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691 results for “Magnetic field”
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>
Dataset for the published article "ITER relevant multi-emissive sheaths at normal magnetic field inclination"
<p>The data contained in the zip files constitute the main research data of the publication entitled as "<a href="https://iopscience.iop.org/article/10.1088/1741-4326/acaabd">ITER relevant multi-emissive sheaths at normal magnetic field inclination</a>" [1]. All the datasets constitute post-processed output from the 2D3V SPICE2 Particle-In-Cell (PIC) code. All the PIC simulations have been performed by M. Komm and A. Podolnik. The input is specified by the plasma density, the electron temperature and the surface temperature. The plasma parameters are relevant to partially mitigated ITER edge-localized modes (ELMs). The output concerns the incident plasma current densities, the emitted electron current densities and their standard deviation, the normal wall electrostatic field, the average electron incident energy, the average electron incident angle with respect to the wall normal and the virtual cathode depth. </p> <p>The assumptions below are followed in all simulations: (i) The Bohm pre-sheath structure is unaltered by the escaping emitted electrons, since the ions are injected at the plasma boundary with a speed distribution satisfying the Bohm criterion. (ii) Irrespective of the emission, the wall is biased with respect to the plasma boundary with a magnitude fixed by the ambipolarity of the plasma fluxes. (iii) The sheath is collisionless. (iv) The wall is perfectly planar. (v) A homogeneous quasi-neutral plasma boundary and an infinite emitting wall with a homogeneous prescribed surface temperature are considered.</p> <p>Sheaths that form between plasma-facing components (PFCs) and standard scrape-off-layer plasmas can be described by the classical model of one-dimensional magnetized multi-positive ion sheaths. There are various conditions that need to be satisfied for this model to be valid such as negligible cross-field drifts, low collisionality and weak electron emission.</p> <p>In contemporary metallic tokamaks, the weak emission condition is violated in the divertor region during intra-ELM as well as inter-ELM periods; thermionic emission being an effective electron emission mechanism from hot tungsten PFCs. As a result of the localized ELM-wetted area, the incident plasma currents can be assumed to remain nearly ambipolar and thus the non-ambipolar current should be equal to the emitted current that escapes to the Bohm pre-sheath. This escaping current density generates a strong volumetric Lorentz force that drives melt layer motion leading to macroscopic PFC erosion. At very elevated surface temperatures, the nominal thermionic current densities are so large that they become incompatible with the classical Bohm pre-sheath structure. As a consequence, space charge accumulation in the sheath leads to the formation of a virtual cathode that limits the escaping thermionic current to a constant value causing the recapture of a fraction of the thermo-electrons. Thus, there is a transition from a monotonic to a non-monotonic potential profile, with the latter known as the space-charge limited (SCL) regime of the emissive sheath. In the case of oblique magnetic field inclination angles, the SCL transition is still realized, but further complications arise due to the suppression of the nominal thermionic current by recapture during Larmor gyration. In contemporary tokamaks, this transition generally occurs at temperatures below the tungsten melting point, thus particular attention has been paid to the SCL sheaths, since they nearly exclusively surround the molten tungsten PFCs. The thermionic emissive sheath in the SCL regime has been thoroughly investigated in our previous works, where an accurate semi-empirical expression for the limited value of the escaping thermionic current as function of the plasma conditions and magnetic field inclination angle was constructed on the basis of systematic PIC simulations [2-4].</p> <p>On the other hand, during ITER intra-ELM periods, the predicted elevated electron temperatures and high plasma densities of the pre-sheath edge should have a strong impact on the emissive sheath established above hot tungsten PFCs. In particular, the high plasma electron temperatures could enable significant contributions from electron-induced electron emission (secondary electron emission and electron backscattering), the intense normal surface electrostatic fields indicate that thermionic emission is coupled with field emission (in the Schottky regime) and the strong plasma currents suggest that virtual cathodes are formed at much higher surface temperatures (so that the monotonic potential profile regime is of primary interest for melt motion). In order to explore this novel multi-emissive sheath regime, a a comprehensive tungsten electron emission model has been implemented that features accurate analytical descriptions of the yields, energy and angular distributions for the processes of field-assisted thermionic emission, secondary electron emission and electron backscattering [5]. In the present publication [1], at normal magnetic field inclinations, highly accurate analytical semi-empirical expressions are provided for the secondary electron emission current, electron backscattering current and thermionic current in the monotonic regime as well as for the total escaping current in the SCL regime. These semi-empirical expressions have been benchmarked against comprehensive PIC simulations, whose primary post-processed data are provided herein.</p> <p>[1] P. Tolias, M. Komm, S. Ratynskaia and A. Podolnik, "ITER relevant multi-emissive sheaths at normal magnetic field inclination", Nucl. Fusion 63 (2023) 026007.<br> [2] M. Komm, S. Ratynskaia, P. Tolias, J. Cavalier, R. Dejarnac, J. P. Gunn and A. Podolnik, "On thermionic emission from plasma-facing components in tokamak-relevant conditions", Plasma Phys. Control. Fusion 59 (2017) 094002.<br> [3] M. Komm, P. Tolias, S. Ratynskaia, R. Dejarnac, J. P. Gunn, K. Krieger, A. Podolnik, R. A. Pitts and R. Panek, "Simulations of thermionic suppression during tungsten transient melting experiments", Phys. Scr. T170 (2017) 014069.<br> [4] M. Komm, S. Ratynskaia, P. Tolias and A. Podolnik, "Space-charge limited thermionic sheaths in magnetized fusion plasmas", Nucl. Fusion 60 (2020) 054002.<br> [5] P. Tolias, M. Komm, S. Ratynskaia and A. Podolnik, "Origin and nature of the emissive sheath surrounding hot tungsten tokamak surfaces", Nucl. Mater. Energy 25 (2020) 100818.</p> <p> </p>
