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2,113 results for “High resolution”
MED-GOLD Indicators for the Wine pilot over Douro Valley based on High Resolution Climate projections
<p>Indicators of interest for the Wine sector over the Douro Valley using high resolution climate projections.</p> <ol> <li>GDD (Growing Degree Days) - summation of daily differences between daily temperature averages and 10 for the period April-October</li> <li>GST (Growing Season Temperature) - average of daily average temperatures for the period April-October</li> <li>SprR (Spring Rain) - Precipitation accumulated between 21st April-to 21st June,</li> <li>HarvR (Harvest Rain)-Precipitation accumulated between 21 August and 21 October</li> <li>SU35 -number of days with temperature higher than 35°C for the period April-October,</li> <li>WSDI (Warm Spell Duration Index) -days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for the period April-October.</li> </ol> <p>The results are based on an sub-ensemble of five RCMs from the EURO-CORDEX modelling experiment which have been statistically downscaled to 1km x1km horizontal resolution using the PTHRES gridded dataset as the reference dataset. More details can be found in Raül Marcos-Matamoros, (2018). Report on the methodology followed to implement the wine pilot services. Zenodo. https://doi.org/10.5281/zenodo.4543337</p> <p>Datasets computed by National Observatory of Athens, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No.776467</p> <p> </p> <p> </p>
A river on fiber: high resolution fluvial monitoring with distributed acoustic sensing – Data, Matlab Scripts and App
<p>Matlab software and data associated with Roth et al. (submitted to Seismica, 2025).</p>
Animated E3SM V1 High Resolution Labrador Sea Ice Thickness and Concentration with mid-20th Century Atmospheric Constituents
<p>This animated GIF file visualizes daily grid-cell mean sea ice thickness and concentration for the Labrador Sea region from version 1 of the Energy Exascale Earth System Model (E3SM) using 25km Atmosphere/Land and 8-16km Sea Ice-Ocean resolutions as described in the manuscript "The DOE E3SM coupled model version 1: Description 1 and results at high resolution". River routing is resolved at 0.125˚. This fully coupled simulation was spun-up for 6 years, and then proceeded for 50 subsequent years using HighResMIP protocols for continuuous 1950s atmospheric constituents. This animation shows sea thickness evolution in each frame for five model years 46 to 50, inclusive, with rendered transparency determined from sea ice concentration to demonstrate the influence of ocean eddies around the southern tip of Greenland. The coastline is the true model boundary. The animation is best viewed using a web browser.</p>
Animated E3SM V1 High Resolution Full Coupled Sea Ice Thickness and Extent with mid-20th Century Atmospheric Constituents
<p>This animated GIF file visualizes daily grid-cell mean sea ice thickness from version 1 of the Energy Exascale Earth System Model (E3SM) using 25km Atmosphere/Land and 8-16km Sea Ice-Ocean resolutions as described in the manuscript "The DOE E3SM coupled mo del version 1: Description 1 and results at high resolution". River routing is resolved at 0.125˚. This fully coupled simulation was spun-up for 6 years, and then proceeded for 50 subsequent years using HighResMIP protocols for continuuous 1950s atmospheric constituents. The animation shows both pan-Arctic and Southern Ocean daily sea thickness evolution in each frame for model years 46 to 55, truncated at 15% sea ice concentration, and is best viewed from within a web browser.</p>
Animated E3SM V1 High Resolution Mertz Polynya Sea Ice Thickness and Concentration with mid-20th Century Atmospheric Constituents
<p>This animated GIF file visualizes daily grid-cell mean sea ice thickness and concentration for the Mertz Glacier Polynya region of the East Antarctic coast from version 1 of the Energy Exascale Earth System Model (E3SM) using 25km Atmosphere/Land and 8-16km Sea Ice-Ocean resolutions as described in the manuscript "The DOE E3SM coupled model version 1: Description 1 and results at high resolution". River routing is resolved at 0.125˚. This fully coupled simulation was spun-up for 6 years, and then proceeded for 50 subsequent years using HighResMIP protocols for continuuous 1950s atmospheric constituents. This animation shows sea thickness evolution in each frame for ten model years 46 to 55, inclusive, with shading transparency determined by sea ice concentration, and grid cell outlines dissappearing where there is less than 0.1% sea ice concentration. The coastline is the true model boundary. This animation is best viewed using a web browser.</p>
Super resolution enhancement of Landsat imagery and detections of high-latitude lakes
