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105 results for “Anisotropic”
Data: Dynamics of star clusters with tangentially anisotropic velocity distribution (Pavlik+ 2024)
<p>This dataset represents the results of our <em>N</em>-body simulations of star clusters (SCs). The initial conditions of the models are fully described in the referenced journal article. In short, the SCs start from isotropic, radially anisotropic or tangentially anisotropic initial velocity distributions, and each model is evolved in an external Galactic tidal field, for two different choices of the filling factor.</p>
Three-dimensional magnetic reconnection in particle-in-cell simulations of anisotropic plasma turbulence (Simulation Data)
<p>This folder contains the output of the following simulation: </p> <p>We use the explicit Plasma Simulation Code (PSC, Germaschewski et al.2016) to simulate eight anisotropic counter-propagating Alfvén waves in an ion-electron plasma. The anisotropy of the initial fluctuation is set up according to the theory of critical balance by Sridhar & Goldreich (1994) and Goldreich & Sridhar (1995) at the small scale end of the inertial range: <span class="math-tex">\(k_{\parallel} d_{i} = C (|k_{\perp}|d_{i})^{2/3}\)</span>, where <span class="math-tex">\(C= 10^{-4/3}\)</span>. The normalization parameters are the speed of light <span class="math-tex">\(c = 1\)</span>, the vacuum permittivity <span class="math-tex">\(\epsilon_{0} = 1\)</span>, the magnetic permeability <span class="math-tex">\(\mu_{0} = 1\)</span>, the Boltzmann constant <span class="math-tex">\(k_{b}=1\)</span>, the elementary charge <span class="math-tex">\(q=1\)</span>, the ion mass <span class="math-tex">\(m_{i}=1\)</span>, the density of ions and electrons <span class="math-tex">\(n_{i}=n_{e}=1\)</span> and the ion inertial length <span class="math-tex">\(d_{i}=c/\omega_{pi}\)</span> where <span class="math-tex">\(\omega_{pi}=\sqrt{n_{i}q^{2}/m_{i}\epsilon_{0}}\)</span> is the ion plasma frequency. We set <span class="math-tex">\(\beta_{s,\parallel}=1\)</span> and <span class="math-tex">\(T_{s,\parallel}/T_{s,\perp}=1\)</span>, where <span class="math-tex">\(\beta_{s,\parallel}=2 n_s \mu_{0} k_{B}T_{s,\parallel}/B_{0}^{2}\)</span> is the ratio between the plasma pressure parallel to the background magnetic field <span class="math-tex">\(\mathbf{B}_{0}\)</span> and the magnetic pressure and $T_{s,\parallel}$ is the parallel temperature. The magnetic field is normalised to <span class="math-tex">\(B_{0}=V_{A}/c\)</span>, where <span class="math-tex">\(V_{A}=B_{0} / \sqrt{\mu_{0}n_{i}m_{i}}\)</span> is the ion Alfvén speed. We use 100 particles per cell (100 ions and 100 electrons), a mass ratio of <span class="math-tex">\(m_{i}/m_{e} = 100\)</span> so that <span class="math-tex">\(d_e = 0.1 d_{i}\)</span> where <span class="math-tex">\(m_{e}\)</span> is the electron mass and <span class="math-tex">\(d_{e}\)</span> is the electron inertial length. The simulation box size is <span class="math-tex">\(L_{x} \times L_{y} \times L_{z} = 24d_{i}\times24d_{i}\times125d_{i}\)</span> and the spatial resolution is <span class="math-tex">\(\Delta x =\Delta y = \Delta z = 0.06d_{i}\)</span>. We use a time step <span class="math-tex">\(\Delta t =0.06/ \omega_{pi}\)</span>. In our normalisation, the Debye length <span class="math-tex">\(\lambda_{D}=d_{i}\sqrt{\beta_{i}/2}V_{A}/c\)</span> defines the minimum spatial distance that needs to be resolve in the simulation and <span class="math-tex">\(\lambda_D=0.07d_i\)</span>.</p> <p>This output corresponds to <span class="math-tex">\(t=120 \omega_{pi}\)</span>. </p> <p>These data were produced using the Data Intensive at Leicester (DIaL) facility provided by the DiRAC project<br> dp126 "Identifying and Quantifying the Role of Magnetic Reconnection in Space Plasma Turbulence".</p>
Temperature-dependent Lamb wave signals in highly anisotropic CFRP
