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Supplement to: Electron energy partition across interplanetary shocks
<p><strong>Quick Summary:</strong></p> <p>The three files herein comprise supplemental information and standalone datasets for a three-part study of <em>Electron energy partition across interplanetary shocks</em> that describe the modeling of solar wind electron velocity distribution functions (VDFs) near interplanetary shocks observed by the <em>Wind</em> spacecraft. Part I of the study (published in the <em>The Astrophysical Journal Supplement Series</em> on July 3, 2019 doi:10.3847/1538-4365/ab22bd) describes the methodology and how the two ASCII files (i.e., those stored here) were created and their contents. Part I also explains the nuances of the analysis, the limitations of the dataset, and how to use the data within the two ASCII files. Parts II and III (in preparation) present the statistical results and the detailed analysis of these results in the context of the dependence on relevant interplanetary shock parameters. Below are the descriptions of each data product starting with the PDF supplemental file to the three-part study and then the associated ASCII files. First we provide some background/definitions of jargon and terms used in each.</p> <p><strong>Solar Wind Electrons:</strong></p> <p>The solar wind electron VDF below ~1 keV is comprised of cold, dense core (subscript c or ec) population with thermal energies typically in the ~5-15 eV range, a hot, tenuous halo (subscript h or eh) population with thermal energies typically >20-30 eV, and an anti-sunward, field-aligned beam called the strahl or beam/strahl (subscript b or eb) population with thermal energies typically ~few 10s of eV. Most previous work modeled the core as a bi-Maxwellian and the halo and beam/strahl as bi-kappa VDFs. The work described in Part I (and the PDF supplement stored here) show that the core is more accurately described by a self-similar model VDF, which reduces to a bi-Maxwellian under appropriate conditions/limits and deviation from Maxwellian quantifies inelasticity in the plasma collisions. That is, if the plasma were controlled by elastic Coulomb particle-particle collisions (e.g., in the low corona or chromosphere or photosphere), the VDF would relax to a Maxwellian in the absence of other forces. When the plasma particles undergo inelastic collisions, the VDF profile changes from a Gaussian to something more like a "flattop" or box-like shape.</p> <p><strong>Wind Spacecraft:</strong></p> <p>The Wind spacecraft (<a href="http://wind.nasa.gov">https://wind.nasa.gov</a>) was launched on November 1, 1994 and currently orbits the first Lagrange point between the Earth and sun. It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, <strong>B</strong><sub>o</sub>. The instruments used in this study and these datasets are the fluxgate magnetometer (MFI), the radio receivers (WAVES), ion Faraday cups (SWE), and the electron and ion electrostatic analyzers (3DP). The MFI measures 3-vector <strong>B</strong><sub>o</sub> at ~11 samples per second (sps); the SWE measures reduced VDFs of the thermal proton and alpha-particle populations from which velocity moments are derived and used herein; WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density; and 3DP observes full 4π steradian VDFs of electrons and ions from a few eV to ~30 keV which provide both ion velocity moments and the electron VDFs modeled herein.</p> <p><strong>PDF Supplement Description:</strong></p> <p>The PDF document contains descriptions and definitions of relevant interplanetary shock parameters and shock analysis techniques used by the Harvard Smithsonian Center for Astrophysics' Wind shock database at <a href="https://www.cfa.harvard.edu/shocks/wi_data/">https://www.cfa.harvard.edu/shocks/wi_data/</a>. It describes the details of the symbols/parameters used on the database website and their translation to plasma parameters or shock parameters. The PDF also defines the shock normal finding techniques listed as two-four character inputs on the database website. The PDF file lists the shocks analyzed and their relevant parameters in two tables, with the second listing the relevant critical Mach numbers. Next the PDF provides some extra statistics of the analysis performed in the three-part study on <em>Electron energy partition across interplanetary shocks</em> in the form of histograms comparing differences for different selection criteria (e.g., low versus high Mach number shocks). Finally, there are detailed descriptions and definitions of the model functions used to fit to the solar wind electron VDFs.</p> <p>Both ASCII files have detailed headers outlining and defining the parameters contained therein. They also provide column headings where the labels/names of each are defined and/or described in the header. The headers also provide links to the analysis software used to perform the model fits to the VDFs. We will first describe the contents of the file labeled Wind_ip_shock_3dp_fit_constraints_electrons.txt (FCONSTS for brevity) and then the file labeled Wind_ip_shock_3dp_fit_results_electrons.txt (FRESULTS for brevity). Below use the following definitions:</p> <ul> <li><span class="math-tex">\(N_{s}\)</span> = number density of species <em>s</em> [cm-3] (s = ec for core, eh for halo, eb for beam/strahl, p for proton, etc.)