Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4,376
datasets available to search
ShareScore release 0.9.0
Dataset results
4,376 results for “magnetism”
MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging
Open the record for dataset details and reuse information.
Dataset of "Asparagine-Modified Magnetic Graphene Oxide: An Efficient and Green Nanocatalyst for Synthesis of 5-oxodihydropyrano[3,2-c]chromenes and dihydropyrano[2,3- c]pyrazole derivatives and the Density functional theory calculation".
<p>The primary focus of this study involved the fabrication of a novel nanocatalyst Fe3O4-supported asparagine functionalized graphene oxide (Fe3O4@GO-N-(Asparagine)). The catalyst was synthesized through a four-step procedure.</p>
Data from the behavioural and Magnetic resonance imaging of the Ts66Yah and Ts65Dn male model of Down syndrome
<p>Please find enclosed the behavioural and Magnetic Resonnance Imaging (MRI) variables used for comparing the Ts66Yah DS models with the parental line Ts65Dn. The raw data are found as two CVS files</p> <p>- Behavioural phenoParameters_Ts65Dn_Ts66Yah.csv</p> <p>- MRI phenoParameters_Ts65Dn_Ts66Yah.csv</p> <p>while the processed data used for the GDAPHEN analysis (https://github.com/YaH44/GDAPHEN/releases/tag/Public) are available as Excel docs.</p> <p> </p> <p>The processing has been done with a low level of imputation for missing data detailed in the Formating_decision_phenoParameters_Ts65Dn_Ts66Yah. ...</p>
Dataset for "Magnetic catalysis in the (2+1)-dimensional Gross-Neveu model"
<p>We study the Gross-Neveu model in 2 + 1 dimensions in an external magnetic field B. We<br> first summarize known mean-field results, obtained in the limit of large flavor number N f , before<br> presenting lattice results using the overlap discretization to study one reducible fermion flavor,<br> N f = 1. Our findings indicate that the magnetic catalysis phenomenon, i.e., an increase of the chiral<br> condensate with the magnetic field, persists beyond the mean-field limit for temperatures below the<br> chiral phase transition and that the critical temperature grows with increasing magnetic field. This<br> is in contrast to the situation in QCD, where the broken phase shrinks with increasing B while the<br> condensate exhibits a non-monotonic B-dependence close to the chiral crossover, and we comment on<br> this discrepancy. We do not find any trace of inhomogeneous phases induced by the magnetic field.</p> <p> </p> <p>If you use this data, please cite the corresponding paper:<br> https://doi.org/10.48550/arXiv.2302.05279 (or better the not-yet-existing published version)</p>
Nuclear Magnetic Resonance values for the Eptachori, Pentalofos and Tsotyli formations in West Macedonia
<p>The data comprises work under the Project Pilot Strategy GA No. 101022664, funded by the European Union. </p> <p>The work relates to rock samples collected in 2022 in West Macedonia, Greece. For full details, please refer to the following:</p> <ol> <li>Tsotyli formation: <a href="https://app.geosamples.org/sample/igsn/IE5770001">https://app.geosamples.org/sample/igsn/IE5770001</a> - <strong>WGS84 Lat : 40.3075, </strong><strong>WGS84 Long : 21.3354</strong></li> <li>Pentalofos formation: <a href="https://app.geosamples.org/sample/igsn/IE5770002">https://app.geosamples.org/sample/igsn/IE5770002</a> - <strong>WGS84 Lat : 40.1332,</strong> <strong>WGS84 Long : 21.1997</strong></li> <li>Eptachori formation: <a href="https://app.geosamples.org/sample/igsn/IE5770003">https://app.geosamples.org/sample/igsn/IE5770003</a> - <strong>WGS84 Lat : 40.1332, </strong><strong>WGS84 Long : 21.1997</strong></li> </ol> <p>The focus of the work is related to CO2 storage in appropriate saline aquifers in West Macedonia. The bulk samples were shipped to IFP Energies for porosity and permeability laboratory investigation conducted by Nuclear Magnetic Resonance techniques. </p> <p>Further to the raw data from the NMR, a depiction of the latter is provided in the corresponding figures</p> <p> </p>
Marine magnetic anomaly data from high resolution surveys off the SW Portuguese coast
