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490 results for “Propagation”
Tracking Knowledge Propagation Across Wikipedia Languages
<p>We present a dataset of <em>inter-language knowledge propagation</em> in Wikipedia. Covering the entire 309 language editions and 33M articles, the dataset aims to track the full propagation history of Wikipedia concepts, and allow follow up research on building predictive models of them. For this purpose, we align all the Wikipedia articles in a language-agnostic manner according to the concept they cover, which results in 13M propagation instances. To the best of our knowledge, this dataset is the first to explore the full inter-language propagation at a large scale. Together with the dataset, a holistic overview of the propagation and key insights about the underlying structural factors are provided to aid future research. For example, we find that although long cascades are unusual, the propagation tends to continue further once it reaches more than four language editions. We also find that the size of language editions are associated with the speed of propagation. We believe the dataset not only contributes to the prior literature on Wikipedia growth but also enables new use cases such as edit recommendation for addressing knowledge gaps, detection of disinformation, and cultural relationship analysis.</p>
Energetic Proton Propagation and Acceleration Simulated for the Bastille Day Event of July 14, 2000
<p>This includes data from the EPREM+CORHEL simulation run presented in "Energetic Proton Propagation and Acceleration Simulated for the Bastille Day Event of July 14, 2000" (Astrophysical Journal). The eight files ending in '.nc' contain the EPREM stream-observer data used to create Figures 5 & 7. The data was saved in the self-describing <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF4</a> format. The HTML files contain the following interactive figures, which you can open in your internet browser:</p> <ul> <li><strong>cos_theta-e10.0-t44.html</strong> cosine of the flow angle (Figure 7)</li> <li><strong>divV-e10.0-t44.html</strong> velocity divergence (Figure 7)</li> <li><strong>flux-e10.0-t44-log.html</strong> differential flux of 10-MeV protons (Figure 7)</li> <li><strong>peak_flux-e10.0-t44.html</strong> relative peak flux of 10-MeV protons (Figure 5)</li> <li><strong>peak_flux-e100.0-t44.html</strong> relative peak flux of 100-MeV protons (not shown in paper)</li> <li><strong>tau_p-e10.0-t44-log.html</strong> theoretical acceleration rate (Figure 7)</li> </ul>
Data for "Direct evidence reveals transmitter signal propagation in the magnetosphere"
<p>The data and codes for figures in "Direct evidence reveals transmitter signal propagation in the magnetosphere"</p>
Propagation of optical pulses through a periodic dielectric structure (Bragg Grating) designed as a delay line interferometer. Example of a designed fiber Bragg grating.
<p>Propagation of optical pulses through a periodic dielectric structure (Bragg Grating) designed as a delay line interferometer. <br> <br> A 9 cm fiber Bragg grating is designed (and fabricated) for this purpose.</p> <p>The top video shows the simulated propagation of a single optical pulse.</p> <p>The bottom video shows the simulated propagation of a sequence of optical pulses, with relative pi-phase shifts in the last pulse, showing both constructive and destructive interferences effect</p>
Flash propagation and inferred charge structure relative to radar-observed ice alignment signatures in a small Florida Mesoscale Convective System
<p>Data for paper of above title, submitted to <em>Geophysical Research Letters</em>, June 2017. Manuscript number: 2017GL072767</p>
eclipse radio propagation 2017
<p>Bill Riches, WA2DVU</p> <p>39.08, -7486</p> <p>MPG audio file start 1400UT</p> <p>Antenna: Mosley 20 meter beam at 60 feet pointing west.</p> <p>Receiver: HP 3586B Selective Voltmeter</p> <p>FX Reference: Lucent KS-24361 GPS</p> <p>FX measurement technique: I use the HP3586B to receive WWV on 10 mHZ. I send the 15625 hz IF output of the HP 3586B to Spectrum Lab. The 3586 is locked to my GPS. Spectrum lab calculates error every 60 seconds and generates an Excel chart of the frequency difference. Computer clock is corrected with NBS program, computer sound card is corrected with 1 PPS pulse from GPS fed to Spectrum lab. </p>
Data for: When Correlation Matters: On Uncertainty Propagation In The Case Of Data Disaggregation
