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490 results for “Propagation”
Armoricaphyton chateaupannense - Propagation Phase Contrast X-Ray Synchrotron Microtomography Dataset
<p>Includes:</p> <p>1) Propagation phase contrast X-ray synchrotron microtomography (PPC-SRμCT) dataset to study the three-dimensional structure of the permineralized wood from <em>Armoricaphyton chateaupannense</em>, using the ID19 beamline of the European Synchrotron Radiation Facility (ESRF), Grenoble, France.</p> <p><strong>Dataset Information (also see scan_log.xml):</strong></p> <ul> <li>Number of image in dataset: 2159 images</li> <li>Images prefix: plante_</li> <li>Image x/y size: 3763 x 2048 px</li> <li>Image type: 16-bit TIFFs (with Pack Bits compression)</li> <li>Image size on disk: 14.8 MB each</li> <li>Scan date: 31-Oct-2008</li> <li>Scan energy: 30keV </li> <li>Voxels size: 0.551 um</li> <li>Filters: Al_1_mm Al_0.5_mm Diam_U</li> <li>Projection number: 4000</li> <li>Projection rotation: 360 degs</li> <li>Magnification: x20</li> <li>Source-sample distance: 145000</li> <li>Scan type: continuous</li> </ul> <p>Note: these images have been cropped from their original scan output size.</p> <p>2) Two supplemental videos of the 3D model.</p>
Modelling of excitation propagation on computer models of insoles colonised by fungal mycelium. Videos and potential difference recordings.
<p>We used an artistic image of the mycelium network projected onto a $364 \times 985$ nodes grid. <br> The original image $M=(m_{ij})_{1 \leq j \leq n_i, 1 \leq j \leq n_j}$, $m_{ij} \in \{ r_{ij}, g_{ij}, b_{ij} \}$, where $n_i=364$ and $n_j=985$, and $1 \leq r, g, b \leq 255$, was converted to a conductive matrix $C=(m_{ij})_{1 \leq i,j \leq n}$ derived from the image as follows: $m_{ij}=1$ if $r_{ij}>170$, $g_{ij}>170$ and $b_{ij}<200$; a dilution operation was applied to $C$. </p> <p>FitzHugh-Nagumo (FHN) equations is a qualitative approximation of the Hodgkin-Huxley model of electrical activity of living cells:<br> \begin{eqnarray}<br> \frac{\partial v}{\partial t} & = & c_1 u (u-a) (1-u) - c_2 u v + I + D_u \nabla^2 \\<br> \frac{\partial v}{\partial t} & = & b (u - v),<br> \end{eqnarray}<br> where $u$ is a value of a trans-membrane potential, $v$ a variable accountable for a total slow ionic current, or a recovery variable responsible for a slow negative feedback, $I$ {is} a value of an external stimulation current. The current through intra-cellular spaces is approximated by<br> $D_u \nabla^2$, where $D_u$ is a conductance. The term $D_u \nabla^2 u$ governs a passive spread of the current. The terms $c_2 u (u-a) (1-u)$ and $b (u - v)$ describe the ionic currents. The term $u (u-a) (1-u)$ has two stable fixed points $u=0$ and $u=1$ and one unstable point $u=a$, where $a$ is a threshold of an excitation.</p> <p>We integrated the system using the Euler method with the five-node Laplace operator, a time step $\Delta t=0.015$ and a grid point spacing $\Delta x = 2$, while other parameters were $D_u=1$, $a=0.13$, $b=0.013$, $c_1=0.26$. We controlled excitability of the medium by varying $c_2$ from 0.05 (fully excitable) to 0.015 (non excitable). Boundaries are considered to be impermeable: $\partial u/\partial \mathbf{n}=0$, where $\mathbf{n}$ is a vector normal to the boundary. </p> <p>To record dynamics of excitation in the network, as if in laboratory experiments, we simulated electrodes by calculating a potential $p^t_x$ at an electrode location $x$ as $p_x = \sum_{y: |x-y|<2} (u_x - v_x)$. Configuration of electrodes $1, \cdots, 16$ is shown in Fig.~\ref{fig:mycelium}c. Time-lapse snapshots provided in the paper were recorded at every 100\textsuperscript{th} time step, and we display sites with $u >0.04$; videos and figures were produced by saving a frame of the simulation every 100\textsuperscript{th} step of the numerical integration and assembling the saved frames into the video with a play rate of 30 fps. </p> <p>Insole_01: Excitation started at electrode E2</p> <p>Insole_10: Excitation started at electrode E1</p> <p>Insole_11: Excitation started at electrodes E1 and E2</p> <p> </p>
The propagation of gravity waves in Titan's stratosphere
<p>The model code and figure data of our article "The propagation of gravity waves in Titan's stratosphere". <a href="https://zenodo.org/api/files/a647c3b7-0796-4a3a-96bc-779957496aad/GW-simulation-program.txt">GW-simulation-program.txt</a> is the Mathematica code used to simulate gravity wave propagation. <a href="https://zenodo.org/api/files/a647c3b7-0796-4a3a-96bc-779957496aad/simulations-nowind.rar">simulations-nowind.rar</a> and <a href="https://zenodo.org/api/files/a647c3b7-0796-4a3a-96bc-779957496aad/simulations-wind.rar">simulations-wind.rar</a> are the simulation results for gravity wave with its horizontal propagation direction perpendicular or not perpendicular to the background wind, respectively. <a href="https://zenodo.org/api/files/a647c3b7-0796-4a3a-96bc-779957496aad/FigureData.rar">FigureData.rar</a> contains several data files for figures in our article.</p>
