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163 results for “tensor”
DFT calculated dielectric tensor Sb2S3
<p>Dielectric tensor of Sb2S3.</p> <p>Files generated for imaginary and real parts.</p> <p>Structure of the file: eV // dielectric function</p> <p>Details on the calculations are available at https://doi.org/10.1016/j.isci.2022.104377</p>
Estimation of best-fitting force, moment tensor, and depth for the 2022 Hunga-Tonga submarine volcanic eruption: Misfit plots in model parameter space
<p>Supporting documents for the correction of the publication <em>Multi‐Event Explosive Seismic Source for the 2022 Mw 6.3 Hunga Tonga Submarine Volcanic Eruption, </em>published in The Seismic Record (<a href="https://doi.org/10.1785/0320220027">https://doi.org/10.1785/0320220027</a>).</p>
Zenodo Collection: Estimation of best-fitting force, moment tensor, and depth for the 2022 Hunga-Tonga submarine volcanic eruption
<p>This Zenodo collection contains figures and results from grid searches used to estimate point source parameters (force or moment tensor) for the main seismic subevent of the 2022 Hunga-Tonga submarine volcanic eruption. The collection includes misfit maps and waveform fits for the best-fitting force or moment tensor, as well as MTUQ weight files and a zipped version of the MTUQ code. These results were obtained using various software tools, including MTUQ, Axisem, Instaseis, Syngine, Obspy, GMT, and PyGMT. The collection was prepared for a manuscript in review for Geophysical Journal International.</p>
Painting a more complete picture of white matter microstructure with myelin water and tensor valued diffusion
<p>Data supporting the publication <strong>Painting a more complete picture of white matter microstructure with myelin water and tensor valued diffusion. </strong></p> <p>V1.0: Includes atlases of MRI measures described in the paper, tracts generated specifically for the study, and a sample subject's data.</p> <p>Processing code available at: https://github.com/sharadab/microstructure_processing_paper </p>
MRI Diffusion Tensor Tractography to Monitor Peripheral Nerve Recovery After Severe Crush or Cut/Repair Nerve Injury
ClinicalTrials.gov study NCT02960516. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Diffractive tensorized unit for million-TOPS general-purpose computing
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Direct measurement of the quantum metric tensor in solids
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Hypermultiplexed integrated-photonics-based optical tensor processor
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Silicon-29 NMR Experimental Datasets used in Statistical Learning of NMR tensors from 2D Isotropic/Anisotropic Correlation Nuclear Magnetic Resonance Spectra
<p>Processed silicon-29 Magic-Angle Flipping and Magic-Angle Turning Nuclear Magnetic Resonance spectra used as input to the smooth-LASSO linear inversion algorithm along with their corresponding NMR tensor parameter distributions as described in the paper "Statistical Learning of NMR tensors from 2D Isotropic/Anisotropic Correlation Nuclear Magnetic Resonance Spectra", by Srivastava and Grandinetti. </p> <p>Files with names containing "MAF" or "MAT" are the corresponding experimental Si-29 NMR MAF or MAT dataset on the composition given in the filename. Files with names containing "inverse" are the corresponding NMR tensor parameter distributions obtained from the inversion of the corresponding experimental MAF and MAT dataset with the composition given in the filename. </p> <p><br> </p> <p>Details of the csdf dataset format are given in <a href="https://doi.org/10.1371/journal.pone.0225953"><em>PLOS ONE,</em> 15(1): e0225953 (2020)</a>, "Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data," D. Srivastava, T. Vosegaard, D. Massiot, and P.J. Grandinetti. The data within csdf files can be accessed with the Python package <a href="https://csdmpy.readthedocs.io/en/stable">csdmpy</a>, or other CSDM-compliant software.</p> <p> </p>
Learning topological states from randomized measurements using variational tensor network tomography
<p>Dataset for paper <strong>Learning topological states from randomized measurements using variational tensor network tomography.</strong><br>The numerical code can be found at the repo: https://github.com/teng10/tn-shadow-qst</p>
Datasets for Muscle : semi-non negative joint decomposition of multiple single cell tensors
<p>This link contains the processed single cell Hi-C (scHi-C) and DNA methylation datasets of <a href="https://www.nature.com/articles/s41592-019-0502-z">Li et al. 2019</a> and <a href="https://www.nature.com/articles/s41586-020-03182-8">Liu et al. 2021. </a>in two separate folders. The datasets are used for the <a href="https://github.com/kp223/Muscle">Github page</a> of <a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=gUgGLY0AAAAJ&citation_for_view=gUgGLY0AAAAJ:u5HHmVD_uO8C">Muscle</a>, which employs a semi-non negative joint tensor decomposition framework for multi-omics data analysis, incorporating scHi-C tensors. The datasets are uploaded in '.qs' format in R (gzipped for the Liu et al. 2021 data), and the chromosome size files are in text format. Further details about the data can be found on the GitHub page. The files </p>
Global Marine Gravity Gradient Tensor Inverted from Altimetry-derived Deflections of the Vertical: CUGB2023GRAD
