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67 results for “dipole”

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zenodo44/100

Models and source data for MuML dipole fitting

<p>Source data, models, and scripts necessary to reproduce the results of: &quot;Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles&quot; (M. Veit, D. M. Wilkins, Y. Yang, R. A. DiStasio Jr., M. Ceriotti, arXiv: 2003.12437). The model is a combination of symmetry-adapted Gaussian process regression (SA-GPR) for atomic dipoles and scalar GPR for atomic partial charges, which are fit together to reproduce the molecule&#39;s total dipole moment. Source data, kernel matrices, weights, residuals, and scripts for fitting and plotting the results are included.</p>

opencc-by-4.0May 2020View details →
zenodo44/100

Dipole localisation predictions data set

<p>This data set contains prediction of ten dipole localisation algorithms computed using a simulated artificial lateral line (potential flow).</p>

opencc-by-4.0May 2021View details →
zenodo44/100

Experimental data for Berry curvature dipole senses topological transition in a moiré superlattice

<p>This experimental dataset was used in our study of &quot;Berry curvature dipole senses topological transition in a moir&eacute; superlattice&quot;.</p>

opencc-by-4.0Aug 2021View details →
zenodo40/100

Dataset: Bacterioplankton metabolism of phytoplankton lysates across a cyclone-anticyclone eddy dipole impacts the cycling of semi-labile organic matter in the photic zone

<p>This dataset contains both field data and the results of dilution batch-culture bioassay experiments characterizing bacterioplankton usage of ambient and added dissolved organic matter across&nbsp;a cyclone to anticyclone spatial transect in the North Pacific Subtropical Gyre. Field data include total organic carbon, total nitrogen, bacterioplankton cell abundances, ammonia monooxygenase subunit A gene concentrations, and 16S rRNA gene amplicons. Experimental data include&nbsp;time-resolved changes in total organic carbon, nitrogen species, and bacterioplankton cell abundances.</p>

