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44 results for “isotropic”
An isotropic EPI database for rat brain resting-state fMRI
Open the record for dataset details and reuse information.
Ultrasonic guided-waves in quasi-isotropic and cross-ply composite beams using a finite element model
<p>Ultrasonic guided-wave data taken from numerical evaluations of a finite element model (FEM) considering two composite beam structures and applying three noise levels in signal-to-noise ratio (SNR):</p> <ul> <li>Quasi-isotropic carbon fiber composite beam (dimensions: 1m x 4mm x 2mm) with a [+45/-45/90/0]_s layup and a transverse crack (1mm deep at 0.40m from excitation) in it and the following noise levels (the file directories are specified in parentheses): <ul> <li>Noisefree (/Quasi-Isotropic_Crack/Noisefree/MeasurementCrack_NF.mat)</li> <li>SNR 10 dB (/Quasi-Isotropic_Crack/SNR_10dB/MeasurementCrack_10dB.mat)</li> <li>SNR 5 dB (/Quasi-Isotropic_Crack/SNR_05dB/MeasurementCrack_5dB.mat)</li> </ul> </li> <li>Cross-ply carbon fiber composite beam (dimensions: 1m x 3mm x 2mm) with [0_{2}/90]_s stacking sequence and a delamination (5mm long at 0.45m from excitation) in it and the following noise levels: <ul> <li>Noisefree (/Cross-Ply_Delam/Noisefree/MeasurementDelam_NF.mat)</li> <li>SNR 10 dB (/Cross-Ply_Delam/SNR_10dB/MeasurementDelam_10dB.mat)</li> <li>SNR 5 dB (/Cross-Ply_Delam/SNR_05dB/MeasurementDelam_5dB.mat)</li> </ul> </li> </ul> <p>FEM notes: solid elements (i.e. C3D8R in Abaqus) with the following dimensions: 0.5mm thick, 0.5mm width, and 0.2mm long. Damage modes are modeled by node duplication.</p> <p>Ultrasonic tests data: 6-cycle Hanning windowed sinusoid centered at 100 kHz of frequency. Excitation signal at one end of the beam in the vertical direction. Sensor situated at 0.2m from the point of excitation and measuring for 720 \mu s. First antisymmetric mode (A0) is excited.</p>
Dataset for Reference-free Isotropic Super-resolution For Volumetric Fluorescence Microscopy
<p>Dataset for a research paper titled "Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy". The images were acquired using two modalities: confocal fluorescence microscopy (CFM) and open-top light-sheet microscopy (OT-LSM). For details about the imaging, please refer to the paper (Link to be uploaded later). </p> <p>A. CFM</p> <ul> <li>CFM image of a cortical region of a Thy 1-eYFP mouse brain. </li> <li>Lateral resolution estimated as 1.24 micron and Z-depth interval of 3 micron</li> </ul> <ol> <li>Input image ["CFM_input_xy-view.tif] [Figure 2] </li> <li>Reference image acquired by rotating the sample by 90 degrees ["CFM_rotated-and-registered_xz-view.tif'] [Figure 2] </li> </ol> <p>B. OT-LSM </p> <ul> <li>OT-LSM image of a cortical region of a Thy 1-eYFP mouse brain.</li> <li>Lateral resolution estimated as 0.5 micron and axial resolution estimated as 4.6 micron. </li> <li>For testing of artifact correction, the microscope was poorly calibrated on purpose. </li> </ul> <ol> <li>Input image for artifact correction ["OT-LSM_artifact-correction_input_volume_xy-view.tif"] [Figure 4]</li> <li>Ground-truth image for artificial blurring ["OT-LSM_artificial-blurring_GT.tif"][Supplementary Figure 14]</li> <li>Input image for artificial blurring ["OT-LSM_artificial-blurring_gau-z-blurred-std-10.tif"][Supplementary Figure 14]</li> <li>Input image for PSF deconvolution ["input_volume_PSF-deconvolution.tif"][Figure 3]</li> </ol> <p>C. Simulation </p> <ul> <li>Jupyter notebook to generate a 3D image volume for simulation ["Data Generator for Simulation.ipynb"] [Figure 1] </li> </ul>
Isotropic and Anisotropic g-Factor Corrections in GaAs Quantum Dots
<p>Supporting data for</p> <p>"Isotropic and Anisotropic g-factor Corrections in GaAs Quantum Dots", Phys. Rev. Lett. 127, 057701 – Published 29 July 2021</p>
Experimental verification of isotropic auxetic behaviour of hierarchical samples
<p>Video of experimental test on a hierarchical auxetic and isotropic <a href="https://www.sciencedirect.com/topics/engineering/porous-medium">p</a>orous sample with extremely negative Poisson’s ratio, related to the publication:</p> <p>M. Morvaridi, G. Carta, F. Bosia, A. S. Gliozzi, N. M. Pugno, D. Misseroni, M. Brun, "Hierarchical auxetic and isotropic porous medium with extremely negative Poisson’s ratio", Extreme Mechanics Letters 48, 101405 (2021), https://doi.org/10.1016/j.eml.2021.101405.</p>
Dataset: 2D particle-in-cell (PIC) simulation of the magnetic reconnection for the paper "Electron mixing and isotropization in the exhaust of asymmetric magnetic reconnection with a guide field"
<p>This repository contains pubilicly available numerical data of a 2D magnetic reconnection event, which includes the field data and plasma moment data. The simulation is performed with the VPIC code. The simulated data are used for the paper "Electron mixing and isotropization in the exhaust of asymmetric magnetic reconnection with a guide field". </p>
Mechanical and hydraulic transport properties of transverse-isotropic Gneiss deformed under deep reservoir stress and pressure conditions.
