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4,376 results for “magnetization”
IODP Expedition 356 Magnetic susceptibility (point or contact system)
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>).
IODP Expedition 356 Magnetic remanence (SRM-longcore)
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.
Data for: Neuromorphic weighted sums with magnetic skyrmions
<h3>Description</h3> <p>The following experimental data were obtained on lithography devices made of magnetic multilayer tracks and thin tantalum transverse electrodes by Kerr microscopy and anomalous Hall effect measurements. The results, demonstrating the weighted sum operation using magnetic skyrmions, are published in T. da Câmara Santa Clara Gomes et al., Neuromorphic weighted sums with magnetic skyrmions, Nature Electronics (2024). Please find in the README additional information regarding the data files and the variables.</p> <h3>Abstract</h3> <div> <p>Integrating magnetic skyrmions into neuromorphic computing could help improve hardware efficiency and computational power. However, developing a scalable implementation of the weighted sum of neuron signals — a core operation in neural networks — has remained a challenge. Here, we show that weighted sum operations can be performed in a compact, biologically-inspired manner by using the non-volatile and particle-like characteristics of magnetic skyrmions that make them easily countable and summable. The skyrmions are electrically generated in numbers proportional to the input with an efficiency given by a non-volatile weight. The chiral particles are then directed using localized current injections to a location where their presence is quantified through non-perturbative electrical measurements. Our experimental demonstration, which currently has two inputs, can be scaled to accommodate multiple inputs and outputs using a crossbar array design, potentially nearing the energy efficiency observed in biological systems.</p> </div>
Magnetic arch plasma expansion in a cluster of two ECR plasma sources (RPA and FC measurements)
<p>- Data from: Magnetic arch plasma expansion in a cluster of two ECR plasma sources (RPA and FC measurements)</p> <p>- Authors: Célian Boyé, Jaume Navarro-Cavallé, Mario Merino</p> <p>- Contact email: <a href="mailto:cboye@ing.uc3m.es" target="_blank" rel="noopener">cboye@ing.uc3m.es</a></p> <p>- Date: 2024-10-24</p> <p>- Version: 1.0</p> <p>- License: This dataset is made available under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a></p> <p> </p> <h2>Abstract</h2> <p>This dataset contains the raw experimental data employed in:</p> <p>Célian Boyé, Jaume Navarro-Cavallé, Mario Merino, "Magnetic arch plasma expansion in a cluster of two ECR plasma sources", Journal of Electric Propulsion.</p> <p>Which is currently submitted.</p> <p> </p> <h2>Dataset description</h2> <p>The experimental data is gathered by means of a Retarding Potential Analyzer (RPA) and a Faraday Cup (FC). The probes have been set on a polar probing arm system to scan the central horizontal plane of the setup, aligned with the axis of symmetry of the assembly and pointing toward the origin at the exit plane of the source(s).</p> <p>The RPA data is provided separately for every spatial position inspected for each configuration (S0, S1, D0, DA, DB). It is collected by means of an Impedance-Semion Retarded Potential Analyser, with a mean resolving voltage of 1V. The FC data is provided for the DA configuration to support the RPA measurements. </p> <p>Please refer to the corresponding article for further details regarding the data collection.</p> <p> </p> <h2>Data files</h2> <p>The data files are in standard comma separated values .csv format. Many programming languages provide functionalities to load such fields.</p> <ul> <li> <h3>RPA data</h3> </li> </ul> <p>The RPA data is separated through the different configurations:</p> <ul> <li> <ul> <li>S0: single ECR source without applied magnetic field.</li> <li>S1: single ECR source with applied magnetic field.</li> <li>D0: cluster of ECR sources without applied magnetic field.</li> <li>DA: cluster of ECR sources with opposed polarity.</li> <li>DB: cluster of ECR sources with same polarity.</li> </ul> </li> </ul> <p>The angle steps vary through the different configurations. Each file contains 8 headlines. </p> <ul> <li> <ul> <li>The first column contains the voltage applied to the sweeping grid (V).</li> <li>The second to sixth columns contain the current collected by the collector (A).</li> <li>The eventh to eleventh columns contain the derivative of the collected current by the voltage (A/V).</li> </ul> </li> </ul> <ul> <li> <h3>FC data</h3> </li> </ul> <p>The FC data has been probed for the DA configuration. The file contains 2 headlines.</p> <ul> <li> <ul> <li>The first column contains the angle at which the current has been collected (deg).</li> <li>The second column contains the distance from the origin at the exit plane of the cluster (mm).</li> <li>The third column contains the collected current (A).</li> </ul> </li> </ul> <p> </p> <h2>Citation</h2> <p>Works using this dataset or any part of it in any form shall cite it as follows.</p> <p>The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available.</p> <p>Optionally, the dataset may be cited directly by referencing the corresponding DOI: 10.5281/zenodo.13987138</p> <p> </p> <h2>Acknowledgments</h2> <p>This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (project ERC-STG ZARATHUSTRA, grant agreement No 950466). </p>
IODP Expedition 353 Magnetic remanence (SRM-discrete)
Raw data files for the magnetic remanence measurements of discrete and section-half samples on the superconducting rock magnetometer (SRM-DISC and SRM-SECT) are stored on by hole and by expedition, and are designated as discrete samples, section halves, or, rarely, whole-round sections.
