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196 results for “CAM”
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at NUTS3 level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct NUTS3 regions in continetal Europe. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each NUTS3 area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles (EPSG:4326) sourced from Eurostat's official repository (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts</a>). These shapefiles link the air quality data to precise NUTS3 regions through unique identifiers.</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each NUTS3 polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p>
Database of pyroclastic cover deposit thickness measurements (PT-Cam) in peri-volcanic areas of Campania (Italy)
<p>In an eruptive event, tephra deposits (i.e. ash and pumice) disperse in the atmosphere and deposit on the ground surface according to the speed and direction of the wind. Because the geotechnical and hydraulic properties of the unconsolidated pyroclastic fall deposits usually differ from the bedrock, their spatial thickness significantly influences geomorphological and hydrogeological processes such as landscape evolution, erosion, landslide, and hillslope hydrogeology.</p> <p>The PT-Cam database presents the thickness of tephra deposits (i.e. the unconsolidated materials over the bedrock) in Campania region (Italy), measured through in-situ investigations of some territories around the Somma-Vesuvius, Campi Flegrei, Roccamonfina, and Ischia volcanoes during the last decades. The measurements were conducted with probing tests, dynamic penetration tests, trenches, man-made pits, seismic surveys, and outcrops.</p> <p>Explanation for database attribute:</p> <ul> <li>CODE: identification code of the measurement;</li> <li>z: measured thickness expressed in cm;</li> <li>type_z: thickness type (i.e. if investigation has reached to the bedrock the type is “total” otherwise it is “partial”);</li> <li>type_investigation: method of in-situ investigation;</li> <li>locality: municipality to which the measurement point belongs;</li> <li>Longitude, Latitude: km coordinates in UTM WGS 84 system.</li> </ul>
Deciphering the Neurosensory Olfactory Pathway and Associated Neo-Immunometabolic Vulnerabilities Implicated in COVID-Associated Mucormycosis (CAM) and COVID-19 in a Diabetes Backdrop—A Novel Perspective
<p>Raw data files of transcriptomic profiling experiments, which form the basis for our publication (https://www.mdpi.com/2673-4540/3/1/13).</p>
TNO-CAMS European CO2 emissions 2000-2014 v1
<p><strong>Introduction</strong></p> <p>This TNO_CAMS_CO2 emission dataset was prepared by TNO as a contribution to the H2020 project MACC-III and the subsequent Copernicus Atmospheric Monitoring Service. This model-ready historic emission inventory at high spatial resolution (~7x7 km) for UNECE-Europe for 15 consecutive years (2000–2014) providing CO<sub>2</sub> from fossil fuels and CO<sub>2</sub> from biofuels is intended to support modelling and sub-national scale identification of emissions. Where available and considered fit for purpose, we have used CO<sub>2</sub> estimates as reported by the Parties to UNFCCC. The data have been supplemented by other estimates, most notable from the IIASA GAINS model and the JRC EDGAR database to create a complete coverage. The approach to the spatial distribution of the dataset is similar to the TNO-MACC emission dataset for air pollutants ( see Kuenen et al., ACP, 2014).</p> <p>The emission grid consists of UNECE-Europe in WGS84 projection (lon-lat) with a spatial resolution of 1/8 x 1/16 degrees (lon x lat). The lower left of the grid is at lon = -60, lat = 30 and the upper right is at lon = 60, lat = 72.