Rotation of electron beams in the presence of localised, longitudinal magnetic fields
<p>Electron Bessel beams have been generated by inserting an annular aperture in the illumination system of a TEM.</p> <p>These beams have passed through a localised magnetic field.</p> <p>As a result a low amount of image rotation (which is expected to be proportional to the longitudinal component of the magnetic field) is observed in the far field.</p> <p>A measure of this rotation should give access to the magneti field.</p> <p>The two datasets have been acquired in a FEI Titan<sup>3</sup> microscope, operated at 300kV.</p> <p>The file focal_series.tif contains a series of images acquired varying the magnetic field through the objective lens.</p> <p>The file line_profile.ser contains a series of images acquired by scanning the beam over a sample with several magnetised nanopillars.</p> <p>For reference, check the associated publication:<br> <em>Giulio Guzzinati, Armand Béché, Damien McGrouther and Jo Verbeeck</em>, <strong>Prospects for out-of-plane magnetic field measurements through interference of electron vortex modes in the TEM, </strong><a href="https://doi.org/10.1088/2040-8986/ab51fc">Journal of Optics 21 124002 (2019)</a></p>
MHD Model of Ganymede's Magnetosphere: Predicted magnetic field on Juno's trajectory
<p>This dataset contains model results from a magnetohydrodynamic (MHD) model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021. Here we publish predicted magnetic field components on Juno's trajectory that can be compared to MAG measurements and are displayed in Figure 3 of Duling et al. (2022).</p> <p>Each file contains data from one model. The dataset includes all models with parameter variations from Duling et al. (2022). These are summarized in Table 1 of Duling et al. (2022) and displayed in Figure 3 with the gray lines.</p> <p>If not varied, all models are run with the following parameters:</p> <p>Upstream Jovian background magnetic field B<sub>0 </sub>= (−15,24,−75) nT<br> Upstream plasma velocity v<sub>0</sub> = 140 km/s<br> Upstream plasma mass density <span class="math-tex">\(\rho\)</span><sub>0</sub> = 100 amu/cm<sup>3</sup><br> Upstream plasma thermal pressure p<sub>0</sub> = 2.8 nPa<br> Ionization frequency <span class="math-tex">\(\nu_{ion}\)</span> = 2.2e-8/s<br> Atmospheric surface mass density <span class="math-tex">\(n_{n,0}\)</span> = 8e6/cm<sup>3</sup><br> Dipole Gauss coefficient <span class="math-tex">\(g_1^0\)</span> = −716.8 nT</p> <p> </p> <p>The published data files correspond to the following models with each one parameter variation:</p> <table> <thead> <tr> <th scope="col">Parameter</th> <th scope="col">Value</th> <th scope="col">Filename Suffix</th> </tr> </thead> <tbody> <tr> <td>default model</td> <td> - </td> <td>default</td> </tr> <tr> <td>Upstream Jovian background magnetic field (measured before flyby)</td> <td>B<sub>0 </sub>= (−16,3,−70) nT</td> <td>B0before</td> </tr> <tr> <td>Upstream Jovian background magnetic field (measured after flyby)</td> <td>B<sub>0 </sub>= (−14,43,−80) nT</td> <td>B0after</td> </tr> <tr> <td>Upstream plasma velocity (min)</td> <td>v<sub>0</sub> = 120 km/s</td> <td>v-</td> </tr> <tr> <td>Upstream plasma velocity (max)</td> <td>v<sub>0</sub> = 160 km/s</td> <td>v+</td> </tr> <tr> <td>Upstream plasma mass density (min)</td> <td><span class="math-tex">\(\rho\)</span><sub>0</sub> = 10 amu/cm<sup>3</sup></td> <td>rho-</td> </tr> <tr> <td>Upstream plasma mass density (max)</td> <td><span class="math-tex">\(\rho\)</span><sub>0</sub> = 160 amu/cm<sup>3</sup></td> <td>rho+</td> </tr> <tr> <td>Upstream plasma thermal pressure (min)</td> <td>p<sub>0</sub> = 1.0 nPa</td> <td>p-</td> </tr> <tr> <td>Upstream plasma thermal pressure (max)</td> <td>p<sub>0</sub> = 5.0 nPa</td> <td>p+</td> </tr> <tr> <td>Ionization frequency (min)</td> <td> <span class="math-tex">\(\nu_{ion}\)</span> = 0.5e-8/s</td> <td>prod-</td> </tr> <tr> <td>Ionization frequency (max)</td> <td> <span class="math-tex">\(\nu_{ion}\)</span> = 10.0e-8/s</td> <td>prod+</td> </tr> <tr> <td>Atmospheric surface mass density (min)</td> <td> <span class="math-tex">\(n_{n,0}\)</span> = 1.6e6/cm<sup>3</sup></td> <td>nn-</td> </tr> <tr> <td>Atmospheric surface mass density (max)</td> <td> <span class="math-tex">\(n_{n,0}\)</span> = 40e6/cm<sup>3</sup></td> <td>nn+</td> </tr> <tr> <td>Dipole Gauss coefficient (min)</td> <td> <span class="math-tex">\(g_1^0\)</span> = −702.5 nT</td> <td>dipole-</td> </tr> <tr> <td>Dipole Gauss coefficient (max)</td> <td> <span class="math-tex">\(g_1^0\)</span> = −731.1 nT</td> <td>dipole+</td> </tr> </tbody> </table> <p>Magnetic Field components and Juno's position are in GPhiO system. GPhiO is defined by the primary direction z parallel to Jupiter’s rotation axis, the secondary direction y is pointing from Ganymede's towards Jupiter's barycenter and x completes the right-handed system approximately in direction of plasma flow.</p> <p>Columns:</p> <p>Spacecraft time [UTC]<br> Bx modeled magnetic field in GPhiO [nT]<br> By modeled magnetic field in GPhiO [nT]<br> Bz modeled magnetic field in GPhiO [nT]<br> B modeled magnetic field magnitude [nT]<br> x of Juno in GPhiO [km]<br> y of Juno in GPhiO [km]<br> z of Juno in GPhiO [km]</p>
