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381 results for “electromagnetism”
Van Allen Probes Occurrence Rates of Electromagnetic Ion Cyclotron (EMIC) Waves with Rising Tones
<p>CSV files with the values for the occurrence rates of electromagnetic ion cyclotron (EMIC) waves with rising tones observed by the Van Allen Probes from 2012-09-07 to 2016-07-01 from the paper</p><p>Sigsbee, K., Kletzing, C. A., Faden, J., & Smith, C. W. (2023). Occurrence rates of electromagnetic ion cyclotron (EMIC) waves with rising tones in the Van Allen Probes data set. Journal of Geophysical Research: Space Physics, 128, e2022JA030548. https://doi.org/10.1029/2022JA030548 </p><p>The below files contain the values from Figures 5 and 6. The first row of each file gives the lower value of each L shell bin (0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.5, 6.0, 7.0, 7.5). The first column of each file gives the magnetic local time (MLT) values (0-23) for each bin. </p><p>rbspab_lshellmlt_minutes_20120907_to_20160701.csv gives the number of minutes spent by the Van Allen Probes in each bin of L shell and MLT.</p><p>rbspab_emic_lshellmlt_pcnt_20120907_to_20160701.csv gives the percentage of minutes all EMIC waves were observed in each bin of L shell and MLT.</p><p>rbspab_h_lshellmlt_pcnt_20120907_to_20160701.csv gives the percentage of minutes H+ band EMIC waves were observed in each bin of L shell and MLT.</p><p>rbspab_hr_lshellmlt_pcnt_20120907_to_20160701.csv gives the percentage of minutes H+ band EMIC waves with rising tones were observed in each bin of L shell and MLT.</p><p>rbspab_he_lshellmlt_pcnt_20120907_to_20160701.csv gives the percentage of minutes He+ band EMIC waves were observed in each bin of L shell and MLT.</p><p>rbspab_her_lshellmlt_pcnt_20120907_to_20160701.csv gives the percentage of minutes He+ band EMIC waves with rising tones were observed in each bin of L shell and MLT.</p><p>rbspab_o_lshellmlt_pcnt_20120907_to_20160701.csv gives the percentage of minutes O+ band EMIC waves with rising tones were observed in each bin of L shell and MLT.</p><p>The below files contain the values from Figures 7-13. The first row of each file gives the lower value of each bin of the radial distance RXY in the XY SM plane (0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.5, 6.0, 7.0, 7.5) in Earth radii (RE). The first column of each file gives the lower value of each bin of Z SM in RE (-2.0, -1.75, -1.5, -1.25, -1.0, 0.0, 1.0, 1.25, 1.50, 1.75). Separate files are provided for four MLT sectors: midnight (21 MLT to 3 MLT), dawn (3 MLT to 9 MLT), noon (9 MLT to 15 MLT), and dusk (15 MLT to 21 MLT).</p><p>Number of minutes spent by the Van Allen Probes in bins of RXY and Z SM (Figure 7):</p><p>rbspab_rxyzsm_minutes_midnight_20120907_to_20160701.csv, rbspab_rxyzsm_minutes_dawn_20120907_to_20160701.csv, rbspab_rxyzsm_minutes_noon_20120907_to_20160701.csv, rbspab_rxyzsm_minutes_dusk_20120907_to_20160701.csv </p><p>Percentage of minutes all EMIC waves were observed in bins of RXY and Z SM (Figure 8):</p><p>rbspab_emic_rxyzsm_pcnt_midnight_20120907_to_20160701.csv, rbspab_emic_rxyzsm_pcnt_dawn_20120907_to_20160701.csv, rbspab_emic_rxyzsm_pcnt_noon_20120907_to_20160701.csv, rbspab_emic_rxyzsm_pcnt_dusk_20120907_to_20160701.csv </p><p>Percentage of minutes H+ band EMIC waves were observed in bins of RXY and Z SM (Figure 9):</p><p>rbspab_h_rxyzsm_pcnt_midnight_20120907_to_20160701.csv, rbspab_h_rxyzsm_pcnt_dawn_20120907_to_20160701.csv, rbspab_h_rxyzsm_pcnt_noon_20120907_to_20160701.csv, rbspab_h_rxyzsm_pcnt_dusk_20120907_to_20160701.csv </p><p>Percentage of minutes He+ band EMIC waves were observed in bins of RXY and Z SM (Figure 10):</p><p>rbspab_he_rxyzsm_pcnt_midnight_20120907_to_20160701.csv, rbspab_he_rxyzsm_pcnt_dawn_20120907_to_20160701.csv, rbspab_he_rxyzsm_pcnt_noon_20120907_to_20160701.csv, rbspab_he_rxyzsm_pcnt_dusk_20120907_to_20160701.csv </p><p>Percentage of minutes O+ band EMIC waves were observed in bins of RXY and Z SM (Figure 11):</p><p>rbspab_o_rxyzsm_pcnt_midnight_20120907_to_20160701.csv, rbspab_o_rxyzsm_pcnt_dawn_20120907_to_20160701.csv, rbspab_o_rxyzsm_pcnt_noon_20120907_to_20160701.csv, rbspab_o_rxyzsm_pcnt_dusk_20120907_to_20160701.csv </p><p>Percentage of minutes H+ band EMIC waves with rising tones were observed in bins of RXY and Z SM (Figure 12):</p><p>rbspab_hr_rxyzsm_pcnt_midnight_20120907_to_20160701.csv, rbspab_hr_rxyzsm_pcnt_dawn_20120907_to_20160701.csv, rbspab_hr_rxyzsm_pcnt_noon_20120907_to_20160701.csv, rbspab_hr_rxyzsm_pcnt_dusk_20120907_to_20160701.csv </p><p>Percentage of minutes He+ band EMIC waves with rising tones were observed in bins of RXY and Z SM (Figure 13):</p><p>rbspab_her_rxyzsm_pcnt_midnight_20120907_to_20160701.csv, rbspab_her_rxyzsm_pcnt_dawn_20120907_to_20160701.csv, rbspab_her_rxyzsm_pcnt_noon_20120907_to_20160701.csv, rbspab_her_rxyzsm_pcnt_dusk_20120907_to_20160701.csv </p>
