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5,155 results for “resolution”
Four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018)
<p>These data were four-dimensional wind fields retrieved from GIIRS hyperspectral measurements with 15-minute temporal resolution during Typhoon Maria (2018). They were also the output results of the findings of Ma et al. (2021).</p>
ValLAI_Crop: Validation dataset for coarse-resolution satellite LAI product over Chinese Cropland
<p>Numerous validation campaigns have been conducted over the last decade to assess the accuracy of the global leaf area index (LAI) products. Accurate and comprehensive validations for coarse-resolution LAI products are still very difficult due to lack of enough high-quality field measurements. Here we developed a fine resolution LAI dataset, consisting of 80 sample plots with an area of 3 km × 3 km in four major agricultural regions in China collected from 2003 to 2017. Instead of the indirect optical measurement method employed in most validation campaigns, the direct destructive method was employed to measure LAI of cropland for all the field experiments to avoid the measurement uncertainties, especially for crops at early growth stages with low height. Fine resolution reference LAI maps were derived from Landsat-5 TM and Landsat-8 OLI surface reflectance products based on the semi-empirical inversion model, which were calibrated using field measurements for each growth stage with an RMSE ranging from 0.22 to 0.95, and a relative root mean square error (RRMSE) ranging from 7.58% to 44.42%. Then, 80 sample plots with an area of 3 km × 3 km were selected as the fine resolution validation dataset from the fine resolution reference LAI maps with a proportion of cropland larger than 75% and one or more in-situ samples were contained in each 3 km × 3 km reference map.</p>
High-resolution digital elevation model of Klados Gorge, Crete, Greece
<p>High-resolution digital elevation model constructed from photogrammetric processing of drone images taken at Klados Gorge, Crete, Greece in 2017. The authors used <em>Agisoft</em> PhotoScan for the photogrammetric processing and to generate 3D spatial data for further use.</p>
Bacteriophage phi29 Structural Model at Atomic Resolution
<p>Bacteriophage phi29 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage phi29 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Bacteriophage SPP1 Structural Model at Atomic Resolution
<p>Bacteriophage SPP1 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage SPP1 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Mycobacteriophage ZoeJ Structural Model at Atomic Resolution
<p>Mycobacteriophage ZoeJ structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose mycobacteriophage ZoeJ using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
Adenovirus D26 Structural Model at Atomic Resolution
<p>Adenovirus D26 structural model at atomic resolution. This structural model has been constructed in UCSF Chimera software putting together all the structures that compose Adenovirus D26 using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com</p>
A High-Resolution Spatial Room Impulse Response Database
<p><strong>A High-Resolution Spatial Room Impulse Response Database:</strong></p> <p>A Database of various SRIRs measured in three rooms, for two receiver and 3 source positions each. The positions are shown in the floor plans. </p> <p><strong>Naming Convention:</strong></p> <p><strong>Receivers</strong>: SMA (DRIRs): spherical microphone array impulse responses on a 2702 sampling point Lebedev grid.</p> <p> KU100 (BRIRs): Neumann KU100 dummy head impulse responses on a 360 sampling point horizontal grid</p> <p> Omni_ir: Omnidirectional impulse responses measured with an Earthworks M30 microphone </p> <p><strong>Receiver positions:</strong> P1, P2 as indicated in the floor plans</p> <p><strong>Source positions:</strong> LSL: left speaker, LSR: right speaker, LSC: center speaker as indicated in the floor plans </p> <p><strong>Rooms: </strong>Audiolab, Classroom, Audimax</p> <p>_______________________________________________________________________________________</p> <p><strong>Contact:</strong><br> Tim Lübeck, Johannes M. Arend, and Christoph Pörschmann<br> TH Köln - University of Applied Sciences<br> Institute of Communications Engineering<br> Department of Acoustics and Audio Signal Processing<br> Betzdorfer Str. 2, D-50679 Cologne, Germany</p> <p><a href="https://www.th-koeln.de/personen/tim.luebeck/">https://www.th-koeln.de/personen/tim.luebeck/</a></p> <p><a href="https://www.th-koeln.de/personen/johannes.arend/">https://www.th-koeln.de/personen/johannes.arend/</a></p> <p><a href="http://www.th-koeln.de/personen/christoph.poerschmann/">https://www.th-koeln.de/personen/christoph.poerschmann/</a><br> <br> </p>
Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery
<p>A <a href="https://www.sciencedirect.com/topics/engineering/photovoltaics">photovoltaic</a> (PV) dataset from satellite and aerial imagery. The dataset includes three groups of PV samples collected at the spatial resolution of 0.8m, 0.3m and 0.1m, namely PV08 from Gaofen-2 and Beijing-2 imagery, PV03 from aerial photography, and PV01 from UAV orthophotos. PV08 contains rooftop and ground PV samples. Ground samples in PV03 are divided into five categories according to their background land use type: shrub land, grassland, cropland, saline-alkali, and water surface. Rooftop samples in PV01 are divided into three categories according to their background roof type: flat concrete, steel tile, and brick. Data document can refer to the preprint https://essd.copernicus.org/preprints/essd-2021-270/</p>
Dataset on UAV High-resolution Images from Grassland with Broad-leaved Dock (Rumex Obtusifolius)
<p>The dataset consists of orthophotos (build from UAV images) from a grassland field in which several <em>Rumex obtusifolius</em> plants were detected. The field is located in Germany (Kleve). The UAV images were acquired at 10, 15, and 30 meters height. Moreover, the labels/annotations from the <em>Rumex obtusifolius</em> plants in the images are also provided. </p>
SMAP-HydroBlocks: Hyper-resolution satellite-based soil moisture over the continental United States
<p><a href="https://waterai.earth/smaphb/">SMAP-HydroBlocks (SMAP-HB)</a> is a hyper-resolution satellite-based surface soil moisture product that combines NASA's Soil Moisture Active-Passive (SMAP) L3 Enhance product, hyper-resolution land surface modeling, radiative transfer modeling, machine learning, and in-situ observations. The dataset was developed over the continental United States at 30-m 6-hourly resolution (2015–2019), and it reports the top 5-cm surface soil moisture in volumetric units (m3/m3).</p> <p>This repository contains the following two versions of the SMAP-HydroBlocks dataset:</p> <ol> <li><strong>SMAP-HB_hru_6h.zip</strong>: SMAP-HydroBlocks data in the Hydrological Response Unit (HRU) space. Storing the data in the HRU space enables the entire 30-m 6-h dataset to be compressed to 33.8 GB. A python script and instructions to post-process and remap the data from the HRU-space into geographic coordinates (latitude, longitude) is provided at <a href="https://github.com/NoemiVergopolan/SMAP-HydroBlocks_postprocessing">GitHub</a>. After post-processed, files are stored in netCDF4 format with a Plate Carrée projection.</li> <li><strong>SMAP-HB_1km_6h.zip</strong>: SMAP-HydroBlocks data at 1-km 6-h resolution. This aggregated version is already post-processed, and thus it is already in geographic coordinates (latitude, longitude), stored in netCDF4 format, with a Plate Carrée projection, and comprising 31.5 GB of data. </li> </ol> <p>Different subsets of the original dataset can be made available on request from Noemi Vergopolan (noemi.v.rocha@gmail.com). Data visualization, updates, and more information is available at <a href="http://waterai.earth/smaphb/">https://waterai.earth/smaphb/</a> </p> <p> </p> <p>Please cite the following paper when using the dataset in any publication:</p> <p>Vergopolan, N., Chaney, N.W., Pan, M. <em>et al.</em> SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US. <em>Sci Data</em> 8<strong>, </strong>264 (2021). <a href="https://doi.org/10.1038/s41597-021-01050-2">https://doi.org/10.1038/s41597-021-01050-2</a></p> <p>Vergopolan, N., Chaney, N. W., Beck, H. E., Pan, M., Sheffield, J., Chan, S., & Wood, E. F. (2020). Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates. Remote Sensing of Environment, 242, 111740. <a href="https://doi.org/10.1016/j.rse.2020.111740">https://doi.org/10.1016/j.rse.2020.111740</a></p> <p> </p> <p>To download all the files via the command line, please try <a href="https://zenodo.org/record/1261813">zenodo_get</a>:</p> <pre><code>pip install zenodo-get zenodo_get 5206725</code></pre>
RELLISUR: A Real Low-Light Image Super-Resolution Dataset
<p>The RELLISUR dataset contains real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. This dataset aims to fill the gap between low-light image enhancement and low-resolution image enhancement (Super-Resolution (SR)) which is currently only being addressed separately in the literature, even though the visibility of real-world images is often limited by both low-light and low-resolution. The dataset contains 12750 paired images of different resolutions and degrees of low-light illumination, to facilitate learning of deep-learning based models that can perform a direct mapping from degraded images with low visibility to high-quality detail rich images of high resolution. The associated paper can be found here: <a title="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf" href="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf" target="_blank" rel="noopener">https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf</a></p>
FIGURE 12 in Wing Tracheation in Chrysopidae and Other Neuropterida (Insecta): A Resolution of the Confusion about Vein Fusion
FIGURE 12. Line drawings of forewings and hind wings of a thorny lacewing and a mantispid lacewing. A. Mucroberotha sp. (Rhachiberothidae). B. Plega sp. (Mantispidae). See figure 2 for explanation of abbreviations.