</li> <li><span class="math-tex">\(B_{o, j}\)</span>= j<sup>th</sup> component (GSE coordinate basis) of quasi-static magnetic field vector [nT]</li> <li><span class="math-tex">\(V_{Ts, j}\)</span> = j<sup>th</sup> component (relative to <strong>B</strong><sub>o</sub>) of thermal speed of species <em>s</em> [km/s] <ul> <li><span class="math-tex">\(V_{Ts,j} = \sqrt{{2 k_{B} T_{s,j} \over m_{s}}}\)</span>, where <span class="math-tex">\(T_{s, j}\)</span> is the j<sup>th</sup> component (relative to <strong>B</strong><sub>o</sub>) of the temperature of species <em>s</em> [eV]</li> </ul> </li> <li><span class="math-tex">\(V_{os, j}\)</span> = j<sup>th</sup> component (relative to <strong>B</strong><sub>o</sub>) of drift speed of species <em>s</em> [km/s] in ion rest frame</li> <li><span class="math-tex">\(V_{s, j}\)</span> = j<sup>th</sup> component (GSE coordinate basis) bulk velocity of species <em>s</em> [km/s] in spacecraft frame</li> <li><span class="math-tex">\(T_{s, tot} = {1 \over 3} (T_{s, \parallel} + 2 \ T_{s, \perp})\)</span>, where <span class="math-tex">\(\parallel(\perp)\)</span> is the parallel(perpendicular) component relative to <strong>B</strong><sub>o</sub></li> <li><span class="math-tex">\(s_{es}\)</span> = exponent for the symmetric self-similar model VDF of species <em>s</em></li> <li><span class="math-tex">\(\kappa_{es}\)</span> = kappa value for the bi-kappa VDF of species <em>s</em></li> <li><span class="math-tex">\(p_{es}(q_{es})\)</span> = parallel(perpendicular) exponent for the asymmetric self-similar model VDF of species <em>s</em></li> <li><span class="math-tex">\(\chi_{s}^{2}\)</span> = least chi-squared of fit to species <em>s</em></li> <li><span class="math-tex">\(\phi_{sc}\)</span> = spacecraft electric potential [eV]</li> <li><span class="math-tex">\(\delta R = \lvert 1 - Median(f^{data}/f^{model}) \rvert\)</span> = excess median deviation of fit [%]</li> </ul> <p><strong>FCONSTS File Description:</strong></p> <p>The FCONSTS file contains all the pertinent information used during the fit process for all VDFs that were analyzed including the fit results. The columns are organized by electron component from core to halo to beam/strahl, in that order, sorted by the time stamp (UTC) of the observed VDF (very first column). The first column in each set of electron component groups is a numerical indicator of the fit status for that component of the i<sup>th</sup> VDF. This is followed by 30 columns consisting of 5 sets of 6 numbers. Each model function has six fit parameters: <span class="math-tex">\(N_{s}\)</span> [0], <span class="math-tex">\(V_{Ts, \parallel}\)</span> [1], <span class="math-tex">\(V_{Ts, \perp}\)</span> [2], <span class="math-tex">\(V_{os, \parallel}\)</span> [3], <span class="math-tex">\(V_{os, \perp}\)</span> [4] (or <span class="math-tex">\(p_{es}\)</span> for asymmetric self-similar model VDF), and exponent of fit (i.e., <span class="math-tex">\(s_{es}\)</span>, <span class="math-tex">\(\kappa_{es}\)</span>, or <span class="math-tex">\(q_{es}\)</span>). Thus, there are six columns for each of the following for each of the three components (i.e., 18 columns for each of the following in total): initial guess values, returned fit values, lower limit constraints, upper limit constraints, and a logical value indicating whether the i<sup>th</sup> fit value sits on the lower (-1) or upper (+1) limit or neither (0). These columns are followed by four more containing the number of iterations necessary to find the fit values, the least chi-squared value of the fit, the degrees of freedom in the fit process, and a two-letter designator of the model fit function used (defined in the ASCII file header).</p> <p><strong>FRESULTS File Description:</strong></p> <p>The FRESULTS file contains the fit results used in the three-part study. Again, the first column starts each row with the time stamp (UTC) of the observed VDF. In the following, all parameters listed with subscript <em>j</em> will correspond to three columns (one for each component) except the drift velocities which only have two for <span class="math-tex">\(\parallel(\perp)\)</span>. That is followed by: <span class="math-tex">\(N_{p}\)</span> (SWE), <span class="math-tex">\(N_{\alpha}\)</span> (SWE), <span class="math-tex">\(N_{i}\)</span> (3DP), <span class="math-tex">\(T_{p, j}\)</span> (SWE), <span class="math-tex">\(T_{\alpha, j}\)</span> (SWE), <span class="math-tex">\(T_{i, j}\)</span> (3DP), <span class="math-tex">\(B_{o, j}\)</span> (MFI), <span class="math-tex">\(V_{p, j}\)</span> (SWE), <span class="math-tex">\(V_{\alpha, j}\)</span> (SWE), <span class="math-tex">\(V_{i, j}\)</span> (3DP), <span class="math-tex">\(\phi_{sc}\)</span> (multiple instruments), <span class="math-tex">\(\delta R\)</span> (3DP), <span class="math-tex">\(N_{ec}\)</span> (fit), <span class="math-tex">\(T_{ec, j}\)</span> (fit), <span class="math-tex">\(V_{oec, j}\)</span> (fit), <span class="math-tex">\(\kappa_{ec}\)</span> (fit), <span class="math-tex">\(s_{es}\)</span> (fit), <span class="math-tex">\(p_{es}\)</span> (fit), <span class="math-tex">\(q_{es}\)</span> (fit), reduced <span class="math-tex">\(\chi_{ec}^{2}\)</span> (fit), core fit status, and repeats for the halo and beam/strahl fits. The last four columns contain, in the following order, the total reduced chi-squared of the model fit of all components combined and fit flags (0 = worst, 10 = best) for each electron component. Note that all possible exponents are provided for each component but only the one that is not set as a fill value corresponds to the functional form used to model that electron component (e.g., if <span class="math-tex">\(s_{ec}\)</span> is the only non-fill exponent for the core, then the core was modeled as a symmetric self-similar VDF).</p>
LMU Fast Decompression Experiment Data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This data is camera images and nozzle pressure gauge voltage traces from rapid decompression shots at the LMU shock tube facility.</p> <p>This data is discussed in the "Materials and Methods" section of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The prefixes of the filenames correspond to the shot dates and times listed in table S1 of the paper. </p> <p>The "_camera.zip" files contains tiff images of the camera frames. The ".ixc" file in each zip lists camera settings in plain text.</p> <p>The ".dat" file contains the voltage measurement of the nozzle pressure gauge. Row 1 is the header, row 2 is the time in seconds, and row 3 is the voltage of the pressure gauge in Volts. The peak pressure in the header can be used to relate the voltage to pressure.</p>
