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4,376 results for “magnetization”
Magnetic Resonance Imaging Glucose Study Dataset
<p>The data has been produced by the Institut für Mikrostrukturtechnik (IMT) at Karlsruher Institut für Technologie (KIT). This dataset represents the DICOM (Digital Imaging and Communications in Medicine) files, which belong to one MRI (Magnetic Resonance Imaging) study and contain a series of images that have been measured with different protocols. The samples shown by the images are tubes, which contain different concentrations of Glucose. The DICOM file headers have metadata tags, which embody additional information about the study and the particular series.</p>
Brownian Relaxation Shakes and Breaks Magnetic Iron Oxide-Polymer Nanocomposites to Release Cargo
<p>Original data supporting the findings of the manuscript and supplementary materials sorted after Figures and their respective panels.</p>
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.
<p>This multi-center dataset consists of 250 expert-annotated magnetic resonance imaging stroke cases. It is the training dataset for the Ischemic Stroke Lesion Segmentation Challenge (ISLES'22).</p> <p>For each case, an expert level annotation of the stroke lesions is included along with the following three imaging sequences: Fluid attenuated inversion recovery (FLAIR), diffusion weighted imaging (DWI, b=1000) and its corresponding apparent diffusion coefficient (ADC) map. All imaging data and annotations are released in the Neuroimaging Informatics Technology Initiative (NIfTI) format (https://nifti.nimh.nih.gov/nifti-1), according to the BIDS convention. All imaging data are released in the native space without prior registration. Prior to release, skull-stripping was performed to de-identify patients.</p> <p>Image acquisition was performed on one of the following devices: 3T Philips MRI scanners (Achieva, Ingenia), 3T Siemens MRI scanner (Verio) or 1.5T Siemens MAGNETOM MRI scanners (Avanto, Aera). All images were obtained by healthcare professionals as part of the clinical imaging routine for stroke patients at three different stroke centers and imaging data was collected retrospectively for different clinical studies. Computer-readable scanner metadata from the Digital Imaging and Communications in Medicine (DICOM) header in the JSON file format is provided with the datasets if available.</p> <p>For a full dataset description, see the <a href="https://arxiv.org/abs/2206.06694">ISLES'22 preprint</a>.</p> <p>More information about the ISLES'22 challenge can be found in <a href="https://isles22.grand-challenge.org/">grand challenge</a> and in our official <a href="http://www.isles-challenge.org/">challenge website</a>.</p> <h3>Please cite the following works when using this dataset:</h3> <ul> <li>de la Rosa, Ezequiel, et al. <strong>DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge.</strong> <em>Nature Communications</em> 16.1 (2025): 7357.</li> <li>Hernandez Petzsche, Moritz R., et al. <strong>ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.</strong> <em>Scientific data</em> 9.1 (2022): 762.</li> </ul>
A highly magnetized environment in a pulsar binary system
<p>A highly magnetized environment in a pulsar binary system</p> <p><strong>Data availability:</strong></p> <ul> <li>IPV and DM, RM variations used for Fig 1 (Demo how to read the code and plot: plot_Fig1.py) <p>DM_Icut_60s_140114.txt, DM_Icut_60s_200621.txt, </p> <p>RM_140114_20s_rmfit.txt,RM_200621_20s_rmfit.txt, </p> <p>ILV_140114_20s.npy, ILV_200621_20s.npy</p> </li> <li>A full table of DM and RM variations (duplicated with the txt files, but with better readability): Li_DM_RM_Ter5A.xlsx</li> <li>Calibrated full stokes data with frequency information for Fig 2 (in psrfit format, can be read in with standard psrchive commands): VEGAS_Ter5A_200621_0001.zap.E2.calib.rot.f32</li> </ul> <p><strong>Code availability</strong></p> <ul> <li><em>The basic process of the raw data and DM, RM fitting:</em></li> </ul> <p>DSPSR (http://dspsr.sourceforge.net) PSRCHIVE (http://psrchive.sourceforge.net)</p> <ul> <li><em>Modeling and fitting:</em></li> </ul> <p>https://github.com/dongzili/Pol_prop_fitting</p> <p>NOTICE: If there are updated data due to new demands, the information or new URL will be posted in https://github.com/dongzili/Pol_prop_fitting</p>
Magnetic Tape Recorder Dataset