<p>The dataset contains signals of propagating Lamb waves in highly anisotropic carbon fibre reinforced polymer (CFRP). The reinforcement is unidirectional along 0 deg. A detailed description of the material and its parameters is given in [1]. The arrangement of piezoelectric actuator A and sensors S1-S7 is shown in figure "plate_angular_pzt_arrangement_50x50.png". Sensors are placed at propagation angles from 0 deg to 90 deg with a step of 15 deg. It should be noted that two piezoelectric transducers bonded to both sides of the plate were used as the actuator. It allowed for exciting Lamb waves with dominant A0 and S0 modes, respectively. Hence, there are two respective zip files with data.</p> <p>The following parameters were used during measurements:</p> <ul> <li>Temperatures: T=[50,40,30,20,10,0,-10,-20,-30,-40,-50];</li> <li>Number of cycles in Hann windowed signals: no_of_cycles=[2,2.5,3];</li> <li>Carrier frequencies of excitation signals [kHz]: frequencies=[20:10:250];</li> <li>Number of averages: 50;</li> <li>Sampling frequency: 10 MHz.</li> </ul> <p>The following equipment was used in the experiment:</p> <ul> <li>Environmental chamber by Angelantoni Test Technologies, model MyDiscovery 600 C;</li> <li>National Instruments waveform generator PXIe-5413;</li> <li>Krohn-Hite voltage amplifier model 7500;</li> <li>Cedrat Technologies LWDS amplifier (used as a charge amplifier);</li> <li>National Instruments oscilloscope PXIe-5105.</li> </ul> <p>Files in CSV format contain environmental chamber data (temperature programme, actual temperature and humidity over time, etc.). This can be read and plotted in Matlab by running the script “Read_plot_environmental_chamber.m”. There is another file “Read_plot_environmental_chamber_plus_DS18B20_RH20_KROHN_A0.m” in which temperature was registered also by DS18B20 digital sensor. It loops over all measurements so that it can be used also for reading signals from “niscope_avg_waveform.mat” in respective subfolders. In particular, sensor signals are stored in the “niscope_avg_waveform” variable, a matrix of dimensions 8192x7.</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 9)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>GBCurvApprx_175_to_5200.zip<br> Single-grain-boundary-contour-point-resolved curvature estimation tracking data for time step 175 to 5200 with 1 time step resolution and all grains</p> <p>CurvatureEvolution.zip,<br> CurvatureEvolutionAverage.zip<br> TopologyTracer analysis capillary driving force evolution with 100 time step resolution based on the<br> GBContour* data --- not the GBCurvApprx* data.</p> <p>CurvatureEvolutionSuccessful.zip<br> TopologyTracer analysis matrix of segment-length-averaged capillary migration speed for the surviving<br> grains at 1 time step temporal resolution based on the GBCurvApprx* data</p> <p>CurvatureEvolutionUnsuccessful.zip<br> TopologyTracer analysis matrix of segment-length-averaged capillary migration speed for the false<br> positive sub-grains, see paper for details, based on the GBCurvApprx* data</p> <p>UnbiasedGrowthMeasures.zip<br> TopologyTracer analysis temporal evolution of long-range characterisation with the xi metric, see paper for details</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 7)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_450_to_5296.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 450 to 5296 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 5)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_171_to_259.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 171 to 259 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 3)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_50_to_99.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 50 to 99 with 1 time step resolution</p> <p> </p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 2)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_1_to_49.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 1 to 49 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 4)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_100_to_170.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 100 to 170 with 1 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX3D)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>0Synthesis.zip --- Microstructure synthesis<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds and MicrostructureDiagnostics.uds meta data for the grains<br> -parameters.xml parameterization of microstructure synthesis<br> -Scripts and additional input referred to by parameters.xml</p> <p>1Coarsening.zip --- Simulation<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds meta data for the grains<br> -VoxelizedParameters.xml parameterization of coarsening simulation<br> -NrGrains&EnergyStatistics.dat descriptive stats simulation (Time step, Real time, number of grains, ---, grid size)<br> -Network.zip implicit 3d x,y,z unsigned int ID container detailing the