Compressible Hydrodynamics Simulation Data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This data is a 2D cross-section from a 3D compressible hydrodynamics simulation (Hyburn / AMRex code) of a rapid decompression / shock tube experiment at Special Technologies Laboratory. The simulated shot is a pure argon gas decompression from 1000Psi to atmosphere. </p> <p>This data is used in figures 3 and 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The data is saved as python numpy zipped archives numbered by the timestep in the simulation. Files starting with 'tube' contain data from inside the shock tube. Files starting with 'near_vent' contain data from the expansion chamber above the nozzle. All units are in SI.</p> <p>Each .npz file is an array file generated with python numpy.savez(). It can be opened with:</p> <p><em>import numpy as np</em></p> <p><em>data = np.load('<name>.npz')</em></p> <p>The data is an python dictionary. The dictionary keys can be displayed with:</p> <p><em>print(data.files)</em></p> <p>The numpy arrays can be accessed by keyname:</p> <p><em>print(data['keyname'])</em></p> <p>The key names correspond to physical quantities (density, temperature, etc.). All particle quantities are 0 as the simulation did not include particles.</p>
Predicted times of bow Shock crossings at Venus from the ESA/Venus Express mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br> Planet: <strong>Venus</strong><br> Radius: <strong>R<sub>V</sub> = 6051.8 km</strong> (volumetric mean planetary radius)<br> Spacecraft: <strong>ESA/Venus Express</strong><br> Spacecraft coordinates system: <strong>Venus Solar Orbital (VSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>VSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>VSO</sub></em> towards Venus’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>VSO</sub></em>–<em>Y<sub>VSO</sub></em> plane passing through the centre of Venus,</li> <li><em>Y<sub>VSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/04/2006 to 25/11/2014</strong><br> Total number N of candidate bow shock crossings in the database: <strong>N = 4950</strong><br> Number of quasi-parallel bow shock crossings: <strong>N<sub>||</sub> = 844</strong><br> Number of quasi-perpendicular bow shock crossings: <strong>N<sub><span class="math-tex">\(\perp\)</span></sub> = 4106</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br> The original Venus Express/MAG data repository on which these algorithms were applied is available on ESA's Planetary Science Archive system (PSA) at: https://archives.esac.esa.int/psa/ftp/VENUS-EXPRESS/MAG/. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br> To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described for the Mars case in:<br> Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2021), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, e2021JA029942. <a href="https://doi.org/10.1029/2021JA029942">https://doi.org/10.1029/2021JA029942</a></p> <p>They consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on 2D or 3D existing fits for prediction of the Venus bow shock position. The original fits were taken from 2D conic fits in the plane <span class="math-tex">\(\left(X_\text{VSO}, \sqrt{Y_\text{VSO}^2+Z_\text{VSO}^2}\right)\)</span>performed on the datasets of <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>).</li> <li>Corrector algorithm based on magnetic field measurements.</li> </ol> <p>We also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted <span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub></span> (ThetaBn). Assuming a locally smooth shock surface, this gives a first indication of the geometry of the shock, so that:</p> <ul> <li><span class="math-tex">45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup></span>: quasi-perpendicular shock condition</li> <li><span class="math-tex"><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub><span class="math-tex">\(\geq\)</span>135<sup>∘</sup></span>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>For details, see <strong>Simon Wedlund et al. (2022)</strong> above, §2.3 pp. 10-12.</p> <p><strong>VARIABLES DESCRIPTION</strong></p> <p>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in Venus Express' database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Venus Solar Orbital coordinates of the shock, in units of Venus radius <em>R</em><sub>V </sub>(<em>R</em><sub>V</sub> = 6051.8 km):<br> <em>X<sub>VSO</sub></em>,<sub> </sub><em>Y<sub>VSO</sub></em>, <em>Z<sub>VSO</sub></em> and Euclidean distance <span class="math-tex">\(R_{VSO} = \sqrt{X_{VSO}^2 + Y_{VSO}^2 + Z_{VSO}^2}\)</span> (in <em>R<sub>V</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = <span class="math-tex">\(\tan^{-1}{Y_{VSO}^2+Z_{VSO}^2 \over X_{VSO}^2}\)</span> (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface <span class="math-tex">\(\theta_{Bn}\)</span> (ThetaBn, in º, calculated with atan2(norm(cross(<strong>B</strong>,<strong>ñ</strong>),dot(<strong>B</strong>,<strong>ñ</strong>)), with <strong>B</strong> the magnetic field vector and <strong>ñ</strong> the vector normal to the shock surface): <ul> <li>45 < ThetaBn < 135 deg: quasi-<span class="math-tex">\(\perp\)</span> shock</li> <li>ThetaBn <span class="math-tex">\(\leq\)</span> 45 deg & ThetaBn <span class="math-tex">\(\geq\)</span> 135 deg: quasi-|| shock</li> </ul> </li> <li>Interplanetary Magnetic Field (IMF) upstream average vector in VSO coordinates, <em>B<sub>x</sub></em>, <em>B<sub>y</sub></em>, <em>B<sub>z</sub></em> (in nT).</li> <li>Flag for direction of crossing: <ul> <li>flag = 0: magnetosheath <span class="math-tex">\(\longrightarrow\)</span> solar wind (2447 events)</li> <li>flag = 1: solar wind <span class="math-tex">\(\longrightarrow\)</span> magnetosheath (2503 events)</li> </ul> </li> </ul> <p><strong>WARNING</strong></p> <ol> <li>This version of the database is currently in a preliminary stage of application and, as such, is not fully tested. Solar wind upstream magnetic field values (IMF) are given only as a first approximation for each orbit segment. See point 2 for caveats. For carefully manually picked shock crossings, the user is referred to the database of:<br> <strong>Persson et al. (2023)</strong>, Venusian bow shock crossings manually identified from measurements by the ASPERA-4 and MAG instruments onboard Venus Express, <em>Zenodo</em> (<a href="http://doi.org/10.5281/zenodo.7679677">https://doi.org/10.5281/zenodo.7679677</a>)</li> <li>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic fields. This is aimed at giving a first approximation of the shock area times in the Venus Express data. It is particularly suited to statistical studies and region identification in the Venus Express datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.040 R<sub>V</sub> (with R<sub>V</sub> = 6051.8 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</li> </ol> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br> C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. </p> <p><strong>LICENSE AND RIGHTS</strong><br> This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences, 2022-10-05<br> Contact email: cyril.simon.wedlund@gmail.com</p>