</p> <p>The grid files TXT (.csv) & netcdf (.nc) both contain annual total emissions per grid cell for the year 2000-2014. A separate file has been prepared for each year. </p> <p>The unit in the .csv files is Mg/gridcell/yr</p> <p>The unit in the .nc files is kg/gridcell/yr</p> <p>Sectoral breakdown uses the SNAP classification. Compared to the default SNAP1 sectors (1 to 10), a couple of refinements have been made to the sectors:</p> <ul> <li> <p>SNAP 3 and SNAP 4 are grouped as SNAP 34</p> </li> <li> <p>SNAP 7 is split in SNAP 71 to 75</p> </li> </ul> <p>The dataset is described in </p> <p>Denier van der Gon, H.A.C., J.J.P. Kuenen, G. Janssens-Maenhout, U. Döring, S. Jonkers, A.J.H. Visschedijk., TNO_CAMS high resolution European emission inventory for anthropogenic CO<sub>2</sub> for 2000-2014 and future years following two different pathways, ESSD, in preparation, 2017.</p> <p> </p>
Model input data for the FACETS downscaling simulation with the CAM-MPAS model
<p>The archived file contains input data necessary to reproduce the set of simulations described in Sakaguchi et al., submitted to GWD, "Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM-MPAS variable-resolution model", using the experimental CAM-MPAS code further modified by Sakaguchi and Harrop (2022) for long-term AMIP-type simulations.</p>
Digital Assets for "Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey"
<p>These are morphological catalogs and trained <a href="https://github.com/aritraghsh09/GaMPEN">GaMPEN</a> models for Hyper Suprime-Cam galaxies. Please refer to <a href="https://gampen.readthedocs.io/en/latest/Public_data.html">https://gampen.readthedocs.io/en/latest/Public_data.html</a> and <a href="https://arxiv.org/abs/2212.00051">https://arxiv.org/abs/2212.00051</a> for more details about this data release. </p> <p> </p> <p><strong>Catalog Files</strong></p> <ol> <li>g_0_025_preds_summary.csv --> Structural parameter catalog for z < 0.25 HSC g-band galaxies </li> <li>r_025_050_preds_summary.csv --> Structural parameter catalog for 0.25 < z < 0.50 HSC r-band galaxies </li> <li>i_050_075_preds_summary.csv --> Structural parameter catalog for 0.50 < z < 0.75 HSC i-band galaxies </li> </ol> <p> </p> <p><strong>Trained PyTorch Model Files</strong></p> <ol> <li>g_0_025_real_data.pt --> Trained Model for z < 0.25 HSC g-band galaxies </li> <li>r_025_050_real_data.pt --> Trained Model for 0.25 < z < 0.50 HSC r-band galaxies </li> <li>i_050_075_real_data.pt --> Trained Model for 0.50 < z < 0.75 HSC i-band galaxies </li> <li>sim_g_0_025.pt --> Trained Model for Simulated z < 0.25 HSC g-band galaxies </li> <li>sim_r_025_050.pt --> Trained Model for Simulated 0.25 < z < 0.50 HSC r-band galaxies </li> <li>sim_i_050_075.pt --> Trained Model for Simulated 0.50 < z < 0.75 HSC i-band galaxies </li> </ol>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at ZIP-code level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct ZIP-code areas in Germany. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each ZIP-code area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles provided by ESRI Deutschland (<a href="https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0">https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0</a>). These shapefiles link the air quality data to precise ZIP-code areas .</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each ZIP-code polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p> <p>Generated using Copernicus Atmosphere Monitoring Service Information 2013-2022</p>
Human Colorectal Tissue OCT Dataset: Neoplastic and Non-Neoplastic Samples from Chorioallantoic Membrane (CAM) Assays
