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691 results for “magnetic field”
Figure R1: Current evolution of the magnetic field of the coil we employed in the present study.
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Data and code for "Quasiparticle effects in magnetic-field-resilient 3D transmons"
<p>This folder contains all data files and Jupyter notebooks needed to recreate the figures of our publication, <br>"Quasiparticle effects in magnetic-field-resilient 3D transmons",<br>written by J. Krause, G. Marchegiani, L. M. Janssen, G. Catelani, Yoichi Ando, and C. Dickel</p> <p>Measurements were done with quantify-core (https://quantify-os.org/docs/quantify-core/)</p> <p>Required python packages are quantify-core (tested to run with version 0.7.4) and all its dependencies, as well as cmcrameri (colormaps used for plots).<br>Qutip is used for master equation solving and Hamiltonian modeling (used version 4.7.5) and hmmlearn (version 0.3.2) for fitting gaussian hidden-markov-models.<br>From a clean conda environment one should just install the above 4 packages and it should work.</p> <p>Content:<br>Data/ <br>Contains the plotted measurement data used in the figures of the paper mostly in csv format.<br>The datasets are loaded and plotted in the respective notebooks for all figures. </p> <p>Data/quantify_datasets/<br>Contains a few quantify datasets (raw data format of quantify measurements)</p> <p>Data/20220710_parity_paper_device_afm/<br>Contains .tiff images and .txt files with AFM data of the device</p> <p>Figures/<br>Contains all paper figures as .pdf files</p> <p>Fits/transmon_spectrum_including_EJ_harmonics<br>Contains some auxiliary fit results so the fits of flux arcs do not have to be repeated.</p> <p>Fits/Gap_vs_Bpar<br>Contains numerical results for the superconducting gap Delta as a function of magnetic field for aluminum films of different thickness.<br>Used to check the cavity data and not used for other modeling.<br>Imported in Jupyter_notebooks/models_transmon_spectrum_including_higher_harmonics.py</p> <p>Jupyter_notebooks/<br>Contains the jupyter notebooks with the code that creates the figures including the relevant calculations and fits and the loading of the data.<br>The notebook name gives the figure(s) it creates.<br>There are several auxiliary .py files and two notebooks that do not make paper figures:</p> <p>Jupyter_notebooks/fit_params.py<br>Contains the fit parameters for the quasiparticle modeling and the transmon spectrum</p> <p>Jupyter_notebooks/models_qp_dynamics.py<br>Contains the functions for quasiparticle modeling vs field and temperature.</p> <p>Jupyter_notebooks/models_transmon_spectrum_including_higher_harmonics.py<br>Contains the models for the field dependent spectrum</p> <p>Jupyter_notebooks/models_cavity_transmon_interaction.py<br>Contains qutip model for the fitting of the transmon-cavity Hamiltonian to estimate the bare-dressed changes.</p> <p>Jupyter_notebooks/fit_quasiparticle_model.ipynb<br>Notebook that shows how the quasiparticle model can be fit to the data<br>Takes a bit longer to execute</p> <p>Jupyter_notebooks/fit_cavity_transmon_interaction.ipynb<br>Looks at a resonator-transmon model to understand the bare-dressed difference and estimate the coupling.<br>Not really used in the paper, except to estimate G. </p>
Inlist files for "Detectability of axisymmetric magnetic fields from the core to the surface of oscillating post-main sequence stars"
<p>Concerned article: "Detectability of axisymmetric magnetic fields from the core to the surface of oscillating post-main sequence stars" by Bhattacharya et al. (submitted).<br>Corresponding author: Shatanik Bhattacharya</p> <p>Inlists for the proof-of-concept stellar models used in this project have been provided here for reproducibility.</p> <p>For the red-giant model, the inlist was executed with MESA version r22.05.1 and MESA-SDK version x86 64-linux-22.6.1. Model 500 (age 4.056 Gyr) was used as the RG in this project.</p> <p>For the sub-giant models, the inlist was executed with MESA version r23.05.1 and MESA-SDK version x86 64-linux-22.6.1. Models 345 (age 3.624 Gyr) and 350 (age 3.702 Gyr) were used as the MSG and LSG models respectively.</p>