<p>This is the data repository for our study on “When Correlation Matters: On Uncertainty Propagation In The Case Of Data Disaggregation” submitted to the Journal of Industrial Ecology (JIE).</p> <p>I contains:</p> <ul> <li>The data behind the all numeric plots</li> <li>The data needed to reproduce the case-study results in the Supplementary Information (check out V1)</li> </ul> <p> </p> <p>To reproduce our results you need to download the files from this repo, our code from Github (https://github.com/simschul/uncertainty_disaggregation) and put the data into the `./data` folder. </p> <p>The data is an intermediate output from an earlier study: https://essd.copernicus.org/articles/16/2669/2024/essd-16-2669-2024.html </p> <p>For more information how those intermediate results were created please refer to the paper and the code (https://github.com/simschul/uncertainty_GHG_accounts)</p> <p> </p> <p> </p>
Large-Scale Traveling Ionospheric Disturbances over the European sector during the geomagnetic storm on March 23-24, 2023: energy deposition in the source regions and the propagation characteristics
<p>IMAGE 2D Ionospheric Equivalent Currents for 23 and 24 March 2023 (https://space.fmi.fi/image/). </p> <p><em>We thank the institutes who maintain the IMAGE Magnetometer Array (<a href="https://space.fmi.fi/image/">https://space.fmi.fi/image/</a>): Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway), Finnish Meteorological Institute (Finland), Institute of Geophysics Polish Academy of Sciences (Poland), GFZ German Research Centre for Geosciences (Germany), Geological Survey of Sweden (Sweden), Swedish Institute of Space Physics (Sweden), Sodankylä Geophysical Observatory of the University of Oulu (Finland), DTU Technical University of Denmark (Denmark), and Science Institute of the University of Iceland (Iceland). The provisioning of data from AAL, GOT, HAS, NRA, VXJ, FKP, ROE, BFE, BOR, HOV, SCO, KUL, and NAQ is supported by the ESA contracts number 4000128139/19/D/CT as well as 4000138064/22/D/KS. The authors would like to thank Dr. Liisa Juusola for providing the IMAGE 2D Ionospheric Equivalent Currents data.</em></p>
Data repository for: Frictional weakening leads to unconventional singularities during dynamic rupture propagation
<p>Laboratory data to accompany publication <span>Frictional weakening leads to unconventional singularities during dynamic rupture propagation</span>, submitted to Earth and Planetary Science Letters.</p> <p>For any further queries please contact federica.paglialunga@epfl.ch</p>
Propagating Gottesman-Kitaev-Preskill states encoded in an optical oscillator
<p>Gottesman-Kitaev-Preskill (GKP) qubit in a single Bosonic harmonic oscillator is an efficient logical qubit for mitigating errors in a quantum computer. The entangling gates and syndrome measurements for quantum error correction only require noise-robust linear operations, a toolbox that is naturally available and scalable in optical system. To date, however, GKP qubits have been only demonstrated at mechanical and microwave frequency in a highly nonlinear stationary system. In this work, we realize a GKP state in propagating light at the telecommunication wavelength and demonstrate homodyne measurements on the GKP states without loss corrections. Our states do not only show nonclassicality and non-Gaussianity at room temperature and atmospheric pressure, but the propagating wave property also permits large-scale quantum computation with strong compatibility to telecommunication technology.</p>
Linguistic features of Twitter rumor propagation trees on the CLNews19-20 dataset
<p>Linguistic features applied to 140 Twitter rumor propagation trees (53 of type 1: false rumors, and 87 of type 2: true rumors) about Chilean topics collected during the Chilean social outbreak (2019-2020). These rumor propagation trees come from the CLNews19-20 dataset (DOI <a href="../doi/10.5281/zenodo.5851204">10.5281/zenodo.5851204</a>). There are 38 different linguistics features applied:</p> <ul> <li>number of paragraphs</li> <li>total number of sentences</li> <li>standard deviation of sentences</li> <li>mean words</li> <li>maximum number of words</li> <li>mean characters</li> <li>maximum number of characters</li> <li>mean characters without spaces</li> <li>maximum number of characters without spaces</li> <li>adjective idf</li> <li>minimum number of adpositions</li> <li>maximum number of adpositions</li> <li>mean adpositions</li> <li>median adpositions</li> <li>maximum number of auxiliaries</li> <li>total number of auxiliaries</li> <li>mean auxiliaries</li> <li>median auxiliaries</li> <li>auxiliary idf</li> <li>auxiliary tfidf</li> <li>standard deviation of numerals</li> <li>proper noun idf</li> <li>minimum number of symbols</li> <li>maximum number of symbols</li> <li>total number of symbols</li> <li>mean symbols</li> <li>median symbols</li> <li>symbol idf</li> <li>symbol tfidf</li> <li>number of paragraphs</li> <li>MDT conditionals</li> <li>MDT counterarguments</li> <li>MDT Connectors Opinion Justifiers</li> <li>MDT Connectors Opinion Generalizers</li> <li>AS VeryPositive Affin</li> <li>AS Negative Nrc</li> <li>AS Angry Nrc</li> <li>AS Fear Nrc</li> </ul>