<p>"This is the ReadMe file corresponding to the study entitled: "Mechanical and hydraulic transport properties of transverse-isotropic<br> Gneiss deformed under deep reservoir stress and pressure conditions" <br> <br> "By M. Acosta, & M. Violay." <br> <br> This study has been published in the International Journal of Rock Mechanics and Mining Sciences in June 2020. <br> https://doi.org/10.1016/j.ijrmms.2020.104235<br> <br> This Read-Me file has been last edited on 2020-06-31 <br> <br> This readme file describes the data repository and supplementary files accompanying the above publication. <br> For any further queries please contact mateo.acosta@epfl.ch <br> <br> The following files are included: <br> <br> --- Regarding Figure 3. <br> <br> "1) ""Acosta_et_al_2020_Figure3Data.xlsx"" " <br> This is the processed data from the experiments described in Figure1 of the article. <br> "In this .xlsx File, each sheet corresponds to one figure panel as follows: " <br> <br> Fig.3: One experiment example<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>--- Regarding Figure 4. <br> <br> "1) ""Acosta_et_al_2020_Figure4Data.xlsx"" " <br> This is the processed data from the experiments described in Figure1 of the article. <br> "In this .xlsx File, each sheet corresponds to one figure panel as follows: " <br> <br> Fig.4a&g: Beta=0deg<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>Fig.4b&h: Beta=30deg<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>Fig.4c&i: Beta=45deg<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>Fig.4d&j: Beta=60deg<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>Fig.4e&k: Beta=90deg<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>Fig.4f&l: LPG<br> Column A: Axial strain in (%) ; Column B: Effective axial stress (in MPa); Column C: Axial strain for AE's in (%) ; Column D: Acoustic emission hits (in #); Column D: Axial strain for porosity change in (%) ; Column E: Porosity change (in %);</p> <p>--- Regarding all other Figures, the tables provided in the article allow reproduction of these.</p> <p> </p>
Driven Wattwins, a 2-DOF isotropic horological oscillator
<p>Videos of a silicon prototype of Wattwins, a 2-DOF compliant mechanical oscillator, driven by a watch movement. The two first eigenfrequencies of the oscillator are tuned and matched in order to have elliptical trajectories at the end effector with a constant rotational frequency at 15.5Hz.</p>
Data for Wind Energy Science paper "Modal dynamics of structures with bladed isotropic rotors and its complexity for 2-bladed rotors"
<p>The files are data files with model input and Matlab files with model parameters and function that sets up the block matrices of the dynamic model used in the paper. The Matlab script "test_repo.m" shows how to this function with all input.</p>
Thermal diffusivity of isotropic graphite from 23 °C to 3000 °C
<p>This dataset contains thermal diffusivity values determined from 23 °C to 3000 °C by seven laboratories on a batch of specimens machined in the same block of isotropic graphite. </p>
Data from: In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution (PART I)
<p>This whole-brain in vivo diffusion MRI dataset was acquired at 760 µm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy subject. It was acquired using state-of-the-art acquisition hardware and advanced reconstruction to achieve high SNR at such resolution, including a high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition, and parallel imaging reconstruction with advanced ghost reduction algorithms. With its unprecedented high resolution, SNR and image quality, it could help explore the fine-scale structures of in vivo human brain, and further advance the understanding of human brain connectivity. This dataset can also be used as a test bed for further technical development of new modeling, sub-sampling strategies, denoising and processing algorithms for in vivo high resolution dMRI. Whole brain anatomical T<sub>1</sub>-weighted and T<sub>2</sub>-weighted images at submillimeter scale, field maps and the code for preprocessing pipeline are also made available in the repository.</p>