Dataset for the "a parameterization for cloud organization and propagation by evaporation-driven cold pools edges"
<p>When the negatively buoyant air in the cloud downdrafts reaches the surface, it spreads out horizontally, producing cold pools. A cold pool can trigger new convective cells. However, when combined with the ambient vertical wind shear, it can also connect and upscale them into large mesoscale convective systems (MCS). Given the broad spectrum of scales of the atmospheric phenomenon involving the interaction between cold pools and the MCS, a parameterization was designed here. Then, it is coupled with a classical convection parameterization to be applied in an atmospheric model with an insufficient spatial resolution to explicitly resolve convection and the sub-cloud layer. A new scalar quantity related to the deficit of moist static energy detrained by the downdrafts mass flux is proposed. This quantity is subject to grid-scale advection, mixing, and a sink term representing dissipation processes. The model is then applied to simulate moist convection development over a large portion of tropical land in the Amazon Basin in a wet and dry-to-wet 10-days period. Our results show that the cold pool edge parameterization improves the organization, longevity, propagation, and severity of simulated MCS over the Amazon and other different continental areas.</p><p> </p>
Dataset for the publication "Reversal of nanomagnets by propagating magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory"
<p>Raw data associated to the manuscript ‘’Reversal of nanomagnets by propagating<br> magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory‘’, Nature Communications (2023); doi: <a href="https://deref-web.de/mail/client/P1XojCfdiYA/dereferrer/?redirectUrl=https%3A%2F%2Fdoi.org%2F10.1038%2Fs41467-023-37078-8">https://doi.org/10.1038/s41467-023-37078-8</a><br> Information about file formats and measurement parameters are described in text files in the specific folders. For micromagnetic simulations Mumax 3.10 was used. The simulation scripts (*.mx3 files) and exemplary plotting scripts in Python 3.9 (*.py files) are included.</p> <p>Paper abstract:<br> Despite the unprecedented downscaling of CMOS integrated circuits, memory-intensive machine learning and artificial intelligence applications are limited by data conversion between memory and processor. There is a challenging quest for novel approaches to overcome this so-called von Neumann bottleneck. Magnons are the quanta of spin waves. Their angular momentum enables power-efficient computation without charge flow. The conversion problem would be solved if spin wave amplitudes could be stored directly in a<br> magnetic memory. Here, we report the reversal of ferromagnetic nanostripes by spin waves which propagate in an underlying spin-wave bus. Thereby, the charge-free angular momentum flow is stored after transmission over a macroscopic distance. We show that the spin waves can reverse large arrays of ferromagnetic stripes at a strikingly small power level. Combined with the already existing wave logic, our discovery is path-breaking for the new era of magnonics-based in-memory computation and beyond von Neumann computer architectures</p>
Data for: Heat induces multiomic and phenotypic stress propagation in zebrafish embryos
<p>This contains the data for the manuscript Feugere et al., "Heat induces multiomic and phenotypic stress propagation in zebrafish embryos" (2023). Zebrafish embryos were exposed to thermal stress ("TS") and stress metabolites ("SM") released by heat-stressed conspecifics in a two-way factorial design ("TSxSM"). The folder includes raw molecular data (cortisol levels, HSP70 protein levels, and gene expression acquired with LAMP and RNA-seq) and raw phenotypic data (morphology, hatching, survival, and behaviour) of zebrafish <em>Danio rerio </em>at 1 day and 4 days of development.</p> <p>The .csv files contain all quantitative data, whilst the .tab files contain the gene count data required for gene expression analysis. The data were analysed in R using the code shared in the "TSxSM2.stats.Rmd" file. The "Metadata" document provides the reader with an extensive description of each file.</p>
Nuclear Data Uncertainty Propagation for the Molten Salt Fast Reactor Design (dataset)