<p>This dataset contains <strong>magnetic anomaly grids</strong> that result from the full processing of marine magnetic data collected off the SW Portuguese coast between 2014 and 2019. A total area of ~4400 km<sup>2</sup> was surveyed with average line spacing of 1 nautic mile. Surveys covered the continental shelf and in some regions reaching up to 2500 m bathymetric levels. Total magnetic field data were acquired with a G882 Cesium vapor marine magnetometer towed, towed at sea surface.</p> <p><strong>Full processing</strong> of magnetic data included: layback correction; noise removal; IGRF subtraction; base station correction; line leveling; minimum curvature gridding. The resulting sea level magnetic anomaly grid was further processed for upward continuation and reduction to the pole, providing additional outputs. </p> <p>The following grids are provided in <strong>georeferenced geotiff format</strong>:</p> <ul> <li>Magnetic anomaly (sealevel)</li> <li>Magnetic anomaly reduced to the pole (sealevel)</li> <li>Magnetic anomaly upward continued to 200 m height </li> <li>Magnetic anomaly upward continued to 200 m height, reduced to the pole</li> <li>Magnetic anomaly upward continued to 3000 m height </li> <li>Magnetic anomaly upward continued to 3000 m height, reduced to the pole</li> </ul> <p><strong>Published in</strong>: Neres, M., P. Terrinha, J. Noiva, P. Brito, M. Rosa, L. Batista, C. Ribeiro (2023). <em>New Late Cretaceous and CAMP magmatic sources off West Iberia, from high-resolution magnetic surveys on the continental shelf.</em> <strong>Tectonics</strong>. doi: 10.1029/2022TC007637</p> <p> </p>
Dataset for "The magnetized (2+1)-dimensional Gross-Neveu model at finite density"
<p>We perform a lattice study of the (2+1)-dimensional Gross-Neveu model in a background magnetic field <em>B</em> and at non-zero chemical potential <em>μ</em>. The complex-action problem arising in our simulations using overlap fermions is under control. For <em>B</em>=0 we observe a first-order phase transition in <em>μ</em> even at non-vanishing temperatures. Our main finding, however, is that the rich phase structure found in the limit of infinite flavor number <em>N</em>f is washed out by the fluctuations present at <em>N</em>f=1. We find no evidence for inverse magnetic catalysis, i.e., the decrease of the order parameter of chiral symmetry breaking with <em>B</em> for <em>μ</em> close to the chiral phase transition. Instead, the magnetic field tends to enhance the breakdown of chiral symmetry for all values of <em>μ</em> below the transition. Moreover, we find no trace of spatial inhomogeneities in the order parameter. We briefly comment on the potential relevance of our results for QCD.</p> <p>If you use this data, please cite the corresponding paper:<br> https://doi.org/10.48550/arXiv.2304.14812 (or better the not-yet-existing published version)</p>
Flow Magnetic Tweezers example video
<p>The example video contains a section of a field-of-view from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils.</p>
Labelled magnetic reconnection simulation data set
<p>Numerical simulations have been performed on Marconi at CINECA (Italy) under the ISCRA initiative. The corresponding data can be found at: <a href="https://doi.org/10.5281/zenodo.3935887">https://doi.org/10.5281/zenodo.3935887</a></p>
Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions
<p>This dataset provides various acquisitions for T2 mapping of the MnCl2 array of the NIST phantom at 1.5T. Data were acquired on a MAGNETOM Sola (Siemens Healthcare, Erlangen, Germany), with an 18-channel body coil and a 32-channel spine coil (12 elements used). It gathers original acquisitions from Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12.</p> <p>The dataset is composed of DICOM images from:</p> <p>i) Gold-standard single-echo spin echo (SE) sequences acquired at variable TE;</p> <p>ii) Alternative reference multi-echo spin echo (MESE) acquisitions;</p> <p>iii) Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images at variable TE in three orthogonal orientations.</p> <p>The acquisition parameters are further detailed in the ReadMe.txt file provided along with the images.</p> <p>These acquisitions were repeated independently on three different days during the month of January 2020.</p> <p>These data are made publicly available as a support for further reproducibility studies as well as for the validation of new T2 relaxometry strategies.</p> <p>Works using any of these data should cite the following two references:</p> <p>- Lajous H. et al. (2020) T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_12</p> <p>- Lajous, Hélène, Ledoux, Jean-Baptiste, Hilbert, Tom, van Heeswijk, Ruud B., & Bach Cuadra, Meritxell. (2020). Dataset T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3931812</p>