opencc-by-4.0Jan 2020View details →
zenodo40/100

Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus

<p>Description for Zenodo Data Set DOI:10.5281/zenodo.45507</p> <p>This dataset accompanies the article, submitted to Physics of Plasmas, titled &quot;Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus,&quot; by Garnier, Mauel, Roberts, Kesner, and Woskov.&nbsp;</p> <p>---------------------------------------------</p> <p>Data is presented as HDF5 datafiles&nbsp;<br /> (see https://www.hdfgroup.org/HDF5/)&nbsp;<br /> as HDF4 datafiles<br /> (see https://www.hdfgroup.org/release4/doc/index.html)<br /> and as CSV datafiles&nbsp;<br /> (see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).</p> <p>Data files are associated with FIGURES 2, 3, 4, 5, 6</p> <p>---------------------------------------------<br /> start of figure list<br /> ---------------------------------------------<br /> FIGURE 1: &nbsp; No data set</p> <p>---------------------------------------------<br /> FIGURE 2: &nbsp; (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer&nbsp;<br /> S140529016_DensityData.hdf &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br /> time range: &nbsp; &nbsp; 5.00 sec - 7.00 sec<br /> time sample: &nbsp; &nbsp;8 micro-sec<br /> samples: &nbsp; &nbsp; &nbsp; &nbsp;250,000 x 4 channels + 250,000 (total)<br /> Unit: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; radian &quot;Interferometer&quot;<br /> Unit: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 1.0E18 particles &quot;Total-Particles&quot;</p> <p>16-channel Photodiode Array 1 &nbsp;&nbsp;<br /> S140529016_PDAData.hdf &nbsp;<br /> time range: &nbsp; &nbsp; 5.00 sec - 7.00 sec<br /> time sample: &nbsp; &nbsp;20 micro-sec<br /> samples: &nbsp; &nbsp; &nbsp; &nbsp;100,000 x 16 channels<br /> Unit: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; A.U. &quot;PDA-1&quot;</p> <p>TOTAL ECRH Injected Heating Power<br /> S140529016_ECRHData.hdf &nbsp; &nbsp;&nbsp;<br /> time range: &nbsp; &nbsp; 5.00 sec - 7.00 sec<br /> time sample: &nbsp; &nbsp;20 micro-sec<br /> samples: &nbsp; &nbsp; &nbsp; &nbsp;100,000<br /> Unit: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; kW &quot;Microwave-Power&quot;</p> <p>S140529016_LoopVoltage.hdf &nbsp;<br /> time range: &nbsp; &nbsp; 5.00 sec - 7.00 sec<br /> time sample: &nbsp; &nbsp;80 micro-sec<br /> samples: &nbsp; &nbsp; &nbsp; &nbsp;25,000&nbsp;<br /> Unit: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; milli-Volt x sec &quot;Loop-Voltage&quot;</p> <p>---------------------------------------------<br /> FIGURE 3(a): &nbsp; &nbsp;(HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer&nbsp;<br /> S140529016_DensityData.hdf &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br /> time range: &nbsp; &nbsp; 5.00 sec - 7.00 sec<br /> time sample: &nbsp; &nbsp;8 micro-sec<br /> samples: &nbsp; &nbsp; &nbsp; &nbsp;250,000 x 4 channels + 250,000 (total)<br /> Unit: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; radian &quot;Interferometer&quot;<br /> Unit: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 1.0E18 particles &quot;Total-Particles&quot;</p> <p>16-channel Photodiode Array 1 &nbsp;&nbsp;<br /> S140529016_PDAData.hdf &nbsp;<br /> time range: &nbsp; &nbsp; 5.00 sec - 7.00 sec<br /> time sample: &nbsp; &nbsp;20 micro-sec<br /> samples: &nbsp; &nbsp; &nbsp; &nbsp;100,000 x 16 channels<br /> Unit: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; A.U. &quot;PDA-1&quot;</p> <p>---------------------------------------------<br /> FIGURE 4 (a), (b), (c): &nbsp; (CSV Files)</p> <p>time period: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;5.0 - 6.0 sec<br /> Ensemble Window: &nbsp; &nbsp; &nbsp; &nbsp;8 msec<br /> sample period: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;8 micro-sec<br /> Nyquist Freq: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 62.475 kHz</p> <p>Line-Density-Coherence.csv<br /> Frequency (Hz) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Hz<br /> d(nl-1)^2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless<br /> d(nl-2)^2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless &nbsp;&nbsp;<br /> d(nl-3)^2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless<br /> d(nl-4)^2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless<br /> Kappa 1-2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless<br /> Kappa 1-3 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless<br /> Kappa 1-4 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless</p> <p>Isat-Coherence.csv &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br /> Frequency (Hz) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Hz<br /> d(I)^2 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;dimensionless<br /> Kappa 8deg &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;dimensionless<br /> Kappa 16deg &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless<br /> Kappa 24deg &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless</p> <p>Float-Potential-Coherence.csv<br /> Frequency (Hz) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Hz<br /> d(Pot)^2/Te^ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;dimensionless<br /> Kappa 8deg &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;dimensionless<br /> Kappa 16deg &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless<br /> Kappa 24deg &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless</p> <p>---------------------------------------------<br /> FIGURE 5 (a), (b): &nbsp; (CSV Files)</p> <p>Figure 5(a)<br /> time period: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;5.0 - 6.0 sec<br /> Ensemble Window: &nbsp; &nbsp; &nbsp; &nbsp;8 msec<br /> sample period: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;8 micro-sec<br /> Nyquist Freq: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 62.475 kHz</p> <p>Float-Isat-CrossPhase.csv &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;<br /> Frequency (Hz) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Hz<br /> alpha-Float &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; degree<br /> alpha-Isat &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;degree</p> <p>Figure 5(b)<br /> time period: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;6.02 - 6.05 sec<br /> Ensemble Window: &nbsp; &nbsp; &nbsp; &nbsp;1.6 msec<br /> sample period: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;8 micro-sec<br /> Nyquist Freq: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 62.475 kHz</p> <p>Float-Isat-DuringCrossPhase.csv&nbsp;<br /> Frequency (Hz) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Hz<br /> alpha-Float &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; degree<br /> alpha-Isat &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;degree<br /> kappa-Float &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; dimensionless<br /> kappa-Isat &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;dimensionless<br /> ---------------------------------------------<br /> FIGURE 6: &nbsp; No data set</p> <p>---------------------------------------------<br /> FIGURE 7: &nbsp; No data set</p> <p>---------------------------------------------<br /> FIGURE 8: &nbsp; No data set</p> <p>---------------------------------------------<br /> FIGURE 9: &nbsp; No data set</p> <p>---------------------------------------------<br /> FIGURE 10: &nbsp;No data set</p> <p>---------------------------------------------<br /> end of figure list<br /> ---------------------------------------------<br /> ---------------------------------------------<br /> start of file list<br /> ---------------------------------------------<br /> Filename &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Size &nbsp; &nbsp;<br /> ----------------------------------------------<br /> Potential-Time-Angle-Data.h5 &nbsp; &nbsp; &nbsp; &nbsp;254.68 KB &nbsp; &nbsp; &nbsp; &nbsp;<br /> All-Probe-Data.h5 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 9.81 MB &nbsp; &nbsp; &nbsp;<br /> Average-Probe-Data.h5 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 1.06 MB &nbsp; &nbsp; &nbsp;<br /> Average-Isat-Data.h5 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;422.22 KB &nbsp; &nbsp; &nbsp; &nbsp;<br /> S140529016_PDAData.hdf &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;6.80 MB &nbsp; &nbsp; &nbsp;<br /> S140529016_DensityData.hdf &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;6.00 MB &nbsp; &nbsp; &nbsp;<br /> S140529016_ECRHData.hdf &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 803.39 KB &nbsp; &nbsp; &nbsp; &nbsp;<br /> S140529016_LoopVoltage.hdf &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;203.39 KB &nbsp;&nbsp;<br /> Line-Density-Coherence.csv &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;420.59 KB &nbsp; &nbsp; &nbsp; &nbsp;<br /> Isat-Coherence.csv &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;258.51 KB &nbsp; &nbsp; &nbsp; &nbsp;<br /> Float-Potential-Coherence.csv &nbsp; &nbsp; &nbsp; 257.90 KB<br /> Float-Isat-DuringCrossPhase.csv &nbsp; &nbsp; 48.87 KB &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br /> Float-Isat-CrossPhase.csv &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 138.47 KB &nbsp; &nbsp;<br /> LDX-Pellet-Supplementary.pdf &nbsp; &nbsp; &nbsp; &nbsp;1.84 MB &nbsp; &nbsp; &nbsp;<br /> S140529016_frPlots.mp4 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;3.24 MB &nbsp; &nbsp; &nbsp;<br /> ---------------------------------------------<br /> end of file list<br /> ---------------------------------------------</p>