Electromagnetic data (FDEM and ERT) collected in the Venice coastland (Zennare basin)
<p>FDEM and ERT data collected southern of the Venice lagoon (Italy) in the Zennare basin in 2019-2020.</p> <p>FDEM_ZENNARE37.csv: Raw output of Quadrature and Inphase values for the 6 frequencies adopted with the GEM2 FDEM probe.<br> ERT_ROUGHoutput_Zennare.dat: Apparent resistivity data as retrieved with the 48 channels Syscal Pro georesistivimeter ERT.</p>
Datasets for "Pulsational pair-instability supernovae in gravitational-wave and electromagnetic transients" from Hendriks et al 2023.
<p>Data related to the paper "Pulsational pair-instability supernovae in gravitational-wave and electromagnetic transients" by Hendriks et al 2023 <a href="https://doi.org/10.1093/mnras/stad2857">https://doi.org/10.1093/mnras/stad2857</a>.</p><ul><li>`EVENTS_V2.2.2_SEMI_HIGH_RES*.tar.gz: main PPISN prescription variation simulation results for the GW mergers. These contain configurations for the populations and the convolved merger results which in turn contain merger rates, merger properties and events that preceded the mergers (RLOF episodes, SNe). These results are used in figures 2, 3, 6, and 7. Figure 8 uses the SFR used in one of these simulations.</li><li>`EVENTS_V2.2.2_MID_RES*.tar.gz`: PPISNe prescription variation results for the transient rate evolution. These contain configurations for the populations and the convolved merger results which in turn contain merger rates, merger properties and events that preceded the mergers (RLOF episodes, SNe). These results are used in figure 4.</li><li>`grid_single_mass_metallicity_data.tar.gz`: data containing single-star remnant-mass data as a function of initial mass vs. final mass for our fiducial model and three variations: Farmer 2019 PPISN prescription, M_extra_ppisn_ML=10 Msun (i.e. where 10 solarmass of additional mass loss occur for each PPISN), M_co_shift_ppisn=-5 Msun (i.e. the CO core mass range that undergoes PPISN is shifted to lower masses by 5 solarmass). This data is used in figure 5.</li><li>`schematic_overview_data.tar.gz`: data containing single-star remnant-mass data as a function of pre-SN core mass for our fiducial models and several variations: Farmer 2019 PPISN prescription, M_extra_ppisn_ML = 5 Msun, M_co_shift_ppisn=-5 Msun, M_co_shift_ppisn=+5 Msun. This data is used in figure 1.</li><li>`paper_ppisne_scripts-main.tar.gz`: git-repository that contains the routines to generate the figures. The readme in this script should contain enough information, but relevant to the data here: the user needs to store the files contained in this zenodo repository in a directory that they point to at with an environment variable called `paper_PPISNe_Hendriks2023_data_dir`. These scripts are also hosted on <a href="https://gitlab.com/dhendriks/paper_ppisne_scripts">https://gitlab.com/dhendriks/paper_ppisne_scripts</a></li></ul>
Space Weather ElectroMagnetic Database for Ireland (SWEMDI)
<p>This is a database containing electromagnetic (EM) data that can contribute to better understand and quantify the electric fields caused by space weather events at the Earth's surface, and the physical properties of Ireland’s lithosphere. The database is named Space Weather Electromagnetic Database for Ireland (SWEMDI).</p> <p>It contains measured electromagnetic time series using magnetotelluric equipment, electromagnetic tensor relationships, 3D electrical resistivity model of Ireland's lithosphere, modelled electric and magnetic time series for Ireland between 1991 and 2018, documents and publications that used parts of this database, and a series of scripts that were used to generate the database.</p>
Optimal neutron-star mass ranges to constrain the equation of state of nuclear matter with electromagnetic and gravitational-wave observations: EOS library
<p>This repository includes a library of equations of state (EOS) and stellar models presented in the publications Weih et al. (2019) (see also the related identifier) and Most et al. (2018). The library includes ~ 3 Million physically plausible EOSs that fulfill a number of astrophysical and nuclear constraints. See the README for more information. </p>