<p>A commercial OCT system (Telesto II-1325 LR spectral domain OCT (SD-OCT)) was used to imaging two colon cell lines implanted in a chick embryo chorioallantoic membrane (CAM) assay. This is a high-performance imaging system designed for in vivo and ex vivo imaging of biological tissues.</p> <p>The CAM is a highly vascularized extra-embryonic membrane connected to the developing embryo through an easily accessible circulatory system that allows the successful engraftment of a variety of foreign tissues, such as tumor explants or cancer cell lines. Neoplastic and non-neoplastic tumors were developed from RKO and NCM460 cell lines, respectively.</p> <p> </p> <p>OCT B-scan images were collected from 30 CAM models, 15 with RKO-cells and 15 with NCM460-cells. The collected images are avalilable in format <strong>OCT </strong>format (<strong>Neoplastic_OCT.zip/Non-neoplastic_OCT.zip</strong>), <strong>TIFF </strong>format (<strong>Neoplastic_tiff.zip/Non-neoplastic_tiff.zip</strong>), and <strong>MAT </strong>format (<strong>Neoplastic_mat.zip/Non-neoplastic_mat.zip</strong>).</p> <p> </p> <p>Since, the attenuation of near-infrared light in biological samples has proven to be a powerful tool for tissue characterization the <strong>attenuation coefficient of light</strong> was calculated for each image. This data is available in the <strong>Neoplastic_processed.zip</strong> and <strong>Non-neoplastic_processed.zip</strong> folders.</p>
Рис. 2. Фотографии Viscosia orientalis sp. nov., гоΛотип самца (А, В, Г, Е, З, И) и паратип самки (Б, Á, Ж, К). А, Б — общий виΑ; В — переΑний конец теΛа; Г, Á, Е — гоΛова; Ж — теΛо в обΛасти вуΛьвы; З — теΛо в обΛасти кΛоаки; И, К — заΑний конец теΛа. Масштаб: А, Б — 100 мкм; В, Ж — 50 мкм; И, К — 20 мкм; Á, З — 10 мкм; Г, Е — 5 мкм Fig. 2. Light micrograph of Viscosia sp. nov., male holotype (А, В, Г, Е, З, И) and female paratype (Б, Á, Ж, К). А, Б — general view; В — anterior body end; Г, Á, Е — head; Ж — vulva region; З — cloaca region; И, К — posterior body end. Scale bars: А, Б — 100 μm; В, Ж — 50 μm; И, К — 20 μm; Á, З — 10 μm; Г, К — 5 μm in Sp. Nov. And Sp. Nov. (Nematoda, Enoplida) From The Mouth Of The Cam River In Vietnam
Рис. 2. Фотографии Viscosia orientalis sp. nov., гоΛотип самца (А, В, Г, Е, З, И) и паратип самки (Б, Á, Ж, К). А, Б — общий виΑ; В — переΑний конец теΛа; Г, Á, Е — гоΛова; Ж — теΛо в обΛасти вуΛьвы; З — теΛо в обΛасти кΛоаки; И, К — заΑний конец теΛа. Масштаб: А, Б — 100 мкм; В, Ж — 50 мкм; И, К — 20 мкм; Á, З — 10 мкм; Г, Е — 5 мкм Fig. 2. Light micrograph of Viscosia sp. nov., male holotype (А, В, Г, Е, З, И) and female paratype (Б, Á, Ж, К). А, Б — general view; В — anterior body end; Г, Á, Е — head; Ж — vulva region; З — cloaca region; И, К — posterior body end. Scale bars: А, Б — 100 μm; В, Ж — 50 μm; И, К — 20 μm; Á, З — 10 μm; Г, К — 5 μm
Рис. 1. Viscosia orientalis sp. nov., гоΛотип самца (А, Б, Á) и паратипа самки (В, Г). А — гоΛова; Б — переΑний конец теΛа; В, Á — заΑний конец теΛа; Г — теΛо в обΛасти вуΛьвы. Масштаб: А — 15 мкм; В — 25 мкм; Á — 30 мкм; Г — 60 мкм; Б — 80 мкм Fig. 1. Viscosia orientalis sp. nov., male holotype (А, Б, Á) and female paratype (В, Г). А — head; Б — anterior body end; В, Á — posterior body end; Г — vulva region. Scale bars: А — 15 μm; В — 25 μm; Á — 30 μm; Г — 60 μm; Б — 80 μm in Sp. Nov. And Sp. Nov. (Nematoda, Enoplida) From The Mouth Of The Cam River In Vietnam
Рис. 1. Viscosia orientalis sp. nov., гоΛотип самца (А, Б, Á) и паратипа самки (В, Г). А — гоΛова; Б — переΑний конец теΛа; В, Á — заΑний конец теΛа; Г — теΛо в обΛасти вуΛьвы. Масштаб: А — 15 мкм; В — 25 мкм; Á — 30 мкм; Г — 60 мкм; Б — 80 мкм Fig. 1. Viscosia orientalis sp. nov., male holotype (А, Б, Á) and female paratype (В, Г). А — head; Б — anterior body end; В, Á — posterior body end; Г — vulva region. Scale bars: А — 15 μm; В — 25 μm; Á — 30 μm; Г — 60 μm; Б — 80 μm
Рис. 4. Фотографии Halalaimus borealis sp. nov., гоΛотип самца (А, В, Á, Ж, З) и паратип самки (Б, Г, Е, И). А, Б — общий виΑ; Á, Г — гоΛова; Á — переΑний конец теΛа; Е — теΛо в обΛасти вуΛьвы; Ж — теΛо в обΛасти кΛоаки; З, И — заΑний конец теΛа. Масштаб: А, Б — 200 мкм; Á, И — 50 мкм; З — 20 мкм; Е — 10 мкм; В, Г, Ж — 5 мкм Fig. 4. Light micrograph of Halalaimus borealis sp. nov., male holotype (А, В, Á, Ж, З) and female paratype (Б, Г, Е, И). А, Б — general view; В, Г — head; Á — anterior body end; Е — vulva region; Ж — cloaca region; З, И — posterior body end. Scale bars: А, Б — 200 μm; Á, И — 50 μm; З — 20 μm; Е — 10 μm; В, Г, Ж — 5 μm in Sp. Nov. And Sp. Nov. (Nematoda, Enoplida) From The Mouth Of The Cam River In Vietnam
