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490 results for “Propagation”
Streamer propagation in humid air
<p>This dataset includes the input and output files for the paper: Streamer propagation in humid air.</p> <p><strong>Input files</strong>:</p> <p># <em>Plasma-chemistry and transport coefficients</em></p> <p>chemistry_files/*.txt</p> <p># <em>Configuration files</em></p> <p>config_files/*.cfg</p> <p># <em>Initial conditions (densities)</em></p> <p>config_files/m_user.f90</p> <p><strong>Output files (output_files)</strong>:</p> <p>*.silo</p> <p>*.txt</p> <p>Output data generated with the software afivo-streamer (https://gitlab.com/MD-CWI-NL/afivo-streamer) corresponding to the commit 1ff2676ba48a5eb568f06c7b11a548629a5ff20c</p>
Streamer propagation in humid air
<p>This dataset includes the input and output files for the paper: Streamer propagation in humid air.</p> <p><strong>Input files</strong>:</p> <p># <em>Plasma-chemistry and transport coefficients</em></p> <p>chemistry_files/*.txt</p> <p># <em>Configuration files</em></p> <p>config_files/*.cfg</p> <p># <em>Initial conditions (densities)</em></p> <p>config_files/m_user.f90</p> <p><strong>Output files (output_files)</strong>:</p> <p>*.silo</p> <p>*.txt</p> <p>Output data generated with the software afivo-streamer (https://gitlab.com/MD-CWI-NL/afivo-streamer) corresponding to the commit 1ff2676ba48a5eb568f06c7b11a548629a5ff20c</p> <p> </p>
Data Set : Seismic Wave Propagation Simulations in Indo Gangetic Basin using Spectral Element Method
<p>Indo Gangetic (IG) basin is one of the largest alluvial basins in the world. The surrounding Himalayan topography and the geometry of the basin make the IG basin unique. The analysis of seismic response of the basin is important as the region is seismically active with more than 40% of Indian population residing in it. This online database consists of the input files for performing the spectral finite element simulation for IG basin by incorporating the 3D variation of material properties and basin geometry. The input files consists of mesher, solver and CMTSOLUTION files for SPECFEM3D Cartesian (Version-3) simulation.</p>
Constraining Andean Propagation of Exhumation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context - Supporting Information
<p>Supporting information accompanying the publication "Constraining Andean Propagation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context" published in Tectonics. The dataset contains apatite and zircon (U-Th-Sm)/He and apatite fission track data from the Tilcara Range and San Lucas block, Jujuy, Argentina, as well as additional QTQt thermal models that are discussed in the paper.</p> <p>Table S1 contains full single-grain results from apatite fission track, apatite (AHe) (U-Th-Sm)/He and zircon (ZHe) (U-Th-Sm)/He analyses. Outliers are marked in grey and are not included in the weighted mean age. Figure S1 supports (U-Th-Sm)/He data graphically. Apatite fission track (AFT) data is supported by radial plots in Figure S2. Figure S3 shows QTQt thermal models using either AHe, AFT or ZHe single-grain ages. All of the models results are explained in the main text.</p>
Supplementary audio files: Propagation effects in the synthesis of wind turbine noise
<p>The audio files are supplementary files required for the audio article titled: "Propagation effects in the synthesis of wind turbine noise". Each audio file refers to a signal of a test obtained from the wind turbine noise model which is described in the article. </p> <p>The audio files can be read as: NNx-x-TETIN-eeeeeee.mp3</p> <p>NNx-x refers to the Test case and case number for the syntheisized trailing edge and turbulent inflow noise, eeeeeee is the description of the specific case.<br> (tauTT- angle of the receiver, ff - for free field, GPE for ground and propagation effects included, GPE_Turb for ground and propagation effects included with turbulence scattering)</p> <p>eg: C2-2-TETIN_tau80_GPE_Turb.mp3 is the synthesized sound for Test case C2-2 in the article which includes the ground and propagation effects and also scattering due to turbulence. The receiver is at an angle of 80° with respect to the wind direction. </p>