<p>This repository contains the datasets collected and used in the research project:</p> <p>O. Mikkonen, A. Wright, E. Moliner and V. Välimäki, “Neural Modeling Of Magnetic Tape Recorders,”<br> in <em>Proceedings of the International Conference on Digital Audio Effects (DAFx)</em>,<br> Copenhagen, Denmark, 4-7 September 2023.</p> <p>A pre-print of the article is available in <a href="https://arxiv.org/abs/2305.16862">arXiv</a>.<br> The code is open-source and published in <a href="https://github.com/01tot10/neural-tape-modeling">GitHub</a>.<br> The accompanying web page can be found from <a href="http://research.spa.aalto.fi/publications/papers/dafx23-neural-tape/">here</a>.</p> <p><strong>Overview</strong></p> <p>The data is divided into various subsets, stored in separate directories. The data contains both <em>toy data</em> generated using a software emulation of a reel-to-reel tape recorder, as well as <em>real data</em> collected from a physical device. The various subsets can be used for training, validating, and testing neural network behavior, similarly as was done in the research article.</p> <p><strong>Toy and Real Data</strong></p> <p>The <em>toy data</em> was generated using <a href="https://github.com/jatinchowdhury18/AnalogTapeModel/">CHOWTape</a>, a physically modeled reel-to-reel tape recorder. The subsets generated with the software emulation are denoted with the string `CHOWTAPE`. Two variants of the toy data was produced: in the first variant, the fluctuating delay produced by the simulated tape transport was disabled, and in the second kind, the delay was enabled. The latter variants are denoted with the string `WOWFLUTTER`.</p> <p>The <em>real data</em> is collected using an Akai 4000D reel-to-reel tape recorder. The corresponding subsets are denoted with the string `AKAI`. Two tape speeds were used during the recording: 3 3/4 IPS (inches per second) and 7 1/2 IPS, with the corresponding subsets denoted with '3.75IPS' and '7.5IPS' respectively. On top of this, two different brands of magnetic tape were used for capturing the datasets with different tape speeds: Maxell and Scotch, with the corresponding subsets denoted with 'MAXELL' and 'SCOTCH' respectively.</p> <p><strong>Directories</strong></p> <p>For training the models, a fraction of the inputs from <a href="https://zenodo.org/record/3824876">SignalTrain LA2A Dataset</a> was used. The training, validation, and testing can be replicated using the subsets:</p> <ul> <li>ReelToReel_Dataset_MiniPulse100_AKAI_*/ (hysteretic nonlinearity, real data)</li> <li>ReelToReel_Dataset_Mini192kHzPulse100_AKAI_*/ (delay generator, real data)</li> <li>Silence_AKAI_*/ (noise generator, real data)</li> <li>ReelToReel_Dataset_MiniPulse100_CHOWTAPE*/ (hysteretic nonlinearity, toy data)</li> <li>ReelToReel_Dataset_MiniPulse100_CHOWTAPE_F[0.6]_SL[60]_TRAJECTORIES/ (delay generator, toy data)</li> </ul> <p>For visualizing the model behavior, the following subsets can be used:</p> <ul> <li>LogSweepsContinuousPulse100_*/ (nonlinear magnitude responses)</li> <li>SinesFadedShortContinuousPulse100*/ (magnetic hysteresis curves)</li> </ul> <p><strong>Directory structure</strong></p> <p>Each directory/subset is made of up of further subdirectories that are most often used to separate the training, validation and test sets from each other. Thus, a typical directory will look like the following:<br> ```<br> [DIRECTORY_NAME]<br> ├── Train<br> │ ├── input_x_.wav<br> │ ...<br> │ ├── target_x_.wav<br> │ ...<br> └── Val<br> │ ├── input_y_.wav<br> │ ...<br> │ ├── target_y_.wav<br> │ ...<br> ├── Test<br> │ ├── input_z_.wav<br> │ ...<br> │ ├── target_z_.wav<br> │ ...<br> ```</p> <p>While not all of the audio is used for training purposes, all of the subsets share part of this structure to make the corresponding datasets compatible with the dataloader that was used.</p> <p>The input and target files denoted with the same number `x`, e.g. `input_100_.wav` and `target_100_.wav` make up a pair, such that the target audio is the input audio processed with one of the used effects. In some of the cases, a third file named `trajectory_x_.npy` can be found, which consists of the corresponding pre-extracted delay trajectory in the `NumPy` binary file format.</p> <p><strong>Revision History</strong></p> <ul> <li>Version 1.1.0 <ul> <li>Added high-resolution (192kHz) dataset for configuration (SCOTCH, 3.75 IPS)</li> </ul> </li> <li>Version 1.0.0 <ul> <li>Initial publish</li> </ul> </li> </ul>