Predicted times of bow Shock crossings at Venus from the ESA/Venus Express mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br> Planet: <strong>Venus</strong><br> Radius: <strong>R<sub>V</sub> = 6051.8 km</strong> (volumetric mean planetary radius)<br> Spacecraft: <strong>ESA/Venus Express</strong><br> Spacecraft coordinates system: <strong>Venus Solar Orbital (VSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>VSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>VSO</sub></em> towards Venus’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>VSO</sub></em>–<em>Y<sub>VSO</sub></em> plane passing through the centre of Venus,</li> <li><em>Y<sub>VSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/04/2006 to 25/11/2014</strong><br> Total number N of candidate bow shock crossings in the database: <strong>N = 4950</strong><br> Number of quasi-parallel bow shock crossings: <strong>N<sub>||</sub> = 844</strong><br> Number of quasi-perpendicular bow shock crossings: <strong>N<sub><span class="math-tex">\(\perp\)</span></sub> = 4106</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br> The original Venus Express/MAG data repository on which these algorithms were applied is available on ESA's Planetary Science Archive system (PSA) at: https://archives.esac.esa.int/psa/ftp/VENUS-EXPRESS/MAG/. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br> To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described for the Mars case in:<br> Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2021), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, e2021JA029942. <a href="https://doi.org/10.1029/2021JA029942">https://doi.org/10.1029/2021JA029942</a></p> <p>They consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on 2D or 3D existing fits for prediction of the Venus bow shock position. The original fits were taken from 2D conic fits in the plane <span class="math-tex">\(\left(X_\text{VSO}, \sqrt{Y_\text{VSO}^2+Z_\text{VSO}^2}\right)\)</span>performed on the datasets of <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>).</li> <li>Corrector algorithm based on magnetic field measurements.</li> </ol> <p>We also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted <span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub></span> (ThetaBn). Assuming a locally smooth shock surface, this gives a first indication of the geometry of the shock, so that:</p> <ul> <li><span class="math-tex">45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup></span>: quasi-perpendicular shock condition</li> <li><span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub><span class="math-tex">\(\geq\)</span>135<sup>∘</sup></span>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>For details, see <strong>Simon Wedlund et al. (2022)</strong> above, §2.3 pp. 10-12.</p> <p><strong>VARIABLES DESCRIPTION</strong></p> <p>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in Venus Express' database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Venus Solar Orbital coordinates of the shock, in units of Venus radius <em>R</em><sub>V </sub>(<em>R</em><sub>V</sub> = 6051.8 km):<br> <em>X<sub>VSO</sub></em>,<sub> </sub><em>Y<sub>VSO</sub></em>, <em>Z<sub>VSO</sub></em> and Euclidean distance <span class="math-tex">\(R_{VSO} = \sqrt{X_{VSO}^2 + Y_{VSO}^2 + Z_{VSO}^2}\)</span> (in <em>R<sub>V</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = <span class="math-tex">\(\tan^{-1}{Y_{VSO}^2+Z_{VSO}^2 \over X_{VSO}^2}\)</span> (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface <span class="math-tex">\(\theta_{Bn}\)</span> (ThetaBn, in º, calculated with atan2(norm(cross(<strong>B</strong>,<strong>ñ</strong>),dot(<strong>B</strong>,<strong>ñ</strong>)), with <strong>B</strong> the magnetic field vector and <strong>ñ</strong> the vector normal to the shock surface): <ul> <li>45 < ThetaBn < 135 deg: quasi-<span class="math-tex">\(\perp\)</span> shock</li> <li>ThetaBn <span class="math-tex">\(\leq\)</span> 45 deg & ThetaBn <span class="math-tex">\(\geq\)</span> 135 deg: quasi-|| shock</li> </ul> </li> <li>Interplanetary Magnetic Field (IMF) upstream average vector in VSO coordinates, <em>B<sub>x</sub></em>, <em>B<sub>y</sub></em>, <em>B<sub>z</sub></em> (in nT).</li> <li>Flag for direction of crossing: <ul> <li>flag = 0: magnetosheath <span class="math-tex">\(\longrightarrow\)</span> solar wind (2447 events)</li> <li>flag = 1: solar wind <span class="math-tex">\(\longrightarrow\)</span> magnetosheath (2503 events)</li> </ul> </li> </ul> <p><strong>WARNING</strong></p> <ol> <li>This version of the database is currently in a preliminary stage of application and, as such, is not fully tested. Solar wind upstream magnetic field values (IMF) are given only as a first approximation for each orbit segment. See point 2 for caveats. For carefully manually picked shock crossings, the user is referred to the database of:<br> <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>)</li> <li>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic fields. This is aimed at giving a first approximation of the shock area times in the Venus Express data. It is particularly suited to statistical studies and region identification in the Venus Express datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.040 R<sub>V</sub> (with R<sub>V</sub> = 6051.8 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</li> </ol> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br> C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. </p> <p><strong>LICENSE AND RIGHTS</strong><br> This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences, 2022-10-05<br> Contact email: cyril.simon.wedlund@gmail.com</p>
Data and simulation files for "Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations"