Pre-processed ex vivo MRI data for manuscript titled "Neuroanatomical and cognitive biomarkers of alpha-synuclein propagation in a mouse model of synucleinopathy prior to onset of motor symptoms""
<p>Repository for <em>ex vivo</em> magnetic resonance imaging data from the project titled "Presymptomatic neuroanatomical and cognitive biomarkers of alpha-synuclein propagation in a mouse model of synucleinopathy"</p> <p>Contains the pre-processed <em>ex vivo</em> T1-weighted images (Bruker 7T; 70 micron isotropic voxel resolution) for M83 alpha-synuclein A53T hemizygous mice that received either a phosphate buffered saline (PBS) or alpha-synuclein pre-formed fibrils (PFF) injection in the right dorsal striatum. Full subject list can be viewed with the "subject_list.csv" file. More details are available in the manuscript. </p>
Dataset for Twitter Sentiment Analysis on Criminal Data Propagation using Naive Bayes Algorithm
<p>This study presents a dataset tailored for conducting sentiment analysis on Twitter regarding the propagation of criminal data. Leveraging the Naive Bayes algorithm, the dataset aims to facilitate research into public perceptions surrounding the dissemination of criminal data on social media platforms. Through a currated collection of tweets, researchers can explore the nuanced sentiments and attitudes expressed by users in response to this phenomenon.</p>
Propagation, dissipation and breakdown in quantum anomalous Hall edge states probed by microwave edge plasmons
<p>Here we upload the raw data from the manuscript entitled “Propagation, dissipation and breakdown in quantum anomalous Hall edge states probed by microwave edge plasmons ”, by T. Röper, H. Thomas, D. Rosenbach, A. Uday, G. Lippertz, A. Denis, P. Morfin, A.A. Taskin, Y. Ando and E. Bocquillon. We provide Jupyter notebooks to load, process, and plot all results. The datasets contain measurements on 4 devices. Each device has its own Jupyter notebook. The necessary Python packages are listed in the file called "requirements.txt". <br><br><br></p>
Physics-based Simulations of 3D Wave Propagation - Case study deriving from the Le Teil earthquake
<p>This dataset contains 4,000 simulation results of the 3D elastic wave equation in a setting deriving from the Le Teil earthquake (France, 2019). The elastic wave equation governs the propagation of waves in a 3D propagation medium. Two types of data are given in this dataset: a materials dataset and a velocity dataset.</p> <h2>Materials dataset</h2> <p>Each material describes the propagation domain used for one numerical simulation. It is built from non-stationary random fields added to the reference 1D velocity profile and corresponds to the velocity of shear waves. The minimum value is 1500m/s and the maximum is 4500m/s. All materials contain a 1800m-thick bottom layer with a constant velocity of 4500m/s. </p> <p>All materials are 3D arrays of shape 32 x 32 x 32.They correspond to a physical size of 9.6 x 9.6 x 9.6km³. </p> <h3>Practical use</h3> <p>Materials are provided as `.npy` arrays, readable with python: `a = np.load(‘materials0-1999.npy’)`<br>Each file contains 2000 materials. Therefore, `a` is of shape (2000, 32, 32, 32). Indices correspond to the material index, the x coordinate (from West to East), the y coordinate (from South to North), and the z coordinate (from bottom to top). </p> <h2>Velocity dataset</h2> <p>The velocity dataset contains the velocity wavefields simulated at the surface of each propagation domain. They have been generated by solving the 3D elastic wave equation with the high-performance computing code SEM3D based on the Spectral Element Method (https://github.com/sem3d/SEM). To each material described above corresponds one velocity field, obtained by the propagation of waves through this material.