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2,113 results for “High resolution”
GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets for China from 1901 to 2020 generated by machine learning
<p>The high spatial resolution and century-long Standardized Precipitation Evapotranspiration Index (SPEI) dataset with a spatial resolution of 0.0083 degrees (~1 km) was spatially downscaled from the global SPEI data with a 0.5 degrees spatial resolution (https://spei.csic.es/database.html) based on machine learning integrated with high spatial resolution climatic and topographic variables. The 1-km SPEI datasets are across the land areas of China from January 1901 to December 2020, including 1-month, 3-month, 6-month and 12-month SPEIs. The unit of the data is 0.01. The dataset was evaluated using the root zone soil moisture and the historical drought events, and the evaluation indicated that the high spatial resolution SPEI dataset is reliable.</p> <p>Data Information: </p> <p>GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets over China from 1901 to 2020 generated by machine learning</p> <p>Publication: </p> <p><span>He, Q., Wang, M., Liu, K., & Wang, B. (2025). High-resolution Standardized Precipitation Evapotranspiration Index (SPEI) reveals trends in drought and vegetation water availability in China. <em>Geography and Sustainability</em>, <em>6</em>(2), 100228. https://doi.org/10.1016/j.geosus.2024.08.007</span></p> <p></p> <p>----------------------------------------------------data description---------------------------------------------</p> <p>This is a gridded dataset for the Standardized Precipitation Evapotranspiration Index (SPEI) at a spatial resolution of 1 km over the main terrestrial lands of China for each month during 1901-2020, which is generated using the Gaussian process regression (GPR) based on the Global SPEI database (https://spei.csic.es/database.html) integrated with high spatial resolution climatic and topographic variables. Four timescales of SPEI were generated: 1-month (SPEI-1), 3-month (SPEI-3), 6-month (SPEI-6) and 12-month (SPEI-12). The details are as follows:</p> <p>Region: China</p> <p>Temporal Extent: January 1901 to December 2020</p> <p>Spatial resolution: 0.0083° (~1 km)</p> <p>Temporal resolution: month</p> <p>Timescales: 1-month, 3-month, 6-month and 12-month</p> <p>Data format: GeoTIFF</p> <p>Unit: unitless (0.01)</p> <p>Geographic coordinate system: WGS 1984</p> <p>---------------------------------------------------dataset filename---------------------------------------------</p> <p>The file name specifically shows the data information.</p> <p>For example,</p> <p>“SPEI_1_2020_1.tif” means “1-month SPEI of January 2020”.</p> <p>“SPEI_3_2020_1.tif” means “3-month SPEI of January 2020”.</p> <p>All the file names are formatted in “SPEI_timescale_year_month”</p> <p>timescale: 1, 3, 6 and 12 indicate 1-month, 3-month, 6-month and 12-month, respectively</p> <p>year: from 1901 to 2020</p> <p>month: from 1 to 12</p> <p>--------------------------------------------------storage information-------------------------------------------</p> <p>The high-resolution SPEI dataset is stored in TIFF format using WGS 1984 coordinate system. The data type is int16 with a scale factor of 0.01. The nodata value is -32768. The dataset requires multiplication by 0.01 during application to obtain the actual value ranges.</p> <p>The data were compressed into .rar format every 10 years for each timescale SPEI.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 8
<p>Future projections of precipitation by the BM10 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 5
<p>Future projections of precipitation by the BM1 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 3
<p>Future projections of 2-meter minimum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 2
<p>Future projections of 2-meter maximum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 11
<p>Future projections of 2-meter maximum, mean and minimum temperatures by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 1
<p>Input data (ERA5 and the seven GCMs) used to train the CNN models (BMlinear, BM1, BM10 and BMdense) used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 9
<p>Historical projections of all predictands (2-meter maximum, mean and minimum temperatures, and precipitation) by all CNN models (BMlinear, BM1, BM10, BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia". Each CNN model architecture is available in the file "model.json" and its optimized weights for each case are available in the file "model_weights.h5".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 7
<p>Future projections of precipitation by the BMdense model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
Data from: High-resolution crossover maps for each bivalent of Zea mays using recombination nodules