Рис. 4. Фотографии Halalaimus borealis sp. nov., гоΛотип самца (А, В, Á, Ж, З) и паратип самки (Б, Г, Е, И). А, Б — общий виΑ; Á, Г — гоΛова; Á — переΑний конец теΛа; Е — теΛо в обΛасти вуΛьвы; Ж — теΛо в обΛасти кΛоаки; З, И — заΑний конец теΛа. Масштаб: А, Б — 200 мкм; Á, И — 50 мкм; З — 20 мкм; Е — 10 мкм; В, Г, Ж — 5 мкм Fig. 4. Light micrograph of Halalaimus borealis sp. nov., male holotype (А, В, Á, Ж, З) and female paratype (Б, Г, Е, И). А, Б — general view; В, Г — head; Á — anterior body end; Е — vulva region; Ж — cloaca region; З, И — posterior body end. Scale bars: А, Б — 200 μm; Á, И — 50 μm; З — 20 μm; Е — 10 μm; В, Г, Ж — 5 μm
Рис. 3. Halalaimus borealis sp. nov., гоΛотип самца (А, В, Е) и паратип самки (Б, Г). А — переΑний конец теΛа; Б — теΛо в обΛасти вуΛьвы; В, Г — заΑний конец теΛа; Е — спикуΛы и руΛек. Масштаб: А, Б — 20 мкм; В, Г, Á — 30 мкм Fig. 3. Halalaimus borealis sp. nov., male holotype (А, В, Е) and female paratype (Б, Г). А — anterior body end; Б — vulva region; В, Г — posterior body end; Е — spicules and gubernaculum. Scale bars: А, Б — 20 μm; В, Г Á — 30 μm in Sp. Nov. And Sp. Nov. (Nematoda, Enoplida) From The Mouth Of The Cam River In Vietnam
Рис. 3. Halalaimus borealis sp. nov., гоΛотип самца (А, В, Е) и паратип самки (Б, Г). А — переΑний конец теΛа; Б — теΛо в обΛасти вуΛьвы; В, Г — заΑний конец теΛа; Е — спикуΛы и руΛек. Масштаб: А, Б — 20 мкм; В, Г, Á — 30 мкм Fig. 3. Halalaimus borealis sp. nov., male holotype (А, В, Е) and female paratype (Б, Г). А — anterior body end; Б — vulva region; В, Г — posterior body end; Е — spicules and gubernaculum. Scale bars: А, Б — 20 μm; В, Г Á — 30 μm
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network - CAMS data for experiments
<p>Contains data generated by the CAMS model (during a global reanalysis), used to train and evaluate the models presented article "Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network" - DOI of this article will be provided as soon as it is available.</p> <p>This data can be downloaded from the Copernicus Atmospheric Data Store (https://ads.atmosphere.copernicus.eu/#!/home), and is also hosted by the ICARE Data and Services Center (https://www.icare.univ-lille.fr/).</p> <p>This dataset only contains the specific data collection used for the experiments presented in aforementioned article. It is only a portion of the data available from these two websites.</p>
Alfaroa colombiana Lozano, Hern. Cam. & Espinal from Colombia collected by W. López #
<p><strong>File Name</strong>: <span>TOLI-26134-BET-02-C1-1020.jpg</span></p> <p><strong>CÓDIGO FOTO</strong>: <span>TOLI-26134-BET-02-C1-1020-</span></p> <p><strong>Fotografía</strong>: <span>SI</span></p> <p><strong>Nº TOLI</strong>: <span>TOLI-26134</span></p> <p><strong>PARCELA</strong>: <span>BET-02</span></p> <p><strong>CÓDIGO</strong>: <span>C1-1020</span></p> <p><strong>NUEVOS COLECTORES</strong>: <span>Wilmar López Oviedo</span></p> <p><strong>COLECTORES</strong>: <span>W. López</span></p> <p><strong>Nº MUESTRAS MONTADAS</strong>: <span>1</span></p> <p><strong>Homologación</strong>: <span>Homologado</span></p> <p><strong>Nueva fecha del evento </strong>: <span>18/01/2019.</span></p> <p><strong>Fecha del evento</strong>: <span>18/01/2019.</span></p> <p><strong>Proyecto </strong>: <span>Recursos Botánicos Disponibles en Línea (BRAVO) para la flora Colombiana</span></p> <p><strong>Hábitat</strong>: <span>Bosque seco pre-montano (bs-PM)</span></p> <p><strong>Continente</strong>: <span>SA</span></p> <p><strong>Pais</strong>: <span>Colombia</span></p> <p><strong>Estado/Provincia</strong>: <span>Santander</span></p> <p><strong>Municipio</strong>: <span>Betulia</span></p> <p><strong>Localidad</strong>: <span>Reserva Cuchilla El Ramo</span></p> <p><strong>Elevación minima en metros</strong>: <span>1600</span></p> <p><strong>Elevación maxima en metros</strong>: <span>2200</span></p> <p><strong>Latitud</strong>: <span>6.9180</span></p> <p><strong>Longitud original</strong>: <span>-73.3010</span></p> <p><strong>datum