Spectral emissivity of isotropic graphite from 1290 K to 2300 K
<p>This dataset contains spectral emissivity values determined from 1290 K to 2300 K by two laboratories on a batch of specimens machined in the same block of isotropic graphite.</p>
Specific heat of isotropic graphite from 1000 °C to 2800 °C
<p>This dataset contains specific heat values determined from 1000 °C to 2800 °C by three laboratories on a batch of specimens machined in the same block of isotropic graphite.</p>
Isotropic Transport Benchmarks
<p>Time dependent transport solutions for multiple sources and initial conditions through integration of Green's functions of the transport operator.</p>
Isotropic negative thermal expansion in ZrW2O8 and HfW2O8 from 1100 ℃ to 1275 ℃
<p>This is a repository of synchrotron, powder, XRD Data for ZrW2O8 and HfW2O8 samples from 25 C to ~1300 C showing negative thermal expansion. </p>
DNS dataset for modelling homogeneous ignition processes of clustering solid particle clouds in isotropic turbulence
<h2>Abstract</h2> <p>This dataset is being published to enable the development of models for igniting and combusting solid particles in isotropic turbulence using flamelet tabulated chemistry. This dataset is generated using the forced homogeneous isotropic turbulence in order to investigate the effect of active turbulent forces on the ignition of particles, and it is used as the supplementary material for the manuscript "<em>Modeling homogeneous ignition processes of clustering </em><em>solid particle clouds in isotropic turbulence</em>", which was accepted for publication in the special issue of Fuel Journal for the proceeding of the 4th international Oxyflame workshop. The particles are chosen to have near unity Stokes numbers, which promote particle clustering to study the ignition phenomenon for particle clusters, which is common during ignition and combustion of particle clouds in large industrial solid fuel-powered burners. Since the ignition process of clustering solid particle clouds is transient, different time instances during the ignition process are presented to facilitate the modelling effort for the transient ignition behaviour of the particles. The dataset consists of gas-phase data, particle data and the most important routines required to process the dataset. </p> <p>This database is a valuable resource for users developing solid fuel ignition and combustion in turbulent conditions. </p> <p>It should be noted that this dataset is a reduced version of the full dataset in order to size limitations in the sharing platforms. More information and full dataset can be provided upon request. For more information, contact: p.farmand@itv.rwth-aachen.de</p> <h2>Technical details</h2> <p>The data provided in this dataset contains gas phase data, particle data, and some post-processing scripts for visualization of the data. The data is generated in forced homogeneous isotropic turbulence (HIT) with an initial preferential concentration of the particles in a hot atmosphere to study the impact of particle clustering on ignition. Simulations were performed within a region with the physical size of 12.8mm * 12.8mm * 12.8mm with periodic boundary conditions in all directions. The domain size is discretized with a three-dimensional cartesian mesh with a resolution of Δx = 50 μm. A forced isotropic turbulent field with Re_λ=30 and the Kolmogorov length scale η =100 microns has been chosen. The dispersed phase consists of 10,000 particles of Colombian coal with D_p= 20 microns and T0=300K and with an apparent density of 700kg/m3. Non-reactive particles are first randomly distributed in the box filled with air with 20% oxygen and an initial gas temperature of T = 1500K, which is relevant to practical PCC applications. These conditions lead to an initial Stokes number of around 5, for which a clustering behaviour in particle cloud motion is expected. The employed forced