</p> <p>Velocity fields were recorded by a grid of 16 x 16 virtual sensors located at the surface of the propagation domain between 150m and 450m (600m between consecutive sensors). Each sensor records the 3-component velocity with a 100Hz sampling between 0s and 20s. </p> <p>Computational details: The computational mesh was designed with elements of size 300m and 7 Gauss-Lobato-Legendre quadrature points. It can accurately represent the propagation of waves up to 5Hz frequency. Waves were generated by a point-wise source placed at the bottom of the domain, inside the constant layer (the position of the source is 4800, 4800, -8400m). The seismic source derives from the Le Teil earthquake [Delouis et al., 2021, doi:10.5802/crgeos.78]. The seismic source is described by a moment tensor with fixed orientation (strike = 48°, dip = 45°, and rake = 88°) and amplitude (moment magnitude M0=2.47 · 10^16 N.m).</p> <h3>Practical use</h3> <p>Results are given in .feather dataframes, readable with pandas library in Python: v = pd.read_feather(‘velocity0-99.feather’). Each dataframe contains 100 simulation results. Each row of the dataframe has the following format: </p> <table> <tbody> <tr> <td>run</td> <td>field</td> <td>x</td> <td>y</td> <td>z</td> <td>0.0</td> <td>0.01</td> <td>0.02</td> <td>...</td> <td>19.98</td> <td>19.99</td> </tr> <tr> <td>12</td> <td>Veloc E</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>1.1e-5</td> <td>1.0e-5</td> </tr> <tr> <td>12</td> <td>Veloc N</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>3e-6</td> <td>3e-6</td> </tr> <tr> <td>12</td> <td>Veloc Z</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>-2.6e-5</td> <td>-2.7e-5</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>where `run` indicates the index of the material used in this simulation, `field` indicates the component of the velocity field (`Veloc E` for East-West, `Veloc N` for North-South, `Veloc Z` for Vertical). `x`, `y`, `z` are the coordinates of the sensor (in meters). The next 2000 columns contain the velocity field for times 0, 0.01, …, 19.99.</p> <h1>Related work</h1> <p>This dataset was used to fine-tune a Factorized Fourier Neural Operator (F-FNO, Lehmann et al. 2024, doi:10.1016/j.cma.2023.116718) to predict ground motion wavefields from 3D geologies. The code to train the F-FNO is available at https://github.com/lehmannfa/HEMEW3D</p>
Fig. 1. Vriesea incurvata plantlets after 180 in In vitro propagation of Vriesea incurvata: conservation of a bromeliad endemic to the Atlantic Forest
Fig. 1. Vriesea incurvata plantlets after 180 days in MS medium with different macronutrient combinations (25N and 25M) and sucrose concentrations (10, 30 and 60 g L-1). 25M - 25% of the original concentrations of the macronutrients; 25N - 25% of the original concentrations of the nitrogenous salts. Scale bars = 1 cm.
Channel Measurements from Sitarjevec Mine for Propagation Modelling in a Cave Environment
<p>The dataset contains data collected during a measurement campaign using the <a href="https://www.qorvo.com/products/p/DW1000">DecaWave1000 </a>UWB pulse radio module in the <a href="https://rudniksitarjevec.si/en">Sitarjevec</a> mine in Litija, Slovenia. </p> <p>The measurements include complex channel impulse responses (CIRs) collected for 40 predefined positions with the following system parameters:</p> <ul> <li><strong>CONF1:</strong> Channel 4, DataRate 110, PRFR 16, PreambleLenght 4096, PreambleCode 7;</li> <li><strong>CONF2: </strong>Channel 4, DataRate 110, PRFR 64, PreambleLenght 4096, PreambleCode 17;</li> <li><strong>CONF3: </strong>Channel 7, DataRate 110, PRFR 16, PreambleLenght 4096, PreambleCode 7;</li> <li><strong>CONF4: </strong>Channel 7, DataRate 110, PRFR 64, PreambleLenght 4096, PreambleCode 17.</li> </ul> <p><strong>Measurement setup</strong></p> <p>Measurements were performed using the DecaWave1000 UWB pulse radio module in Segment 1 of the mine. Four points (Point A, Point B, Point C, and Point D) were selected for defining the positions of the nodes as shown in Figure 1. Point A denotes the main entrance, the entrance to the measurement segment is represented by Point B, and the beginning and the end of the measurement environment are denoted by Point C, and Point D, respectively. The length of the mine between Point A and Point D is approximately 60 m. </p> <p>The nodes were mounted on stands 1.4 m above the ground, which