<p>In this repository, we provide data files in connection to our paper “Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations” accepted for publication in the Astrophysical Journals and soon available on Arxiv.</p> <p>In the publication, we perform a joint analysis of observations of five blazars with the Fermi Large Area Telescope (LAT) and the High Energy Stereoscopic System (H.E.S.S.) in order to search for signatures of a gamma-ray halo around these sources. The non-detection of such extended emission allows us to place lower limits on the intergalactic magnetic field (IGMF).</p> <p>In this repository, we provide our data analysis products of both H.E.S.S. and LAT data for the case when a template for the halo flux is <em>not</em> included in the data. Furthermore, we provide files that contain the log likelihood profiles as functions of the IGMF in case the halo emission <em>is</em> included. Lastly, we also provide our template files for the halo, generated with <a href="https://crpropa.github.io/CRPropa3/">CRPropa 3</a>.</p> <p>Below, we provide minimal code examples to demonstrate how to read in the specific files.</p> <p><strong>H.E.S.S. observational results</strong></p> <p>We provide the best-fit spectral parameters as well as the flux points (spectral energy distribution; SED) for the H.E.S.S. observations of the five blazars under consideration. The corresponding files are:</p> <ul> <li>hess_fit_result_*.fits which contain the best-fit parameters,</li> <li>hess_sed_file_*.fits which contain the flux points.</li> </ul> <p>In the file names above, the '*' should be replaced with a the corresponding source name, e.g. 1ES0229+200. The files can be read in using astropy:</p> <pre><code class="language-python">from astropy.table import Table src = "1ES0229+200" best_fit_pars = Table.read("hess_fit_result_1ES0229+200.fits") sed = Table.read("hess_sed_file_1ES0229+200.fits")</code></pre> <p><strong>Fermi observational results</strong></p> <p>For Fermi-LAT, we provide the SED files as well as the best-fit models for the region of interests. These files are called:</p> <ul> <li>fermi_avg_file_*.npy provides the best-fit ROI model</li> <li>fermi_sed_file_*.npy provides the SED.</li> </ul> <p>Both of these files are generated with <a href="https://fermipy.readthedocs.io/en/latest/">fermipy</a> and can be read-in the following way:</p> <pre><code class="language-python">import numpy as np # first a little helper function since the # fermipy analysis was run under python 2.7 def convert(data): if isinstance(data, bytes): return data.decode('ascii') if isinstance(data, dict): return dict(map(convert, data.items())) if isinstance(data, tuple): return map(convert, data) return data # Load the ROI fit roi_fit_file = "fermi_avg_file_1ES0229+200.npy" roi_fit = np.load(avg_file, allow_pickle=True, encoding="latin1").flat[0] # if you want to inspect the dictionaries in python 3, you need to run the convert function. # For example, to inspect the central source of the ROI # you would first get the source name src_fgl_name = roi_fit['config']['selection']['target'] # and then you can get the dictionary for the central source src_dict = convert(roi_fit['sources'])[src_fgl_name] # Load the SED sed_file = "fermi_sed_file_1ES0229+200.npy" sed = np.load(sed_file, allow_pickle=True, encoding='latin1').flat[0] # to plot the SED, you can use the SEDPlotter class from fermipy from fermipy.plotting import SEDPlotter SEDPlotter.plot_sed(sed)</code></pre> <p><strong>Likelihood profiles</strong></p> <p>The likelihood profiles as function of the IGMF strengths are provided in the files logl_profile_*_*yr.npz. Their are provided for all five sources and all tested blazar activity times of 10, 10<sup>4</sup>, and 10<sup>7</sup> years. They can be read in with the following code snippet:</p> <pre><code class="language-python">import numpy as np logl = dict(np.load("logl_profile_1ES0229+200_1.0e+07yr.npz")) b_fields = np.array([1.00000e-16, 3.16228e-16, 1.00000e-15, 3.16228e-15, 1.00000e-14, 3.16228e-14, 1.00000e-13]) for k, v in logl.items(): print(k,v)</code></pre> <p>As the print command shows, the python dictionary contains 3 entries: "fermi_only" are the likelihood values for the Fermi data as a function of magnetic field, "combined" are the likelihood values from Fermi and H.E.S.S. combined, and "ps" is the likelihood value of the Fit without halo to the H.E.S.S. data only.</p> <p><strong>Halo simulations</strong></p> <p>Lastly, we also provide the output simulations files from CRPropa. For details how the simulations were run, please consult the accompanying paper, in particular Section 3.1 and Appendix C. For each source redshift, a tar file is provided, which in itself contains 7 hdf5 files with the simulation outputs for each tested magnetic field strength. The name of the files is casc_file_z*.tar.gz. After unpacking the files, they can be read in with your favorite hdf5 library; in python you would need to install h5py. We recommend that you check out <a href="https://github.com/me-manu/simCRpropa">this github repository</a> which provides an advanced python wrapper for CRPropa and functions to read in the files. In particular, you can use <a href="https://github.com/me-manu/simCRpropa/blob/b3f39b5c77c6b97d19f7db387427d857690444d2/simCRpropa/cascmaps.py#L28">this function</a> to read in the files. It also writes a new hdf5 file with parallel transport applied. The written data is also returned together with the configuration dictionary.</p> <pre><code class="language-python">from simCRpropa.cascmaps import stack_results_lso data, config = stack_results_lso("casc_file_z0.140_B1.00e-16.hdf5", "casc_file_z0.140_B1.00e-16_theta_obs0.0.hdf5" )</code></pre> <p>You can provide arbitrary angles between the observer and the jet angles using the theta_obs keyword. Note, however, that the simulations used a jet opening angle of 3 degrees and going beyond that value will return zero halo photons.</p>
Solar and interplanetary magnetic field data analyzed in "Optimal frequency-domain analysis for spacecraft time series: Introducing the missing-data multitaper power spectrum estimator"
<p>This dataset contains simultaneous measurements of the interplanetary magnetic field magnitude <B> and the sun's radio flux at 10.7 cm <F10.7>. <B> measurements come from a series of spacecraft located at the L1 point, while <F10.7> was measured by the ongoing monitoring program by Canada's Dominion Radio Astrophysical Observatory. Bartels rotation-averaged data were downloaded from NASA's OMNIWeb, https://omniweb.gsfc.nasa.gov/html/ow_data.html. The file contains other solar wind plasma parameters that were not used in the analysis.</p>
NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion"
<p>Liquid-state and solid-state NMR data for "Rapid and simple 13C-hyperpolarization by 1H dissolution dynamic nuclear polarization followed by an in-line magnetic field inversion".</p> <p>The data enclosed are NMR data generated by the software Topspin by Burker Biospin. The experiments are dDNP runs that come in two parts: a solid-state and a liquid-state part.</p> <ul> <li>Experiments from 1 to 9 are reference experiments used to quantify polarization in other experiments</li> <li>Experiments 11-19, 21-29, 31-39, ... 61-69 correspond to 6 dDNP runs performed a different samples from the same batch. The numbers correspond between solid and liquid-state datasets</li> </ul> <p>The codes used to analyze the data are available at in a next upload.</p> <p>Refer to the main text of the paper and its supplementary material at 10.26434/chemrxiv-2023-6gd0l for more information.</p>
Magnetism and anomalous transport in the Weyl semimetal PrAlGe: Possible route to axial gauge fields
<p>The file ManuscriptDataFiles.7z contains the raw experimental data from which the figures are made in the manuscript entitled "Magnetism and anomalous transport in the Weyl semimetal PrAlGe: Possible route to axial gauge fields" that appeared in npj Quantum Materials <strong>5</strong>, 5 (2020).</p> <p>Paper Abstract: In magnetic Weyl semimetals, where magnetism breaks time-reversal symmetry, large magnetically sensitive anomalous transport responses are anticipated that could be useful for topological spintronics. The identification of new magnetic Weyl semimetals is therefore in high demand, particularly since in these systems Weyl node configurations may be easily modified using magnetic fields. Here we explore experimentally the magnetic semimetal PrAlGe, and unveil a direct correspondence between easy-axis Pr ferromagnetism and anomalous Hall and Nernst effects. With sizes of both the anomalous Hall conductivity and Nernst effect in good quantitative agreement with first principles calculations, we identify PrAlGe as a system where magnetic fields can connect directly to Weyl nodes via the Pr magnetization. Furthermore, we find the predominantly easy-axis ferromagnetic ground state co-exists with a low density of nanoscale textured magnetic domain walls. We describe how such nanoscale magnetic textures could serve as a local platform for tunable axial gauge fields of Weyl fermions.</p>
3D motion of flexible ferromagnetic filaments under rotating magnetic field
<p>This repository contains experimental data and numerical results related to the publication: A. Zaben, G. Kitenbergs, A. Cēbers (2020), 3D motion of flexible ferromagnetic filaments under rotating magnetic field. Soft Matter, <a href="https://doi.org/10.1039/D0SM00403K">https://doi.org/10.1039/D0SM00403K</a> / <a href="https://arxiv.org/abs/2003.03737">https://arxiv.org/abs/2003.03737</a>.</p> <p>Figs_data.xlsx contains the data presented in the figures. Experimental_Data.rar contains experimental images used to obtain the results for Fig. 3 and 9. The files are named with the operating frequency, field strength and filament length. Numerical.rar contains numerical results used in Fig.6, 8 and 9. The files are named with Cm values. The results are in .dat files named with Cm values followed by wt (wend_cm_wt). The first column is for time(t) followed by x,y,z values of filament tips. </p> <p> </p>
Dynamo in weakly collisional non-magnetized plasmas impeded by Landau damping of magnetic fields
<p>This dataset contains a collection of simulation inputs and results used in the paper [I. Pusztai et al (2020) Phys. Rev. Lett., Dynamo in weakly collisional non-magnetized plasmas impeded by Landau damping of magnetic fields, https://arxiv.org/abs/2001.11929]. References to figures below refer to this publication. </p> <p>These simulations are performed using the kinetic-Vlasov solver Gkeyll [version: cd65328c077f+ 2228+ default], for more information on the code visit https://gkyl.readthedocs.io/en/latest/index.html, or consult [J. Juno et al (2018) J. Comp. Phys 353, 110].</p> <p>The input files are found with .lua extension in each simulation directory</p> <p>Content:</p> <p>* Galloway-Proctor-flow_Fig1-kinetic-and-Fig2 <br> Kinetic simulation of the Galloway-Proctor flow, corresponding to the solid lines in Fig. 1 and Fig. 2. </p> <p>* Cnu-and-k-scan_Fig3-and-Fig4a<br> This is a parameter scan in wavelength of the magnetic perturbations [ranging from L0 ("L0") to L0/8 ("L0per8"), with baseline domain size L0] and collision frequencies [ranging from 0.05 ("Cnu005") to 1 ("Cnu1") times the baseline values]. These results are presented in Fig. 3 and Fig. 4a.</p> <p>* Magnetization-scan_Fig4b<br> Scan in magnetization shown in Fig. 4 b. The magnetic field varies between 1 and 100 T ["B1" and "B100", respectively].</p> <p>* Roberts-flow_Fig5 <br> Kinetic simulations of the Roberts flow, shown in Fig. 5. The collision frequency is scaled to 0.3 the physical value (dashed lines, "Roberts_Cnu03_Fig5"), and zero (solid lines, "Roberts_Cnu00_Fig5"). </p> <p>* Pencil_Run_12x12x12.tar.gz<br> Input files for PENCIL CODE simulations.</p>
Magnetization versus applied dc magnetic field data for Li0.7[Cr(pyz)2]Cl0.7·(THF)
<p>Magnetization versus applied dc magnetic field data in the –7 to 7 T field range from 1.85 K to 520 K for Li<sub>0.7</sub>[Cr(pyz)<sub>2</sub>]Cl<sub>0.7</sub>·(THF). The data are provided in a tab-delimited text format.</p>
Datasets For "Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data", Blake et al. (2020), JGR