Ionization rate simulation data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This is a set of 3d data containing ionization rates computed from Hyburn hydrodynamic simulations contained in a Matlab .mat file, along with a plot in both .png and Matlab .fig format, and a Matlab script for plotting.</p> <p>This data is used in figure 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The .mat file can be opened in Matlab to examine data. The 3d arrays contained therein can be viewed in various ways, including using the enclosed script with syntax like plot_isosurfaces(xg,yg,zg,density,max(density(:)),pressure,max(pressure(:))) to produce the included isosurface plot.</p> <p>The data arrays contained are:</p> <p>e: electric field magnitude</p> <p>alpha: ionization rate lengths: ionization lengths (equal to 1/alpha)</p> <p>eOverN: electric field divided by gas number density</p> <p>alphaOverN: ionization rate divided by gas number</p> <p>density density: gas mass density</p> <p>pressure: gas pressure</p> <p>x,y,z: spatial coordinates</p> <p>xg,yg,zg: spatial coordinates in 3d meshgrid format, for Matlab plotting</p> <p>The electric field e was artificially generated from velocities in Hyburn output; alpha was computed from BOLSIG+ with Hyburn input; density and pressure data were from Hyburn.</p> <p> </p>
Shock Ramp Compressions Measurements of Iron on the Sandia National Laboratories' Z-Machine
<p>This data contains 1) the apparent velocity data from Velocity Interferometer System for Any Reflector (VISAR) data analyzed using the PointVISAR program for experiments Z3155 and Z3339 and 2) the equation of state results from analyzing the velocity data using a backward integration -- forward Lagrangian analysis.<br> These experiments were performed on the Sandia National Laboratories' Z-Machine, where the iron samples were dynamically compressed via shocked compression to approximately 275 Gpa and further ramp compression to approximately 400 GPa. This covers pressure-temperature regions near the melt line as well as the interior conditions of terrestrial planets.<br> The Z3155 data include four samples, each with two VISAR traces, and the Z3339 data include six samples, each with two or three VISAR traces.<br> The apparent velocity can be corrected to true velocity using the latest lithium fluoride window correction for a 532 nm wavelength.<br> PointVISAR is available as part of the Sandia Matlab AnalysiS Hierarchy (SMASH) toolbox.<br> Details of the backward integration -- forward Lagrangian anaylsis that was used can be found in the related publication.</p> <p>Example data file interpretation: "Z3155_north_panel_bot_sample_01.txt" is the first VISAR trace from the bottom sample of the north panel on experiment Z3155.<br> "Z3155_EoS_combined.txt" is the sample-averaged Equation of State result from experiment Z3155.</p> <p>Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2020-13961 O</p> <p> </p>
Data products and software for `X-ray diagnostics of Cassiopeia A's "Green Monster": evidence for dense shocked circumstellar plasma`
<div> <h2>Data Reproduction Package for the publication ‘X-ray diagnostics of Cassiopeia A’s “Green Monster”: evidence for dense shocked circumstellar plasma’</h2> </div> <div> <h3>Authors: Jacco Vink, Manan Agarwal, Patrick Slane, Ilse De Looze, Dan Milisavljevic, Daniel Patnaude, and Tea Temim.</h3> </div> <div> <h3>Link to paper: <a href="https://doi.org/10.3847/2041-8213/ad2fc5">https://doi.org/10.3847/2041-8213/ad2fc5</a> </h3> <p> </p> </div> <div> <h4>This package was prepared by Jacco Vink and Manan Agarwal (University of Amsterdam)</h4> </div> <div> <h3>Summary</h3> </div> <div> <p>This data reproduction package contains the data files in FITS format used to<br>generate the figures in the paper. The data files concern the revised manuscript, which incorporates changes made in response to the journal’s referee report.</p> </div> <div> <p>The paper is based on Chandra X-ray Observatory (CXO) data of Cassiopeia A taken in 2004. The raw archival data used, maintained by the Chandra Data Archive, can be retrieved using the following DOI link: <a href="https://doi.org/10.25574/cdc.209">https://doi.org/10.25574/cdc.209</a>.</p> </div> <div> <p>Additional James Webb Space Telescope (JWST) data are stored at the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. The data used in the paper can be downloaded through DOI link <a href="https://doi.org/10.17909/szf2-bg42">https://doi.org/10.17909/szf2-bg42</a>.</p> </div> <div> <p>The data produced from the above raw data are stored in the files:</p> </div> <div> <ul> <li>green_monster_image_data.tar.gz</li> <li>spectral_files_and_models.tar.gz</li> <li>imaging_and_pca_code.tar.gz</li> <li>green_monster_pca_input_output.tar.gz</li> </ul> <p>The repository contains JWST/MIRI mosaics of Cassiopeia A which are described in detail in the paper "A JWST Survey of the Supernova Remnant Cassiopeia A", by D. Milisavljevic, T. Temim, I. De Looze, et al.; see https://arxiv.org/abs/2401.02477, to be published in ApJ letters.<br> </p> </div>
SHOCK Trial - Early Revascularization in Acute Myocardial Infarction Complicated by Cardiogenic Shock
<p>The leading cause of death in patients hospitalized for acute myocardial infarction is cardiogenic shock. We conducted a randomized trial to evaluate early revascularization in patients with cardiogenic shock. In patients with cardiogenic shock, emergency revascularization did not significantly reduce overall mortality at 30 days. However, after six months there was a significant survival benefit. Our conclusion was that early revascularization should be strongly considered for patients with acute myocardial infarction complicated by cardiogenic shock. We are making original data that was collected for this trial available here for further analysis.</p>