Dataset for the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest"
<p>Datasets to accompany the article "Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest". Written by J. Elgy and P. D. Ledger (Keele University, 2023).</p> <p>The datasets include data files, meshes, and source code for generating figures from the paper. This requires the open source MPT-Calculator software available at <a href="https://github.com/MPT-Calculator/MPT-Calculator%7D">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch).</p> <p>The authors gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1</p> <p> </p>
Dataset for the article "Computations and Measurements of the Magnetic Polarizability Tensor Characterisation of Highly Conducting and Magnetic Objects"
<p>Datasets to accompany the article "Computations and Measurements of the Magnetic Polarizability Tensor Characterisation of Highly Conducting and Magnetic Objects". Written by J. Elgy, P. D. Ledger, J. L. Davidson, T. Özdeğer and A. J. Peyton. The article has been submitted to "Engineering Computations" (2023).</p> <p>The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch).</p> <p>The datasets also include measurement data for real world objects courtesy of The University of Manchester.</p> <p>J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.<br> J. L. Davidson and A. J. Peyton are grateful for the financial support received from an Innovate UK Grant (reference number 39814).<br> T. Özdeğer and A. J. Peyton are grateful for the financial support received from EPSRC, U.K. through the research grant EP/R002177/1.</p>
Simulations of magnetized Spherical Langmuir probes
<p>Dataset containing Particle-in-Cell simulations of Langmuir probes in magnetized plasma decribed in the paper "Spherical Langmuir probes in magnetized plasma. A model based on Particle-in-Cell simulations". The simulations are done for plasma parameters representative of the lower E-region ionosphere, with probe biases in the electron saturation regime.</p> <p>Each simulation has its own folder denoting the magnetic field strength |B| in Tesla, and probe bias electric potential in Voltage. The folder naming convention uses three digits for numerical values, where the last digit is after the decimal point. i.e "010" is the value 1.0. </p> <p> </p> <p>Plasma parameters used for simulations:</p> <p>Density (e,i) (m−3) 5.9e9<br> Te(K) 1000<br> Ti(K) 1000<br> me(kg) 9.11e-31<br> mi(kg) 4.55e-28</p> <p>The hierarchical structure of the history.xy.h5 files:</p> <p> -current<br> -electrons<br> -dataset<br> -ions<br> -dataset<br> -energy<br> -kinetic<br> -specie 0<br> -specie 1<br> -total<br> -potential<br> -specie 0<br> -specie 1<br> -total<br> -potential<br> -dataset</p> <p>The top-level group "current" is the current to the probe given for electrons and ions. Values are in Ampere. "energy" is the total kinetic and potential energies within the whole simulated domain for electrons (specie 0), ions (specie 1), and both species combined (total). The potential energy is not calculated per species for these simulations, and is therefore set to zero. Values are given in Joule.</p> <p>The top-level group "potential" is the electric potential (bias) of the probe and is given in Volts.</p> <p>Each datapoint is given each 1.5*10^(-9) s, giving it a sampling frequency of 6.67*10^(8) Hz.</p> <p> </p> <p>In addition density data for the whole simulated domain is given in separate H5 files for (rho) electropns, ions, the combined charge density, and (phi) the electric potential. </p> <p> </p> <p>Example python ploting scripts is added for ease of access. </p> <p> </p>
Resolving the Interpretation of Magnetic Coercivity Components from Backfield Isothermal Remanence Curves Using Unmixing of Non-linear Preisach Maps: Application to Loess-Paleosol Sequences
<p>The data set includes:</p> <p>1. Non-linear Preisach measurements for Lunca and Costinești loess-paleosol sections</p> <p>2. IRM coercivity distributions from Lunca and Costinesti interpolated on a common sequence of fields</p> <p>3. Lunca granulometry data</p> <p>4. Median Grain size for Costineși section.</p> <p>5. Magnetic susceptibility data measured at Lunca section (Constantin et al., 2015)</p> <p>6. IRM acquisition curves derived from backfield IRM data through rescaling for Costinesti section</p> <p>7. Costinesti rock magnetic data (Necula et al., 2015)</p>