opencc-zeroJul 2016View details →
zenodo40/100

A magnetic dipole and a magnetic monopole (figure).

<p>A magnetic dipole with North and South poles (left); a magnetic monopole, North pole only (right). Image credit: Institute for Research in Schools 2016.</p> <p>CERN@school DRN: CAS-PUB-MDL-000008-v1.0</p>

opencc-by-4.0Dec 2016View details →
zenodo40/100

Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus

<p><strong>Description for Zenodo Data Set DOI:10.5281/zenodo.220992</strong></p> <p>This dataset accompanies the article, to appear in Physics of Plasmas, titled "Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus," by Garnier, Mauel, Roberts, Kesner, and Woskov. </p> <p>---------------------------------------------</p> <p>Data is presented as HDF5 datafiles <br> (see https://www.hdfgroup.org/HDF5/) <br> as HDF4 datafiles<br> (see https://www.hdfgroup.org/release4/doc/index.html)<br> and as CSV datafiles <br> (see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).</p> <p>Data files are associated with FIGURES 2, 3, 4, 5</p> <p>Data plotted in other figures are derived from the dataset as described<br> in the paper.</p> <p>---------------------------------------------<br> start of figure list<br> ---------------------------------------------<br> <strong>FIGURE 1: </strong>  No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 2:</strong>   (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br> S140529016_DensityData.hdf          <br> time range:     5.00 sec - 7.00 sec<br> time sample:    8 micro-sec<br> samples:        250,000 x 4 channels + 250,000 (total)<br> Unit:           radian "Interferometer"<br> Unit:           1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1   <br> S140529016_PDAData.hdf  <br> time range:     5.00 sec - 7.00 sec<br> time sample:    20 micro-sec<br> samples:        100,000 x 16 channels<br> Unit:           A.U. "PDA-1"</p> <p>TOTAL ECRH Injected Heating Power<br> S140529016_ECRHData.hdf     <br> time range:     5.00 sec - 7.00 sec<br> time sample:    20 micro-sec<br> samples:        100,000<br> Unit:           kW "Microwave-Power"</p> <p>S140529016_LoopVoltage.hdf  <br> time range:     5.00 sec - 7.00 sec<br> time sample:    80 micro-sec<br> samples:        25,000 <br> Unit:           milli-Volt x sec "Loop-Voltage"</p> <p>---------------------------------------------<br> <strong>FIGURE 3(a)</strong>:    (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br> S140529016_DensityData.hdf          <br> time range:     5.00 sec - 7.00 sec<br> time sample:    8 micro-sec<br> samples:        250,000 x 4 channels + 250,000 (total)<br> Unit:           radian "Interferometer"<br> Unit:           1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1   <br> S140529016_PDAData.hdf  <br> time range:     5.00 sec - 7.00 sec<br> time sample:    20 micro-sec<br> samples:        100,000 x 16 channels<br> Unit:           A.U. "PDA-1"</p> <p>---------------------------------------------<br> <strong>FIGURE 4 (a), (b), (c), (d), (e), (f):  </strong> (CSV Files)</p> <p>time period:            5.0 - 6.0 sec<br> Ensemble Window:        8 msec<br> sample period:          8 micro-sec<br> Nyquist Freq:           62.475 kHz</p> <p>(a) Fig4a-Line-Density-Coherence.csv<br> Frequency (Hz)          Hz<br> d(nl-1)^2               dimensionless<br> d(nl-2)^2               dimensionless   <br> d(nl-3)^2               dimensionless<br> d(nl-4)^2               dimensionless<br> Lambda 1-2              dimensionless<br> Lambda 1-3              dimensionless<br> Lambda 1-4              dimensionless</p> <p>(b) Fig4b-Isat-Coherence.csv                       <br> Frequency (Hz)          Hz<br> d(I)^2                  dimensionless<br> Lambda 8deg             dimensionless<br> Lambda 16deg            dimensionless<br> Lambda 24deg            dimensionless</p> <p>(c) Fig4c-Float-Potential-Coherence.csv<br> Frequency (Hz)          Hz<br> d(Pot)^2/Te^            dimensionless<br> Lambda 8deg             dimensionless<br> Lambda 16deg            dimensionless<br> Lambda 24deg            dimensionless</p> <p>(d) Fig4d-Line-Density-Coherence.csv<br> Frequency (Hz)          Hz<br> d(nl-1)^2               dimensionless<br> d(nl-2)^2               dimensionless   <br> d(nl-3)^2               dimensionless<br> d(nl-4)^2               dimensionless<br> Lambda 1-2              dimensionless<br> Lambda 1-3              dimensionless<br> Lambda 1-4              dimensionless</p> <p>(e) Fig4e-Isat-Coherence.csv                       <br> Frequency (Hz)          Hz<br> d(I)^2                  dimensionless<br> Lambda 8deg             dimensionless<br> Lambda 16deg            dimensionless<br> Lambda 24deg            dimensionless</p> <p>(f) Fig4f-Float-Potential-Coherence.csv<br> Frequency (Hz)          Hz<br> d(Pot)^2/Te^            dimensionless<br> Lambda 8deg             dimensionless<br> Lambda 16deg            dimensionless<br> Lambda 24deg            dimensionless</p> <p>---------------------------------------------<br> <strong>FIGURE 5 (a), (b):</strong>   (CSV Files)</p> <p>Figure 5(a)<br> time period:            5.0 - 6.0 sec<br> Ensemble Window:        8 msec<br> sample period:          8 micro-sec<br> Nyquist Freq:           62.475 kHz</p> <p>(a) Fig5a-Float-Isat-CrossPhase.csv                <br> Frequency (Hz)          Hz<br> alpha-Float             degree<br> alpha-Isat              degree</p> <p>Figure 5(b)<br> time period:            6.02 - 6.05 sec<br> Ensemble Window:        1.6 msec<br> sample period:          8 micro-sec<br> Nyquist Freq:           62.475 kHz</p> <p>(b) Fig5b-Float-Isat-DuringCrossPhase.csv <br> Frequency (Hz)          Hz<br> alpha-Float             degree<br> alpha-Isat              degree<br> kappa-Float             dimensionless<br> kappa-Isat              dimensionless<br> ---------------------------------------------<br> <strong>FIGURE 6: </strong>  No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 7: </strong>  No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 8:</strong>   No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 9: </strong>  No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 10:</strong>  No data set</p> <p>---------------------------------------------<br> end of figure list<br> ---------------------------------------------<br> ---------------------------------------------<br> <strong>start of file list</strong><br> ---------------------------------------------<br> Filename                            Size    <br> ----------------------------------------------<br> All-Probe-Data.h5                       9.8 MB<br> Average-Isat-Data.h5                    422 KB<br> Average-Probe-Data.h5                   1.1 MB<br> Fig4a-Line-Density-Coherence.csv        413 KB<br> Fig4b-Isat-Coherence.csv                251 KB<br> Fig4c-Float-Potential-Coherence.csv     250 KB<br> Fig4d-Line-Density-Coherence.csv        103 KB<br> Fig4e-Isat-Coherence.csv                62 KB<br> Fig4f-Float-Potential-Coherence.csv     62 KB<br> Fig5a-Float-Isat-CrossPhase.csv         138 KB<br> Fig5b-Float-Isat-DuringCrossPhase.csv   36 KB<br> Potential-Time-Angle-Data.h5            255 KB<br> S140529016_DensityData.hdf              6 MB<br> S140529016_ECRHData.hdf                 803 KB<br> S140529016_LoopVoltage.hdf              203 KB<br> S140529016_PDAData.hdf                  6.8 MB</p> <p><br> LDX-Pellet-Supplementary.pdf            1.8 MB<br> S140529016_frPlots.mp4                  3.2 MB<br> ---------------------------------------------<br> <strong>end of file list</strong><br> ---------------------------------------------</p> <p> </p> <p> </p>