FIGURE 13 in Wing Tracheation in Chrysopidae and Other Neuropterida (Insecta): A Resolution of the Confusion about Vein Fusion
FIGURE 13. Microphotograph and line drawing of the wing base of Plega sp. (Mantispidae), showing the basal fusion of R and M, ventral view.
FIGURE 9 in Wing Tracheation in Chrysopidae and Other Neuropterida (Insecta): A Resolution of the Confusion about Vein Fusion
FIGURE 9. Line drawings of forewing and hind wing of Oedosmylus sp. (Osmylidae). See figure 2 for explanation of abbreviations.
FIGURE 6 in Wing Tracheation in Chrysopidae and Other Neuropterida (Insecta): A Resolution of the Confusion about Vein Fusion
FIGURE 6. Line drawings of forewings and hind wings of an antlion and owlfly. A. Acheron trux (Walker) (Ascalaphidae). B. Undetermined species of Dendroleontini (Myrmeleontidae). See figure 2 for explanation of abbreviations.
FIGURE 3 in Wing Tracheation in Chrysopidae and Other Neuropterida (Insecta): A Resolution of the Confusion about Vein Fusion
FIGURE 3. Line drawings of forewing and hind wing of Psychopsis insolens McLachlan (Psychopsidae); see figure 2 for explanation of abbreviations.
FIGURE 16 in Wing Tracheation in Chrysopidae and Other Neuropterida (Insecta): A Resolution of the Confusion about Vein Fusion
FIGURE 16. Line drawing Cryptochrysa chloros Freitas and Penny (Chrysopidae: Chrysopinae) A. Apparent wing venation of forewing, including traditional terminology. B. Forewing and hind wing, using revised, current vein terminology. Abbreviations: im, (intramedian cell); dcc, (distal cubital cell); PsM, (pseudomedial); PsC, (pseudocubital); i.g., (inner gradates); o.g., (outer gradates). See figure 2 for explanation of other abbreviations.
FIGURE 15 in Wing Tracheation in Chrysopidae and Other Neuropterida (Insecta): A Resolution of the Confusion about Vein Fusion
FIGURE 15. Line drawings of forewings and hind wings of green lacewings (Chrysopidae). A. Hypochrysa elegans (Burmeister) (Nothochrysinae). B. Apochrysa lutea (Walker) (Apochrysinae), with a detail of fusion in PsM (pseudomedial) and PsC (pseudocubital) (inset). See figure 2 for explanation of other abbreviations.
FIGURE 2 in Wing Tracheation in Chrysopidae and Other Neuropterida (Insecta): A Resolution of the Confusion about Vein Fusion
FIGURE 2. Line drawings of forewings and hind wings of Megaloptera and Raphidioptera. A. Protochauliodes aridus Maddux (Megaloptera), longitudinal veins: Sc (subcosta; dark blue), RA (radius anterior; orange), RP (radius posterior; green), MA (media anterior; purple), MP (media posterior; yellow), CuA (cubitus anterior; light blue), CuP (cubitus anterior; red), A (anal; brown); crossveins: 1rp-ma (first crossvein between RP and MA), 1cua-cup (first crossvein between CuA and CuP); cells: rarp1 (first interradial cell); mamp1 (first intermedial cell), mcu2 (second medial cell). B. Agulla adnixa (Hagen) (Raphidioptera).
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.