<p>Datasets and sample Python codes for the 2020 paper <em>"Estimating Maximum Extent of Auroral Equatorward Boundary using Historical and Simulated Surface Magnetic Field Data"</em>, by Blake et al., submitted to the Journal of Gephysical Research, Space Physics. </p> <p>Up-to-date Python codes can be found at <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p>The complete SWMF simulation folders (including parameter and log files etc.) can be requested from <a href="https://ccmc.gsfc.nasa.gov/index.php">NASA's Community Coordinated Modeling Center</a>.</p> <p>#########</p> <p><strong>Data/ </strong>contains the following:</p> <p><strong>Data/HIST_DATA.txt </strong>contains the minimum Dst values and calculated maximum extents of the auroral equatorward boundaries for 25 years of INTERMAGNET data (1991-2016). The fourth column is the standard deviation of the calculated auroral boundary in degrees. </p> <p><strong>Data/Boundary_Fits.csv </strong>contains the calculated minimum Dst values, and calculated auroral boundaries using Method 1 and Method 2 (see main paper's ttext), for each of the 15 SWMF simulations. Also included are the uncertainties for each calculation.</p> <p><strong>Data/SWMF_outputs/ </strong>contains 15<strong> </strong>.txt files,<strong> </strong>each of which correspond to an SWMF simulation of the same name given in Table 1 in the main text. These data are for the magnetic longitude, magnetic latitude and maximum calculated <em>E<sub>H</sub> </em>(V/km) for each simulation.</p> <p>#########</p> <p><strong>Codes/ </strong>contains two python scripts, and some sample data. These scripts correspond to Section 2 in the main text:</p> <p>1) <strong>Boundary_Calc.py</strong> calculates the extent of the auroral boundary using magnetic latitudes and maximum calculated <em>E<sub>H</sub></em> values from multiple INTERMAGNET sites. </p> <p>2) <strong>Efield_Calc.py </strong>calculates the E-field for a single INTERMAGNET site using the Quebec 1-D resistivity model.</p> <p>A more detailed description of these codes can be found here: <a href="https://github.com/TerminusEst/Auroral_Boundary_Geomag">https://github.com/TerminusEst/Auroral_Boundary_Geomag</a></p> <p> </p>
Symmetry breaking in spin spirals and skyrmions by in-plane and canted magnetic fields
<p>The influence of in-plane and canted magnetic fields on spin spirals and skyrmions in atomic bilayer<br> islands of palladium and iron on an Ir(111) substrate is investigated by scanning tunneling microscopy<br> at low temperatures. It is shown that the spin spiral propagation direction is determined by the island’s<br> border which can be explained by equilibrium state calculations on a triangular lattice.Wefind a<br> different response of spin spirals to in-plane magnetic fields for a propagation direction parallel to the<br> applied field as compared to perpendicular, which originates from their cycloidal nature. As a result,<br> the spin spiral propagation direction may be reorientated by in-plane fields. Furthermore, it is<br> demonstrated that also skyrmions are distorted in canted fields which allows to determine the sense of<br> magnetization rotation as enforced by the interfacial Dzyaloshinskii–Moriya interaction.</p>
Predicted times, spatial coordinates of bow shock crossings and shock geometry at Mars from the NASA/MAVEN mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br>Planet: <strong>Mars</strong><br>Radius: <strong>R<sub>M</sub> = 3389.5 km</strong> (volumetric mean planetary radius)<br>Spacecraft: <strong>NASA/Mars Atmosphere and Volatile Evolution (MAVEN)</strong><br>Spacecraft coordinates system: <strong>Mars Solar Orbital (MSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>MSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>MSO</sub></em> towards Mars’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>MSO</sub></em>–<em>Y<sub>MSO</sub></em> plane passing through the centre of Mars,</li> <li><em>Y<sub>MSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/11/2014 to 30/04/2024</strong> (Mars Years MY32 to MY36 included, part of MY37).<br>Total number N of candidate bow shock crossings in the database: <strong>N = 20107</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br>The original MAVEN/MAG data repository on which these algorithms were applied is available on NASA's Planetary Data System (PDS) at <a href="https://doi.org/10.17189/1414178">https://doi.org/10.17189/1414178</a>. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br>To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described in:<br>Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2022), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, 1-33, e2021JA029942, <a href="https://doi. org/10.1029/2021JA029942">https://doi. org/10.1029/2021JA029942</a>. </p> <p>Also available at: <a href="https://doi.org/10.1002/essoar.10507942.1">https://doi.org/10.1002/essoar.10507942.1 </a> and as arXiv e-print: <a href="https://doi.org/10.48550/arXiv.2109.04366">https://doi.org/10.48550/arXiv.2109.04366</a></p> <p>These algorithms consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on J. Gruesbeck's 3D model (<a href="https://doi.org/10.1029/2018JA025366">Gruesbeck et al. 2018</a>) for prediction of Mars bow shock position</li> <li>Corrector algorithm based on magnetic field measurements (magnitude and fluctuations).</li> </ol> <p><strong>REMARK ON VERSIONS</strong><br>From Version 3 onwards, we also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted \(\theta_{Bn}\)(ThetaBn). Assuming a smooth shock surface and the 3D model of Gruesbeck et al. (2018, all points), this gives a first indication of the geometry of the shock, so that:</p> <ul> <li>45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup>: quasi-perpendicular shock condition</li> <li><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub>≥135<sup>∘</sup>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>From Version 4 onwards, we also added the solar longitude Ls (in degrees).</p> <p>For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. Note that due to minor adjustments in the code, some of the ThetaBn angles calculated here for the examples of Fig. 6 in Simon Wedlund et al. (2022) may slightly differ from the values quoted in the paper.