<p>This repository contains the dataset, post-processing script and models needed to reproduce the results presented in the article "Nuclear Data Uncertainty Propagation for the Molten Salt Fast Reactor Design", published in the special issue of Nuclear Science and Engineering dedicated to the 1st Young Molten Salt Reactor Conference (held in Lecco in June 6th and 9th 2022).</p> <p>The dataset includes:</p> <ul> <li>the perturbed nuclear data files (in ACE and ENDF-6 formats) generated with the open-source, python package <a href="https://github.com/luca-fiorito-11/sandy">SANDY</a> and processed with the processing code <a href="https://github.com/njoy">NJOY</a> by using the <a href="https://github.com/nicoloabrate/ndl">NDL</a> code.</li> <li>the <a href="https://serpent.vtt.fi/serpent/">Serpent 2</a> Monte Carlo calculations for two models of the Molten Salt Fast Reactor design, conceived during the <a href="http://samofar.eu/">SAMOFAR</a> EU project</li> </ul> <p>The python scripts include pre- and post-processing tools used to generate the perturbed data and to analyse the Serpent 2 output.</p>
Dataset on the Human Body as a Signal Propagation Medium
<p><strong>Overview:</strong> This is a large-scale dataset with impedance and signal loss data recorded on volunteer test subjects using low-voltage alternate current sine-shaped signals. The signal frequencies are from 50 kHz to 20 MHz.</p> <p><strong>Applications:</strong> The intention of this dataset is to allow to investigate the human body as a signal propagation medium, and capture information related to how the properties of the human body (age, sex, composition etc.), the measurement locations, and the signal frequencies impact the signal loss over the human body.</p> <p><strong>Overview statistics:</strong></p> <ul> <li>Number of subjects: 30</li> <li>Number of transmitter locations: 6</li> <li>Number of receiver locations: 6</li> <li>Number of measurement frequencies: 19</li> <li>Input voltage: 1 V</li> <li>Load resistance: 50 ohm and 1 megaohm</li> </ul> <p><strong>Measurement group statistics:</strong></p> <ul> <li>Height: 174.10 (7.15)</li> <li>Weight: 72.85 (16.26)</li> <li>BMI: 23.94 (4.70)</li> <li>Body fat %: 21.53 (7.55)</li> <li>Age group: 29.00 (11.25)</li> <li>Male/female ratio: 50%</li> </ul> <p><strong>Included files:</strong></p> <ul> <li>experiment_protocol_description.docx - protocol used in the experiments</li> <li>electrode_placement_schematic.png - schematic of placement locations</li> <li>electrode_placement_photo.jpg - visualization on the experiment, on a volunteer subject</li> <li>RawData - the full measurement results and experiment info sheets</li> <li>all_measurements.csv - the most important results extracted to .csv</li> <li>all_measurements_filtered.csv - same, but after z-score filtering</li> <li>all_measurements_by_freq.csv - the most important results extracted to .csv, single frequency per row</li> <li>all_measurements_by_freq_filtered.csv - same, but after z-score filtering</li> <li>summary_of_subjects.csv - key statistics on the subjects from the experiment info sheets</li> <li>process_json_files.py - script that creates .csv from the raw data</li> <li>filter_results.py - outlier removal based on z-score</li> <li>plot_sample_curves.py - visualization of a randomly selected measurement result subset</li> <li>plot_measurement_group.py - visualization of the measurement group</li> </ul> <p><br> CSV file columns:</p> <ul> <li>subject_id - participant's random unique ID</li> <li>experiment_id - measurement session's number for the participant</li> <li>height - participant's height, cm</li> <li>weight - participant's weight, kg</li> <li>BMI - body mass index, computed from the valued above</li> <li>body_fat_% - body fat composition, as measured by bioimpedance scales</li> <li>age_group - age rounded to 10 years, e.g. 20, 30, 40 etc.</li> <li>male - 1 if male, 0 if female</li> <li>tx_point - transmitter point number</li> <li>rx_point - receiver point number</li> <li>distance - distance, in relative units, between the tx and rx points. Not scaled in terms of participant's height and limb lengths!</li> <li>tx_point_fat_level - transmitter point location's average fat content metric. Not scaled for each participant individually.</li> <li>rx_point_fat_level - receiver point location's average fat content metric. Not scaled for each participant individually.</li> <li>total_fat_level - sum of rx and tx fat levels</li> <li>bias - constant term to simplify data analytics, always equal to 1.0</li> </ul> <p>CSV file columns, frequency-specific:</p> <ul> <li>tx_abs_Z_... - transmitter-side impedance, as computed by the `process_json_files.py` script from the voltage drop</li> <li>rx_gain_50_f_... - experimentally measured gain on the receiver, in dB, using 50 ohm load impedance</li> <li>rx_gain_1M_f_... - experimentally measured gain on the receiver, in dB, using 1 megaohm load impedance</li> </ul> <p><br> <strong>Acknowledgments:</strong> The dataset collection was funded by the Latvian Council of Science, project “Body-Coupled Communication for Body Area Networks”, project No. lzp-2020/1-0358.</p> <p><strong>References:</strong> For a more detailed information, see this article: J. Ormanis, V. Medvedevs, A. Sevcenko, V. Aristovs, V. Abolins, and A. Elsts. Dataset on the Human Body as a Signal Propagation Medium for Body Coupled Communication. Submitted to Elsevier Data in Brief, 2023.</p> <p><strong>Contact information:</strong> info@edi.lv</p>