Data set for: Real-space Imaging of Confined Magnetic Skyrmion Tubes
<p>This repository contains the scripts and notebooks to reproduce the figures, simulations and numerical data shown in <strong>Real-space Imaging of Confined Magnetic Skyrmion Tubes</strong> by <em>M. T. Birch, D. Cortés-Ortuño, L. A. Turnbull, M. N. Wilson, F. Groß, N. Träger, A. Laurenson, N. Bukin, S. H. Moody, M. Weigand, G. Schütz, H. Popescu, R. Fan, P. Steadman, J. A. T. Verezhak, G. Balakrishnan, J. C. Loudon, A. C. Twitchett-Harrison, O. Hovorka, H. Fangohr, F. Ogrin, J. Gräfe and P. D. Hatton.</em></p> <p>Both simulation and experimental data analysis are performed using Python with the Matplotlib, Jupyter, Scipy, Numpy and h5py libraries.</p> <p>Jupyter notebooks are provided to process the experimental data and reproduce the STXM, X-Ray Holography and LTEM images, which are shown as Figures 2, 3, 4 and 5 in the paper.</p> <p>Simulation scripts are based on the finite difference micromagnetic code OOMMF with the extension to simulate DMI for materials with symmetry class <em>T</em>: [oommf-extension-dmi-t](https://github.com/joommf/oommf-extension-dmi-t)</p> <p>The analysis of OOMMF's output files, which are in the `OMF` format, are processed using the [OOMMFPy](https://github.com/davidcortesortuno/oommfpy) library, which can calculate the topological charge in a 2D slice.</p> <p>Three-dimensional visualisations of the magnetic states are performed using Paraview. In order to get VTK files for visualisation, convert the `OMF` files into `.vtk` using the `OOMMFPy` library.</p> <p> </p> <p>Latest version of this Data Set can be found at the Github repository:</p> <p><a href="https://github.com/davidcortesortuno/paper-2020_real-space_imaging_of_confined_magnetic_skyrmion_tubes">https://github.com/davidcortesortuno/paper-2020_real-space_imaging_of_confined_magnetic_skyrmion_tubes</a></p>
IODP Expedition 361 Magnetic susceptibility (section half)
<p>Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).</p>
Simulation data of "Controlling the chirping of chorus waves via magnetic field inhomogeneity"
<p>Simulation data of "Controlling the chirping of chorus waves via magnetic field inhomogeneity", including waveform recorded at certain locations, part of 2D wave field and wave spectrogram obtained with 2D FFT. </p>
Nanoscale Imaging of High-Field Magnetic Hysteresis in Meteoritic Metal Using X-Ray Holography
<p>Data of magnetisation (two datasets) of the cloudy zone of Tazewell IIICD iron meteorite. Data was obtained using X-ray holography. Magnetization data is a 3D matrix containing magnetisation data in form of data[x location][y location][applied field], applied field values is provided in a separate file.</p> <p>Further details about this dataset and conditions of measurements can be found in Blukis et al., 2020 submitted to Geochemistry, Geophysics, Geosystems</p>
Mid to late Pleistocene IODP Expedition 354 Bengal Fan 8⁰ North transect age models, sedimentation rate stack, magnetic susceptibility stack, and XRF data
<p>Mid to late Pleistocene age models for the International Ocean Discovery Program (IODP) Expedition 354 8⁰ North drilling transect. Stacked records of sedimentation rates and magnetic susceptibility. U-channel XRF scans of calcareous clay sediments at Site U1452.</p> <p> </p> <p><strong>Abstract:</strong></p> <p>We investigate chronology and age uncertainty for the middle to upper Pleistocene lower Bengal Fan using a novel age-depth modeling approach that factors litho-, magneto-, bio-, cyclo-, and seismic stratigraphic constraints, based on results from the International Ocean Discovery Program Expedition 354 Bengal Fan and analysis of the GeoB97-020/027 seismic line. The initial chronostratigraphic framework is established using regionally extensive hemipelagic sediment units and only age-depth models of fan deposits that respect the superposition of channel-levee systems between sites are accepted. In doing so, we reconstruct signals of regional sediment accumulation rate and lithogenic sediment input through the perspective of a two-dimensional ~320 km transect at 8⁰ N that are consistent with more distal and more ambiguous regional records. This chronology allows us to discuss the depositional history of the middle to upper Pleistocene lower Bengal Fan within the context of sea level, climate, and tectonic controls. We hypothesize, based on the timing of accumulation rate changes, that progradation and intensification of the Bengal Fan’s channel-levee system at 8⁰ N was largely driven by increases in sea level amplitude during this time. However, it is also possible this progradation was influenced by changes in Pleistocene climate and increased Himalayan erosion rates, driving greater sediment flux to the fan.</p> <p> </p>
Reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - II. Implementation of magnetic braking in MESA and implications for the evolution of surface rotation in OB stars "
<p>This is a reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - II. Implementation of magnetic braking in MESA and implications for the evolution of surface rotation in OB stars" by Keszthelyi et al. (2020), https://doi.org/10.1093/mnras/staa237</p>
IODP Expedition 362 Magnetic susceptibility (section half)
<p>Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).</p>
IODP Expedition 362 Magnetic remanence (spinner)
<p>Magnetic remanence was measured on discrete samples by an Agico JR-6A spinner magnetometer, first as natural remanent magnetization (NRM) and then after demagnetization or remagnetization steps were performed on the samples (e.g., alternating field [AF] demagnetization, thermal demagnetization [TD], or isothermal remanent magnetization [IRM]).</p>
IODP Expedition 362 Magnetic remanence (SRM-longcore)
<p>Magnetic remanence was measured on section halves (and rarely on whole-round sections) using a 2G Enterprises 760R cryogenic magnetometer, first as natural remanent magnetization (NRM) and then after demagnetization steps were performed on the samples by alternating field (AF) demagnetizer coils mounted in-line within the instrument.</p>
Detecting axisymmetric magnetic fields using gravity modes in intermediate-mass stars
<p>Typical MESA and GYRE inlists associated with <a href="https://ui.adsabs.harvard.edu/abs/2020arXiv200502411V/abstract">Van Beeck et al. (2020)</a>. MESA version 10398 and GYRE version 5.2.</p> <p>Context: Angular momentum (AM) transport models of stellar interiors require improvements to explain the strong extraction of AM from stellar cores that is observed with asteroseismology. One of the often invoked mediators of AM transport are internal magnetic fields, even though their properties, observational signatures and influence on stellar evolution are largely unknown.</p> <p>Aims: We study how a fossil, axisymmetric internal magnetic field affects period spacing patterns of dipolar gravity mode oscillations in main-sequence stars with masses of 1.3, 2.0 and 3.0 <span class="math-tex">\(\mathrm{M}_{\odot}\)</span> . We assess the influence of fundamental stellar parameters on the magnitude of pulsation mode frequency shifts.</p> <p>Methods: We compute dipolar gravity mode frequency shifts due to a fossil, axisymmetric poloidal-toroidal internal magnetic field for a grid of stellar evolution models, varying stellar fundamental parameters. Rigid rotation is taken into account using the traditional approximation of rotation and the influence of the magnetic field is computed using a perturbative approach.</p> <p>Results: We find magnetic signatures for dipolar gravity mode oscillations in terminal-age main-sequence stars that are measurable for a near-core field strength larger than 10<sup>5</sup> G. The predicted signatures differ appreciably from those due to rotation.</p> <p>Conclusions: Our formalism demonstrates the potential for the future detection and characterization of strong fossil, axisymmetric internal magnetic fields in gravity-mode pulsators near the end of core-hydrogen burning from Kepler photometry, if such fields exist.</p> <blockquote> <p>The publication date is the date of acceptance.</p> </blockquote> <p>J. Van Beeck would like to thank researchers M. Michielsen, C. Johnston, and dr. M. G. Pedersen for their valuable input in the MESA and GYRE computations.</p>
Spherical harmonic model of the magnetic field of Mars from Morschhauser et al. (2014)
<p><strong>Morschhauser2014.txt.gz</strong> is a gzipped file of the magnetic potential coefficients of Mars as published by Morschhauser et al. (2014). This is the same as the file ts01.txt in the supplemental materials of this manuscript.</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.