is not flat. They were placed on a straight line connecting Point C and Point D. The ANCHOR node was positioned in Point C, and the TAG node was moved along the reference line with a step of 1 m up to a maximum distance of 40 m. The nodes were always positioned so the antenna was centred above the reference line. </p> <p><strong>Folder structure</strong></p> <p>The measurements collected for each of the TAG node positions are stored in the corresponding folder. The folders are named m_n, with n=1, ..., 40 corresponding to the TAG's distance to the ANCHOR. Each folder contains four CSV files holding the CIR data measured using the four pre-defined configurations of the UWB system. Each CSV file stores CIRs of approximately 200 repeated measurements as separate records. The CIRs hold complex values of the 156 strongest multipath components corresponding to the taps in the DecaWave1000 accumulator, each of which represents a 1 ns sample interval. </p> <p><strong>Authors</strong></p> <p>Teodora Kocevska, Aleš Simončič, Grega Morano, Tomaž Javornik, and Andrej Hrovat</p> <p>Department of Communication Systems</p> <p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p> <p>teodora.kocevska@ijs.si</p>
BRAIN Journal-An Enhancement over Texture Feature Based Multiclass Image Classification under Unknown Noise-Figure 4. Feed Forward Back propagation Neural Network
<p>This BPNN provides a computationally efficient<br> method for changing the weights in feed forward network, with differentiable activation function<br> units, to learn a training set of input-output data. Being a gradient descent method it minimizes the<br> total squared error of the output computed by the net. The aim is to train the network to achieve a<br> balance between the ability to respond correctly to the input patterns that are used for training and<br> the ability to provide good response to the input that are similar. A typical back propagation<br> network of input layer, one hidden layer and output layer is shown in figure 4.</p>
Figure 3. Distribution of Q3 values-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The estimated accuracy for the α- helices (QH), β- strands (QE), C-coil states (QC), and three<br> state together (Q3) for the system is shown in Figure 3.</p>
Figure 2. PAM250 matrix for the encoded sequence-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The PAM matrix (Dayhoff et al., 1978) describes the probability that original amino acid<br> will be replaced by another amino acid over a defined evolutionary interval. The unit of<br> evolutionary divergence is defined as the interval in which 1% of the amino acids have been<br> changed between two sequences. The work uses PAM250, which assumes the occurrence of 250-<br> point mutations per 100 amino acids.<br> So, for the given the protein sequence GIVEQCCASVCSLYQLENYCN, A will be replaced<br> by 1 -3 0 1 -3 -1 0 5 -2 -3 -4 -2 -3 -5 0 1 0 -7 -5 -1 as shown in Figure 2.</p>
Figure 1: Snapshot of the CB396 dataset-Secondary Structure Prediction of Protein using Resilient Back Propagation Learning AlgorithmSecondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm
<p>The dataset used for this work is CB396. This dataset contains 396 non-redundant sequences<br> derived from the 3Dee database created by Cuff and Barton (Cuff & Barton, 1999). It contains 396<br> proteins with their respective secondary structure as shown in Figure 1.</p>
Data, plotting scripts, and figures for "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures"
<p>This bundle of files contains all the data and plotting scripts for "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures", as well as the figures themselves.</p> <p>These results are part of the paper:</p> <p>Tejas Chandrashekhar Mulky and Kyle E. Niemeyer. "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures," 2018. Accepted for publication in <em>Proceedings of the Combustion Institute</em>, available via <a href="https://arxiv.org/abs/1806.08396">https://arxiv.org/abs/1806.08396</a></p>
Excitation waves propagating on the streets of Barcelona. Numerical integration of Oregonator equations.
