Ciencias de la Atmósfera of the Universidad Nacional Autónoma de México (http://www.atmosfera.unam.mx/espectroscopia/index.html).</p> <p>The dataset covers measurements made between November 2012 and February 2014 applying two different FTIR spectrometers. The first instrument offers very high resolution spectra and contributes to NDACC (Network for the Detection of Atmospheric Composition Change). It is located at the mountain observatory of Altzomoni (ALTZ) about 1700m above the Mexico City basin. The second instrument has a medium spectral resolution and is located inside of Mexico City at the Universidad Nacional Autónoma de México (UNAM) at a horizontal distance of about 60km to the mountain observatory.</p> <p>The here provided dataset consists of two NETCDF data-files for each station and a MATLAB script for reading the NETCDF files. The files “ALTZ_IFS125_O3.nc” and “UNAM_IFS125_O3.nc” contain the retrieved O<sub>3</sub> state vectors, the O<sub>3</sub> averaging kernels and the O<sub>3</sub> a priori profiles, together with auxiliary data: observation time, observation geometry, instrumental settings, atmospheric temperature and humidity profiles. The data as well as the method for combining the two different observations are presented in Plaza-Medina et al. (2017), which should be consulted for more details.</p> <p>The files “ALTZ_IFS125_O3_Jac+Gain.nc” and “UNAM_IFS125_O3_Jac+Gain.nc” contain the Jacobians (for O<sub>3</sub> as well as for error sources) and the Gain matrix, together with the auxiliary data. The MATLAB script “readNETCDF_and_combine2FTIR.m” reads the NETCDF files and performs the operations needed for the generation of a combined product, thereby exploiting the synergetic effects of two observations made in coincidence but at different ground altitudes.</p> <p>A related dataset with Altzomoni O<sub>3</sub> profiles obtained by applying slightly different retrieval settings is available at the NDACC database (ftp://ftp.cpc.ncep.noaa.gov/ndacc/station/altzomoni/hdf/ftir/). Further datasets of atmospheric parameters as measured by different techniques are available at the webpage of the Red Universitario de Observaciones Atmosfericas (www.ruoa.unam.mx).</p>
K5JBT 40m I/Q Solar Eclipse Observations
<p>John Thurmond K5JBT</p> <p>Location: 29.762948N, -95.707518W (Houston, TX)</p> <p>Antenna: Inverted-L (ca. 25 ft vertical, 15 ft lateral oriented NE)</p> <p>Receiver type: Flex 6300</p> <p>Antenna was tuned for 20m, but also recorded on 40m</p>
K5JBT 20m I/Q Solar Eclipse Observations
<p>John Thurmond K5JBT</p> <p>Location: 29.762948N, -95.707518W (Houston, TX)</p> <p>Antenna: Inverted-L (ca. 25 ft vertical, 15 ft lateral oriented NE)</p> <p>Receiver type: Flex 6300</p> <p>Antenna was tuned for 20m, but also recorded on 40m</p>
A dataset of ground-based vertical profile observations of aerosol, NO2 and HCHO from the hyperspectral vertical remote sensing network in China (2019-2023)
<p>Vertical <span>profile </span>observations of atmospheric composition are crucial for understanding the generation, evolution, and transport of regional air pollution. However, existing technological limitations and costs have resulted in a scarcity of vertical profil<span>e</span> data. This study <span>introduces </span>a high-<span>time-</span>resolution (approximately 15 minutes) dataset of vertical <span>profile </span>observations of atmospheric composition (aerosols, NO2, and HCHO) conducted using passive remote sensing technology across 32 sites in seven major regions of China from 2019 to 2023. The study meticulously documents the vertical distribution, seasonal <span>variations and </span>diurnal <span>pattern</span> of these pollutants, revealing long-term trends in atmospheric composition across various regions of China. This dataset provides essential scientific evidence for regional environmental management and policy-making. Its sharing <span>would </span>facilitate the scientific community <span>in </span>explor<span>ing</span> of source-receptor relationships, investigating the impacts of atmospheric composition on regional and global climate <span>and </span>feedback mechanisms.</p>
Seasonal Variation of Thermospheric Composition Observed by NASA GOLD
We examine characteristics of the seasonal variation of thermospheric composition using column number density ratio ∑O/N2 observed by the NASA Global Observations of Limb and Disk (GOLD) mission from low-mid to mid-high latitudes. We found that the ∑O/N2 seasonal variation is hemispherically asymmetric: in the southern hemisphere, it exhibits the well-known annual and seminal pattern, with highs near the equinoxes, and primary and secondary lows near the solstices. In the northern hemisphere, it is dominated by an annual variation, with a minor semiannual component with the highs shifting towards the wintertime. We also found that the durations of the December and June solstice seasons in terms of thermospheric composition are highly variable with longitude. Our hypothesis is that ion-neutral collisional heating in the equatorial ionization anomaly region and auroral Joule heating play substantial roles in this longitudinal dependency.