isotropic turbulence ensures maintaining the same turbulence statistics during non-reactive and reactive simulations, as summarised in the following table:</p> <table> <tbody> <tr> <td> <p><em>time [ms]</em></p> </td> <td> <p><em>Re_</em><em>λ</em></p> </td> <td> <p><em>Re_</em><em>Turb</em></p> </td> <td> <p><em>η[m]</em></p> </td> <td> <p><em>l_</em><em>t</em><em>[m]</em></p> </td> <td> <p><em>t_</em><em>η</em><em>[ms]</em></p> </td> <td> <p><em>t_</em><em>l</em><em>[ms]</em></p> </td> <td> <p><em>St</em></p> </td> </tr> <tr> <td>0</td> <td>30.7</td> <td>141.8</td> <td>1.04e-4</td> <td>4.27e-3</td> <td>4.47e-2</td> <td>5.33e-1</td> <td>6.199</td> </tr> <tr> <td>0.46</td> <td>30.9</td> <td>143.9</td> <td>9.94e-5</td> <td>4.13e-3</td> <td>4.16e-2</td> <td>4.99e-1</td> <td>6.207</td> </tr> <tr> <td>0.5</td> <td>30.5</td> <td>140.1</td> <td>9.96e-5</td> <td>4.06e-3</td> <td>4.27e-2</td> <td>5.05e-1</td> <td>6.333</td> </tr> <tr> <td>0.55</td> <td>29.4</td> <td>129.8</td> <td>1.01e-4</td> <td>3.88e-3</td> <td>4.34e-2</td> <td>4.94e-1</td> <td>6.032</td> </tr> <tr> <td>0.6</td> <td>28.5</td> <td>122.1</td> <td>1.02e-4</td> <td>3.76e-3</td> <td>4.46e-2</td> <td>4.92e-1</td> <td>5.775</td> </tr> <tr> <td>0.65</td> <td>27.7</td> <td>115.2</td> <td>1.04e-4</td> <td>3.66e-3</td> <td>4.48e-2</td> <td>4.81e-1</td> <td>5.365</td> </tr> </tbody> </table> <p><em>η and t_η are the respective Kolmogorov length and time scales, and l_t and t_l correspond to the integral length and time scales.</em></p> <p> </p> <h3>Gas phase and particle data:</h3> <p>The gas phase data has HDF5 formation, which contains selected scalars relevant to model developments. Since the ignition process is a transient process, two different time instances, t=0.5ms with the maximum number of ignited regions during the ignition process and t=0.65ms at the end of the ignition process, are chosen. Before starting the reactive simulations, a non-reactive simulation for 20.5ms was performed to form the particle clusters, and then the reactive simulation was started. Therefore, the data.out_2.100E-02.h5 corresponds to t = 0.5ms and data.out_2.115E-02.h5 corresponds to t = 0.65ms. Each HDF5 dataset has the following structure:</p> <p>Group Flow:</p> <ul> <li>U, V, W</li> </ul> <p>Group scalar: </p> <ul> <li>CO, CO2, H2O, C2H2, O2, N2, OH, progress variable (PV), temperature(T), enthalpy(h), heat capacity (Cp), density(RHO), pressure(P), mixture fraction(Z), dissipation rate, heat conductivity</li> </ul> <p>Group C_dot:</p> <ul> <li>Molar production rate of CO, CO2, O2, OH</li> </ul> <p>Group ST:</p> <ul> <li>Mass fraction rate of OH</li> </ul> <p>Group var (data required for subfilter analysis and PDF modelling):</p> <ul> <li>RHO * h</li> <li>RHO * h * h</li> <li>RHO *<em> </em>PV</li> <li>RHO *<em> </em>PV <em>* </em>PV</li> <li>RHO *<em> </em>RHO</li> <li>RHO *<em> </em>RHO <em>* </em>RHO</li> <li>RHO * T</li> <li>RHO *<em> </em>T <em>* </em>T</li> <li>RHO * Z</li> <li>RHO *<em> </em>Z <em>* </em>Z</li> </ul> <p>These gas phase data can be used to study the Eulerian field, which is impacted by the particles through the source terms, which are obtained from the Lagrangian framework. For the particle data, two CSV files containing information about each particle's position and temperature are provided. </p> <p>In each CSV file, which corresponds to the same time instance as the gas phase data, this structure can be found:</p> <ul> <li>Points_0, Points_1, Points_2: particle position x,y,z</li> <li>coal_defaultT: particle temperature</li> </ul> <h3>Scripts:</h3> <p>Different scripts are provided for a reader to enable the first-time use of the data, such as visualising the gas phase quantities, calculating the conditional mean and statistical analysis, and clustering analysis for the particles. Here is a brief information regarding the scripts:</p> <ul> <li><strong>hdf5_2D_Con_Mean_Plots.m</strong>: This script can be used for loading the HDF5 file and visualising the field, joint PDF, and joint correlation of different quantities with respect to different parameters. This script requires another module to calculate the conditional mean of the data.</li> <li><strong>Compute_ConditionalMean_histograms_2D.m: </strong>This module calculates the conditional average of a 2D array.</li> <li><strong>clustering_voronoi_3D.m</strong>: This script can analyse the particle data and calculate the clustering limit based on the Voronoi algorithm. It can also filter the clustered particles and separate different clusters using the nearest neighbour and DB-SCAN methods.</li> </ul> <p> </p> <p> </p>