High Granularity Electromagnetic Shower Images
<p>This is a limited subset of the data used for training in <strong>arXiv:2005.05334</strong>. The network architectures and instructions to generate more data are available at <a href="https://github.com/FLC-QU-hep/getting_high">here. </a></p> <p>Electromagnetic calorimeter for the ILD consists of 30 active silicon layers in a tungsten absorber stack with 20 layers of 2.1 mm followed by 10 layers of 4.2 mm thickness respectively. We project the sensors onto a rectangular grid of 30×30×30 cells. Each cell in this grid corresponds to exactly one sensor, resulting in total of 27k channels.</p> <p>The file has the following structure:</p> <ul> <li> Group named <em>30x30</em> <ul> <li> <em>energy</em> : Dataset {1000, 1}</li> <li> <em> layers</em> : Dataset {1000, 30, 30, 30}</li> </ul> </li> </ul> <p>The <em>energy</em> specifies the true energy of the incoming photons in units of GeV, where <em>layers</em> represent the energy deposited (MeV) in 30 layers of the calorimeter in an image data format. This file contains approximately 24.000 showers.</p>
Transient Electromagnetic data from the Los Humeros geothermal field in Mexico: raw data
<p>The dataset is composed of raw data from TerraTEM (from Monex GeoScope, single loop) from the Los Humeros geothermal field, Mexico and their locations in a .csv file.</p> <p>The data were gathered under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund CONACYT-SENER, Project 2015-04-268074.</p>
Photon wave function and the electromagnetic vacuum
<p>The association of the density of states theory to the vector potential quantization of the electromagnetic field leads naturally to the definition of the photon quantization volume, ascribing an intrinsic physical geometrical property to a single photon. Photons are not point particles and constitute a particular case in particles standard model. Based upon the vector potential with quantized amplitude, we define a photon wave function representing an amplitude probability for the photon localization normalized with respect to the photon quantization volume. In addition, the established photon wave function satisfies Maxwell's propagation equation and Schrödinger's equation with the relativistic massless particle Hamiltonian as well as a Schrödinger-like equation for the vector potential. </p><p>The electromagnetic vacuum derives straightforward from the photon vector potential function and has both classical and quantum representations. It permits to remedy to the zero-point energy shortcomings and singularities in quantum electrodynamics. Finally, the photon wave function is naturally related to the electromagnetic vacuum states putting the basis for understanding entanglement. </p>
Electromagnetic Calorimeter Shower Images of CaloFlow
<p>These are the calorimeter showers that were used to train and evaluate the normalizing flows of "<a href="https://arxiv.org/abs/2106.05285">CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows</a>" and "<a href="https://arxiv.org/abs/2110.11377">CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows</a>". The training and evaluation scripts can be found in <a href="https://gitlab.com/claudius-krause/caloflow">this git repository</a>.</p> <p>The samples were created with the same GEANT4 configuration file as the original CaloGAN samples. Said configuration can be found at the <a href="https://github.com/hep-lbdl/CaloGAN">CaloGAN repository</a>; the original CaloGAN samples are available at <a href="https://doi.org/10.17632/pvn3xc3wy5.1">this DOI</a>.</p> <p>Samples for each particle (eplus, gamma, piplus) are stored in a separate .tar.gz file. Each tarball contains the following files:</p> <ul> <li> <p>train_particle.hdf5: 70,000 events used to train CaloFlow I and II.</p> </li> <li> <p>test_particle.hdf5: 30,000 events used for model selection of CaloFlow I and II.</p> </li> <li> <p>train_cls_particle.hdf5: 60,000 events used to train the evaluation classifier.</p> </li> <li> <p>val_cls_particle.hdf5: 20,000 events used for model selection and calibration of the evaluation classifier.</p> </li> <li> <p>test_cls_particle.hdf5: 20,000 events used for the evaluation run of the evaluation classifier.</p> </li> </ul> <p>Each .hdf5 file has the same structure as the <a href="https://doi.org/10.17632/pvn3xc3wy5.1">original CaloGAN data</a>.</p>
DL-RMD: A geophysically constrained electromagnetic resistivity model database for deep learning applications (Dataset)
