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135 results for “Water content”
Monitoring water content variations from seismic noise in a controlled laboratory experiment: PART 2 [Dataset]
<p>This dataset has been obtained with an original laboratory experiment aimed at assessing the sensitivity of passive seismic interferometry imaging (PII) to controlled fluctuations in water content. Multiple controlled cycles of water imbibition and draining at the base of the sandbox produce significant variations in the seismic wavefield and especially in dominant surface waves. PART 2: seismic measurements from 1501 to 2700minutes</p>
Monitoring water content variations from seismic noise in a controlled laboratory experiment: PART 1 [Dataset]
<p>This dataset has been obtained with an original laboratory experiment aimed at assessing the sensitivity of passive seismic interferometry imaging (PII) to controlled fluctuations in water content. Multiple controlled cycles of water imbibition and draining at the base of the sandbox produce significant variations in the seismic wavefield and especially in dominant surface waves. PART 1: seismic measurements from 0001 to 1500minutes</p>
Liquid water content and vertical velocity from RICO-based LES simulations
<p>The RICO-based simulations by implementing the LES module of WRF model 4.0 generate the raw data. The simulation continued for 40 hours with a time step of one second. The domain size is 12.8×12.8×4 km3, with a resolution of 100 m and 40 m in the horizontal and vertical orientation, respectively. The outputs during 8~40 hour were retained every 10 minutes. Two parameters (LWC and vertical velocity) were then extracted by using MATLAB to create the following dataset. The "q" and "w" in the filename represent LWC and vertical velocity, respectively, and the "a"~"e" in the filename represents the corresponding minute in each hour (e.g. "q24a" means LWC value at 24h10m). The unit of "q" and "w" are "kg/kg" and "m/s' respectively. </p>
Effects of FODMAPs on Small Bowel Water Content: an MRI Study
ClinicalTrials.gov study NCT01459406. IPD Sharing: Not stated. Countries: 1. Publications: 10.
A Study of the Effect of Bran, Psyllium and Nopal on Intestinal Water Content Using Magnetic Resonance Imaging
ClinicalTrials.gov study NCT03263065. IPD Sharing: NO. Countries: 1. Publications: 1.
Effects of Bolus and Continuous Nasogastric Feeding on Small Bowel Water Content and Blood Flow
ClinicalTrials.gov study NCT01557673. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Data from: Investigation of nitrogen and phosphorus contents in water in the tributaries of Danjiangkou Reservoir
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Data from: Identifying conservation priorities for gorgonian forests in Italian coastal waters with multiple methods including citizen science and social media content analysis
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Irrigation well water in Nebraska: essential nutrient contents and other properties
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Data from: Estimating field capacity from volumetric soil water content time series using automated processing algorithms
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Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks
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Data from: Contrasting water, dry matter and air contents distinguish orthophylls, sclerophylls and succophylls (leaf succulents)
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Soil water content determined at saturation and water holding capacity and calculated at 50% water filled pore space
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Dataset for: Physics-informed neural networks with monotonicity constraints for Richardson-Richards equation: Estimation of constitutive relationships and soil water flux density from volumetric water content measurements by Toshiyuki Bandai and Teamrat A. Ghezzehei
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Development and uncertainty assessment of pedotransfer functions for predicting water contents at specific pressure heads
<p>There has been much effort to improve the performance of pedotransfer functions (PTFs) using intelligent algorithms, but the issue of covariate shift, i.e. different probability distributions in training and testing datasets, and its impact on prediction uncertainty of PTFs has been rarely addressed. The common practice in PTF generation is to randomly separate the dataset in training and testing subsets, and outcomes of this random selection may be different if the process is subject to covariate shift. We evaluated the impact of covariate shift generated by data shuffling and detected by Kolmogorov-Smirnov test for prediction of water contents using soil databases from Denmark and Brazil. The soil water contents at different pressure heads were predicted by developing linear and stepwise regression besides machine learning based PTFs including Gaussian regression process and ensemble method. Regression based PTFs for the Brazilian dataset resulted in better predictions compared to machine learning methods that estimated high water contents in Danish soils more accurately. One hundred PTFs were developed for water content at specific pressure heads by data shuffling generating covariate shift. From these, a hundred sets of fitted van Genuchten parameters were obtained representing the generated uncertainty. Data shuffling led to covariate shift, resulting in uncertainty in water content prediction by the PTFs. Inherent variability of data may lead to increased prediction uncertainty. For correlated data, simple regression models performed as good as sophisticated machine learning methods. Using PTF-predicted water contents for van Genuchten retention parameter fitting may lead to a high uncertainty.</p>
data for "A novel three-phase interface premelting theory for determining unfrozen water content in unsaturated soil"
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Experimental moisture content and water saturation and modeled output NAPL-phase saturation
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Assessing the Effect of Lettuce on Intestinal Water Content Through Magnetic Resonance Imaging of the Small Bowel
ClinicalTrials.gov study NCT02939716. IPD Sharing: NO. Countries: 1. Publications: 0.
Soil Water Content at MMWD
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Development and uncertainty assessment of pedotransfer functions for predicting water contents at specific pressure heads
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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.