Dataset for Sakata et al. (2024) "Effects of an Intrinsic Magnetic Field on Ion Escape from Ancient Mars Based on MAESTRO Multifluid MHD Simulations"
<div> <p>This dataset contains the simulation results in Sakata et al. (2024), Effects of an Intrinsic Magnetic Field on Ion Escape from Ancient Mars Based on MAESTRO Multifluid MHD Simulations, Journal of Geophysical Research: Space Physics.</p> </div>
Supporting data for "The Initial Magnetic Field Distribution in AB Stars"
<p>These are the inlist and run_star_extras required to reproduce the results of 'The Initial Magnetic Field Distribution in AB Stars', originally used with MESA r15140. The history output files are also included.</p>
data for Thermodynamics of correlated electrons in a magnetic field
<p>DQMC data and Jupyter/Python data analysis scripts</p>
Observations of natural positive leader featuring stepwise propagation in low frequency magnetic field
<p>The high-speed video, B-field and E-field data of two cases in the manuscript is presented here.</p>
Directional control of neurite outgrowth: emerging technologies for Parkinson's Disease using magnetic nanoparticles and magnetic field gradients
<p>Data set for publication '<strong>Directional control of neurite outgrowth: emerging technologies for Parkinson’s Disease using magnetic nanoparticles and magnetic field gradients'</strong></p> <p><strong>Journal of The Royal Society Interface</strong></p> <p><strong>2022</strong></p>
Data - Magnetic-field-assisted nanochain formation of intermixed catalytic CoPd nanoparticles
<p>This DOI address contains data regarding the manuscript entitled "Magnetic-field-assisted nanochain formation of intermixed catalytic CoPd nanoparticles", published in TBD</p> <p>Authors: Calle Preger, Lisa Rämisch, Johan Zetterberg, Sara Blomberg and Maria E. Messing</p> <p>This DOI contains:</p> <ul> <li>XEDS results used for histogram in Figure 2</li> <li>SEM images used for data points in Figure 3</li> <li>Results from simulations providing solid line in Figure 3</li> </ul>
Dephasing-tolerant quantum sensing of transverse magnetic fields with spin qudits. Open data set
<p>Data supporting Figs. 1, 2, 3, 4 of the related manuscript.</p>
Supplemental Material for the paper "Hamiltonian model for electron heating by electromagnetic waves during magnetic reconnection with a strong guide field"
<p>Video clip showing the trajectories of two close particles in the (x,px) phase space while interacting with a wave.</p> <p>Red and green dots are the particle positions, superimposed to the instantaneous energy levels.</p>
Dataset for "Particle-in-cell simulations of the fast magnetosonic mode in a dipole magnetic field: 1D along the radial direction"
<p>Dataset used to produce figures in the paper "Particle-in-cell simulations of the fast magnetosonic mode in a dipole magnetic field: 1D along the radial direction"</p>
Supporting data for "Spectroscopy of Quantum Dot Orbitals with In-Plane Magnetic Fields"
<p>Supporting data for<br> "Hyperfine-phonon spin relaxation in a single-electron GaAs quantum dot"<br> Leon C. Camenzind, Liuqi Yu, Peter Stano, Jeramy D. Zimmerman, Arthur C. Gossard, Daniel Loss & Dominik M. Zumbühl</p> <p><em>Phys. Rev. Lett. <strong>122</strong>, 207701 – Published 22 May 2019</em></p>
Replication Data for: (111)-oriented, single crystal diamond tips for nanoscale scanning probe imaging of out-of-plane magnetic fields
<p>Data repository for: <strong>(111)-oriented, single crystal diamond tips for nanoscale scanning probe imaging of out-of-plane magnetic fields</strong></p> <p><em>Data description.pdf </em>describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.</p>
Data for "Magnetic field independent sub-gap states in hybrid Rashba nanowires"
<p>Data for the publication "Magnetic field independent sub-gap states in hybrid Rashba nanowires"</p>
CSES (ZH-1) Satellite magnetic field detection data with whistlers
Open the record for dataset details and reuse information.