Predicted times, spatial coordinates of bow shock crossings and shock geometry at Mars from the NASA/MAVEN mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br>Planet: <strong>Mars</strong><br>Radius: <strong>R<sub>M</sub> = 3389.5 km</strong> (volumetric mean planetary radius)<br>Spacecraft: <strong>NASA/Mars Atmosphere and Volatile Evolution (MAVEN)</strong><br>Spacecraft coordinates system: <strong>Mars Solar Orbital (MSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>MSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>MSO</sub></em> towards Mars’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>MSO</sub></em>–<em>Y<sub>MSO</sub></em> plane passing through the centre of Mars,</li> <li><em>Y<sub>MSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/11/2014 to 30/04/2024</strong> (Mars Years MY32 to MY36 included, part of MY37).<br>Total number N of candidate bow shock crossings in the database: <strong>N = 20107</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br>The original MAVEN/MAG data repository on which these algorithms were applied is available on NASA's Planetary Data System (PDS) at <a href="https://doi.org/10.17189/1414178">https://doi.org/10.17189/1414178</a>. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br>To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described in:<br>Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2022), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, 1-33, e2021JA029942, <a href="https://doi. org/10.1029/2021JA029942">https://doi. org/10.1029/2021JA029942</a>. </p> <p>Also available at: <a href="https://doi.org/10.1002/essoar.10507942.1">https://doi.org/10.1002/essoar.10507942.1 </a> and as arXiv e-print: <a href="https://doi.org/10.48550/arXiv.2109.04366">https://doi.org/10.48550/arXiv.2109.04366</a></p> <p>These algorithms consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on J. Gruesbeck's 3D model (<a href="https://doi.org/10.1029/2018JA025366">Gruesbeck et al. 2018</a>) for prediction of Mars bow shock position</li> <li>Corrector algorithm based on magnetic field measurements (magnitude and fluctuations).</li> </ol> <p><strong>REMARK ON VERSIONS</strong><br>From Version 3 onwards, we also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted \(\theta_{Bn}\)(ThetaBn). Assuming a smooth shock surface and the 3D model of Gruesbeck et al. (2018, all points), this gives a first indication of the geometry of the shock, so that:</p> <ul> <li>45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup>: quasi-perpendicular shock condition</li> <li><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub>≥135<sup>∘</sup>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>From Version 4 onwards, we also added the solar longitude Ls (in degrees).</p> <p>For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. Note that due to minor adjustments in the code, some of the ThetaBn angles calculated here for the examples of Fig. 6 in Simon Wedlund et al. (2022) may slightly differ from the values quoted in the paper.</p> <p><strong>VARIABLES DESCRIPTION</strong><br>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in MAVEN's database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Mars Solar Orbital coordinates of the shock, in units of Mars radius <em>R</em><sub><em>M</em> </sub>(<em>R<sub>M</sub></em> = 3389.5 km):<br><em>X<sub>MSO</sub></em>,<sub> </sub><em>Y<sub>MSO</sub></em>, <em>Z<sub>MSO</sub></em> and Euclidean distance \(R_{MSO} = \sqrt{X_{MSO}^2 + Y_{MSO}^2 + Z_{MSO}^2}\) (in <em>R<sub>M</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = \(\tan^{-1}{Y_{MSO}^2+Z_{MSO}^2 \over X_{MSO}^2}\) (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface \(\theta_{Bn}\) (ThetaBn, in º) <ul> <li>45 < ThetaBn < 135 deg: quasi-⊥ shock</li> <li>ThetaBn ≤45 deg & ThetaBn ≥ 135 deg: quasi-|| shock</li> </ul> </li> <li>Solar longitude Ls, in degrees.</li> <li>Flag for crossing: <ul> <li>sheath \(\longrightarrow\) solar wind, flag = 0.</li> <li>solar wind \(\longrightarrow\) sheath, flag = 1.</li> </ul> </li> </ul> <p><strong>WARNING</strong><br>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic field. It is aimed at giving a first approximation of the shock area times in the MAVEN data. It is particularly suited to statistical studies and region identification in the MAVEN datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma suite bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.075 R<sub>M</sub> (with R<sub>M</sub> = 3389.5 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</p> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br>C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. C. Möstl thanks the Austrian Science Fund FWF projects P31659-N27, P31521-N27. A. Beth thanks the Swedish National Space Agency (SNSA) and its support with the grant 108/18. This database was notably used to add to the Helio4Cast database which monitors solar wind parameters in the solar system (<a href="https://doi.org/10.6084/m9.figshare.6356420">https://doi.org/10.6084/m9.figshare.6356420</a>). Helio4Cast is available at <a href="http://www.helioforecast.space/icmecat">www.helioforecast.space/icmeca</a>t and <a href="http://www.helioforecast.space/sircat">www.helioforecast.space/sircat</a>. </p> <p><strong>LICENSE AND RIGHTS</strong><br>This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences (ÖAW), 2021-09-08<br>Version 2 (c) CSW @ ÖAW/IWF, 2021-11-30 -- Addition of R_MSO and SZA<br>Version 3 (c) CSW @ ÖAW/IWF, 2022-02-09 -- Addition of ThetaBn<br>Version 4 (c) CSW @ ÖAW/IWF, 2025-03-20 -- Addition of Ls, Bx, By, Bz and Bt.</p> <p> </p> <p><br>Contact email: cyril.simon.wedlund@gmail.com</p>
Interplanetary shock data base