Propagating mechanisms of the 2016 Summer BSISO Event: air-sea coupling, vorticity, and moisture
<pre>This repository contains the data from the WRF+HYCOM coupled simulations and WRF simulations for the BSISO event in July and August, 2016. </pre>
Perceptual history propagates down to early levels of sensory analysis
<p>One function of perceptual systems is to construct and maintain a reliable representation of the environment. A useful strategy intrinsic to modern “Bayesian” theories of perception is to take advantage of the relative stability of the input and use perceptual history (priors) to predict current perception. This strategy is efficient but can lead to stimuli being biased toward perceptual history, clearly revealed in a phenomenon known as serial dependence. However, it is still unclear whether serial dependence biases sensory encoding or only perceptual decisions. We leveraged on the “surround tilt illusion”—where tilted flanking stimuli strongly bias perceived orientation—to measure its influence on the pattern of serial dependence, which is typically maximal for similar orientations of past and present stimuli. Maximal serial dependence for a neutral stimulus preceded by an illusory one occurred when the perceived, not the physical, orientations of the two stimuli matched, suggesting that the priors biasing current perception incorporate the effect of the illusion. However, maximal serial dependence of illusory stimuli induced by neutral stimuli occurred when their physical (not perceived) orientations were matched, suggesting that priors interact with incoming sensory signals before they are biased by flanking stimuli. The evidence suggests that priors are high-level constructs incorporating contextual information, which interact directly with early sensory signals, not with highly processed perceptual representations.</p>
The imprint of crustal density heterogeneities on regional seismic wave propagation - dataset
<p>This dataset should provide complete synthetic seismograms and software</p> <p>(python tools for random media generation, signal comparison and histogram stacking)</p> <p>that were used in the publication:</p> <p>Płonka, A., Blom, N., and Fichtner, A.: The imprint of crustal density heterogeneities on regional seismic wave propagation, Solid Earth, 7, 1591-1608, doi:10.5194/se-7-1591-2016, 2016.</p>
Supporting materials for manuscript entitled "Investigation of guided wave propagation in pipes fully- and partially-embedded in concrete"
<p>This set contains data in support of some of the figures appearing in an open access manuscript entitled 'Investigation of guided wave propagation in pipes fully- and partially-embedded in concrete,' published at the Journal of the Acoustical Society of America (DOI:10.1121/1.4972118) by the authors.</p> <p>The data set contains the numerical output from finite element (FE) modelling, Semi-analytical FE (SAFE) modelling, simulations using the Disperse software, and experimental measurements of guided wave transmission loss in full-scale laboratory tests. The set contains three separate data files. Details on the specific data is provided within an extra file ('readme' file).</p>
Software for optimizing treatment to slow the spatial propagation of invasive species: Code and results
<p>Slowing the spread of invasive species is a major challenge. How can we achieve this goal in the most cost-effective manner? This package includes the complete code and simulation results that help finding the optimal, most cost-effective treatment to slow the spread of a propagating species. This package accompanies the paper "Optimizing strategies for slowing the spread of invasive species" by Adam Lampert (PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1011996). The file general_model_code.zip contains the code for the general model; the file spongy_moth_model_code.zip contains the code for the spongy moth model; and the file general_model_simulation_results.zip contains the results for the general model; and the file spongy_moth_model_simulation_results.zip contains the results for the spongy moth model.</p>
Accompanying data for the paper "Two-scale concurrent simulations for crack propagation using FEM-DEM bridging coupling" : Mode-I
