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9 results for “VOD”
GLAB-VOD: Global L-band AI-Based Vegetation Optical Depth Dataset Based on Machine Learning and Remote Sensing
<p>GLAB VOD is a Global L-band Ai-Based vegetation optical depth dataset with 18-day temporal and 25 km spatial resolution, covering 2002 to 2020. The dataset is created using a neural network with SMOS-SMAP-INRAE-BORDEAUX (SMOSMAP-IB) VOD product as a target (over 2015-2020) and brightness temperatures (TB) from the SMOS, AMSR-E, and AMSR-2 spaceborne missions alongside with a novel soil moisture dataset (CASM) as inputs. The GLAB-VOD dataset was created using a recently developed methodology previously used to create a long-term consistent soil moisture dataset CASM, adapted to the VOD retrievals. First, the TB and VOD signals were divided into fixed seasonal cycle and residuals, where the residual part of the signal contains sub-seasonal periodic signals, trends, extremes, and noise. Then, a multi-staged neural network training scheme was used to achieve internally consistent predictions by merging data from different sources without introducing biases or compromising data distribution. A side-product of this project is GLAB TB - a global long-term brightness temperature dataset that matches SMOS TB quality and spawns back to 2002. GLAB TB has daily temporal resolution and 25 km spatial resolution. </p>
Dataset: Vodafone Group Public Limited Company (VOD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
A global daily seamless 9-km Vegetation Optical Depth (VOD) product from 2010 to 2021
<p> </p> <p><strong>(I) DESCRIPTION</strong>:</p> <p>· A <strong>global daily seamless 9-km Vegetation Optical Depth (VOD)</strong> product is generated through gap-filling and spatiotemporal fusion model. This daily products start <strong>from Jan 01, 2010 to Jul 31, 2021 </strong>(about 20GB memory after uncompressing all zip files).</p> <p>· To further validate the effectiveness of these products, three verification ways are employed as follow: 1) Time series validation; 2) Simulated missing-region validation; And 3) Data comparison validation.</p> <p>· It is important to note that the original data contain missing dates, and these corresponding gaps are also present in our dataset.</p> <p><strong>(II) DATA FORMATTING AND FILE NAMES </strong></p> <p>For the convenience of our readers, we have two formats of data available for download.</p> <p><strong>1) MAT file (Version v1)</strong></p> <p>Data from 2010 to 2021 are stored separately into folders for the corresponding years, with each folder containing daily `.mat` files. The naming convention for the data is “YYYYXXZZ,” where YYYY is the 4-digit year, XX is the 2-digit month, and ZZ is the 2-digit date. The geographic scope is global and the grid size is 4000*2000.</p> <p>MATFILES (.mat): The folders with matfiles contain individual files for:</p> <p>1. Vegetation Optical Depth: VOD_seamless_9km_ YYYYXXZZ.mat</p> <p>2. Latitude/Longitude: VOD_9km_Coordinates.mat</p> <p><strong>2) NetCDF file (Version v2)</strong></p> <p>The year-by-year daily data from 2010 to 2021 are stored in the ‘.nc’ files for the corresponding years. The daily data within each year into one NetCDF file. <span>The variable names are named as VOD_xxxxyydd, where xxxx represents the year, yy represents the month, and dd represents the day. The longitude variable is named “lon” with a dimension of 4000×1, and the latitude variable is named “lat” with a dimension of 2000×1. </span></p> <p><span>It should be noted that these NetCDF files are saved using the netCDF4 library in Python, with the dimension order being (lat, lon). When reading these NetCDF files in MATLAB, the default data dimension order is (lon, lat). Therefore, it is necessary to transpose the variables to match the correct dimension order.</span></p>
8-day Vegetation optical depth (VOD) and normalised difference vegetation index (NDVI)- based estimated degree of curing (DOC) for Australia
<p>This is a gridded degree of curing (DOC) dataset over Australia based on vegetation optical depth (VOD) and normalised difference vegetation index (NDVI) that can reasonably reproduce groundbased observations in space and time.</p> <p>The gridded DOC data is produced via estimation models using the VOD dataset from AMSR-E (0.1 degree; 8-day) and NDVI dataset from MODIS Terra MOD09A1 (0.005 degree; 8-day). The estimation models are derived from the calibration and evaluation of VOD and NDVI datset with field observed DOC over Australia. Matlab was used for the calibration and evaluation of these models.</p> <p>There are 2 variations based on the following estimation models: DOC_M1 = 145.57-260.82(NDVI)+137.19(VOD)(NDVI) DOC_M2 = 48.70+147.60(VOD)-259.95(VOD)(NDVI) The domain covered is Australia with a 0.05 degree spatial resolution. Temporal resolution is 8-day composites from 04/07/2002 to 26/06/2011 .</p> <p>These experiments were executed by Waisin Chaivaranont of the ARC Centre of Excellence for Climate System Science (ARCCSS) research program "The role of land surface forcing and feedbacks for regional climate".</p>
European VOD Registry
ClinicalTrials.gov study NCT03032016. IPD Sharing: NO. Countries: 4. Publications: 1.
Pharmacogenetic Study of Antimitotic Therapies Involved in Hepatic VOD in Children With Nephroblastoma or ALL
ClinicalTrials.gov study NCT04168788. IPD Sharing: NO. Countries: 1. Publications: 0.
Pharmacokinetic Study of Lipo-PGE1 for Prevention of VOD After HSCT
ClinicalTrials.gov study NCT02338440. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Pharmacogenetics of VOD in Children With HSCT
ClinicalTrials.gov study NCT03664427. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Accuracy of Ultrasound Exam, Including Doppler and Elastography, in the Diagnosis of VOD, Following Bone Marrow Transplantation
ClinicalTrials.gov study NCT04660266. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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