HIWIND Balloon and Antarctica Jang Bogo FPI High Latitude Conjugate Thermospheric Wind Observations and Simulations
The data files contain thermospheric wind observations and simulations at JBS observatory and HIWIND.
Data for "Early and Widespread Emergence of Regional Warming is Robust to Observational and Model Uncertainty"
<p>These data can be used to reproduce all figures in "Early and Widespread Emergence of Regional Warming is Robust to Observational and Model Uncertainty". Figure code is hosted at https://github.com/jshaw35/RegionalToE_ShawAndLenssen/releases/tag/v1.0</p>
Data sets used in "Is there a tropical response to recent observed Southern Ocean cooling?"
The dataset contains processed data for the GRL publication (to be submitted) "Is there a tropical response to recent observed Southern Ocean cooling?" The dataset includes global maps of linear trends of various fields presented in the figures, as well as pattern correlation coefficients for the three experiment ensembles using CESM1: Large Ensemble, Tropical Pacific Pacemaker Experiment, and Southern Ocean Pacemaker Experiment.
WRF-Solar AOD550 & Clear-Sky Irradiance Forecasts & Verifying Observations over CONUS
<p>This dataset is fully described in and is a companion of Lee et al. (2022):</p> <p>Lee, J. A., P. A. Jiménez, J. Dudhia, and Y.-M. Saint-Drenan, 2023: Impacts of the aerosol representation in WRF-Solar clear-sky irradiance forecasts over CONUS. J. Appl. Meteor. Climatol., 62, 227–250, https://doi.org/10.1175/JAMC-D-22-0059.1.</p> <p>Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) The default Tegen climatology; 2) Imposing AOD forecasts from the GEOS-5 model; 3) Imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model; and 4) The Thompson-Eidhammer aerosol-aware water/ice-friendly aerosol climatology. Over eight months of these 15-min output forecasts are compared against high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected due to the lesser sensitivity of GHI to aerosol loading. Hourly-average clear-sky DNI and GHI was also analyzed, and additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is.</p> <p>wrf_aeronet_aod550_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 317 MB</p> <p>WRF-Solar AOD550 spatially interpolated to AERONET stations and AERONET observed AOD550. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 15 min to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>wrf_surfrad_solrad_inst_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 102 MB</p> <p>WRF-Solar clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 15 min to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>wrf_surfrad_solrad_hrly_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 26 MB</p> <p>WRF-Solar clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. All values are averaged to time-ending 1-hourly averages. Organized in arrays sorted by model cycle time (once daily at 09 UTC from 20191119 to 20200730) and model lead time (every 1 h to 45 h). The four WRF-Solar experiments correspond to the four from Lee et al. (2022).</p> <p>cams_surfrad_solrad_hrly_dni_ghi_by_cycle_20191119-20200730.nc:</p> <p>NetCDF, 26 MB</p> <p>CAMS clear-sky DNI and clear-sky GHI interpolated to SURFRAD & SOLRAD stations, and SURFRAD & SOLRAD observed clear-sky DNI and clear-sky GHI. All values are averaged to time-ending 1-hourly averages. Organized in arrays sorted by model cycle time (once daily at 00 UTC from 20191119 to 20200730) and model lead time (every 1 h to 54 h).</p>
WACCMX DART Ionosphere Data Assimilation Observing System Simulation Experiment
This dataset contains output from the data assimilation version of the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCMX DART). The dataset includes model output fields of electron column density, temperature, and zonal wind as well as DART observation space diagnostics. The results are based on a series of Observing System Simulation Experiments that are aimed at evaluating the assimilation of ionospheric observations in WACCMX DART. Included are files from the OSSE truth simulation, as well as from three experiments: Experiment 1: No ionosphere data assimilation. Experiment 2: Ionosphere data assimilation without thermosphere constraint. Experiment 3: Ionosphere data assimilation with thermosphere constraint. The dataset is in support of the publication "Assimilation of Ionosphere Observations in the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCMX)".