Data for performing wavepacket propagation using Fourier Neural Operators
<p>Datasets for training Fourier neural operator (FNO) [Li, Z., et al, 2021] and the trained models in <strong>Accelerating wavepacket propagation with Machine Learning </strong>by Kanishka Singh, Ka Hei Lee, Daniel Peláez and Annika Bande.</p> <ul> <li>data-gaussian-pulse-10000.pickle: Training dataset for 2D FNO model. The dataset contains 10000 wavefunction propagations under 1D double well which is generated by the split-operator method. Each propagation is influenced by a different laser pulse.</li> <li>data-gaussian-pulse-10000.pt: The trained 2D FNO model for wavefunction propagation under 1D double well influenced by laser pulse.</li> <li>anharmonic_FNO_dens.pt: Training dataset for 3D FNO model. The dataset contains 5100 wavefunction propagations under 2D anharmonic potential which is generated by the multi-configurational time-dependent Hartree (MCTDH) method [Beck, M., 2000]. The dataset is structured and a dictionary with input 'x_conv' and output 'y'.</li> <li>alllambda-anharmonic.pt: The trained 3D FNO model for wavefunction propagation under 2D anharmonic potential.</li> <li>anharmonic_FNO_dens-potential.pt: The 2D anharmonic potential of the 2D wavefunction propagations.</li> <li>anharmonic_FNO_dens-true.pt: The ground truth of the 2D wavefunction propagations by the MCTDH method.</li> <li>anharmonic_FNO_dens-pred.pt: The prediction of the 2D wavefunction propagations by the 3D FNO model.</li> </ul>
Raw data for Figures 1-4 for journal article: "Transcutaneous and percutaneous bone conduction sound propagation in single-sided deaf patients and cadaveric human whole heads"
<p>This is a data set containing the raw data for figures 1-4 from the journal article:</p> <p>"Transcutaneous and percutaneous bone conduction sound propagation in single-sided deaf patients and cadaveric human whole heads"</p> <p>Original article DOI: 10.1080/14992027.2021.1903586</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/34097554/</p> <p> </p> <p>The data is contained within plots in word files, created with Microsofft Office (v18).</p>
Raw data for journal article: "Wave propagation across the skull under bone conduction: Dependence on coupling methods"
<p>This is a data set containing the raw data for figures 3-6 from the journal article:</p> <p>"Wave propagation across the skull under bone conduction: Dependence on coupling<br>methods"</p> <p>Original article DOI: 10.1121/10.0009676</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/35364950/</p> <p> </p> <p>The Fig 3 and 4 data are contained within MATLAB figure (.fig) files, all saved with MATLAB version R2020a.</p> <p>Fig 5 and 6 data are 3D velocity data for 5 cadaver heads (CH1-5) and FEM predictions.</p> <p>This data are stored within a folder structure indicating the stimulation condition (defined in the journal article). For example "Cadaver head data\CH1\Attract" contains cadaver head data for cadaver head 1 (CH1) with stimulation "Attract", as defined in the journal article above.</p> <p>For each combination of cadaver head (or FEM) and stimulation condition there is a TXT file (comma delimited) for the real and imaginary data at each stimulation frequency, and orthogonal velocity axis (X,Y,Z based on the anatomical coordinate system defined in the journal article) as well as the combined (maximum) velocity vector. The data set also includes a TXT file with the position (in same coordinate system the velocity data) of each measurement point and a list of stimulation frequencies.</p>
Propagational Isotropy of Large Scale Traveling Ionospheric Disturbances Over Australia And New Zealand due to the 2022 Tonga Volcanic Eruption
<p>This data repository contains global TEC processed data from 14 - 16 January 2022. The original data were obtained from the GNSS-TEC database available at https://stdb2.isee.nagoya-u.ac.jp/GPS/GPS-TEC/ provided by the Institute for Space-Earth Environment Research, Nagoya University. The data is in .mat format (binary Matlab file) with the following data matrices:</p> <ol> <li>Coordinates (geographic coordinates - Latitude, Longitude)</li> <li>dTEC1 (detrended TEC)</li> <li>TimeTEC_combined (time series absolute TEC for each geographic coordinate)</li> </ol> <p>Data has a time resolution of 5 min in each column. </p>
Eclipse Propagation data KQ4NRO WSPRLite Beacon on 20m band from EM64k0 (92% totality zone)