opencc-by-4.0Jul 2016View details →
zenodo40/100

NEP models for PTAF including PES and dipole

<p>(This version is identical to the previous version 10.5281/zenodo.10255268 with the only change that the nep.txt files are added separately to provide easier interfacing with parsing scripts.)</p> <p>NEP models used in https://doi.org/10.48550/arXiv.2311.09739 and https://doi.org/10.48550/arXiv.2312.05233.</p> <p>The DFT data is obtained using the ASE ORCA interface with the following parameters:</p> <p>calc = ORCA(label='orcacalc', maxiter=500, charge=-1, mult=1, orcasimpleinput='PBE def2-TZVP TIGHTSCF engrad')</p> <p>train.xyz collects the relevant structures with associated information to train the NEP models on.</p> <p>pes.zip and dipole.zip include inputs (nep.in), final NEP models (nep.txt), as well as loss and scatter plots.</p>

opencc-by-4.0Dec 2023View details →
zenodo40/100

Continuum-continuum Coulomb dipole integrals

<p>Supplementary data tables for the article <a href="https://arxiv.org/abs/2407.14160" target="_blank" rel="noopener">Angular momentum dependence in multiphoton ionization and attosecond time delays</a>.</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2024View details →
zenodo40/100

Localizing On-scalp MEG Sensors using an Array of Magnetic Dipole Coils

<p>Matlab scripts and data necessary to reproduce the results from the PLOS ONE paper. For more information see README.</p>

opencc-by-4.0Nov 2017View details →
zenodo40/100

Source Code and Simulation Results: Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna

<h3>Summary</h3> <p>This publication supplements the article "Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna" with tabulated data and Matlab code that allows the reproduction of the results. Within the article, the chiral behavior of single and double plasmonic nano antennas made from silver is numerically investigated with a focus on the coupling of a linear polarized dipole as an excitation source to the helix.</p> <h3>Simulation Setup - FEM Simulations</h3> <p>The script "run_wavlengthscan.m" allows to reproduce all simulations of the article. It can be chosen between the single and double helices, by specifying the keys parameter "keys.doppelhelix" where 0 gives a single and 1 a double helix. The number of turns can be specified by choosing "keys.case". The dipol is located within a 20nm thick hBN substrate layer, on glass (BK7). Results of the Purcell enhancement can be plotted using the scripts "display_results_single_helix.m" and "display_results_doublehelix.m" in the folder "results". The far-field plots can be reproduced using the scripts "display_farfiel_polarization_single_helix.m" and "display_farfiel_polarization_double_helix.m" of the folder "FunctionsAndScripts".</p> <p>The template for the mesh&nbsp;is contained&nbsp;in the folder&nbsp;"generate_grid_file",&nbsp;where the parameters of the helix (for&nbsp;example:&nbsp;radius, tube radius, and&nbsp;pitch height) can&nbsp;be modified.</p> <p>Within the folder&nbsp;"project3D"&nbsp;all required .jcm files are stored. Copy the&nbsp;"grid.jcm"&nbsp;file with the geometry of interest to this folder to perform simulations.</p> <p>All required keys parameters for the JCM template files (.jcmt, jcmpt) are set within the functions "set_numerical_parameter.m" and "set_physical_parameters.m", contained in the folder "FunctionsAndScripts". Therein, the function "set_sources.m" specifies the parameters for the dipole excitation, such as the position, and the strength (equivalent to the polarization).</p> <h3>Semi-Analytical Model</h3> <p>The Jupyter notebook "Semi_Analytical_Plasmonic_Helix.ipynb" contains the commented Python script for the semi-analytical design tool used to obtain far-field radiation patterns of the single helix. This semi-analytical design tool is based on an analytical model developed in [4]. The script can be divided into three parts. First, the single helix is defined, and a linear wavelength scaling law [5] is used to determine the illuminating wavelengths at which Fabry-P&eacute;rot resonances occur. Second, the overlap integral between the mode current on the helix and the incident electric field is evaluated for a given direction of incident light. Thirdly, the direction of incidence is varied to obtain the far-field radiation patterns. The script allows for the radiation patterns to be exported as a .csv file. Alternatively, the radiation patterns can be plotted directly using the provided single_plot functions.</p> <h3>Material</h3> <p>The material data has&nbsp;been taken&nbsp;from the&nbsp;<a href="https://refractiveindex.info/" target="_blank" rel="noopener">refractiveindex.info</a> database. For silver the data is taken from tabulated data from Johnson and Christy [1] . The dispersion relation for hBN comes from [2] and tabulated data for glass (BK7) from [3]. The MATLAB script "material_properties_plot.m" plots the material fits above the wavelengths of interest. The required tabulated data is given in the folder "material_data".</p> <p>With&nbsp;'material_properties_plot.m'&nbsp;the fits to the material data can be reproduced and plotted.</p> <h3>Usage</h3> <p>The .zip folder Helix_FEM contains all data and scripts to reproduce the plots from the 3D FEM simulations.</p> <p>The Jupyter Notebook Semi_Analytical_Plasmonic_Helix reprouces the results from the semi-analytical model.</p> <h3>Requirements</h3> <ul> <li>JCMsuite (at least 5.4.0)</li> <li>MATLAB (tested with version R2023b)</li> <li>Python&nbsp;(tested with Version 3.10.9)</li> <li>Jupyter Notebook (tested with 6.5.2)&nbsp;</li> </ul> <p>To run the simulations&nbsp;with&nbsp;JCMsuite&nbsp;you must replace corresponding placeholders with 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> <h3>References</h3> <p>[1] P. B. Johnson and R.-W. Christy, &ldquo;Optical constants of the noble metals,&rdquo;&nbsp;Phys. Rev. B 6, 4370 (1972).</p> <p>[2] S.-Y. Lee, T.-Y. Jeong, S. Jung, and K.-J. Yee, &ldquo;Refractive index dispersion of hexagonal boron nitride in the visible and near-infrared,&rdquo; Phys. Status Solidi B 256,&nbsp; 1800417 (2019).</p> <p>[3] &ldquo;SCHOTT Zemax catalogue 2017-01-20b,&rdquo; (2017).</p> <div>[4] K.&nbsp;H&ouml;flich et al., "Resonant behavior of a single plasmonic helix."&nbsp;Optica 6,&nbsp;1098(2019).</div> <div>&nbsp;</div> <div>[5]L. Novotny, "Effective wavelength scaling for optical antennas", Phys. Rev. Lett. 98,266802 (2007).</div>

opencc-by-4.0Jan 2024View details →
zenodo40/100

Supplemental Material: Consolidating the concept of low-energy magnetic dipole decay radiation

<p>This record consists of all shell model calculation results used in Midtb&oslash; <em>et al</em>.,&nbsp;<em>Consolidating the picture of low-energy magnetic dipole decay radiation,&nbsp;</em>Phys. Rev. C (2018, accepted),&nbsp;arXiv:1807.04036 [nucl-th].</p> <p>All calculations are performed using KSHELL (arXiv:1310.5431 [nucl-th]). For details on our calculations we refer to our article.</p>

opencc-by-4.0Nov 2018View details →
zenodo40/100

Data for 'Improved predictability of the Indian Ocean Dipole using seasonally modulated ENSO forcing forecasts'