microstructure</p> <p>Texture_Faces_1_to_1261.zip<br> Grain boundary network geometry and grain meta data tracking data</p> <p>Network.zip<br> Rendering images for evolution of the grain boundary network and volume properties</p> <p>2DataAnalysis --- Data analyses<br> -All other archive Matlab analysis scripts and TopologyTracer results<br> </p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 1)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>0Synthesis.zip --- Microstructure synthesis<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds and MicrostructureDiagnostics.uds meta data for the grains<br> -parameters.xml parameterization of microstructure synthesis<br> -Scripts and additional input referred to by parameters.xml</p> <p>1Coarsening.zip --- Simulation<br> -Container.raw 3D implicit x,y,z right-handed coordinate system ID field describing the microstructure IDs refer to<br> -Microstructure.uds meta data for the grains<br> -VoxelizedParameters.xml parameterization of coarsening simulation<br> -NrGrains&EnergyStatistics.dat descriptive stats simulation (Time step, Real time, number of grains, ---, grid size)<br> -Network.zip implicit 3d x,y,z unsigned int ID container detailing the microstructure</p> <p>1CoarseningTrackCurvature.zip<br> -Files of the re-run to the simulation along which capillary data were tracked on the fly in each time step</p> <p>GBContourPoints_100_to_5296.zip<br> -Grain boundary face network for time steps 100 to 5296 at 100 time step resolution</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 8)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>GBCurvApprx_1_to_174.zip<br> Single-grain-boundary-contour-point-resolved curvature estimation tracking data for time step 1 to 174 with 1 time step resolution and all grains</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 10)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>NetworkEvolutionPNG.zip<br> Rendered images of the evolving microstructure at very high resolution and reduced size FullHD</p> <p>PRX2DNetwork.gif and PRX2DNetwork.gifx<br> Video of the evolving microstructure and corresponding gif-x video generation file</p> <p>TrackingParallelRXEVO_Resolution_1.zip<br> TopologyTracer analysis of recrystallized volume fraction over time</p> <p>TrackingParallelSEE1002000.zip<br> TopologyTracer analysis of single-grain-resolved stored elastic energy driving forces segment length averaged</p> <p>TrackingParallelTrackingBK_Resolution_1.zip,<br> TrackingParallelTrackingBK_Resolution_100.zip<br> TopologyTracer analysis tracking backwards (BK) in time the evolution of the successful grains with different temporal resolution</p> <p>TrackingParallelTrackingFW_FID100.zip<br> TopologyTracer analysis tracking forward (FW) in time the metadata for all grains at time step 100</p> <p>TrackingParallelTrackingFW_Resolution_1.zip,<br> TrackingParallelTrackingFW_Resolution_20.zip,<br> TrackingParallelTrackingFW_Resolution_100.zip<br> TopologyTracer analysis tracking forward (FW) in time the evolution of all grains with different temporal resolution</p> <p>MPIEDevBranchPRX2D.zip<br> TopologyTracer analysis rendering of images and analysis scripts</p> <p>MPIEDevBranchPRX2DCapTrack.zip,<br> MPIEDevBranchPRX2DMajorRevPCA.zip,<br> MPIEDevBranchTrackSuccessful02.zip<br> TopologyTracer analysis of capillary tracking data in correlation with size and topology evolution and PCA</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 6)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_260_to_449.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 260 to 449 with 1 time step resolution</p>
Figure reproduction for "Accelerating small angle scattering experiments on anisotropic samples using kernel density estimation"
<p>These datasets and a Jupyter notebook reproduce figures in <a href="https://www.nature.com/articles/s41598-018-37345-5">a publication by Saito et al in Scientific Reports</a>. The notebook also serves as a demo for kernel density estimation (smoothing) of 2D data using Python. Details are described in the notebook. If you have no idea about ipynb format, please see HTML version with your web browser instead. It contains exactly the same codes and results as ipynb version.</p>
A Dataset for In-situ synchrotron tomography experiments to investigate anisotropic damage of line pipe steel