Pressure and chemical substituion in the Kitaev magnet alpha-RuCl3
<p>These dataset is the result of investigation of the effect of pressure and chemical substituion in the Kitaev magnet alpha-RuCl3.<br> <br> These data corresponds to the experimental results discussed in the open access publications: https://doi.org/10.1103/PhysRevB.97.241108 and https://doi.org/10.1103/PhysRevB.99.214410.<br> <br> Every odd column contains temperature values. Every even column contains the physical quantity (magnetization or specific heat depending on the title of the files) measured at the temperature indicated in the previous column under the conditions described in the third line of the column. These conditions can be pressure or composition change as indicated in the title of the file.<br> Physical units used are Kelvin, emu/mol/Oe and J/mol/K for temperature, magnetization and specific heat respectively as indicated in the second line of the table.</p>
Magnetic hyperthermia with e-Fe2O3 nanoparticles
<p>These data correspond to the figures in the paper: Gu Y. et al. RSC Adv., 2020, 10, 28786–28797. doi:10.1039/d0ra04361c.</p>
Datasets for "Gravitational waves from the chiral magnetic effect"
<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 "Gravitational waves from the chiral magnetic effect" by A. Brandenburg, Y. He, T. Kahniashvili, M. Rheinhardt, and J. Schober. If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Dataset from "In silico assessment of collateral eddy current heating in biocompatible implants subjected to magnetic hyperthermia treatments"
<p>This dataset from the publication entitled "Dataset from "In silico assessment of collateral eddy current heating in biocompatible implants subjected to magnetic hyperthermia treatments" contains simulated data of magnetic hyperthermia treatments for three different indications: colorectal cancer, prostate cancer and head & neck cancer. Since the aim of the study is to evaluate the risk of thermal damage caused by the collateral heating of two common types of passive prostheses (hip and dental implants), eddy currents induced in these implants upon interacting with the externally applied ac field during treatment have been computed for all the evaluated regions. Two different alloys for the implants have been considered for each case as well: Ti6Al4V and CoCrMo. At the same time, besides temperature, the specific abosorption rate (SAR) have been also computed to work out the energy deposition in tissues.</p> <p>Calculations have been carried out using a het exchange model with and without thermoregulation.</p> <p>log-log SAR vs T plots have been obtained and proposed as a quick means to pre-check treatment feasibility in each patient. These graphs are thought to be included in treatment planning prior to the clinical procedure.</p> <p>Other parameters taken into account have been the treatment time (5 and 30 minutes), and the maximum tolerable temperature threshold (1 or 5 ºC, as indicated by the ICNIRP commission), all for three main types of tissues, namely fat, bone and muscle. Each tissue have been simulated using three different field intensities (5, 10 and 15 mT).</p> <p>The field frequency has been 300 kHz in all cases.</p> <p>The files "Dataset_description.doc" and "file_scheme.txt" contain the structure and description of the files that make up the dataset.</p> <p>UPDATES FROM PREVIOUS VERSIONS: simulations of the dental implant without thermoregulation have been added.</p>
Data from: An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles
<p><strong> Data from: An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles</strong></p> <p>- Authors: Pedro Jimenez, Luis Chacon, Mario Merino</p> <p>- Contact email: pejimene@ing.uc3m.es</p> <p>- Date: 2024-02-09</p> <p>- Keywords: electric propulsion, plasma simulation, magnetic nozzles, implicit particle-in-cell (PIC)</p> <p>- Version: 1.2</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.8081962</p> <p>- License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0">Open Data Commons Attribution License</a></p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the journal article:</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S0021999124000755?via%3Dihub">Pedro Jimenez, Luis Chacon, Mario Merino, "An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles"</a></p> <p>The data in this repository are the results of kinetic plasma simulations as described in the reference. For further information on the setup for the simulation please refer to the article.