<p>Deep learning algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale data sets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database of a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. The presented database can potentially be used to build surrogate models of well-known processes and aid in labour intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, it will incorporate consistency and credibility in deep learning models. We urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database.</p>
Marine time domain electromagnetic data and true model for 2.5D inversion
<p>Dataset contains the description of complex 3D geoelectric model (with bathymetry, curved surfaces of geoelectric layers, target bodies simulated HC deposits, and background inhomogeneities) and marine time domain electromagnetic data calculated via finite element modeling. Noised data sets have been used for geometric 2.5D inversion.</p>
Chesley et al., 2023 - Southern Hikurangi Margin Electromagnetic Data from HT-RESIST trench-crossing profile
<p>Chesley_etal_2023_South-Hikurangi-EM-data.txt contains controlled-source electromagnetic data and magnetotelluric data from the southern trench-crossing profile of the Hikurangi Trench Regional Electromagnetic Survey to Image the Subduction Thrust (HT-RESIST) project (see https://emlab.ldeo.columbia.edu/index.php/category/ht-resist/ for information regarding the survey). Chesley_etal_2023_South-Hikurangi-bathymetry.txt is the associated bathymetry file. </p>
Meshing strategies for 3D geo-electromagnetic modeling in the presence of metallic infrastructure
<p>Accompanying data to journal article</p> <blockquote> <p>Castillo-Reyes, O., Rulff, P., Schankee Um, E., Amor-Martin, A. (2023) Meshing strategies for 3D geo-electromagnetic modeling in the presence of metallic infrastructure. Accepted for publication in Computational Geosciences.</p> </blockquote>
Dataset related to the publication "Electromagnetic Amplification of Microwave Phonons in Nonlinear Resonant Microcavities", DOI: 10.1109/TMTT.2018.2855176
<p>This folder contains the raw data from which the graphs in paper "Electromagnetic Amplification of Microwave Phonons in Nonlinear Resonant Microcavities", DOI: 10.1109/TMTT.2018.2855176, have been obtained.</p>
Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>.</p> <p>The R code was written for R version 3.6.3.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p>
Data release - A Standard Siren Cosmological Measurement from the Potential GW190521 Electromagnetic Counterpart ZTF19abanrhr
<p>Data release accompanying the manuscript</p> <p> "<strong>A Standard Siren Cosmological Measurement from the Potential GW190521 Electromagnetic Counterpart ZTF19abanrhr</strong>" - <a href="https://arxiv.org/abs/2009.14057">Chen et al. (2020)</a></p> <p>assuming an association between the LIGO-Virgo gravitational wave signal <a href="https://www.gw-openscience.org/eventapi/html/O3_Discovery_Papers/GW190521/">GW190521</a> and the electromagnetic signal ZTF19abanrhr as identified by <a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.251102">Graham et al 2020</a>.</p> <p>The posterior samples for the GW analyses are available from <a href="https://doi.org/10.5281/zenodo.4057130">Isi (2020)</a> and <a href="https://dcc.ligo.org/LIGO-P2000158/public">LVC (2020)</a> respectively.</p>
Vocal drum sounds in Human Beatboxing: an acoustic and articulatory exploration using electromagnetic articulography
<p>This dataset constitutes the supplementary material of a paper in review in the Journal of the Acoustical Society of America (JASA)</p>
Transient Electromagnetic data from the Acoculco area in Mexico: raw data
<p>The dataset is composed of raw data from TerraTEM (from Monex GeoScope, single loop) from the Acoculco area, Mexico and their locations in a .csv file.</p> <p>The data were gathered under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund CONACYT-SENER, Project 2015-04-268074.</p>
Source code and simulation results: Poles and zeros of electromagnetic quantities in photonic systems