</p> <p><strong>VARIABLES DESCRIPTION</strong><br>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in MAVEN's database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Mars Solar Orbital coordinates of the shock, in units of Mars radius <em>R</em><sub><em>M</em> </sub>(<em>R<sub>M</sub></em> = 3389.5 km):<br><em>X<sub>MSO</sub></em>,<sub> </sub><em>Y<sub>MSO</sub></em>, <em>Z<sub>MSO</sub></em> and Euclidean distance \(R_{MSO} = \sqrt{X_{MSO}^2 + Y_{MSO}^2 + Z_{MSO}^2}\) (in <em>R<sub>M</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = \(\tan^{-1}{Y_{MSO}^2+Z_{MSO}^2 \over X_{MSO}^2}\) (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface \(\theta_{Bn}\) (ThetaBn, in º) <ul> <li>45 < ThetaBn < 135 deg: quasi-⊥ shock</li> <li>ThetaBn ≤45 deg & ThetaBn ≥ 135 deg: quasi-|| shock</li> </ul> </li> <li>Solar longitude Ls, in degrees.</li> <li>Flag for crossing: <ul> <li>sheath \(\longrightarrow\) solar wind, flag = 0.</li> <li>solar wind \(\longrightarrow\) sheath, flag = 1.</li> </ul> </li> </ul> <p><strong>WARNING</strong><br>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic field. It is aimed at giving a first approximation of the shock area times in the MAVEN data. It is particularly suited to statistical studies and region identification in the MAVEN datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma suite bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.075 R<sub>M</sub> (with R<sub>M</sub> = 3389.5 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</p> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br>C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. C. Möstl thanks the Austrian Science Fund FWF projects P31659-N27, P31521-N27. A. Beth thanks the Swedish National Space Agency (SNSA) and its support with the grant 108/18. This database was notably used to add to the Helio4Cast database which monitors solar wind parameters in the solar system (<a href="https://doi.org/10.6084/m9.figshare.6356420">https://doi.org/10.6084/m9.figshare.6356420</a>). Helio4Cast is available at <a href="http://www.helioforecast.space/icmecat">www.helioforecast.space/icmeca</a>t and <a href="http://www.helioforecast.space/sircat">www.helioforecast.space/sircat</a>. </p> <p><strong>LICENSE AND RIGHTS</strong><br>This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences (ÖAW), 2021-09-08<br>Version 2 (c) CSW @ ÖAW/IWF, 2021-11-30 -- Addition of R_MSO and SZA<br>Version 3 (c) CSW @ ÖAW/IWF, 2022-02-09 -- Addition of ThetaBn<br>Version 4 (c) CSW @ ÖAW/IWF, 2025-03-20 -- Addition of Ls, Bx, By, Bz and Bt.</p> <p> </p> <p><br>Contact email: cyril.simon.wedlund@gmail.com</p>
Derivation of Hemispheric Ionospheric Current Functions From Ground-Level Magnetic Fields
<p>These files provide the input and output data for the Figures shown in the paper "Derivation of<br> Hemispheric Ionospheric Current Functions From Ground-Level Magnetic Fields" by Daniel Weimer, published in the Journal of Geophysical Research, Space Physics, paper number 2018JA026191, doi:10.1029/2018JA026191</p> <p>Two IDL program files and the original, PDF versions of the figures are included.</p> <p>The data are provided as IDL "SAVE" files, readable in IDL with the "RESTORE" command. NetCDF<br> versions are included, readable with any NetCDF software library. These NetCDF files have the same<br> names as the ".xdr" files, except they have the extension ".nc". Scalar variables (length 1) are put<br> into the Global Attributes in these files.</p> <p>The files in this archive are:</p> <p>Figures:<br> Figure_1.PDF<br> Figure_2.PDF<br> Figure_3.PDF<br> Figure_4.PDF<br> Figure_5.PDF<br> Figure_6.PDF</p> <p>IDL Programs:<br> spherical_cap_90_fits.pro : Routines for fitting magnetic field data on a hemispheric cap (90<br> degrees), using spherical harmonics having both internal and external sources (or external alone),<br> and functions for evaluating the results as equivalent currents, or the magnetic field components.</p> <p> AllLegendre.pro : Required by spherical_cap_90_fits.pro, provides computations of Associated<br> Legendre Polynominals as arrays, for all combinations of l and m, up to Lmax and Mmax, as well as first derivatives.</p> <p>Data Files:</p> <p> Figures_1_2_3_4_dB_ModelData.xdr : Magnetic field values (output from the 2013 empirical model),<br> shown in Figure 1, and used to calculate the coefficients used to make Figures 2, 3, and 4.<br> Contents:<br> ALLLATS FLOAT = Array[8640] , array of latitude values, degrees<br> ALLMLTS FLOAT = Array[8640] , array of Magnetic Local Time (MLT) values, hours<br> DBNS FLOAT = Array[8640] , array of northward magnetic field values<br> DBES FLOAT = Array[8640] , array of eastward magnetic field values<br> DBVS FLOAT = Array[8640] , array of vertical (downward) magnetic field values<br> NLATS FLOAT = 180.000<br> NMLTS INT = 48<br> empirical model inputs:<br> BT FLOAT = 10.0000 , magnitude of the IMF<br> ANGLE FLOAT = 180.000 , IMF clock angle<br> F107 FLOAT = 120.000 , F10.7 solar index<br> SWVEL FLOAT = 400.000 , solar wind velocity<br> TILTA FLOAT = 0.00000 , dipole tilt angle</p> <p> Figures_2_3_4_SCHA90FitResults.xdr : The coefficients obtained from the fits, used to generate<br> Figures 2, 3, and 4.<br> Contents:<br> SPHCE DOUBLE = Array[115] , external spherical harmonic coefficients<br> SPHCI DOUBLE = Array[115] , interal spherical harmonic coefficients<br> NOINT_SPHCE DOUBLE = Array[115] , external spherical harmonic coefficients,<br> derived without using the internal terms in the fitting of the magnetic potential<br> The following variables are documented in the IDL program spherical_cap_90_fits.pro:<br> MAXL INT = 34<br> MAXM INT = 3<br> ODD INT = 1<br> EVEN INT = 0<br> CSIZE INT = 115<br> LS INT = Array[115]<br> MS INT = Array[115]<br> AB BYTE = Array[115]</p> <p> Figures_3_4_dB_ModelData-Dst.xdr : Magnetic field values, after subtraction of the ring current<br> magnetic field, used to calculate the coefficients to make Figures 3 and 4.<br> Contents: Same variables as in file Figures_1_2_3_4_dB_ModelData.xdr</p> <p> Figures_3_4_SCHA90FitResults-Dst.xdr : The coefficients obtained from fitting the magnetic field<br> that had the ring current subtracted, used to make Figures 3, and 4.