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1,255
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1,255 results for “High-resolution”
Data from: Seasonally varying contributions to urban CO2 in the Chicago, IL, USA region: insights from a high-resolution CO2 concentration and δ13C record
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Data from: Spatially consistent high-resolution land surface temperature mosaics for thermophysical mapping of the Mojave Desert
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Data from: Redescription of Phymolepis cuifengshanensis (Antiarcha: Yunnanolepididae) using high-resolution computed tomography and new insights into anatomical details of the endocranium in antiarchs
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Data from: Testing the effects of ant invasions on non-ant arthropods with high-resolution taxonomic data
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Data from: Quantifying urban ecosystem services based on high-resolution data of urban green space: an assessment for Rotterdam, the Netherlands
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Data from: impacts of El Niño and La Niña on interannual snow accumulation in the Andes: results from a high-resolution 31 year reanalysis
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Data for: High-resolution land value maps reveal underestimation of conservation costs in the United States
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Data from: High-resolution mapping reveals hundreds of genetic incompatibilities in hybridizing fish species
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Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
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Data from: Rapid identification of chloroplast haplotypes using High-Resolution Melting analysis
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Data from: High-resolution chemical dissection of a model eukaryote reveals targets, pathways and gene functions
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Data from: From lidar waveforms to vegetation products: 7380 km2 of high-resolution airborne and simulated GEDI data over Sierra Nevada, California
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SnowClim v1.0: High-resolution snow model and data for the western United States
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Data from: High-resolution estimates of crossover and noncrossover recombination from a captive baboon colony
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Monthly mean temperature and salinity outputs from the Southern Ocean high-resolution (SOhi) simulation
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Arid or Cloudy: Characterizing the Atmosphere of the super-Earth 55 Cancri e using High-Resolution Spectroscopy
<p>These raw data and models are supplementary to the submission of the manuscript.</p>
High-resolution (100 m) urban land surface dataset for the Hong Kong-Shenzhen area
<p>In recent years, with rapidly developing computational capabilities, high-resolution techniques have been continuously employed in the atmospheric models. Thus, researchers become capable of applying these high-resolution models to produce detailed meteorological scenarios, which empowers studies of urban-scale climatology which requires finer grid spacing. WRF ARW/Noah LSM/UCM model is often used in urban climate researches. However, the default input land surface data in urban areas is not enough precise, especially for the data describing in China. In this study, we developed a series of methods to produce the high-resolution urban land surface dataset which includes the land cover, vegetation coverage, urban morphology, artificial impervious area and anthropogenic heat data in Hong Kong-Shenzhen area. Moreover, a sensitivity test was conducted to examine the influence of urban land surface data accuracy on urban climate modeling quality. In short, the article proposes the methodological framework for producing the urban land surface dataset and presents how the urban land surface accuracy affects urban climate modeling quality.</p>
LROC NAC-based high-resolution (2 m per pixel) digital elevation models (DEMs) of twelve regions on the Moon containing ring-moat dome structures (RMDSs)
<p>The DEMs were constructed based on Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) images using shape from shading constrained by lower-resolution stereo data. They have been introduced in the following publication, where more details can be found:</p> <p> </p> <p>F. Zhang, J. W. Head, C. Wöhler, R. Bugiolacchi, L. Wilson, A. T. Basilevsky, A. Grumpe, Y. L. Zou.</p> <p>Ring-Moat Dome Structures (RMDSs) in the Lunar Maria: Statistical, Compositional, and Morphological Characterization and Assessment of Theories of Origin.</p> <p>Journal of Geophysical Research: Planets, 125, e2019JE005967. https://doi.org/10.1029/2019JE005967</p> <p> </p> <p>The file format of the DEMs is GEOTIFF, the pixel values indicate evelations in meters. The DEMs are numbered as in Table 1 of the paper.</p>
High-resolution cone-beam scan of twenty-one walnuts with two dosage levels
<p>We release tomographic scans of 21 walnuts with two levels of radiation dosage for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The dataset collected with higher dosage is referred to as the "<em><strong>good</strong></em>" <strong>dataset</strong>; and the other as the "<em><strong>noisy</strong></em>" <strong>dataset</strong>, as a way to distinguish between the two dosage levels.</p> <p>The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel. To reduce the sizes of the datasets the detector pixels were binned pair-wise to get 972-by-768 detector pixels.</p> <p>All datasets were collected over a 360 degrees in circular and continuous motion with 501 projections distributed evenly over the full circle. The uploaded datasets are not normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 70kV, 20W, respectively. The total scanning time were 20 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.</p> <p>These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>m.j.lagerwerf [at] cwi.nl</li> <li>s.b.coban [at] cwi.nl or</li> </ul>
Restore Centre of Excellence: High-resolution mapping of Louisina vegetation remote sensing data for surge modelling
<p>Satellite derived Leaf Area Index map of the Louisiana coast, translated into plant dimensions using field data from CMRS stations and dedicated project sampling. Plant dimensions have been used to prescribe hydraulic roughness fields for a hydrodynamic model (Delft3D) used to asses the effect of wetlands on storm surge levels.</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.