<h4><strong>Summary</strong></h4> <p>This publication supplements the article "Poles and zeros of electromagnetic quantities in photonic systems" with tabulated data and matlab code that allows to reproduce the results. The article elaborates how evaluating resonances based on contour integrals of scalar electromagnetic quantities extends to computing zeros. Furthermore, direct differentiation of underlying scattering problems is used to compute sensitivities with respect to design parameters.</p> <h4><strong>Structure</strong></h4> <p>The script 'main_text.m' can be used to reproduce the results provided in the paper. In tabulated form the results are contained in the directory <strong>tabulated</strong>. Furthermore, the script 'supplement.m' can be used to reproduce results presented in the supplement. The directory <strong>RPExpand </strong>contains the software RPExpand v2, which is available on <a href="https://doi.org/10.5281/zenodo.10371002">Zenodo</a> with additional examples. </p> <h4><strong>Compute residues</strong></h4> <p>The modal expansion of the Fourier transform is based on its residues at the dominant resonances. If the poles are simple, which often is the case, the residues can be obtained directly from the eigenvectors of the generalized eigenvalue problem used to obtain the poles or the zeros. Introducing the Vandermonde matrix</p> <p>\(V = \begin{bmatrix} 1 & \dots & 1 \\ w_1 & \dots & w_M \\ \vdots & & \vdots \\ w_1^{M-1} &\dots & w_M^{M-1} \end{bmatrix}\),</p> <p>the Hankel matrix \(H\) can be written as \(H = V A V^T\) with \(A\) being the diagonal matrix \(\mathrm{diag}(a_1,\dots,a_M)\) containing the residues \(a_m \). This decomposition is a consequence of the Cauchy's reisdue theorem if the poles are simple. Furthermore, we now that \(V^{-T}\) solves the generalized eigenproblem \(H^<X = HX\Omega\) (Eq. 2 in the original paper) and hence the eigenvectors we get from Matlabs eig routine are \(X = V^{-T}D\) where \(D\) is some scaling. It follows that we obtain the residues using \(A = X^T H X (X V^T)^{-2}\)</p> <h4><strong>Derivatives</strong></h4> <p>Similarly, our framework provides a straight forward approach to the derivatives of zeros and poles if they are simple. Using direct differentiation we have access to partial derivatives of the quantity \(q(\omega)\) and hence the derivatives of the moments \(s_k = \frac{1}{2\pi i} \oint_C \omega^k q(\omega) \mathrm{d}\omega\). For the zeros the inverse \(1/q(\omega)\) and the respective derivative are considered. Using Cauchy's residue theorem the derivatives \(\frac{\partial w_m}{\partial p}\)are solutions of the linear system of equations \(\frac{\partial s_k}{\partial p} = \sum_{m = 1}^{M}\left[k\omega_m^{k-1}\frac{\partial w_m}{\partial p} a_m + \omega_m^k\frac{\partial a_m}{\partial p} \right]\).</p> <h4><strong>Higher order singularities</strong></h4> <p>Finding higher order poles and zeros is possible without further adaptation. Computing derivatives and residues requires some special care. The moments are then given by \(s_k = \sum_{m=1}^{M} \sum_{n = 1}^{N_m} a_{m,n} \frac{k! \, \omega^{k-n+1}}{(k-n+1)!(n-1)!}\)with \(a_{m,n}\) being the residue of the pole \(\omega_m\) and \(n \) refers to the order. Accordingly expressions for the derivatives are available.</p> <h4><strong>Error estimates</strong></h4> <p>The estimated errors in Table 1 refer to the number of integration points, i.e. we are interested in the question how close we get with a given number of integration points to the exact solution of the chosen approximate model of the physical system. Due to propagation of the error the convergence of the derivatives is shifted towards a larger number of integration points.</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (version 5.4.3 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holder in 'zeros_poles.m' by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <h4><strong>References</strong></h4> <p>[1] Felix Binkowski, Fridtjof Betz, Rémi Colom, Patrice Genevet, Sven Burger, Poles and zeros of electromagnetic quantities in photonic systems, https://doi.org/10.48550/arXiv.2307.04654</p> <p>[2] Anthony P. Austin, Peter Kravanja, Lloyd N. Trefethen, Numerical algorithms based on analytic function values at roots of unity, SIAM Journal of Numerical Analysis 52, 1795 (2014), https://doi.org/10.1137/130931035</p> <p>[3] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p>
Cassini Magnetometer Data Products Associated With Ring-Saturn Electromagnetic Coupling
<p>This dataset shows the measurements made by the Cassini magnetometer as it traversed ring-connected magnetic field lines during the Cassini Grand Finale orbits. The L-shell mapping of the azimuthal magnetic field component and associated field-aligned currents are provided, and can be used to regenerate the Cassini magnetometer data products in Figure 2 of the manuscript "Current Events at Saturn: Ring-Planet Electromagnetic Coupling" (Agiwal et al., 2024). </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.