geodésico</strong>: <span>WGS 84</span></p> <p><strong>Latitud decimal</strong>: <span>6.9180</span></p> <p><strong>Longitud decimal</strong>: <span>-73.3010</span></p> <p><strong>Nombre cientifico</strong>: <span>Alfaroa colombiana Lozano, Hern. Cam. & Espinal </span></p> <p><strong>Reino</strong>: <span>Plantae</span></p> <p><strong>Filo</strong>: <span>Magnoliophyta</span></p> <p><strong>Clase</strong>: <span>Equisetopsida</span></p> <p><strong>Orden</strong>: <span>Fagales</span></p> <p><strong>Familia nueva</strong>: <span>Juglandaceae</span></p> <p><strong>Género nuevo</strong>: <span>Alfaroa </span></p> <p><strong>especie nueva</strong>: <span>colombiana </span></p> <p><strong>Autoría del nombre científico</strong>: <span>Lozano, Hern. Cam. & Espinal </span></p> <p><strong></strong>: <span>Juglandaceae</span></p> <p><strong>genero herbario</strong>: <span>Alfaroa</span></p> <p><strong>especie herbario</strong>: <span>colombiana</span></p> <p><strong>Especie de herbario para TNRS</strong>: <span>Alfaroa colombiana</span></p> <p><strong>Especie corregida herbario y desde TNRS</strong>: <span>Alfaroa colombiana</span></p> <p><strong>Familia corregida desde TNRS</strong>: <span>Juglandaceae</span></p> <p><strong></strong>: <span>5129</span></p>
Global continuous 0.05 degree atmospheric carbon dioxide dataset (GCXCO2) based OCO-2 satellite, CAMS and CarbonTracker simulation data from 2000 to 2020
<p>This dataset provides global seamless 8-day XCO2 (column-averaged CO2 dry air mole fraction) with a spatial resolution of 0.05 degree from 2000 to 2020. The unit is ppm. The detailed process and product validation accuracy can be found in our paper at https://doi.org/10.1016/j.scitotenv.2024.177051</p>
Phone Cam Test: Denver Museum
This was made with 76 photos taken from my Samsung Galaxy S6. No clean up or editing in an external 3d program raw photo scan from RealityCapture. Created in RealityCapture by Capturing Reality from 76 images in 00h:03m:53s. Source: Objaverse 1.0 / Sketchfab
5GMETA simulated C-ITS CAM data
<p>This dataset contains messages with information about simulated vehicles travelling in Torino, Italy. Each vehicle moves at constant speed. The JSON-formatted ETSI C-ITS Cooperative Awareness Message v1.4.1 messages are generated at variable frequency (mostly 10Hz).</p> <p>This dataset has been generated using the ETSI C-ITS CAM Simulator by LINKS Foundation, the same simulation environment provided to the participant of the second 5GMETA hackathon.</p> <p>A readme file and a usage example are included.</p> <p>This dataset can be used to build a proof of concept of the 5GMETA platform.</p>
CAM Global RCE simulations TC track, radial profiles, and filtered precipitation files
<p>This dataset includes processed output from the Community Atmosphere Model (CAM), version 5, the atmospheric component of the Community Earth System Model (CESM2). CAM was run in a global rotating radiative convective equilibrium (RCE) aquaplanet configuration for 2 years, with the first 2 months discarded to allow for spin-up. The globally-uniform sea surface temperature (SST) was varied from 295 to 305 K in 1 K increments, producing a total of 11 model simulations. More details about CAM and the RCE configuration can be found in the associated manuscript in <em>JGR: Atmospheres.</em> The TempestExtremes software package (https://github.com/ClimateGlobalChange/tempestextremes) was used to track tropical cyclones (TCs) in the raw model output. Specifically DetectNodes and StitchNodes were used to locate potential TCs based on sea level pressure minima and then stitch these TC candidates into tracks based on spatial proximity. NodeFileEditor was used to calculate a radial wind profile at each timestep in each TCs' lifetime, and from these radial profiles, the TCs' outer sizes were estimated based on the radii of 8 m/s winds, outside the radii of maximum winds. Lastly NodeFileFilter was used to extract all precipitation within these calculated outer sizes at each timestep in each TC's lifetime. This dataset contains the TC track files, the TC radial wind profile and outer size files, and the filtered TC precipitation files. Note that while the track and radial profile files contain data from TCs all over the global domain, the filtered TC precipitation files only contain precipitation froms TCs between 40°S and 40°N because that's the domain we used for the TC precipitation analysis in the manuscript. </p>