<p>This interplanetary shock data base was compiled with Wind, Advanced Composition Explorer (ACE), and Deep Space Climate Observatory (DSCOVR) observations collected at the Lagrangian point L1. The list ranges from January 1995 to December 2024 with 650 events. Many shock parameters are included, such as shock impact angle, shock speed, Mach numbers, compression ratios, and IMF (interplanetary magnetic field) Bz in the upstream and downstream regions. The list also brings geomagnetic activity information such as minimum SMR values in a time interval of 2 hours after shock impact. The author intends to update this list annually.</p> <p>There are three files: (i) full_shock_list_2024.txt, a text file with 650 events; (ii) full_shock_params.cdf, a cdf file with detailed information about each specific shock events; and (iii) read_shock.py, a file that contains a short python routine to read information about a specific shock event. The SpacePy package (<a href="https://spacepy.github.io/spacepy.html#:~:text=SpacePy%3A%20Space%20Science%20Tools%20for,at%20the%20space%20science%20community.">https://spacepy.github.io/spacepy.html#:~:text=SpacePy%3A%20Space%20Science%20Tools%20for,at%20the%20space%20science%20community.</a>) is required to extract shock information from the cdf file.</p> <p>Example (shock number 142, 26 June 2000):</p> <p>from read_shock import read_shock_cdf</p> <p>read_shock_cdf(142)</p> <p>------------------------------------------------------------------------------------<br>sn date UTS UTM<br><a href="tel:142 2000 06 23 1226">142 2000 06 23 1226</a> 1226<br>Spacecraft is ac<br>Position: X = 239.9 Re; Y = 36.7 Re; Z = -0.7 Re</p> <p>Time windows<br>Upstream: 5 to 10 minutes before shock<br>Downstream: 5 to 10 minutes after shock</p> <p>Solar wind plasma and IMF<br> Bx By Bz Vx Vy Vz N T<br>Upstream 5.448 -3.946 -4.568 -399.958 18.948 -14.969 6.941 99731.6<br>Downstream 14.317 -1.766 -16.125 -508.734 37.628 -117.578 17.429 224496.1</p> <p>Computed parameters<br> dp1 dp2 Xdp Xb Xn vs_rh vA cs<br>1.864 7.989 4.286 2.661 2.511 604.778 67.317 52.384</p> <p>Minimum SMR index in the 2-hour window following shock impact: -9.60 nT</p> <p> nx ny nz thxn phiyn thbn vs vfms Ma Ms<br>MC -0.323 -0.944 0.070 108.831 175.779 78.309 127.332 85.704 0.255 0.200<br>MX1 -0.796 0.191 -0.575 142.729 -71.590 72.348 578.268 86.195 3.681 2.874<br>MX2 -0.798 0.133 -0.587 142.962 -77.223 74.365 579.175 86.010 3.693 2.890<br>MX3 -0.798 0.107 -0.592 142.980 -79.739 75.273 578.920 85.933 3.694 2.894<br>VC -0.722 0.124 -0.681 136.205 -79.682 80.717 551.670 85.556 3.720 2.927</p> <p>Type cdf['SHOCK'].attrs for a description of all shock variables and parameters.</p> <p>More details about this list and methods for shock normal calculations can be found in:</p> <p>Oliveira, D. M. (2023). Interplanetary Shock Data Base. Frontiers in Astronomy and Space Science. (Under review) </p>
Martian shock crossings dataset
<p>Lists of martian bow shock crossings with the corresponding extrapolated terminator altitudes (RTD). DATA_MAVEN.txt includes 3837 shock crossings by the Mars Atmosphere and Volatile EvolutioN spacecraft from November 2014 to April 2017 published by Fang et al. (2017) and Gruesbeck et al. (2018). DATA_MEX.txt includes 11820 shock crossings by the Mars express spacecraft published by Hall et al. (2016) and Sanchez-Cano et al. (2019). DATA_MGS.txt includes 544 bow shock crossings from the Mars Global Surveyor spacecraft from September 1997 to September 1998.</p> <p>Each files the following columns (with UTC time) : Year Month Day Hour Minutes Seconds RTD</p>
Example input files and output data for 1D hydrodynamic simulations of shock compressed iron
<p>Example input files and output data for 1D hydrodynamic simulations of shock compressed iron. Input files consists of 3 examples from the SIMEX github wiki page for a 50 micron CH ablator with 5 micro Fe foil (laser pulse is a 6 ns flat top pulse, 1064 nm with 0.3 TW/cm<sup>2</sup>). Output data are from Esther hydrocode in .txt format and the SIMEX opmd.h5 format.</p>
Photographic record of land degradation and resilience in Dogu'a Tembien after the shock of the Tigray war (northern Ethiopia)
<p><span>Following two years of combat, blockade, and power outage, the Tigray war in northern Ethiopia has had a substantial negative impact on the environment (2020–2022). This photographic dataset, part of a rare study carried out by the same research team before and after a war, compares 26-year legacy data on land degradation, with post-war observations at 56 sites in the Dogu'a Tembien district of Tigray (13°39'N, 39°30'E), at elevations ranging from 1600 to 2800 meters.</span></p> <p><span>With 30 years of environmental research experience in Tigray, we remained as a lone research team after the start of the war and collected ground data at previous research sites during the war. This culminated in international partners returning to the Dogu'a Tembien district in 2023 after they had been absent for four years due to coronavirus restrictions and the Tigray War. We visited 56 previously investigated sites—which have been documented in 45 prior publications—through transect walks, where we mostly made qualitative observations and discussions regarding the processes of land degradation and recovery. This included degradation processes like sheet and rill erosion</span><span>, gully erosion</span><span>, landslides</span><span></span><span>, deforestation</span><span>, as well as the most common rehabilitation approaches, i.e. stone bunds</span><span>, check dams</span><span>, exclosures</span><span>, improved hydrological cycle</span><span>, and integrated catchment management</span><span>. Local farmers and other village residents, along with experts who either reside in or have a good understanding of the research area, participated in the group observations.</span></p>
Climate induced economic shocks using CLIMRISK
<p>Various risk measures of climate induced economic shocks using CLIMRISK. Metrics include year of exceeding 1 billion in climate damages, year of exceeding 5% annual GDP lost and a multivariate risk index (1 billion, 5% GDP and 3 degrees temperature exceedance).</p>
Supply Chain Shocks due to extreme weather events
<p>Projected supply chain shocks due to extreme weather events measured in annual percentage change in a country-sector's export activity compared to the baseline period</p>
Lists of magnetopause and bow shock crossings, as measured by Juno/Waves and Juno/MAG.