<p>This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m<sup>2</sup> for 2017; 13 m<sup>2</sup> for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.</p> <p>All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in <strong>Table 1</strong>. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.</p> <p>Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey</p> <p>For an interactive map demo of these datasets via Google Earth Engine Apps, visit: <a href="https://ekyzivat.users.earthengine.app/view/super-resolution-demo">https://ekyzivat.users.earthengine.app/view/super-resolution-demo</a></p> <p><strong>Table 1</strong>: File naming scheme based on region, with some regions requiring two-scene mosaics.</p> <table> <tbody> <tr> <td> <p><strong>Region</strong></p> </td> <td> <p><strong>Landsat ID</strong></p> </td> <td> <p><strong>Mosaic name</strong></p> </td> </tr> <tr> <td> <p><strong>Yukon Flats Basin</strong></p> </td> <td> <p>LC08_L2SP_068014_20170708_20200903_02_T1</p> </td> <td> <p>LC08_20170708_yflats_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_068013_20170708_20201015_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Old Crow Flats</strong></p> </td> <td> <p>LC08_L2SP_067012_20170903_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Mackenzie River Delta</strong></p> </td> <td> <p>LC08_L2SP_064011_20170728_20200903_02_T1</p> </td> <td> <p>LC08_20170728_inuvik_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_064012_20170728_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield Margin</strong></p> </td> <td> <p>LC08_L2SP_050015_20170811_20200903_02_T1</p> </td> <td> <p>LC08_20170811_cshield-margin_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_048016_20170829_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield near Baker Creek</strong></p> </td> <td> <p>LC08_L2SP_046016_20170831_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Canadian Shield near Daring Lake</strong></p> </td> <td> <p>LC08_L2SP_045015_20170723_20201015_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Peace-Athabasca Delta</strong></p> </td> <td> <p>LC08_L2SP_043019_20170810_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes North 1</strong></p> </td> <td> <p>LC08_L2SP_041021_20170812_20200903_02_T1</p> </td> <td> <p>LC08_20170812_potholes-north1_cog.tif</p> </td> </tr> <tr> <td> <p><strong>“</strong></p> </td> <td> <p>LC08_L2SP_041022_20170812_20200903_02_T1</p> </td> <td> <p>“</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes North 2</strong></p> </td> <td> <p>LC08_L2SP_038023_20170823_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Prairie Potholes South</strong></p> </td> <td> <p>LC08_L2SP_031027_20170907_20200903_02_T1</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Fairbanks </strong></p> </td> <td> <p>LT05_L2SP_070014_19850831_20200918_02_T1</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p><strong>References:</strong></p> <p>Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. <em>Remote Sensing</em>, <em>11</em>(18), 2163. <a href="https://doi.org/10.3390/rs11182163">https://doi.org/10.3390/rs11182163</a></p> <p>Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. <a href="https://doi.org/10.3334/ORNLDAAC/1707">https://doi.org/10.3334/ORNLDAAC/1707</a></p> <p>Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. <a href="https://doi.org/10.1080/07038992.2021.1924646">https://doi.org/10.1080/07038992.2021.1924646</a></p> <p>Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. <em>Canadian Journal of Remote Sensing</em>, <em>47</em>(2), 261–275. <a href="https://doi.org/10.1080/07038992.2021.1924646">https://doi.org/10.1080/07038992.2021.1924646</a></p> <p>Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. <em>Environ. Res. Lett</em>, <em>16</em>, 35010. <a href="https://doi.org/10.1088/1748-9326/abc848">https://doi.org/10.1088/1748-9326/abc848</a></p> <p>Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. <a href="https://doi.org/10.3334/ORNLDAAC/1859">https://doi.org/10.3334/ORNLDAAC/1859</a></p>
HRPlanesv2 - High Resolution Satellite Imagery for Aircraft Detection
<p>The HRPlanesv2 dataset contains 2120 VHR Google Earth images. To further improve experiment results, images of airports from many different regions with various uses (civil/military/joint) selected and labeled. A total of 14,335 aircrafts have been labelled. Each image is stored as a ".jpg" file of size 4800 x 2703 pixels and each label is stored as YOLO ".txt" format. Dataset has been split in three parts as 70% train, %20 validation and test. The aircrafts in the images in the train and validation datasets have a percentage of 80 or more in size.</p>
A High Resolution (3km) Reanalysis Database for Mediterranean Coastal Winds Downscaled from ERA5, using the WRF Model