IODP Expedition 353 Magnetic remanence (SRM-longcore)
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.
IODP Expedition 353 Magnetic susceptibility (point or contact system)
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>).
IODP Expedition 359 Magnetic susceptibility (point or contact system)
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>).
IODP Expedition 359 Magnetic susceptibility (whole round)
Magnetic susceptibility was measured on whole-round sections (and rarely section halves) on the Whole-Round Multisensor Logger (WRMSL) and/or Special Task Multisensor Logger (STMSL) using a Bartington MS2 meter and a 90 mm or 80 mm MS2C loop. As volume of the sample is not controlled for this experiment, susceptibility units are recorded in instrument units and are not volume-corrected.
IODP Expedition 359 Magnetic remanence (SRM-longcore)
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.
IODP Expedition 359 Magnetic remanence (spinner)
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]).
Datasets for "Turbulent magnetic decay controlled by two conserved quantities"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Turbulent magnetic decay controlled by two conserved quantities" by A. Brandenburg, & A. Banerjee. If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Flow behaviour of magnetic steel powder
<p>These are the data used in the article "Flow behaviour of magnetic steel powder". This dataset includes:</p> <ul> <li>Optical micrographs used to determine the mean size and circularity in each size class after sieving <ul> <li>Scale bars are included for two images and may be used to determine the scale of all micrographs included - all images were taken using the same microscope, camera and software. All images are as-recorded.</li> </ul> </li> <li>X-ray diffractograms used to determine the fraction of martensite present in each sample <ul> <li>Calibration data for the diffraction instrument are included</li> </ul> </li> <li>Vibrating sample magnetometry data, used to determine the saturation and remanent magnetisation.</li> <li>Shear cell metrics derived automatically form the shear cell tests</li> <li>Hall flow times, both with and without drying</li> <li>Angle of repose dat</li> <li>Videos of all angle of repose tests</li> </ul> <p>Other data can be provided on request.</p>
Nuclear Magnetic resonance Dataset of 2D spectra of S100B and Tau to study their protein-protein interaction
<p>Nuclear Magnetic resonance dataset of 2D spectra corresponding to raw data of research published in Nature Communication in a communication entitled "Dynamic interactions and Ca2+ 1 -binding modulate the holdase-type chaperone activity of S100B preventing tau aggregation and seeding" by Moreira G. et al.</p> <p>Dataset corresponds to</p> <p>raw data files in Bruker format of NMR 2D spectra (ser), associated with files of acquisition parameters and processing parameters (pdata),</p> <p>files in .ucsf format that can be read with NMRFAM-Sparky (free download) of 2D spectra (in sub-directory pdata/1)</p> <p>files of chemical shift value lists that can be read as text files or in NMRFAM sparky together with the corresponding ucsf files.</p> <p>physico-chemical conditions are found in title in pdata\1</p> <p>Data were acquired on a Bruker 900-MHz spectrometer equipped with a triple-resonance cryogenic probe (Bruker, Karlsruhe, Germany)</p>
Measured magnetic susceptibility data for different magnetite tracer stacking scenarios
<p>Dataset includes measured data of the volume magnetic susceptibility of 36 artificial soil profiles with various distribution of magnetic tracer. The monitoring was done with Bartington MS2D field probe.</p> <p>The dataset was created for the fitting and calibration of the parameters of a MagHut model. The model and the procedure is described in a manuscript by Zumr D., Li T., Gómez J., Guzmán G., Modelling the response of a field probe for non-destructive measurements of the magnetic susceptibility of soils (to date of the data submission under review).</p>
Magnetic Resonance Imaging Copper Sulfate 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 CuSO4. The DICOM file headers have metadata tags, which embody additional information about the study and the particular series.</p>
BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution
<p><strong>BigBrain-MR</strong> is a novel digital phantom with realistic anatomical detail up to 100-µm resolution, including multiple MRI contrasts and properties that affect image generation. This phantom was generated from the publicly available <a href="https://bigbrainproject.org/">BigBrain histological dataset</a> and from lower-resolution in-vivo 7T-MRI data, using a new image processing framework that allows mapping the general properties of in-vivo data into the fine anatomical scale of BigBrain.</p> <p>The <strong>dataset</strong> includes:</p> <ul> <li>BigBrain original contrast and a new atlas with 20 ROIs;</li> <li>T<sub>1</sub>-weighted image and T<sub>1</sub> map;</li> <li>T<sub>2</sub>*-weighted images and R<sub>2</sub>* map;</li> <li>Magnetic susceptibility map (QSM);</li> <li>Background magnetic field map;</li> <li>Complex coil sensitivity maps (32ch-receive RF array);</li> <li>Bias field map.</li> </ul> <p>Information about each image/map (including data type and amplitude scaling) is provided in <em>data_info.txt</em>.</p> <p>Additionally, we have included a script with <strong>usage examples</strong> in Python that illustrate how the data can be loaded, processed and combined for diverse simulation purposes.</p> <p>BigBrain-MR is presented, described and tested in the following <strong>peer-reviewed article</strong>:</p> <p>C. Sainz Martinez, M. Bach Cuadra, J. Jorge. <em>BigBrain-MR: a new digital phantom with anatomically-realistic magnetic resonance properties at 100-µm resolution for magnetic resonance methods development</em>. NeuroImage 2023. <strong>DOI:</strong> <a href="https://doi.org/10.1016/j.neuroimage.2023.120074">10.1016/j.neuroimage.2023.120074</a></p> <p> </p>
Strong collisionless coupling between an unmagnetized driver plasma and a magnetized background plasma
<p>This repository contains some of the simulation data presented in the recent article in plasma physics titled "Strong collisionless coupling between an unmagnetized driver plasma and a magnetized background plasma" (<a href="https://arxiv.org/abs/2302.00149">https://arxiv.org/abs/2302.00149</a>). The data available are for 1D particle-in-cell (PIC) simulations that consider the interaction between a uniform unmagnetized driver plasma flowing against a uniform magnetized background plasma, for multiple values of the driver density and background magnetic field. The simulations were performed with OSIRIS, a massively parallel and fully-relativistic, PIC code.</p> <p>Using the data from the simulations, we studied the coupling between the plasmas and determined the compression ratio and the velocities of the magnetic cavity and magnetic compression that were visible in the simulations. More information on the simulations and on the obtained results are presented in the article.</p> <p>The datasets contain the main data of some of the simulations presented in the paper (.h5 files), the coupling parameters measured in the simulations (coupling_data.csv), and a Jupyter Notebook file to look at the simulation results from the available datasets (read_dataset.ipynb).</p>
Magnetic field data for "Cosmic Rays in Intermittent Magnetic Fields" (magnetic fields produced by the small-scale dynamo)
<p>Magnetic field data for the kinematic dynamo generated magnetic fields (KS) employed in the cosmic ray test particle simulations of Shukurov et al. 2017, <em>ApJL</em>, <strong>839</strong>, L16 [<a href="https://doi.org/10.3847/2041-8213/aa6aa6">https://doi.org/10.3847/2041-8213/aa6aa6</a>]. One dataset was also used in "Relative distribution of cosmic rays and magnetic fields", Seta et al. 2018, <em>MNRAS</em>, <strong>473 </strong>(4), 4544-4557 [<a href="https://doi.org/10.1093/mnras/stx2606">https://doi.org/10.1093/mnras/stx2606</a>]. These studies investigated the effects of magnetic field structure on charged test particle transport and trapping. See the README file for more details.</p> <p> </p>
Magnetic coupling of divalent metal centers in postsynthetic metal exchanged bimetallic DUT-49 MOFs by EPR spectroscopy
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20201111_ULEI_21_DUT49Mn@7K </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20201111_ULEI_00_DUT49Mn@simulation </strong>folder includes computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>DUT49Cu – </strong>DUT-49(Cu) MOF, <strong>DUT49Mn – </strong>DUT-49(Mn) MOF, <strong>DUT49CuZn – </strong>DUT-49(CuZn) MOF, <strong>DUT49MnCu – </strong>DUT-49(MnCu) MOF.</li> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> </ul> </li> </ul> <p>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</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.