</p> <p><strong>Data Files</strong></p> <p>The data files are in .csv format. They were produced in Julia using <a href="http://csv.juliadata.org/stable/)">CSV.jl</a> and <a href="https://dataframes.juliadata.org/stable/">DataFrames.jl</a> libraries.</p> <p>The files are organised following the order of the figures in the article. All the plots are 1D series, the first column corresponding to the x-axis data. Y-axis data is presented in the following columns, the total number of additional columns is equal to the number of line series. The title of each series is found in the first row of the .csv files. Please find below some specificalities in certain figures:</p> <p>- The columns for the time evolution in <strong>fig6_left.csv</strong> and<strong> fig6_right.csv </strong>(corresponding to the actual left and right columns in the figure i.e. cases A and B) contain a field tag followed by the corresponding time step (e.g. phi_500).</p> <p>- Due to the different number of nodes, steady state fields for cases A and B are saved in <strong>fig8_a-f.csv</strong> while cases AF and BF are saved in <strong>fig8_a-f_fine.csv</strong>.</p> <p>The rest of the data files should be self descripting</p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The prefered means of citation is to reference the publication asociated to the jounal article with DOI: <a href="https://doi.org/10.1016/j.jcp.2024.112826">10.1016/j.jcp.2024.112826</a></p> <p>The BibTex is also provided for the sake of convinience:</p> <pre>@article{jimenez2024implicit, title={An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles}, author={Jim{\'e}nez, Pedro and Chac{\'o}n, Luis and Merino, Mario}, journal={Journal of Computational Physics}, pages={112826}, year={2024}, publisher={Elsevier} }</pre> <p>Optionally the dataset can be cited by referencing the corresponding DOI:</p> <p><a href="https://doi.org/10.5281/zenodo.8081962">https://doi.org/10.5281/zenodo.8081962</a></p> <p><strong>Acknowledgments</strong></p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466).</p>
Magnetic excitation moments for large moons of the giant planets
<p>Text files containing the spatially uniform (degree-1) magnetic oscillations experienced by each large moon of the giant planets as a function of frequency, also known as the excitation moments. All moments are in complex notation relative to the J2000 epoch. Used for determining the strength of induced magnetic fields from the moons from an interior conductivity structure. This dataset is compatible for use with the <a href="https://github.com/itsmoosh/MoonMag" target="_blank" rel="noopener">MoonMag</a> and <a href="https://github.com/vancesteven/PlanetProfile" target="_blank" rel="noopener">PlanetProfile frameworks</a> for calculating induced magnetic fields of target moons. Refer to the publication linked below for more information.</p> <p>All vector components are in IAU coordinates, such that at the body center, +<em>z</em> is directed along the body spin axis, +<em>x</em> is directed approximately toward the parent planet in the plane of an IAU-defined meridian feature, and +<em>y</em> is directed approximately opposite to the orbital velocity to complete the right-handed set. For the uranian moons and Triton, the IAU +<em>z</em> axes are opposite the spin axes because of their angles relative to the solar system invariable plane; the +<em>y</em> axes for these bodies are therefore directed approximately along the orbital velocity vector.</p> <p>Excitation fields are determined by evaluation of SPICE kernels over a time series at the location of the body center. Position information is inserted into a magnetospheric model for the parent planet and complex Fourier coefficients are inverted from the time series using linear least squares optimization. The magnetic field models we use for each planet are:</p> <ul> <li><strong>Jupiter</strong> - JRM33 + C2020 current sheet (Connerney et al., 2022, 2020) except Callisto, for which we use VIP4+K (Connerney et al., 1998; Khurana, 1997)</li> <li><strong>Saturn</strong> - Cassini 11+ (Cao et al., 2020)</li> <li><strong>Uranus</strong> - AH<sub>5</sub> (Herbert, 2009)</li> <li><strong>Neptune</strong> - O8 (Connerney et al., 1991)</li> </ul> <p>ASCII text files are included for all major moons of these planets.</p>