<p>A high resolution (3km) reanalysis database of Mediterranean coastal winds was constructed to support a research on potential sailing mobility in Antiquity. The database was created by downscaling the ERA5 reanalysis database using the WRF numerical prediction model.</p> <p>A detailed description of the reanalysis database is provided in the attached PDF file. The database format is GRIB version 2 and the total volume of the data files is 435GB. The GRIB files are hosted at <a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a> as their total volume exceeds the volume that could be provided by Zenodo. Required files can therefore be downloaded from this location.</p> <p><strong>Link to the GRIB data files and index map:</strong></p> <p><strong><a href="https://coastalwinds.haifa.ac.il">https://coastalwinds.haifa.ac.il</a></strong></p> <p><strong>Acknowledgements:</strong></p> <p>The Data Science Research Center (DSRC) at Haifa University kindly provided funding towards the creation of this data set.</p>
High-resolution large weighing lysimeter measurements with meteorological and soil-hydrological variables from a Mediterranean Savanna
<p>Raw and processed lysimeter weighing and flux data at the instrumental site ES-LMa of six large high-precision weighing lysimeters in a Mediterranean Savanna ecosystem for the period from 2019-06-01 to 2020-05-31. Additionally, meteorological and radiometric data are provided. Additionally, code for the lysimeter processing is provided.</p> <p>Reproducible workflow of the article <strong>Paulus et al. 2022: Resolving seasonal and diel dynamics of non-rainfall water inputs in a Mediterranean ecosystem using lysimeters. HESS, https://doi.org/10.5194/hess-2021-519</strong></p> <p>Variables, units and detailed description are found in the README.html file.</p>
High-resolution maps of material stock, population and employment in Austria from 1985 to 2018 - Supplementary Material
<p>Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.</p> <p>This dataset is a supplement to previously generated detailed maps of the distribution of material stocks, population and employment across Austria from 1985 to 2018 (10.5281/zenodo.7195101).</p> <p>The data are aggregated tabular data used to create illustrations in an accompanying data article.</p> <p><strong>Data format and units</strong></p> <p>This dataset features:</p> <ul> <li>Tabular aggregated data of material stocks, population and employment on a municipality level from 1985 to 2018 (in administrative borders of 2018. <ul> <li>Note: Only layers used to create illustrations in the accompanying data article are presented as tabular data!</li> </ul> </li> <li>Municipalities of Austria as as shape file (from https://www.data.gv.at/katalog/dataset/stat_gliederung-osterreichs-in-gemeinden14f53#resources)</li> <li>Annual population and employment numbers on a federal states level, extracted and aggregated from Statistik Austria (see readme.txt)</li> </ul> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website </a>to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Funding</strong></p> <p>This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).</p>
High-resolution soil moisture data (1km)
<p>High-resolution soil moisture data based on ESA CCI surface soil moisture data in southwestern Europe (Iberia Peninsula).</p> <p>Refs:</p> <p>He, K., Zhao, W., Brocca, L., and Quintana-Seguí, P.: SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation, Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023, 2023.</p> <p> </p>
High-resolution tropical rain-forest canopy climate data
<p><span>Canopy habitats challenge researchers with their intrinsically difficult access. The current scarcity of climatic data from forest canopies limits our understanding of the conditions and environmental variability of these diverse and dynamic habitats. We present 307 days of climate records collected between 2019 and 2020 in the tropical rainforest canopy of the Yasuní National Park, Ecuador. We monitored climate with a 10-minute temporal resolution in the middle crowns of eight canopy trees. The distance between canopy climate stations ranged from 700 m to 10 km. Apart from air temperature, relative humidity, leaf wetness, and photosynthetically active radiation (PAR), measured in each canopy climate station, global radiation, rainfall, and wind speed were measured in different subsets of them. We processed the eight data series to omit erroneous records resulting from sensor failures or lack of the solar-based power supply. In addition to the eight original data series, we present three derived data series, two aggregating canopy climate for valleys or for ridges (from four stations each), and one overall average (from the eight stations). This last derived data series contains 306 days, while the shortest of the original data series covers 22 days and the longest 296 days. In addition to the data, two open-source tools, developed in RStudio, are presented that facilitate data visualization (a dashboard) and data exploration (a filtering app) of the original and aggregated records.</span></p>
CR2MET: A high-resolution precipitation and temperature dataset for the period 1960-2021 in continental Chile.