<p>In this study, anisotropic ductility and associated damage mechanisms of a grade X100 line pipe steel were investigated using in-situ synchrotron-radiation computed tomography (SRCT) of notched round bars. Line pipe materials have anisotropic mechanical properties, such as tensile strength, ductility and toughness. Specimens were tested for loading along both rolling (L) and transverse (T) directions. The <em>in-situ</em> data collected allowed quantifying both specimen deformation (evolution of the cross section) and microscopic damage parameters such as porosity, void shape and void orientation. The data sets provide here are related to the paper <em>"On the origin of the anisotropic damage of X100 line pipe steel, Part I: in-situ synchrotron tomography experiments"</em> being published in <a href="https://www.springer.com/journal/40192">Integrating Materials and Manufacturing Innovation</a>. For each testing direction, dataset are provided using hdf5 and xdmf standarded exchange format. A compressed file is also provided in connection with the analyses explained in the article.</p>
Processed data from SnoHATS and METCRAX II: anisotropic turbulence and geometry of the Reynolds stress tensor in a streamline coordinate system
<p>Datasets used for the paper 'Interpreting turbulence anisotropy in a streamline coordinate system'. Data from SnoHATS and METCRAX II field campaigns. Datasets include turbulent quantities calculated on 30- and 1-min averaging windows for unstable and stable conditions, with prior linear detrending. Planar fit was used in METCRAX II and double rotation in SnoHATS to rotate the flow into the mean wind direction. Datasets include quantities to characterize the anisotropy of the Reynolds stress tensor, such as eigenvalues, eigenvectors, and the angles between the eigenvectors and the streamline coordinate system, defined in the direction of the mean wind vector.</p> <p>1c: one-component Reynolds stress tensor</p> <p>2c: two-component axisymmetric Reynolds stress tensor</p> <p>3c: isotropic Reynolds stress tensor</p>
Dataset of image processing - High-throughput characterization of cortical microtubule arrays response to anisotropic tensile stress
<p>The data set contains the analysis data files from the image analysis workflow developed to quantify cortical microtubules rearrangements in the case of tensile stress (<a href="https://github.com/VergerLab/MT_Angle2Ablation_Workflow">https://github.com/VergerLab/MT_Angle2Ablation_Workflow</a>), generated form a specific dataset (https://doi.org/10.5878/17te-jg54). The files include the intermediary images processed at each step of the image analysis workflow in imageJ, the log files produced by the imageJ macro describing the input and the output images and the text files containing the quantified values. </p>
Dataset for Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries
<p>This dataset provides the raw data to the manuscript</p> <p>"<strong>Link between Anisotropic Electrochemistry and Surface Transformations at Single Crystal Silicon Electrodes: Implications for Lithium Ion Batteries"</strong></p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Electrochemical measurements as cyclic voltammetry using scanning electrochemical cell microscopy for three different Si crystallographic orientations (100, 110, 311) in 1 M LiPF6 in ethylene carbonate - ethyl methyl carbonate ("SECCM/")</li> <li>Scanning electron microscopy and transmission electron microscopy imaging of pristine and cycled samples ("Images/")</li> </ul>
Pattern formation in skyrmionic materials with anisotropic environments
<p>Magnetic Skyrmions have attracted broad attention during recent years because they are regarded as promising candidates as bits of information in novel data storage devices. A broad range of theoretical and experimental investigations have been conducted with the consideration of axisymmetric Skyrmions in isotropic environments. However, one naturally observes a huge variety of anisotropic behavior inmany experimentally relevant materials. In the present work, we investigate the influence of anisotropic environments onto the formation and behavior of the noncollinear spin states of skyrmionic materials by means of Monte Carlo calculations. We find skyrmionic textures which are far from having an axisymmetric shape. Furthermore, we show the possibility to employ periodic modulations of the environment to create skyrmionic tracks.</p>
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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.