Model output of: Excitation of an MJO event in response to a transient sea surface warming in Super-Parameterized CAM
<p>Recent studies have suggested that the Madden-Julian Oscillation (MJO) could be generated in the atmospheric adjustment to equatorial heating anomalies in models properly resolving moist convection. Here we use Super-Parameterized CAM to simulate atmospheric response to transient sea surface warming in the equatorial Indian Ocean, which leads to the excitation of an MJO event, robust across different ensemble members, in addition to the much-expected equatorial Rossby and Kelvin waves. A moist static energy (MSE) budget analysis suggests that longwave and surface turbulent latent heat flux anomalies are predominantly responsible for the MJO excitation; however, these terms oppose its eastward propagation. In contrast, advection of MSE generally weakens the MJO amplitude, but contributes to its eastward propagation. These results highlight the role of the MJO in the moist atmospheric adjustment to equatorial heating, with implications for the MJO mechanisms and prediction.</p>
Postprocessed output from VR-CESM with refinement over the greater Greenland area and a CAM-SE control experiment
<p>This dataset contains post-processed monthly output from two VR-CESM simulations that were performed with CAM5.4 and CLM5, with refinement patches over the greater Greenland area. Data from a standard, quasi-uniform CAM-SE simulations is included as well. These are the data that are analysed and discussed by our paper in The Cryosphere, <a href="https://www.the-cryosphere-discuss.net/tc-2018-257/">https://www.the-cryosphere-discuss.net/tc-2018-257/</a>.</p> <p>The postprocessing involved (A) regridding from the respective unstructured CAM grids to regular latitude-longitude grids to allow for easy plotting and comparison, (B) vertical interpolation of some atmospheric fields to constant pressure levels, and (C) averaging in time to compute climatological means and variances.</p> <p><strong>Contact</strong><br> Leo van Kampenhout (L.vankampenhout@uu.nl)</p> <p><strong>Raw data</strong><br> The raw, ungridded monthly timeseries data are currently available on NCAR's HPSS tape system, under file path /home/lvank/archive_pp, and can be requested through the contact person. There exists also daily data for selected variables.</p> <p><strong>Dataset contents</strong></p> <pre><code>Global_Uniform.tar Global_VR28.tar Global_VR55.tar</code></pre> <p>Atmospheric CAM output regridded to a global regular 1 degree latitude-longitude grid. Variables are PHIS, T200, T500, T700, Z200, Z500, Z700.</p> <pre><code>Greenland_0.25_Uniform.tar Greenland_0.25_VR28.tar Greenland_0.25_VR55.tar</code></pre> <p>Atmospheric CAM output and CLM land model output regridded to a 0.25x0.25 degree grid stretching from 40N - 90N and 100W - 0W. Variables are FLDS, FLNS, FSDS, FSNS, H2OSNO, LHFLX, SHFLX, PHIS, PRECC, PRECL, PRECSC, PRECSL, TGCLDCWP, TREFHT, TSOI_10CM, TS, U10.</p> <pre><code>acab_c2b8_UNI_fdm.004_timmean.nc acab_c2b8_VRGRN_28.005_timmean.nc acab_c2b8_VRGRN_55.005_timmean.nc</code></pre> <p>Downscaled SMB on the 4km CISM grid, in meters of ice equivalent. These files represent time means over the period 1980-1999.</p> <pre><code> cism_thickness.nc </code></pre> <p>CISM ice thickness and ice elevation at 4 km. Note that the ice sheet was non-evolving in our simulations, so both these fields are constant in time.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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