<p>Recombination nodules (RNs) mark sites of crossing over along pachytene synaptonemal complexes (SCs). Thus, RNs provide the highest resolution cytological marker currently available for defining the frequency and distribution of crossovers along the length of chromosomes because they are observed by electron microscopy. Using the maize inbred line KYS, we have prepared an SC karyotype in which each SC was identified by relative length and arm ratio and related to the proper linkage group using inversion heterozygotes. We mapped 4272 RNs on 2080 identified SCs to produce high-resolution maps of RN frequency and distribution on each bivalent. Average RN frequency per bivalent is closely correlated with SC length. The total length of the RN map is about two-fold shorter than most linkage maps, but there is good correspondence between the relative lengths of the different maps when individual bivalents are considered. Each bivalent has a unique distribution of crossing over, but all bivalents share a high frequency of distal RNs and a severe reduction of RNs at and near kinetochores. The frequency of RNs at knobs is either similar to or higher than the average frequency of RNs along the SCs. These RN maps represent an independent measure of crossing over along maize bivalents.</p>
GRACE High-Resolution Trend Mascons - Greenland Ice Sheet (2007-2015)
<p>High-resolution mascon trend solution, computed for the Greenland Ice Sheet over the time period from January 2007 and January 2015, where each mascon regression model (including the trend) has been directly estimated from the Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The GAD product has not been restored, meaning the ocean mascons are consistent with the Level 2 GSM product information.</p><p>Description of columns in the dataset:</p><ol><li>Latitude center (deg)</li><li>Longitude center (deg)</li><li>Mass change trend (cm w.e. / yr)</li><li>Mass change trend uncertainty (cm w.e. / yr)</li><li>Latitude minimum (deg)</li><li>Latitude maximum (deg)</li><li>Longitude minimum (deg)</li><li>Longitude maximum (deg)</li><li>Area of mascon (sq. km)</li><li>Label of mascon</li></ol><p>When citing this dataset, please also include this citation:</p><p>Loomis, B. D., D. Felikson, T. J. Sabaka, and B. Medley (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses. <i> Journal of Geophysical Research: Solid Earth, </i><a href="https://doi.org/10.1029/2021JB023024">https://doi.org/10.1029/2021JB023024</a></p>
High-spatial-resolution monthly precipitation dataset over China during 1901–2017
<p>The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly precipitation from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China.</p> <p>Another data download site is Loess plateau Scientific Data Center (http://loess.geodata.cn/). This is a Chinese website. This website publishes the updated histrorical dataset and future downscaled monthly precipitation under multiple SSP Scenarios and GCMs, with 1 km spatial resolution.</p> <p>/*************/ The dataset is updated yearly. Now, the period of the dataset is from 1901.1 to 2020.12.</p> <p>/*************/ The future 1km dataset from 2021-2100 is published.</p> <p><br> The data provider recommended the below publication as the reference.<br> Peng Shouzhang, Ding Yongxia, Liu Wenzhao, Li Zhi. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 2019, 11, 1931–1946, https://doi.org/10.5194/essd-11-1931-2019.</p>
Ultra-high-resolution CT vs. Conventional Angiography for Detecting Coronary Heart Disease
ClinicalTrials.gov study NCT04272060. IPD Sharing: NO. Countries: 1. Publications: 7.
18F-AV-1451 High Resolution Autopsy Study
ClinicalTrials.gov study NCT02350634. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Single Arm Trial With Resolute Onyx in ONE-Month DAPT for High-Bleeding Risk Patients Who Are Considered One-Month Clear (Onyx ONE Clear)
ClinicalTrials.gov study NCT03647475. IPD Sharing: NO. Countries: 1. Publications: 3.
Multi-Center Trial of High-resolution Transrectal Ultrasound Versus Standard Low-resolution Transrectal Ultrasound for the Identification of Clinically Significant Prostate Cancer
ClinicalTrials.gov study NCT02079025. IPD Sharing: Not stated. Countries: 2. Publications: 1.
High Resolution, 18F-PSMA PET-MRI Before Prostate Cancer HIFU or Radical Prostatectomy
ClinicalTrials.gov study NCT04461509. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
High-resolution, Relational, Resonance-based, Electroencephalic Mirroring (HIRREM) to Relieve Insomnia
ClinicalTrials.gov study NCT01971567. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Investigation of Hemodynamics and Radiomics Based on High Resolution Magnetic Resonance Imaging for Predicting the Outcomes of Intracranial Dissecting Aneurysm
ClinicalTrials.gov study NCT07335029. IPD Sharing: NO. Countries: 1. Publications: 2.
The Effectiveness of High Resolution Microendoscopy for People Living With HIV
ClinicalTrials.gov study NCT04563754. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.