<p>WSPRLite radio beacon data for station KQ4NRO on 14.097 MHz into a EFHW antenna before, during, and after the April 8, 2024 solar eclipse (dataset from April 5 through April 10). Shows receiving call sign, receiving grid square, Rx SNR, drift, Distance, and azimuth from Jackson, MO ( on the centerline of totality). Excel data format. DOI: 10.5281/zenodo.11011444</p>
Eclipse propagation data N4DPH WSPRLite beacon from EM57ko
<p>WSPRLite radio beacon data for station N4DPH on 14.093 MHz into a Wolf River Coil antenna before, during, and after the April 8, 2024 solar eclipse. Shows receiving call sign, receiving grid square, Rx SNR, drift, Distance, and azimuth from Jackson, MO ( on the centerline of totality). Excel data format. DOI: 10.5281/zenodo.11003181 </p>
Dataset for "Towards Centrality and Causality based Vulnerability Propagation Analysis"
<ul> <li> <p><strong><code>aggregated_data.json</code></strong>: This file contains the processed data generated after loading the Goblin Weaver tool. It includes all CVEs, CWEs, and other relevant attributes. Statistical analyses, particularly vulnerability analyses, are performed on this dataset.</p> </li> <li> <p><strong><code>graph_nodes_edges.pkl</code></strong>: This file stores the parsed GraphML-format graph, divided into chunks of nodes and edges to optimize memory usage during computations. All centrality-based measurements are conducted using this file.</p> </li> <li> <p><strong><code>cve_data.csv</code></strong>: This file captures features of nodes along with their one-hop neighbors. It includes attributes such as <code>whether_cve_exists</code>, <code>cve_exists</code>, and <code>cve_num</code>, as well as the source and target nodes involved.</p> </li> <li> <p><strong><code>cve_2_siblings_data.csv</code></strong>: This file documents features of nodes with their two-hop neighbors. It includes attributes like <code>whether_cve_exists</code>, <code>cve_exists</code>, and <code>cve_num</code>, along with details of source nodes and their two-hop neighbor nodes.</p> </li> <li><code><strong>fea_matrix.csv</strong>:</code><code>This file records the matrix of every node based on transformed five types of attributes, including missrelease (freshness missrelease), outdays(freshness outdatedTimeinMs), popularity, speed, and severity. It has been used for correlation analysis. </code></li> </ul>
Comparison of propagation tools CRPropa and PropPy
<p>Data of comparison of propagation tools <a href="https://github.com/CRPropa/CRPropa3">CRPropa</a> (3.1.7) and <a href="https://gitlab.ruhr-uni-bochum.de/reichp2y/proppy">PropPy</a> (1.0.0) for cosmic-ray propagation in plasmoids in active galactic nuclei (AGN) jets as well as in the intergalactic magnetic fields (IGMFs).</p> <p>Data can be reproduced by following the jupyter notebooks presented in the PropPy package: <a href="https://gitlab.ruhr-uni-bochum.de/reichp2y/proppy/-/tree/master/comparison">link</a>.</p> <p><strong>Simulation parameters</strong></p> <p>AGN plasmoids: particle energy = 100 PeV, isotropic 3d Kolmogorov turbulence with <span class="math-tex">\(b_\mathrm{rms} = 1\)</span>G, correlation length = <span class="math-tex">\(10^{11}\)</span> m.</p> <p>UHCRS in IGMFs: particle energy = 10 EeV, isotropic 3d Kolmogorov turbulence with <span class="math-tex">\(b_\mathrm{rms} = 1\)</span>nG, correlation length = 1 Mpc.</p>
Source models for "Across-slab propagation and low stress drops of deep earthquakes in the Kuril subduction zone"
<p>This repository is for the model results for eight deep earthquakes in the Kuril subduction zone modelled using a second-degree moments method in csv format.</p> <p><a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subvertical.csv</a> - Source models with fixed Amin > 5 km, assuming the sub-vertical fault plane reported in the GCMT catalogue. Event is the GCMT event code. Aspect ratio is the ratio (Amin/Amax). Duration is the rupture duration; Amax is the maximum characteristic fault dimension; Amin is the minimum characteristic fault dimension; Phi is the angle between Amax and the strike; v0 is the centroid velocity; Theta is the angle between the centroid velocity and the strike; and mft is the misfit between the data and the higher-order synthetics calculated for the best-fitting source model obtained from the Monte Carlo inversions.</p> <p> </p> <p><a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subhorizontal.csv</a> - Source models with fixed Amin > 5 km, assuming the sub-vertical fault plane reported in the GCMT catalogue. Column headers are the same as in <a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subvertical.csv</a>.</p>
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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.
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.