<h2>Contributions</h2> <ul> <li>Manon Voisin--Leprince: Contributed to writing scripts, launching simulations, and analyzing results</li> <li>Joaquin Garcia-Suarez: Contributed to helping analyze results</li> <li>Guillaume Anciaux: Contributed to supervising the project</li> <li>Jean-François Molinari: Contributed to supervising the project</li> </ul> <p>All authors reviewed the results and contributed to the manuscript</p> <h2>Funding sources</h2> <ul> <li>Grant 200021_197152, entitled <code>Wear across scales</code> by the Swiss National Science Foundation. </li> </ul> <h2>FEM-DEM coupling applications</h2> <p>The data_mode_I folder is composed of:</p> <p>1- The DEM folder which contains the scripts to generate the DEM samples used in the simulations (Mode_I and Mode_II)</p> <p>2- The Mode_I folder which is composed of:</p> <ul> <li> <p>mode_I: Contains the scripts and data of the section "Mode I crack propagation" presented in the paper</p> </li> <li> <p>post_processing_mode_I: Contains the files to conduct the post processing relative to the section "Mode I crack propagation"</p> </li> </ul> <p>Additional README.md files are provided in the subfolders</p> <p>The notebook folder contains scripts to plot the results of the section "Mode I crack propagation". </p> <h2>Mode_II complementary dataset</h2> <p>The Mode-II part of the study can be found at https://doi.org/10.5281/zenodo.14264611</p>
Accompanying data for the paper "Two-scale concurrent simulations for crack propagation using FEM-DEM bridging coupling" : Mode-II
<h2>Contributions</h2> <ul> <li>Manon Voisin--Leprince: Contributed to writing scripts, launching simulations, and analyzing results</li> <li>Joaquin Garcia-Suarez: Contributed to helping analyze results</li> <li>Guillaume Anciaux: Contributed to supervising the project</li> <li>Jean-François Molinari: Contributed to supervising the project</li> </ul> <p>All authors reviewed the results and contributed to the manuscript</p> <h2>Funding sources</h2> <ul> <li>Grant 200021_197152, entitled <code>Wear across scales</code> by the Swiss National Science Foundation. </li> </ul> <h2>FEM-DEM coupling applications</h2> <p>The data folder contains the Mode_II folder which is composed of:</p> <ul> <li> <p>mode_II: Contains the scripts and data of the section "Surface wear during relative sliding" presented in the paper. Only data for the largest case is not provided.</p> </li> <li> <p>post_processing_mode_II: Contains the files to conduct the post processing relative to the section "Surface wear during relative sliding"</p> </li> </ul> <p>Additional README.md files are provided in the subfolders</p> <p>The notebook folder contains scripts to plot the results of the section "Surface wear during relative sliding". </p>
Data for "The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment due to their Propagation with MHD Modes" by Strack & Saur
<div>This dataset contains data from the publication Strack & Saur, 2024 (<a href="https://doi.org/10.1029/2024JA033235">https://doi.org/10.1029/2024JA033235</a>), including the output of our MHD model as well as processed data used in Figures 4, 5, and 6.<br> <div> </div> <div>We use a Cartesian and a spherical coordinate system, both with the origin at the geometric center of Callisto. In the Cartesian system, the z-axis is parallel to Jupiter’s rotation axis, the y-axis points to the center of Jupiter and the x-axis, which completes the right-handed coordinate system, is approximately in direction of Callisto's orbital motion. In the spherical coordinate system, phi=0° is defined on the Jupiter-facing meridian (positive y-axis) and is counted in an easterly direction, i.e., phi=90° is the upstream direction (negative x-axis). Theta is taken from the positive z-axis.<br><br></div> <div> <div> <h2>Simulation Output</h2> <br> <div>The PLUTO simulation code (v4.4, Mignone et al. 2007, http://plutocode.ph.unito.it) was used for the numerical solution of the MHD model. A description of the model equations, boundary conditions and simulation process is given Strack & Saur, 2024.</div> <br> <div>The simulations were performed in spherical geometry (r, theta, phi). Each "*.flt" output file contains the model variables on the simulation grid for a single time step. The respective simulation grid is specified in the "grid.out" file. The model variables are:</div> <ul> <li>rho: Plasma mass density</li> <li>vx1: Plasma bulk velocity, r component</li> <li>vx2: Plasma bulk velocity, theta component</li> <li>vx3: Plasma bulk velocity, phi component</li> <li>Bx1: Magnetic field, r component</li> <li>Bx2: Magnetic field, theta component</li> <li>Bx3: Magnetic field, phi component</li> <li>prs: Thermal plasma pressure</li> </ul> <div> <div>Each simulation output file also contains the following additional variables:</div> <ul> <li>Bpx1: In our case, this is the same as Bx1</li> <li>Bpx2: In our case, this is the same as Bx2</li> <li>Bpx3: In our case, this is the same as Bx3</li> <li>Jx1: Electric current density, r component</li> <li>Jx2: Electric current density, phi component</li> <li>Jx3: Electric current density, theta component</li> </ul> <div>In the output files, all values are in normalized units. The normalization factors (in CGS units) are:</div> <ul> <li>norm_r = 2410e3 cm</li> <li>norm_t = 1.255e1 s</li> <li>norm_rho = 1.594e-24 g/cm^3</li> <li>norm_v = 1.92e7 cm/s</li> <li>norm_B = 8.593e-05 Gauss</li> <li>norm_prs = 5.877e-10 dyne/cm^3</li> <li>norm_J = 8.508e-04 statA/cm^2</li> </ul> <div>Since the simulation output files are in PLUTO's binary ".flt" format, we provide the Python script "read_data.py" to read the simulation data and grid specifications.