<p>The Center for Climate and Resilience Research Meteorological dataset (CR2MET) includes two spatially-distributed products of daily precipitation and maximum/minimum near surface temperatures. The dataset covers the domain of continental Chile over a regular 0.05 degree latitude-longitude grid, and spans the period 1960-2021. Both a products are built on statistical models of the corresponding variables, calibrated against quality-controlled observational records. The CR2MET models are nurtured with a combination of data that includes different variables from ECMWF reanalysis ERA5, topographic parameters and land-surface temperature estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor.</p>
Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation
<p>Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for single-cell based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dementional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as spatial reference, integrates small FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images and demonstrated the advantage of the dual-modality imaging strategy.</p>
High-resolution figures of Hassenbach et al. 2023
<p>High-resolution figures of Hassenbach et al. 2023 "An expanded view on the morphological diversity of long-nosed antlion larvae further supports a decline of silky lacewings in the past 100 million years" in Insects (MDPI).</p>
High-resolution figures of Amaral et al. 2023
<p>High-resolution figures of Amaral et al. 2023 "Expanding the fossil record of soldier fly larvae – an important component of the Cretaceous amber forest" in Diversity (MDPI).</p>
A near-field Head-Related Transfer Function (HRTF) data set of KEMAR with high distance resolution
<p>A near-field Head-Related Transfer Function (HRTF) data set measured on a KEMAR head and torso simulator with high distance resolution and multiple elevations is presented ('KEMAR_NFHRIRmea_1cm.sofa'). HRTFs are measured at 83448 spatial points at distances ranging from 20 to 110 cm, elevations from -25° to 35°, and azimuths from 0° to 355°. The distance resolution of the HRTF data is 1 cm, higher than that of any existing public near-field HRTF databases. Therefore, the dataset enables further exploration of the distance dependence of near-field HRTFs, and is beneficial for applications of realistic and dynamic binaural rendering of nearby sound sources. An additional data set of simulated HRTFs with 1.5 cm distance resolution is also provided ('KEMAR_NFHRIRsim_1.5cm.sofa') for a direct comparison with the measured HRTFs or other purposes.</p>
Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China
<p>Here presented the forcasted results of Potential Morbidity Risk Index (PMRI) for the personal patients with allerigc rhinitis and the public health administrations, and these results are supplied to the published paper of "Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China".</p>
Data from: high-resolution bioclimatic surfaces for southern Peru: an approach to climate reality for biological conservation
<p>Climatic and bioclimatic surfaces were elaborated for southern Peru (Arequipa, Moquegua and Tacna). For the interpolations, meteorological information from in-situ stations, as well as orographic and geographic covariates were used. Statistical evaluations gave good results, showing some differences with other models also performed for the area. These data will contribute to a better understanding of the ecoclimatic requirements of the species in terms of ENMs and SDMs. </p>
High-resolution (5 m) surface water persistence map for 2021 in East-Africa
<p>Raster surface water image highlighting the percentage of time in 2021 that there was water at a certain pixel in East Africa. </p> <p>Script which classifies individual countries: <a href="https://code.earthengine.google.com/3f508773522979dfa62a75bda7750b5f?noload=true">https://code.earthengine.google.com/3f508773522979dfa62a75bda7750b5f?noload=true</a></p> <p>Script which combines the individual maps and filters the end-result: <a href="https://code.earthengine.google.com/ed8ee1bbade0b51f3759565b30da37ab?noload=true">https://code.earthengine.google.com/ed8ee1bbade0b51f3759565b30da37ab?noload=true</a></p> <p> </p>
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.