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3 results for “semi-empirical methods”
Semi-empirical methods SPT inputs for bearing capacity prediction
<p>These datasets presents inputs for bearing capacity prediction methods. These methods are four well-known semi-empirical models for predicting bearing capacity of piles. The data was collected from the works of Lobo (2005), Vianna (2000) and Jr. (1988) and includes 168 load tests and SPT measures taken from severam Brazilian regions. . The file Data.csv is composed only with the numeric values used in each method and the file Data_with_soils.csv includes the soil types for the piles.</p> <p>The suffix 'Dq', 'Mey', 'Av' and 'Tx' represents which method this input was obtained from, corresponding respectively to Decourt and Quaresma (1978), Meyerhof (1983), Aoki and Velloso (1975) and Teixeira (1996).</p> <p>The columns indexes represents:</p> <p>N_pile - Pile number (for reference);<br> SPT_L - SPT result for the pile lenght;<br> SPT_P - SPT result for the pile tip<br> Soil_L - Predominant soil type along the pile lenght;<br> Soil_P - Predominant soil type in the pile tip;<br> L - pile lenght;<br> D - pile diameter;<br> Qu - Pile bearing capacity, obtained through NBR 6122 load test.<br> <br> When using this dataset, please cite the following paper:</p> <p>//<a href="http://soilsandrocks.com/sr-2021-074921">soilsandrocks.com/sr-2021-074921</a></p> <p>DOI: 10.28927/SR.2021.074921</p>
Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]
<p>Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.</p>
Bearing capacity semi-empirical methods inputs for optimization
<p>These datasets presents inputs for bearing capacity prediction methods. These methods are four well-known semi-empirical models for predicting bearing capacity of piles. The data was collected from the works of Lobo (2005), Vianna (2000) and Jr. (1988) and includes load tests and SPT measures taken from severam Brazilian regions. </p> <p>The suffix 'DQ' and 'MH', 'Av' and 'Tx' represents which method this input was obtained from, corresponding respectively to Decourt and Quaresma (1978) and Meyerhof (1983).</p> <p>The columns indexes represents:</p> <p>N_pile - Pile number (for reference);<br> N_l - SPT result for the pile lenght;<br> N_p - SPT result for the pile tip<br> L - pile lenght;<br> D - pile diameter;<br> U - pile perimeter;<br> A - pile section area<br> Qu - Pile bearing capacity, obtained through NBR 6122 load test.<br> <br> When using this dataset, please cite the following paper:</p> <p>//<a href="http://soilsandrocks.com/sr-2021-074921">soilsandrocks.com/sr-2021-074921</a></p> <p>DOI: 10.28927/SR.2021.074921</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)
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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.