NMRduino: A modular, open-source, low-field magnetic resonance platform
<p>The NMRduino is a compact, cost-effective, sub-MHz NMR spectrometer that utilizes readily available open-source hardware and software components. One of its aims is to simplify the processes of instrument setup and data acquisition control to make experimental NMR spectroscopy accessible to a broader audience. In this introductory paper, the key features and potential applications of NMRduino are described to highlight its versatility both for research and education.</p>
EDEN2020 Ovine Diffusion Tensor Magnetic Resonance Tractography Atlas
<p>This dataset has been created to share the first <em>in vivo, </em>population-averaged Diffusion Tensor Magnetic Resonance Imaging (DTI) Ovine Tractography Atlas (OTA), where the course of the main white matter fiber bundles of the ovine brain has been reconstructed. The OTA has been described in the related paper ‘In vivo Diffusion Tensor Magnetic Resonance Tractography of the Sheep Brain: An Atlas of the Ovine White Matter Fiber Bundles’ by Pieri <em>et al. </em>(2019) <a href="https://doi.org/10.3389/fvets.2019.00345">https://doi.org/10.3389/fvets.2019.00345</a></p> <p>In the context of the EU’s Horizon EDEN2020 project, in vivo brain MRI protocol for ovine animal models was optimized on a 1.5T scanner. High resolution conventional MRI scans and DTI sequences (b-value = 1,000 s/mm<sup>2</sup>, 15 directions) were acquired on ten anesthetized sheep <em>ovis aries</em>, to define the diffusion features of normal adult ovine brain tissue. Topography of the ovine cortex was studied, and DTI maps were derived, to perform DTI deterministic tractography reconstruction of the corticospinal tract (CST), corpus callosum (CC), fornix (FX), visual pathway (VP), and occipitofrontal fascicle (OF), bilaterally for all the animals. Binary masks of the tracts were then coregistered and reported in the space of a standard stereotaxic ovine reference system ('ovine_model_05.nii', Nitzsche B. <em>et al.</em>, <em>Front. Neuroanat.</em> 9:69. <a href="https://doi.org/10.3389/fnana.2015.00069">https://doi.org/10.3389/fnana.2015.00069</a>). Finally, these were combined across animals to obtain population probability masks for each tract, representing voxel- by-voxel probability of the presence of the tract in the 10 animals, thus ranged between 0 and 10. </p> <p>Please don't forget to cite this publication when using the Ovine Tractography Atlas: </p> <p>Pieri V., Trovatelli M., Cadioli M., Zani D.D., Brizzola S., Ravasio G., Acocella F., Di Giancamillo M., Malfassi L., Dolera M., Riva M., Bello L., Falini A., & Castellano A. (2019). In vivo Diffusion Tensor Magnetic Resonance Tractography of the Sheep Brain: An Atlas of the Ovine White Matter Fiber Bundles. <em>Front Vet Sci, 6</em>(345), 345 <a href="https://doi.org/10.3389/fvets.2019.00345">https://doi.org/10.3389/fvets.2019.00345</a> </p> <p>This work has been carried out in the context of the EDEN2020 (Enhanced Delivery Ecosystem for Neurosurgery in 2020, www.eden2020.eu) project, that received funding from the European Union’s EU Research and Innovation programme Horizon 2020 under Grant Agreement No. 688279.</p>
A chip-based superconducting magnetic trap for levitating superconducting microparticles
<p>Video files (mp4 format) showing levitation of a spherical 50μm diameter superconducting microparticle at a temperature of 4K (levitation_4K.mp4) and 40mK (levitation_40mK.mp4).<br> Data file for Figure 6: Frequency spectrum of particle motion.</p>
Dataset supporting the paper "Doublet-Singlet-Doublet Transition in a Single Organic Molecule Magnet On-Surface Constructed with up to 3 Aluminum Atoms. Nano Letters 21, 8317 (2021)"
<p>Dataset corresponding to theoretical calculations in the paper "Doublet-Singlet-Doublet Transition in a Single Organic Molecule Magnet On-Surface Constructed with up to 3 Aluminum Atoms" Nano Letters 21, 8317 (2021), <a href="https://doi.org/10.1021/acs.nanolett.1c02881">https://doi.org/10.1021/acs.nanolett.1c02881</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR and POSCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).<br> </li> </ul>
ScienceDex guides
Understand access before you commit
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.