<p>CUGB2023GRAD is a dataset consisting of all six components of Earth's gravity gradient tensor over the oceans. The gravity gradient tensor is inverted from 1 arc-minute grid of altimetry-derived north-south and east-west components of deflection of the vertical. CUGB2023GRAD has a longitudinal extent of 180° W ~180° E and a latitudinal extent of 80° S ~ 80° N.</p> <p>This version used a merge of deflections of the vertical (north_32.1.nc and east_32.1.nc) developed by Scripps Institution of Oceanography, and DTU21GRA-derived deflections of the vertical. DTU21GRA is a highly accurate gravity anomaly model developed by Technical University of Denmark. Both sets of deflections of the vertical were developed from multiple satellite altimetry observations which include: Jason-1, Jason-2, Cryosat-2, SARAL/AltiKa, and Sentinel-3A/B.</p>
Simulation of a realistic brain phantom for Susceptibility Tensor Imaging
<p>The susceptibility tensor brain phantom is an in-silico brain phantom that serves as ground-truth for new and existing Susceptibility Tensor Imaging algorithms. It contains two different brain phantoms with diffusion (chi_dti.nii.gz) and susceptibility (chi_sti.nii.gz) information each one respectively. Additionally, we upload 2 different local phase simulations for each case:</p> <ul> <li>Angles 1 and Angles 2: Simulations of Gradient Echo acquisitions at two different ranges of rotation angles.</li> <li>phi_6_orientations.nii.gz: Local phase of the 6 different acquisitions</li> <li>phi_12_orientations.nii.gz: Local phase of the 12 different acquisitions</li> <li>angles_6.mat; Angles of the simulations at 6 different acquisitions</li> <li>angles_12.mat: Angles of the simulations at 12 different acquisitions </li> </ul>
Data Sets for "The tensor t-function: a definition for functions of third-order tensors"
<p>MATLAB data sets used for numerical tests in</p> <p>K. Lund, The tensor t-function: a definition for functions of third-order tensors, Numerical Linear Algebra with Applications, 27 (3), e2288, 2020. <a href="https://doi.org/10.1002/nla.2288">https://doi.org/10.1002/nla.2288</a></p> <p>The data and associated code were originally published on GitLab (<a href="https://gitlab.com/katlund/bfomfom-main">https://gitlab.com/katlund/bfomfom-main</a>), ca. 2019. The code (drivers, test scripts, etc.) can still be found in the bfomfom repository.</p> <p> </p>
Dataset and code for : "Representing Vector Geographic Information As a Tensor for Deep Learning Based Map Generalisation"
<p>Dataset and code supporting the experiment about map generation in the article: "Representing Vector Geographic Information As a Tensor for Deep Learning-Based Map Generalisation"</p>
Open-source release of tensor-network software
<p>This is a package<sup><a href="https://github.com/aspects-quantum/TFlucn_tedopa#user-content-fn-SBmodel-85d3b1b42ae4f41239f8975ec68008b2">1</a></sup> for calculating FLUCTUATIONS of heat transfer in the Spin-Boson model<sup><a href="https://github.com/aspects-quantum/TFlucn_tedopa#user-content-fn-PRX2020-85d3b1b42ae4f41239f8975ec68008b2">2</a></sup> using the <strong>Time Evolving Density matrices using Orthogonal Polynomial Algorithm (<em>TEDOPA</em>)</strong><sup><a href="https://github.com/aspects-quantum/TFlucn_tedopa#user-content-fn-Prior2010-85d3b1b42ae4f41239f8975ec68008b2">3</a></sup><sup><a href="https://github.com/aspects-quantum/TFlucn_tedopa#user-content-fn-Chin2010-85d3b1b42ae4f41239f8975ec68008b2">4</a></sup>.</p> <p>We employ the <strong>Thermofield-based chain-mapping approach for open quantum systems</strong><sup><a href="https://github.com/aspects-quantum/TFlucn_tedopa#user-content-fn-PRA2015-85d3b1b42ae4f41239f8975ec68008b2">5</a></sup> that enables us to use a vacuum initial matrix product state (pure) for the environment instead of a thermal state (mixed), thereby speeding up the computation greatly.</p> <p>In this package, we use the ITensor library<sup><a href="https://github.com/aspects-quantum/TFlucn_tedopa#user-content-fn-Itensor-85d3b1b42ae4f41239f8975ec68008b2">6</a></sup> in Julia for tensor network manipulations. </p> <p>This package uses <strong>julia = "1.8.2"</strong> version.</p>
Fig. 2. A in Microstructural Impact of Ischemia and Bone Marrow-Derived Cell Therapy Revealed With Diffusion Tensor Magnetic Resonance Imaging Tractography of the Heart In Vivo
Fig. 2. A picture of four male - female pairs of Amblyomma variegatum attached on a cow examined during this survey. © Anne Laudisoit.
Fig. 1 in Microstructural Impact of Ischemia and Bone Marrow-Derived Cell Therapy Revealed With Diffusion Tensor Magnetic Resonance Imaging Tractography of the Heart In Vivo
Fig. 1. Map of Kisangani and Major transhumance routes from probable departure location to Kisangani city, DR Congo.
Data and code for "Tensor product random matrix theory"
<p>The data files and python scripts to generate Figure 3 of the manuscript "Tensor product random matrix theory" are uploaded.</p> <ul> <li>In the 'scripts' folder there are two python files. The file 'SFF.py' was used to generate the dataset. The file 'plot.py' generates Figure 3.</li> <li>In the 'processed data' folder there are .csv files with the data used to generate Figure 3.</li> <li>In the 'figures' folder the generated figure is included.</li> <li>In the 'additional plots' folder, three data sets and corresponding plots not incorporated in the paper are included.</li> </ul>
Adaptive Rational Interpolation and Higher-order SVD for Low-rank Tensor Approximation in Structural Dynamics Simulations
<h1>Vibroacoustic Data Compression and Interpolation</h1> <p>This is the code that accompanies the paper</p> <blockquote> <p>Adaptive Rational Interpolation and Higher-order SVD for Low-rank Tensor Approximation in Structural Dynamics Simulations</p> </blockquote> <p>which we (Jan Heiland, Victor Gosea, Ulrich Römer, Davide Pradovera, Harikrishnan Sreekumar, Sabine Langer) submitted for presentation at the ECC-2025.</p> <p>See the <code>README.md</code> for information and instruction to reproduce the results of the paper.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.