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9
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ShareScore release 0.9.0
Dataset results
9 results for “multi-physics”
Processed data and models in support of manuscript "Deciphering the state of the lower crust and upper mantle with multi-physics inversion"
<p>Data and model files in original format used in the manuscript "Deciphering the state of the lower crust and upper mantle with multi-physics inversion". These files are accompanied by a set of python scripts to reproduce several of the figures in the Manuscript. Please refer to the Manuscript and the included files for further information on data origin and how to use the scripts. A link will be added upon acceptance.</p>
Multi-physic analysis of power electronic control parameters in a simulation framework
<p>Dataset for conference paper "Multi-physic analysis of power electronic control parameters in a simulation framework"</p>
BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
<p>In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge. Existing experimental datasets are often restricted, with limited availability and sparse ground truth data, impeding our understanding of this complex multi-physics phenomena. To bridge this gap, we present the <a href="https://github.com/HPCForge/BubbleML">BubbleML</a> Dataset which leverages physics-driven simulations to provide accurate ground truth information for various boiling scenarios, encompassing nucleate pool boiling, flow boiling, and sub-cooled boiling. This extensive dataset covers a wide range of parameters, including varying gravity conditions, flow rates, sub-cooling levels, and wall superheat, comprising 79 simulations. BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for ML research. Furthermore, we showcase its potential to facilitate exploration of diverse downstream tasks by introducing two benchmarks: (a) optical flow analysis to capture bubble dynamics, and (b) operator networks for learning temperature dynamics. The BubbleML dataset and its benchmarks serve as a catalyst for advancements in ML-driven research on multi-physics phase change phenomena, enabling the development and comparison of state-of-the-art techniques and models.</p>
Subsurface Multi-Physical Monitoring of a Reservoir Landslide with the Fiber-Optic Nerve System
<p>All data used in the study to support this research is available on repository via 10.5281/zenodo.6541529</p>
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
<p>This dataset accompanies the GMD article 'A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0' (https://doi.org/10.5194/egusphere-2024-96).</p> <ul> <li>The input files to run the presented cases using uDALES are contained in 'inputs'.</li> <li>The model outputs are contained in separate folders: 'XCC', 'indoor-outdoor', 'XCB', and 'SEB'. When downloaded, move into a folder called 'outputs' so that the paths defined in the scripts work as intended (see below).</li> <li>The Matlab scripts to plot the figures are contained in 'scripts'.</li> <li>The figures shown in the article are contained in 'figures'.</li> </ul>
Investigating multi-physical process and deformation mechanism of reservoir landslide using integrated multi-source monitoring
<p>Data to support this study are available.</p>
The hydrological fluxes of the Upper Brahmaputra River Basin constrained by a multi-physics ensemble (MPE) modeling approach
<p>The data represent monthly hydrological fluxes for four sub-basins within the Upper Brahmaputra River Basin, where yyyy is the year, mm is the month, MPE is the multi-physics ensemble, P is the precipitation, R is the runoff, and ET is the evapotranspiration. The unit is mm. The upper-bounds and lower-bounds represent the upper and lower bounds of the hydrological fluxes constrained by the MPE, respectively.</p> <p> </p> <p>Reference:<br>Lei, X, P. Lin*, H. Zheng, K. Yang, W. Liu, C. Miao, K. Wang, J. Wang: A multi-physics ensemble modeling approach to constraining the uncertainty of hydrological fluxes in sparsely-gauged river basins. Geophysical Research Letters, (submitted), 2024.<br>Contact:<br>xiangyonglei@stu.pku.edu.cn; peironglinlin@pku.edu.cn</p>
Multi-physics Modeling the Physiology of a Patient in Critical Condition
ClinicalTrials.gov study NCT02905084. IPD Sharing: NO. Countries: 1. Publications: 0.
Development of multi-physics data inversion software with optimization via artificial intelligence
<p><strong>The project proposes the development of an innovative inversion technology for the characterization and monitoring of deep water reservoirs using CSEM (Controlled-Source Electromagnetic Methods), a robust risk reduction tool in the drilling of oil basins, using multiphysics data in the 3D domain. One of the main objectives of this project is to develop, optimize and parallelize CSEM codes, aiming at improving their performance. The construction of inversion algorithms will also be performed, such as Deep Neural Networks (DNNs) trained to transform CSEM data into models.</strong></p>
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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.