<p><a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval_phi_0_050.mov?versionId=285ba608-3f27-422f-b9f2-72137a5525a7">Barcelona_Raval_phi_0_050.mov</a>: Raval. Initial perturbation site is at the beginning of Les Rambles. $\phi=0.050$<br> <br> <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval_phi_0_065.mov?versionId=30d2c271-9058-4587-a1ec-b5fdda78482b">Barcelona_Raval_phi_0_065.mov</a>: Raval. Initial perturbation site is at the beginning of Les Rambles. $\phi=0.065$<br> <br> <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval_phi_0_074.mov?versionId=d0a4c922-b29c-46da-b8fd-618a404b482f">Barcelona_Raval_phi_0_074.mov</a>: Raval. Initial perturbation site is at the beginning of Les Rambles. $\phi=0.074$</p> <p>Barcelona_Gracia_phi_0_0666.mov: Gracia. Initial perturbation site is Sagrada Familia. $\phi=0.0666$</p> <p>====== Description of the model ====</p> <p>Two fragments of Barcelona street map --- Gracia and Raval, were mapped onto a grid of 2500 by 2500 nodes. Nodes of the grid corresponding to streets are considered to be filled with a Belousov-Zhabotinsky medium, i.e. excitable nodes, other nodes are non-excitable. We use two-variable Oregonator equations~\cite{field1974oscillations} adapted to a light-sensitive <br> Belousov-Zhabotinsky (BZ) reaction with applied illumination~\cite{beato2003pulse}:</p> <p>\begin{eqnarray}<br> \frac{\partial u}{\partial t} & = & \frac{1}{\epsilon} (u - u^2 - (f v + \phi)\frac{u-q}{u+q}) + D_u \nabla^2 u \nonumber \\<br> \frac{\partial v}{\partial t} & = & u - v <br> \label{equ:oregonator}<br> \end{eqnarray}</p> <p>The variables $u$ and $v$ represent local concentrations of an activator, or an excitatory component of BZ system, and an inhibitor, or a refractory component. Parameter $\epsilon$ sets up a ratio of the time scale of variables $u$ and $v$, $q$ is a scaling parameter depending on rates of activation/propagation and inhibition, $f$ is a stoichiometric coefficient. </p> <p> We integrated the system using Euler method with five-node Laplace operator, time step $\Delta t=0.001$ and grid point spacing $\Delta x = 0.25$, $\epsilon=0.02$, $f=1.4$, $q=0.002$. We varied value of $\phi$ from the interval $\Phi=[0.05,0.08]$.</p> <p>To generate excitation waves we perturb the medium by square solid domains of excitation, $20 \times 20$ sites in state $u=1.0$, site of the perturbation is shown by red discs in <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Gracia.png?versionId=cfeb3a2a-1f8a-427d-a2b1-41f9893a4d66">Barcelona_Gracia.png </a> and <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval%20point.png?versionId=c0ad4d24-ff59-4287-a817-2c7bf72f509c">Barcelona_Raval point.png</a>. Time-lapse snapshots provided in the paper were recorded at every 150\textsuperscript{th} time step, we display sites with $u >0.04$; videos supplementing figures were produced by saving a frame of the simulation every 50\textsuperscript{th} step of numerical integration and assembling them in the video with play rate 30 fps. All figures in this paper show time lapsed snapshots of waves, initiated just once from a single source of stimulation; these are not trains of waves following each other.</p>
Data used in "Utilizing the Heliophysics/Geospace System Observatory to Understand Particle Injections: Their Scale Sizes and Propagation Directions"
<p>These are the raw data files for the data used in the paper, "Utilizing the Heliophysics/Geospace System Observatory to Understand Particle Injections: Their Scale Sizes and Propagation Directions" by Gabrielse et al. They can be read using the SPEDAS software found here: http://themis.ssl.berkeley.edu/software.shtml. Tplot variables, which are specifically read and plotted by SPEDAS, are included for pertinent THEMIS data.</p>
Example dataset for openPMD conform wavefront propagation data (wavefront domain extension)
<p>This dataset results from a coherent wavefront propagation of 5 keV photons through the SASE1 beamline and the SPB-SFX instrument at European XFEL. The simulation was performed with the software WPG. The dataset was rewritten from the original WPG output into a hdf5 format that complies with the openPMD metadata standard for particle and mesh data and the proposed domain extension of this standard for wavefront data.</p> <p>This dataset is part of the Deliverable D5.1 in Workpackage 5 (Virtual Neutron and X-ray Laboratory) of the Photon and Neutron Open Science Cloud (PaNOSC).</p> <p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823852.</p> <p> </p> <p> </p>
Fig. 2 in Sarcocystis falcatula-like derived from opossum in Northeastern Brazil: In vitro propagation in avian cells, molecular characterization and bioassay in birds
Fig. 2. (A) A mature schyzont of Sarcocystis falcatula-like (Sarco-BA1 strain) in a permanent chicken cell line (UMNSAH/DF-1). May-Grüenwald-Giemsa stain. Bar = 20 μm. (B) Extracellular merozoites of Sarco-BA1 on a monolayer of UMNSAH/DF-1 cells. Bar = 10 μm.
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.