</div> <br> <div>We provide the following simulation data:</div> <br> <div>For Section 4 in Strack & Saur, 2024</div> <ul> <li>`./symmetric_model_reference`: The reference simulation, i.e., moon-magnetosphere interactions only<br>`./symmetric_model_full_A075`: The (main) full simulation with A=0.75, i.e., moon-magnetosphere interactions and induced magnetic field<br>`./symmetric_model_full_A025`: The full simulation with A=0.25<br>`./symmetric_model_full_A050`: The full simulation with A=0.50<br>`./symmetric_model_full_A100`: The full simulation with A=1.00</li> </ul> <div>For Section 5 in Strack & Saur, 2024</div> <div> <ul> <li>`./C03_high_density_reference`: The reference simulation for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_high_density_full`: The full simulation with A=0.85 for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_low_density_reference`: The reference simulation for the C03 flyby with the lower initial plasma mass density</li> <li>`./C03_low_density_full`: The full simulation with A=0.85 for the C03 flyby with the lower initial plasma mass density</li> <li>`./C09_high_density_reference`: The reference simulation for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_high_density_full`: The full simulation with A=0.85 for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_low_density_reference`: The reference simulation for the C09 flyby with the lower initial plasma mass density</li> <li>`./C09_low_density_full`: The full simulation with A=0.85 for the C09 flyby with the lower initial plasma mass density</li> </ul> </div> <br> <div>Note that in the simulation data that is provided for the symmetric model (Section 4), the output numbers of the data files are different. This is because a higher output frequency was used for the reference simulation and the A=0.75 full simulation. All output files for the symmetric full simulations refer to the end of the propagation time span shown in Figure 4. For the reference simulation, the output is provided at the beginning and end of this time span.</div> <div> </div> <div> <div> <h2>Processed Data</h2> <p>In addition to the simulation output, we provide processed data used in Figures 4, 5 and 6 of Strack & Saur, 2024.</p> <p>The directory `./data_figure_4_and_5` contains the following files for each of the four panels in Figure 4:</p> <ul> <li>`fig4_panel_*_reference.csv`: The magnetic field of the reference simulation for the respective profile. Provided are the mean, minimum, and maximum values of each component (Bx, By, Bz) in the analyzed time period.</li> <li>`fig4_panel_*_full_Bx.csv`: The time series of the Bx magnetic field component of the full simulation for the respective profile. Each column contains values for a different position (given in the first row) and each row contains values for a different point in time (given in the first column).</li> <li>`fig4_panel_*_full_By.csv`, `fig4_panel_*_full_Bz.csv`: The time series of the By and Bz magnetic field components, respectively.</li> </ul> <p>The data given for panels a and b are also used in Figure 5.</p> <p>The directory `./data_figure_6` contains a single file `fig6_sample_data.csv` with the data used for Figure 6.</p> <ul> <li>The first three columns of the file give the Cartesian coordinates of the sample points</li> <li>"B_sec_infinity" is the magnitude of the induced magnetic dipole field in a vacuum environment with A=1.0 (Equation 1)</li> <li>"dB_reference" is the numerical variability of the reference simulation in its approximately stationary state</li> <li>The last four columns (e.g. "B_sec_A025") contain the transport altered induced magnetic field magnitudes in the plasma environment for a true dipole amplitude of A=0.25, A=0.50, A=0.75, and A=1.00</li> </ul> <p>Note that length, time and magnetic field in the processed data are given in units of Callisto radii (Rc), seconds and nanotesla.</p> </div> <h2>References:</h2> <div> <div>Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. (2007). PLUTO: A Numerical Code for Computational Astrophysics. The Astrophysical Journal Supplement Series, 170(1), 228–242. https://doi.org/10.1086/513316</div> <br> <div>Strack, D., Saur, J. (2024). The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment Due to Their Propagation With MHD modes. Journal of Geophysical Research: Space Physics, 129(12), https://doi.org/10.1029/2024JA033235</div> </div> </div> </div> </div> </div> </div>
Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network
<p>In this repository we provide raw and processed datasets for the article: “Genome-wide gene expression noise in <em>Escherichia coli </em>is condition-dependent and determined by propagation of noise through the regulatory network<strong>” </strong>by Arantxa Urchueguía, Luca Galbusera, Dany Chauvin, Gwendoline Bellement, Thomas Julou and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: <a href="https://doi.org/10.1101/795369">https://doi.org/10.1101/795369</a>. </p> <p>The repository consists of the following datasets: </p> <p><strong>1. preprocessed_datasets.zip(~22GB)</strong></p> <ul> <li>This dataset contains raw data from the flow cytometry experiments (FACS Canto II, BD Bioscience) in all measured conditions in RData format. Raw fcs files were processed with the tools described in the publication ''Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria" published here: <a href="https://doi.org/10.1371/journal.pone.0240233">https://doi.org/10.1371/journal.pone.0240233</a>. The tools themselves are available here: <a href="https://github.com/vanNimwegenLab/E-Flow">https://github.com/vanNimwegenLab/E-Flow</a>. Included in the files are the outputs of these processing tools together with all raw values that came directly from the flow cytometer. The file <em>directory_structure_in_preprocessed </em>contains information about how the files are organized.</li> </ul> <p><strong>2. info_files: </strong>This is a set of csv files containing detailed information about the experiments done to acquire the preprocessed_datasets as well as annotation files that we used to retrieve promoter information. </p> <p><strong>3. processed_datasets:</strong> These files correspond to the processed datasets from the raw Rdata files under 1 above. The processed data provide mean and variance estimates in fluorescence of E.coli promoters across the different growth conditions. Note that we discarded flow cytometry measurements from promoter/growth-condition combinations that contained abnormal fluorescence distributions (due to contamination) as well as measurements from reporters with annotation mismatches. The folder contains the following clean dataset files that were used in the paper:</p> <ul> <li><strong>FULL_dataset_mean_var_wreplicates:</strong> In this dataset we include the processed means and variances (in both logarithmic and linear scale) of all promoters in each condition. Included as well are replicate measurements for some conditions.. We also include the name and Blattner number of the gene immediately downstream of each promoter, the DNA sequence of each promoter, and regulatory information (number of unique inputs for transcription factors sites and their names) which we obtained from RegulonDB v 10.5 (<a href="https://doi.org/10.1093/nar/gky1077">https://doi.org/10.1093/nar/gky1077</a>). </li> <li><strong>dataset_with_noise_estimates: </strong>In this dataset we provide noise estimates for all promoters expressed above an expression threshold (mean GFP fluorescence at least as large as autofluorescence). Note that the noise estimate correspond to the difference between the promoter’s variance in log-expression and the minimal variance as a function of its mean expression (i.e. the so called noise floor was subtracted). Apart from the mean, variance, noise and promoter features (sequence, name of gene downstream, number of unique regulatory inputs and name of the TFs binding), we also include the parameters used for fitting the minimal noise, i.e. noise floor, in each of the conditions. </li> <li><strong>time_course_data_SI</strong>: This dataset contains mean and variance measurements of one of the plates of the library measured at different time points during growth in Minimal media 0.4M NaCl: 0h (just after dilution), 1h, 2h, 3h, 5h, 6.5h, 8.5h, 10h and 11h. </li> <li><strong>growth_curves_SI</strong>: Growth data (OD<sub>600</sub> as a function of time) for a subset of the promoters from the library across different growth conditions.</li> <li><strong>singlecell_areas_SI: </strong>Single-cell areas estimated using agar patches of cells growing in each condition. Each row of the table contains data for a single-cell. </li> <li><strong>synthetic_promoters_dataset: </strong>This dataset contains mean, variance and noise measurements of a set of constitutive promoters from <a href="https://doi.org/10.7554/eLife.05856.001">https://doi.org/10.7554/eLife.05856.001</a> across different conditions.</li> <li><strong>MARA_results:</strong> All transcription factor activities results explaining measured noise levels in each condition. This data has been obtained after performing Motif Activity Response Analysis on the noise levels of all measured promoters in each condition.</li> </ul>