<br> Contents: Same variables as in file Figures_2_3_4_SCHA90FitResults.xdr</p> <p> Figure_5_dB_ModelDataTilt3x-Dst.xdr : eight sets of magnetic field values, after subtraction of the<br> ring current magnetic field, used to calculate the coefficients to generate Figure 5.<br> Contents: Similar variables as in file Figures_1_2_3_4_dB_ModelData.xdr, except that TILTA is<br> replaced by TILTS, an array with the eight dipole tilt angles. The magnetic field values are<br> replaced by these arrays:<br> DBN3X FLOAT = Array[180, 48, 3] , northward magnetic field, eight sets<br> DBE3X FLOAT = Array[180, 48, 3] , eastward magnetic field, eight sets<br> DBV3X FLOAT = Array[180, 48, 3] , vertical magnetic field, eight sets</p> <p> Figure_5_SCHA90FitResultsTilt3x-Dst.xdr : eight sets of coefficients obtained from fitting the<br> magnetic field, used to make Figure 5.<br> Contents: Same variables as in file Figures_2_3_4_SCHA90FitResults.xdr, except for these arrays:<br> SPHCE3X DOUBLE = Array[115, 3] , external spherical harmonic coefficients, eight sets<br> SPHCI3X DOUBLE = Array[115, 3] , internal spherical harmonic coefficients, eight sets</p> <p> Figure_6_dB_ModelDataClock8x-Dst.xdr : Eight sets of magnetic field values, after subtraction of<br> the ring current magnetic field, used to calculate the coefficients to generate Figure 6.<br> Contents: Similar variables as in file Figures_1_2_3_4_dB_ModelData.xdr, except that ANGLE is<br> replaced by ANGLES, an array with the eight IMF clock angles. The magnetic field values are<br> replaced by these arrays:<br> DBN8X FLOAT = Array[180, 48, 8] , northward magnetic field, eight sets<br> DBE8X FLOAT = Array[180, 48, 8] , eastward magnetic field, eight sets<br> DBV8X FLOAT = Array[180, 48, 8] , vertical magnetic field, eight sets</p> <p> Figure_6_SCHAFitResultsClock8x-Dst.xdr : Eight sets of coefficients obtained from fitting the<br> magnetic field, used to make Figure 6.<br> Contents: Same variables as in file Figures_2_3_4_SCHA90FitResults.xdr, except for these arrays:<br> SPHCE8X DOUBLE = Array[115, 8] , external spherical harmonic coefficients, eight sets<br> SPHCI8X DOUBLE = Array[115, 8] , internal spherical harmonic coefficients, eight sets<br> </p>
Magnetic Field Measurements above a Phonolite Diatreme near Rockeskyll, West Eifel, Germany
Open the record for dataset details and reuse information.
BepiColombo magnetic field data (MPO-MAG) from the two Venus flybys
<p> </p> <p>Vector magnetic field data from the first two Venus flybys (highres and 1-sec res).</p> <p>*Data will be properly archived on ESA's PSA, once the data has been finalized and/or cleaned*</p> <p>Reference frame: VSO</p> <p>Trajectory information is also included. </p>
Fast calculation methods for the magnetic field of particle lattices: Datasets and scripts
<div>*********************************************** README.txt **************************************************</div> <div> </div> <div>Title: Fast calculation methods for the magnetic field of particle lattices: </div> <div>Datasets and scripts</div> <div>Version: 1.0</div> <div>Date of Release: 2024/10/11</div> <div>Identifier: doi:10.5281/zenodo.13930969</div> <div>Permalink: http://dx.doi.org/10.5281/zenodo.13930969</div> <div> </div> <div>*************************************************************************************************************</div> <div> </div> <div>Associated publication: I. Royo-Silvestre, D. Gandia, J. J. Beato-López, E. Garaio, C. Gómez-Polo </div> <div>"Fast calculation methods for the magnetic field of particle lattices" </div> <div>(paper yet to be published)</div> <div> </div> <div>Link to publication: (paper yet to be published)</div> <div> </div> <div>Suggested citation: Please reference the associated publication above when using any datasets or</div> <div> materials described in this README file.</div> <div> </div> <div>Contact information: Isaac Royo Silvestre, </div> <div>Universidad Pública de Navarra, </div> <div>Pamplona, Spain, </div> <div>isaac.royo@unavarra.es</div> <div> </div> <div>License: CC BY 4.0</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>This directory contains the following datasets and supplementary materials:</div> <div> </div> <div> ------------------------------</div> <div> SCRIPTS</div> <div> ------------------------------</div> <div> </div> <div> - scripts.zip Matlab scripts (compressed zip file) used to calculate the magnetic field of </div> <div>lattices of magnetic particles by analytical and semianalytical methods (more information in the associated paper) </div> <div> </div> <div> --------------------------------</div> <div> DATASETS</div> <div> --------------------------------</div> <div> </div> <div> - data.zip: Tabular data required to plot curves (compressed zip file) in csv format,</div> <div>also data used to obtain average values</div> <div> </div> <div> </div> <div>Specific documentation of each file is described in readme files.</div> <div> </div> <div>Refer to the original manuscript (see above) for additional information regarding the collection and generation of these data.</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div> ---------------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'scripts.zip'</div> <div> ---------------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>and multiple .m files. m files are Matlab scripts, text files that can be read using any text editor. However it has to be executed via Matlab, scripts contain documentation as comments.</div> <div> </div> <div> ---------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'data.zip'</div> <div> ---------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>multiple .dat files with data used to obtain averaged valus (see format in the readme.txt </div> <div>contained in the zip), and a folder "curves".</div> <div>The curves folder contains tabular data in .csv files, these files can be used to plot the curves</div> <div>in the manuscript.</div> <p> </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>
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.