Evaluating and Improving Snow in the National Water Model, using Observations from the New York State Mesonet
This dataset repository contains the dataset that supports the study of Minder et al. (2024): Evaluating and Improving Snow in the National Water Model, using Observations from the New York State Mesonet. The datasets in this archive include output from distributed simulations of the WRF Hydro model, output from point simulations with the Noah-MP land surface model, and manual and automated snow water equivalent (SWE) observations at New York State Mesonet (NYSM) station locations. The formatting and file naming conventions for each dataset are described in detail in the minder_etal_NWM_snow_dataset_ReadMe.pdf file.
Modeled and observed river water temperature and discharge in the paper "Riverine heat waves on the rise, outpacing air heat waves"
<div>The observed and modeled data – mean daily water temperature (WT, °C) and mean daily discharge (Q,<em> </em>ft<sup>3</sup>/s) modeled by an LSTM model (averaged over 5 model runs) – for 1471 sites over 1980-2022 can be found here. Out of these 1471 sites, 1276 sites had good model performance and were used to identify and analyse air and riverine heat waves (RHW) in the paper "Riverine heat waves on the rise, outpacing air heat waves". </div> <div> <div> </div> </div>
Dataset from paper: "Mind the Gap: Reconciling tropical forest carbon flux estimates from Earth Observation and National Reporting requires transparency"
<p>This repository contains the processed data used in the publication of Heinrich et al., 2023 (Mind the Gap: Reconciling tropical forest carbon flux estimates from Earth Observation and National Reporting requires transparency). Carbon Balance and Management: https://cbmjournal.biomedcentral.com/articles/10.1186/s13021-023-00240-2 </p><p>When using this data, full reference to the raw data publications and the publication Heinrich et al., 2023 must be made.</p><p>The raw datasets used in this study are all available from their original sources.</p><p> </p>
Land use information and NO2 observations of environmental monitoring stations in China
<p>The land use information for environmental monitoring stations in China is stored in the "<i>site_lu.csv" </i>file. The monthly NO2 observations from 2015 to 2017 are stored in the "<i>NO2_ground.csv</i>" file.</p>
Climate‐driven tree growth and mortality in the Black Forest, Germany: Long‐term observations
<p>Episodic tree mortality can be caused by various reasons. This study describes climate‐driven tree mortality and tree growth in the Black Forest mountain range in Germany. It is based on a 68‐year consistent data series describing the annual mortality of all trees growing in a forest area of almost 250 thousand ha. The study excludes mortality caused by storm, snow and ice, and fire. The sequence of the remaining mortality, the so‐called "desiccated trees," is analyzed and compared with the sequence of the climatic water balance during the growing season and the annual radial growth of Norway spruce in the Black Forest. The annual radial growth series covers 121 years and the climatic water balance series 140 years. These unique time series enable a quantitative assessment of multidecadal drought and heat impacts on growth and mortality of forest trees on a regional spatial scale. Data compiled here suggest that the mortality of desiccated trees in the Black Forest during the last 68 years is driven by the climatic water balance. Decreasing climatic water balance coincided with an increase in tree mortality and growth decline. Consecutive hot and dry summers enhance mortality and growth decline as a consequence of drought legacies lasting several years. The sensitivity of tree growth and mortality to changes in the climatic water balance increases with the decreasing trend of the climatic water balance. The findings identify the climatic water balance as the main driver of mortality and growth variation during the 68‐year observation period on a landscape‐scale including a variety of different sites. They suggest that bark beetle population dynamics modify mortality rates. They as well provide evidence that the mortality during the last 140 years never was as high as in the most recent years.</p>
Direct observation of chirality-induced spin selectivity in electron donor–acceptor molecules. Open data set
<p>Data supporting the original figures 2 and 4 of the related publication.</p>
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.