<p>Abstract of the associated paper: Despite recent progress in seasonal forecast development, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic-dynamical model (SDM) to predict the IOD using seasonally modulated El Ni&ntilde;o-Southern Oscillation (ENSO) forcing together with a seasonal modulation of the Indian Ocean coupled ocean-atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System Version 2 and the SINTEX system. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal-to-noise ratio of the system. Therefore, potential future ENSO improvements in models should also translate to more skillful IOD predictions.</p>

opencc-by-4.0May 2019View details →
zenodo40/100

Figure 9. Dipole eddy evolution from August 7 in Influence of circulation processes on cyanobacteria bloom and phytoplankton succession in the Baltic Sea coastal area

Figure 9. Dipole eddy evolution from August 7 to August 9, 2018 in the suspended matter field from OLCI Sentinel-3A data for August 7 (a) and August 8, 2018 (b) and MSI Sentinel-2B data for August 9, 2008 (c) according Krayushkin et al. (2018).

opencc-by-4.0Dec 2023View details →
zenodo40/100

Confined dipole and exchange spin waves in a bulk chiral magnet with Dzyaloshinskii-Moriya interaction-Data files

<p>Raw data associated to the manuscript &lsquo;Confined dipole and exchange spin waves in a bulk chiral magnet with Dzyaloshinskii-Moriya interaction.&rdquo; File formats are described in info.txt files in the concerning folders. For plotting and data evaluation Matlab 2019b and OriginPro 2018b were used.&nbsp;</p> <p>We acknowledge financial support from the&nbsp;Swiss National Science Foundation (SNSF) via Grant No.&nbsp;171003&nbsp;Sinergia project Nanoskyrmionics, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under&nbsp;Grant No. TRR80 (From Electronic Correlations to Functionality, Project No. 107745057, and Projects No. E1 and&nbsp;No. F7), SPP2137 (Skyrmionics, Project No. 403191981,&nbsp;Grant No. PF393/19), and the excellence cluster MCQST&nbsp;under Germany&rsquo;s Excellence Strategy EXC-2111 (Project No.&nbsp;390814868). Financial support by the European Research Council (ERC) through Advanced Grants No. 291079 (TOPFIT) and No. 788031 (ExQuiSid) is gratefully acknowledged.</p> <p>Paper abstract:</p> <p>The Dzyaloshinskii-Moriya interaction (DMI) has an impact on excited spin waves in the chiral magnet Cu<sub>2</sub>OSeO<sub>3</sub> by means of introducing asymmetry in their dispersion relations. The confined eigenmodes of a chiral magnet are hence no longer the conventional standing spin waves. Here we report a combined experimental and micromagnetic modeling study by broadband microwave spectroscopy, and we observe confined spin waves up to eleventh order in bulk Cu<sub>2</sub>OSeO<sub>3</sub> in the field-polarized state. In micromagnetic simulations we find similarly rich spectra. They indicate the simultaneous excitation of both dipole- and exchange-dominated spin waves with wavelengths down to (47.2 &plusmn; 0.05) nm attributed to the exchange interaction modulation. Our results suggest the DMI to be effective in creating exchange spin waves in a bulk sample without the challenging nanofabrication and thereby in exploring their scattering with noncollinear spin textures.</p>

opencc-by-4.0Jul 2021View details →
zenodo40/100

Fig. 3. Dipole construction for Electric Fish Finder. 1 in Scientific Note Design and construction of an Electric Fish Finder

Fig. 3. Dipole construction for Electric Fish Finder. 1, dipole connector; 2, microphone cable; 3, exposed ground stub; 4, channel-1 unraveled from within cable, cut short, and copper wire exposed only at end; 5, channel-2 with longer length exposed. The dipole is mounted on a pole with duct tape in the field (Fig. 4).

opencc-by-4.0Sep 2007View details →
zenodo40/100

Tip of the Red Giant Branch Bounds on the Neutrino Magnetic Dipole Moment Revisited