Geometric-Phase Microscopy for Quantitative Phase Imaging of Isotropic, Birefringent and SpaceVariant Polarization Samples_experimental dataset
<p>This dataset shares the data presented in the paper "Geometric-Phase Microscopy for Quantitative Phase Imaging of Isotropic, Birefringent and SpaceVariant Polarization Samples" available in open access under http://doi.org/10.1038/s41598-019-40441-9. </p>
Whole-mantle P-wave isotropic and anisotropic tomography beneath Japan and adjacent regions
<p>Dear readers,</p> <p>A file named "Japan_ISO.dat" contains whole-mantle P-wave isotropic tompgraphy beneath Japan and surrounding regions.</p> <p>The data format is as follows:<br>1st column: depth (km) (F8.2)<br>2nd column: latitude (deg) (F8.2)<br>3rd column: longitude (deg) (F8.2)<br>4th column: dVp (%) (F8.2)<br>5th column: Ray hit count (I8)</p> <p>These are values on grid points used for tomographic inversion.</p> <p>A file named "Japan_AAN_iso.dat" contains isotropic component of whole-mantle P-wave azimuthal anisotropy (AAN) tompgraphy beneath Japan and surrounding regions. </p> <p>The data format is as follows:<br>1st column: depth (km) (F8.2)<br>2nd column: latitude (deg) (F8.2)<br>3rd column: longitude (deg) (F8.2)<br>4th column: dVp (%) (F8.2)<br>5th column: Ray hit count (I8)</p> <p>These are values on grid points used for tomographic inversion.</p> <p>A file named "Japan_AAN_ai.dat" contains anisotropic component of whole-mantle P-wave azimuthal anisotropy (AAN) tompgraphy beneath Japan and surrounding regions. </p> <p>The data format is as follows:<br>1st column: depth (km) (F10.3)<br>2nd column: latitude (deg) (F10.3)<br>3rd column: longitude (deg) (F10.3)<br>4th column: Anisotropic amplitude (%) (F10.3)<br>5th column: Fast velocity direction (deg) (F10.3)</p> <p>These values are obtained by interpolation from the values at surrounding anisotropic grid points used for tomographic inversion.</p> <p>We hope it is useful to you.</p> <p>Kind wishes,</p> <p>Genti Toyokuni & Dapeng Zhao<br>Tohoku University, Japan<br>E-mail: toyokuni@tohoku.ac.jp</p> <p>Reference:<br>Toyokuni, G., Zhao, D., & Takada, D. (2024).<br>Whole-mantle isotropic and anisotropic tomography beneath Japan and adjacent regions.<br>Journal of Geophysical Research: Solid Earth, under review.</p>
Whole-mantle P-wave isotropic tomography beneath Australia and New Zealand
<p>Dear readers,</p> <p>A file named "AN_ISO.dat" contains whole-mantle P-wave isotropic tompgraphy beneath Australia and New Zealand.</p> <p>The data format is as follows:<br>1st column: depth (km) (F7.2)<br>2nd column: latitude (deg) (F7.2)<br>3rd column: longitude (deg) (F7.2)<br>4th column: dVp (%) (F7.2)<br>5th column: Ray hit count (I8)</p> <p>These are values on grid points used for tomographic inversion.</p> <p>We hope it is useful to you.</p> <p>Kind wishes,</p> <p>Genti Toyokuni & Dapeng Zhao<br>Tohoku University, Japan<br>E-mail: toyokuni@tohoku.ac.jp</p> <p>Reference:<br>Toyokuni, G. & Zhao, D. (2024).<br>Slab-plume interactions beneath Australia and New Zealand: New insight from whole-mantle tomography.<br>Geochemistry, Geophysics, Geosystems, under review.</p>
Pure Isotropic Proton Solid State NMR raw data
<p>This dataset contains all raw NMR data (in Topspin and JCAMP format) together with the MATLAB scripts used in the JACS publication named: "Pure Isotropic Proton Solid State NMR" (DOI: 10.1021/jacs.1c03315 )</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.