Synthetic dataset of a full wavefield representing the propagation of Lamb waves and their interactions with delaminations
<p>The dataset contains 475 simulated cases of full wavefield of Lamb waves propagation in a plate made of carbon fibre reinforced plastic (CFRP). The simulated 475 cases represent delaminations with different locations, shapes, and sizes. The following random factors were simulated in each case:</p> <ul> <li>delamination geometrical size (ellipse minor and major axis randomly selected from the interval [10 mm, 40 mm],</li> <li>delamination angle (randomly selected from the interval [0◦ , 180◦]),</li> <li>coordinates of the centre of delamination (randomly selected from the interval [0 mm, 250 mm − δ] and [250 mm + δ, 500 mm], where δ = 10 mm).</li> </ul> <p>The guided waves were excited at the centre of the plate by applying equivalent piezoelectric forces. The excitation was in the form of toneburst sine signal modulated by the Hann window. The carrier frequency is assumed 50 kHz, and the modulation frequency is 10 kHz. The total wave propagation time was set to 0.75 ms so that the guided wave can propagate to plate edges and back to the actuator twice. The number of time integration steps was 150000 which was selected for the stability of the central difference scheme.</p> <p>The material is a typical cross-ply CFRP laminate. The stacking sequence [0/90]<sub>4</sub> was used in the model. The properties of a single-ply were as follows [GPa]: C<sub>11</sub> = 52.55, C<sub>12</sub> = 6.51, C<sub>22</sub> = 51.83, C<sub>44</sub> = 2.93, C<sub>55</sub> = 2.92, C<sub>66</sub> = 3.81. The assumed mass density was 1522.4 kg/m3. These properties were selected so that simulated numerically wave front patterns and wavelengths are similar to the wavefields measured by SLDV on CFRP specimens used later on for testing the developed methods for delamination identification. The shortest wavelength of propagating A0 Lamb wave mode was 21.2 mm for numerical simulations and 19.5 mm for experimental measurements.</p> <p><br> In each delamination case, 512 frames were generated to visualise the propagation of Lamb waves and their interactions and reflections from the delamination and edges.<br> The numerically generated dataset resembles the velocity measurements acquired by the scanning laser Doppler vibrometer (SLDV) at the bottom surface of the plate of dimensions 500×500 mm.</p> <p>The uploaded dataset contains two ZIP files:</p> <ol> <li> The first file contains 475 folders regarding all cases of different delaminations and their interaction with Lamb waves. In each case, there are 512 images in PNG format representing the propagation of guided waves.</li> <li> The second file contains: <ul> <li>475 images in PNG format representing the ground truth of the delaminations.</li> <li>CSV file contains all info regarding delaminations.</li> </ul> </li> </ol> <p> </p>
Synthetic recovery of impulse propagation in myocardial infarction via silicon carbide semiconductive nanowires
<p><strong> DataSet for the publication "Synthetic recovery of impulse propagation in myocardial infarction via silicon carbide semiconductive nanowires"</strong></p> <p>Pre-processed Confocal data for Figure 3 and Supplementary Figure 3. Acquired with Leica SP8 Laser-Scanning Confocal Microscope</p> <p>Pre-processed HPICM raw data for Figure 2, Supplementary Figure 2 Acquired with Ionscope Hopping Software</p> <p>Pre-processed double-patch clamp data for Figure 2b-c. Acquired with Clampfit 10.6</p> <p>Pre-processed EP raw data for Figure 6-7-8, supplementary Figure 4. Acquired with Clampfit 10.6</p> <p>Post-processed Kinematic trajectories for Supplementary Figure 5, Supplementary Figure 6 acquired with Video Spot Tracker V 8.00</p> <p> </p> <p> </p> <p> </p>
HDF5 datasets and python scripts to generate figures in "Butterfly distribution of relativistic electrons driven by parallel propagating lower band whistler chorus waves"
<p>HDF5 datasets and python scripts to generate figures in "Butterfly distribution of relativistic electrons driven by parallel propagating lower band whistler chorus waves"</p> <p>RBW simulation datasets in HDF5 format:</p> <ul> <li>300pT.h5 The particle dataset to generate the figures.</li> </ul> <p>Python scripts to generate figures in the manuscript.</p> <p>- Environment: Python 3.6.7 :: Anaconda 4.4.0 (64-bit)</p> <p>- Required modules: matplotlib, numpy, h5py</p> <ul> <li>Figure1.py Generate figure 1.</li> <li>Figure2.py Generate figure 2.</li> <li>Figure3.py Generate figure 3.</li> <li>Figure4.py Generate figure 4.</li> <li>QLDe.py Calculate bounce averaged diffusion coefficients according to Shprits et al. (2006) (doi: https://doi.org/10.1029/ 2006JA011725).</li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
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.