<p>Reproduction Package for the Paper &quot;Tip of the Red Giant Branch Bounds on the Neutrino Magnetic Dipole Moment Revisited&quot;.</p> <p><strong>File Organization</strong></p> <ul> <li>MESA: MESA modifications to include losses due to the neutrino magnetic dipole moment, scripts to run the grid of models, and post processing pipeline scripts including the Worthey \&amp; Lee bolometric correction code.</li> <li>ML_models: Machine learning code to train and use the models as well as the models themselves.</li> <li>analysis: Plotting code to create figures for papers and presentations.</li> <li>makeGrids: Scripts to create the different input grid files to run MESA on.</li> <li>mcmc: Scripts and plots for the MCMC analysis.</li> <li>mesa_data: All MESA models generated in this project.</li> <li>environment.yml: Conda environment for analysis and the mcmc.&nbsp;</li> </ul> <p>More details can be found in the README files within each directory.</p> <p><strong>Citation Policy</strong><br> If you use any part of this reproduction package for independent work, we recommend you cite the following papers:</p> <ul> <li>This paper</li> <li>https://arxiv.org/abs/2303.12069</li> <li>https://arxiv.org/abs/2305.03113</li> <li>Astrophys. J. Suppl. 192, 3 (2011)</li> <li>Astrophys. J. Suppl. 208, 4 (2013)</li> <li>Astrophys. J. Suppl. 234, 34 (2018)</li> <li>Astrophys. J. Suppl. 243, 10 (2019)</li> </ul> <p><strong>Software</strong></p> <p>Python version 3.8, NumPy version 1.22.3, Pandas version 1.4.3, Matplotlib version 3.5.1, Seaborn version 0.11.2, Tensorflow version 2.4.1, corner version 2.2.1, emcee version 3.1.2, MESA version 12778, MESASDK version x86_64-linux-20.3.2.</p>

opencc-by-4.0Jul 2023View details →
zenodo36/100

How do auroral substorms depend on Earth's dipole magnetic moment?

<p>This contains the original simulation data used by the paper &#39;How do auroral substorms depend on Earth&#39;s dipole magnetic moment?&#39;, which will appear in Journal of Geophysical Research - Space Physics. The data were obtained by the global MHD simulation (REPPU) with Level 6.&nbsp;</p> <p>Each VTK file (Visualization Tookkit format) contains the physical variables in the magnetosphere at the expansion onset for Run 1, 2, 3, 4, and 5, including</p> <ul> <li>plasma pressure (P in nPa),</li> <li>velocity vector (V in km/s),</li> <li>current density vector (J in nA/m2),</li> <li>magnetic field vector (B in nT).</li> </ul> <p>The VTK file can be opened by the software VisIT, or paraview.&nbsp;</p> <p>Each sav file (IDL saveset format) contains the physical variables in the ionosphere at the expansion onset for Run 1, 2, 3, 4, and 5, including</p> <ul> <li>time (&#39;tim&#39; in minutes),</li> <li>magnetic latitude (&#39;lat&#39; in deg),</li> <li>magnetic local time (&#39;mlt&#39; in hour),</li> <li>field-aligned current (&#39;fac&#39; in A/m2),</li> <li>electric potential (&#39;pot&#39; in V),</li> <li>ionospheric conductivities (&#39;s11&#39;, &#39;s12&#39;, and &#39;s22&#39; in S).</li> </ul> <p>The sav file can be opened by IDL. By typing&nbsp;<br> IDL&gt; restore,&#39;tablei-run1.sav&#39;<br> you can load the file to the memory of IDL. Type &#39;help&#39; to confirm the variables that are loaded.</p> <p><br> &nbsp;</p>

opencc-by-4.0Aug 2020View details →
zenodo36/100

Data supporting "Stimulated emission does not radiate in a pure dipole pattern"

<p>Image data presented in figure 3 (15_9_series_I.h5) and figure 4 (25_10_series_J.h5) of the manuscript <em>Stimulated emission does not radiate in a pure dipole pattern.&nbsp;</em>Each h5 file contains image data as an EArray as well as relevant metadata. It is recommended to open them using the PYthon Microscopy Environment (PYME; https://zenodo.org/doi/10.5281/zenodo.4289803). They were processed using the included iPython notebooks. Please see the readme.txt file if you wish to use these notebooks.</p>

opencc-by-4.0Mar 2024View details →
zenodo36/100

Dataset: Dynamo models with a Mercury-like magnetic offset dipole

<p>Input and Output Data for reproducing figures and tables in the submitted publication Kolhey et al. (2024) "Dynamo models with a Mercury-like magnetic offset dipole".</p>

opencc-by-4.0Jul 2024View details →

ScienceDex guides

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record