Supporting data for 'Modelling of planar germanium hole qubits in electric and magnetic fields' v2
<p>The data must not be reused in other studies or publications without approval of the authors.</p>
Associated modeling data & materials for manuscript: Observing the evolution of the Sun's global coronal magnetic field over eight months
<p>This archive contains the magnetohydrodynamic (MHD) modeling materials associated with the manuscript:</p> <p>"<em>Observing the evolution of the Sun’s global coronal magnetic field over eight months</em>"</p> <p>by Zihao Yang, Hui Tian, Steven Tomczyk, Xianyu Liu, Sarah Gibson,<br>Richard Morton, and Cooper Downs</p> <p>Science, 386(6717), 76-82, <strong>2024</strong>, DOI: <a title="Observing the evolution of the Sun&rsquo;s global coronal magnetic field over eight months" href="http://doi.org/10.1126/science.ado2993" target="_blank" rel="noopener">10.1126/science.ado2993</a></p> <p>This archive is intended for transparency and reproduceability purposes. It contains the MHD model source code, run inputs, run outputs, and example python scripts for working with the model data.</p> <p># Contents<br>The subfolders are organized as follows:</p> <p>### source<br>This folder contains the the high-performance MHD code "Magnetohydrodynamic Algorithm outside a Sphere" (MAS) and associated files. MAS is written in Fortran. The dependencies are very straightforward. See `README_MAS.txt` and the associated Makefile for compilation instructions.</p> <p>### runs<br>This folder contains the three MHD model runs that are described in the manuscript. See `README_Runs.txt` for more information on the inputs and outputs. Each folder contains all files required for recreating the run.</p> <p>### scripts<br>This folder contains some example python scripts that illustrate how to read the model data files. This includes an example that will convert the raw 3D data to physical units and place all variables on a common, non-staggered mesh. See `README_Scripts.txt` for more information.</p> <p># Additional Notes<br>MAS is developed and maintained by Predictive Science Inc. (PSI) in San Diego California.</p> <p>The version of MAS in the source folder is not the most recent version. It is the exact version of MAS from the main branch that was used for MHDweb CORHEL runs circa 2022 when similar runs were first posted to PSI's website. For this reason, we used this exact version of MAS for the runs described in the manuscript. As such, MAS is licensed here using the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license. Please see the LICENSE file or visit https://creativecommons.org/licenses/by-nc-nd/4.0/ for details. </p> <p>We are currently working on a project that includes a public, open source release of MAS on GitHub, which will be licensed appropriately. This archive is not intended for that purpose.</p> <p>If you have questions, concerns, or issues installing or running this code for reproduceability purposes, please contact Cooper Downs <cdowns@predsci.com>.</p>
MESA files for "She's Got Her Mother's Hair: End-to-End Collapsar Simulations Unveil the Origin of Black Holes' Magnetic Field"
<p>MESA input files, output, and scripts to reproduce Fig. 1 and the appendix figure in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240716745G/abstract">Gottlieb, Renzo, et al. 2024</a><br><br></p>
Simulation dataset for Off-diagonal Ion Pressure Linked to Hall Fields in Collisionless Magnetic Reconnection
<p>The dataset contains simulation data used in the paper of "Off-diagonal Ion Pressure LInked to Hall Fields in Collisionless Magnetic Reconnection". Data are generated from a particle-in-cell simulation using the VPIC code. Please see the paper for the descriptions of the simulation setup.</p> <p>The .gda files in fiels_moments_gda_files.zip are fields and plasma moments data from the time step presented in the paper. Each file contains float-type data arrays with a size of 6720x1x2240, corresponding to x-y-z dimensions, respectively. Data can be read by softwares like IDL, python, matlab, etc., using the standard data reading methods.</p> <p>The file of harris_m100_x60_Hparticle.89586 contains ion particle data for this time step, for the domain of x=[0,60]di, z=[-8,8]di. The file is written in BINARY, for the information of indivial particles. Each data chunk constitues of the following variables in order: x, z, ux, uy, uz, q. Here x and z are positions of the particles, in unit of de; ux, uy, uz are relativistic velocities in unit of the speed of light (c), i.e., ux = gamma*vx, gamma=sqrt(1+ux^2+uy^2+uz^2); q represents the weight of the particles, so the contribution of a particle to the phase space density needs to multiply by this q factor (In the paper, q factors are re-normalized while the relative weights between particles remain the same).</p> <p>An example IDL sentence of reading particle data: readu, lun, x, z, ux, uy, uz, q. Repeat this sentence for different particles.</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.