<p>Lists of magnetopause (boundary_crossings_caracteristics_MP.pdf, boundary_crossings_caracteristics_MP.csv) and bow shock crossings (boundary_crossings_caracteristics_BS.pdf, boundary_crossings_caracteristics_BS.csv), as measured by Juno/Waves and Juno/MAG (see below to download the files).</p> <p><br> For each crossing, number of the crossing (MP#), day of the year (DOY), date (year/month/day format) and time (hours:minutes format) are indicated, as well as the boundary crossed (magnetopause in this case), the direction of the crossing (in: from the magnetosphere to the magnetosheath; out: from the magnetosheath to the magnetosphere). The column “Notes” indicates whether the magnetopause has potentially not been completely crossed (see Fig. 1c of the article). Position of the crossings are given in the Cartesian JSS (Jupiter-­‐De-­Spun-­Sun) and IAU (International Astronomical Union) coordinate systems, and IAU spherical coordinates system. Finally, the dynamic pressure of the solar wind, and the standoff distance of the magnetopause and the bow shock, derived from the model of Joy et al. (2002), are given in the last three columns.</p> <p>header = ['#', "Day of Year", "Date (year/month/day)", "Time (HH:MM)", "Boundary", "In/Out", "Notes", "x (JSS)", "y (JSS)", "z (JSS)", "x (IAU)", "y(IAU)", "z (IAU)", "r (IAU)", "theta (IAU)", "phi (IAU)", "Dynamic Pressure (nPa)", "Magnetopause Standoff Distance (Jovian radius)", "Bow Shock Standoff Distance (Jovian radius)"]</p> <p>A python function to read the lists is provided (read_boundary_crossings_list.py).</p> <p>Example:</p> <pre><code class="language-python">from read_boundary_crossings_list import * (header, indice, date, boundary, direction_crossing, notes, xyz_jss, xyz_iau, rtp_iau, pdyn, standoff_dist_mp, standoff_dist_bs) = read_boundary_crossings_list("boundary_crossings_caracteristics_MP.csv")</code></pre> <p> </p> <p>This dataset is linked to the following publication: Louis, C. K., Jackman, C. M., Hospodarsky, G., O’Kane Hackett, A., Devon-Hurley, E., Zarka, P., et al. (2023). Effect of a magnetospheric compression on Jovian radio emissions: In situ case study using Juno data. Journal of Geophysical Research: Space Physics, 128, e2022JA031155. <a href="https://doi.org/10.1029/2022JA031155">https://doi.org/10.1029/2022JA031155</a></p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Elastic Wave Simulations (Open Scanning Paths)
<p><strong>Elastic Wave Simulations - Open Scanning Paths</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" <a href="http://www.science.org/doi/10.1126/sciadv.adf2037">(Reardon et al., 2023)</a>. If you use these simulated data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>) and the software package k-Wave (DOI: 10.1109/ULTSYM.2014.0037).</p> <p>This dataset contains the normal shear surface velocity in a cylindrical slab of tissue-like material excited by an acoustic source with a Gaussian spatial profile simulated via a pseudo-spectral numerical method. The data is provided as .mat files. The files are separated by the type of scanning path, the scanning speed of the acoustic source, and the parameters of the scanning path. Details of the simulation parameters can be found in our publication.</p> <p><strong>Line Paths</strong> - The acoustic source scanned along a linear trajectory at speeds ranging from 2 m/s to 12 m/s (scanning speed is indicated in the filename).</p> <p><strong>Zigzag Paths</strong> - The acoustic source scanned along a zigzag path on the surface of the simulated medium with x-axis scanning speed <em>v<sub>x</sub></em> = 3, 4, 5, 6 m/s. At all speeds, the ultrasound focus was modulated transverse to its primary motion direction at a speed, <em>v<sub>y</sub></em>, of +-2.5 m/s yielding a zigzag path (2 cm path width). The x-axis scanning speed is designated in the filename.</p> <p><strong>Letter Paths</strong> - The acoustic source scanned the a trajectory in the shape of the letter "Z." Scanning speeds ranged from 2 m/s to 12 m/s (scanning speed is designated in the filename).</p> <p><strong>Focus Control Rate</strong> - The acoustic source scanned along a linear trajectory at 7 m/s but at different focus control sample rates <em>f<sub>c</sub></em>. These paths amount to a courser sampling of the linear trajectory. In lieu of updating the location of the acoustic source at each timepoint in the simulation, we specified a rate at which the location of the acoustic source would be updated. We set <em>f<sub>c</sub></em> to approximately 0.7, 1.4, and 4.2 kHz (designated at the end of the filename as VeryCoarse, Coarse, and Fine, respectively). (Compare with Line_07, which has the finest path sampling and an *f<sub>c</sub>* of approximately 200 kHz.)</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>surfaceData</strong> (NxNxM) - 3D array containing the normal shear velocity of the simulated medium (in m/s) on a NxN Cartesian grid of locations at M timepoints. The simulated tissue medium was cylindrical, so locations outside the circular top surface are NaN.</p> <p><strong>sourceSignals</strong> (NxNxQ) - 3D array containing the acoustic source distribution on the NxN Cartesian grid of locations used to excite the surface of the simulated tissue medium for Q timepoints.</p> <p><strong>sourceEnvelope</strong> (Qx1) - Vector containing the amplitude envelope that was applied to sourceSignals at each timestep Q</p> <p><strong>dt</strong> - The time between adjacent timepoints in seconds (i.e. fs = 1/dt)</p> <p><strong>dx/dy</strong> - The distance between adjacent grid locations in the x/y direction of the Cartesian grid (in m)</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound
<p>This repository contains links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these data please cite our publication found here: <a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>.</p> <p> </p> <p><strong>Abstract From Manuscript</strong></p> <p>Emerging holographic haptic interfaces focus ultrasound in air to enable their users to touch, feel, and manipulate three-dimensional virtual objects. However, current holographic haptic systems furnish tactile sensations that are diffuse and faint, with apparent spatial resolutions that are far coarser than would be theoretically predicted from acoustic focusing. Here, we show how the effective spatial resolution and dynamic range of holographic haptic displays are determined by ultrasound-driven elastic wave transport in soft tissues. Using time-resolved optical imaging and numerical simulations, we show that ultrasound-based holographic displays excite shear shock wave patterns in the skin. The spatial dimensions of these wave patterns can exceed nominal focal dimensions by more than an order of magnitude. Analyses of data from behavioral and vibrometry experiments indicate that shock formation diminishes perceptual acuity. For holographic haptic displays to attain their potential, techniques for circumventing shock wave artifacts, or for exploiting these phenomena, are needed.</p> <p> </p> <p><strong>Dataset Description</strong></p> <p>This dataset comprises surface velocity responses of materials to ultrasound-based holographic haptic displays. The dataset is split into three parts: numerical simulations on a tissue-like material, experimental measurements on a tissue phantom, and in vivo experimental measurements on a human hand. For details on our numerical and experimental procedure, please see our publication.</p> <p> </p> <p><strong>Elastic Wave Simulations</strong></p> <p>The elastic wave simulation dataset contains the surface velocity response of a tissue-like material to an acoustic source scanned across the medium surface and is split into two parts: closed scanning paths (circle and square paths) and open scanning paths (line, zigzag, and letter). These datasets can be found at the following DOIs: 10.5281/zenodo.7686542 and 10.5281/zenodo.7686550.</p> <p> </p> <p><strong>Vibrometry Measurements with Elastomer Plate</strong></p> <p>Data on our tissue phantom was captured via laser doppler vibrometer. This dataset contains the tissue phantom response to focused ultrasound scanned across the tissue phantom surface along linear and zigzag paths. This dataset can be found at the following DOI: 10.5281/zenodo.7686555.</p> <p> </p> <p><strong>Human Hand: Wave Patterns and Perception</strong></p> <p>In vivo measurements on the human hand were captured via laser doppler vibrometer. This dataset contains the skin response to focused ultrasound scanned in a zigzag path from the wrist to the distal end of digit 2 (and vice-versa) of a single participant. We also captured a behavioral dataset that assessed tactile motion direction discrimination. Participants reported the direction of scanning via a two-alternative forced-choice task. Written, informed consent was gathered from all participants in this study, and the protocol was approved by the human subjects committee of our institution. This dataset can be found at the following DOI: 10.5281/zenodo.7686561.</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Elastic Wave Simulations (Closed Scanning Paths)
<p><strong>Elastic Wave Simulations - Closed Scanning Paths</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these simulated data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>) and the software package k-Wave (DOI: 10.1109/ULTSYM.2014.0037).</p> <p>This dataset contains the normal shear surface velocity in a cylindrical slab of tissue-like material excited by an acoustic source with a Gaussian spatial profile simulated via a pseudo-spectral numerical method. The data is provided as .mat files. The files are separated by the type of scanning path, the scanning speed of the acoustic source, and the parameters of the scanning path. Details of the simulation parameters can be found in our publication.</p> <p><strong>Circle Paths</strong> - The acoustic source was scanned at a constant linear speed along a circular trajectories with two different diameters - 1 cm and 3 cm (indicated in the filename) and for at least 2 pattern repetitions. The linear scanning speed ranged from 2 to 20 m/s and is designated in the filename.</p> <p><strong>Square Paths</strong> - The acoustic source was scanned at a constant speed along square trajectories with two different edge lengths - 1 cm and 3 cm (indicated in the filename) and for at least 2 pattern repetitions. The scan speed ranged from 2 m/s to 10 m/s and is designated in the filename.</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>surfaceData</strong> (NxNxM) - 3D array containing the normal shear velocity of the simulated medium (in m/s) on a NxN Cartesian grid of locations at M timepoints. The simulated tissue medium was cylindrical, so locations outside the circular top surface are NaN</p> <p><strong>sourceSignals</strong> (NxNxQ) - 3D array containing the acoustic source distribution on the NxN Cartesian grid of locations used to excite the surface of the simulated tissue medium for Q timepoints</p> <p><strong>sourceEnvelope</strong> (Qx1) - Vector containing the amplitude envelope that was applied to sourceSignals at each timestep</p> <p><strong>nCycles</strong> - Number of pattern repetitions</p> <p><strong>dt</strong> - The time between adjacent timepoints in seconds (i.e. fs = 1/dt)</p> <p><strong>dx/dy</strong> - The distance between adjacent grid locations in the x/y direction of the Cartesian grid (in m)</p>
Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound - Human Hand: Wave Patterns and Perception
<p><strong>Human Hand: Wave Patterns and Perception</strong></p> <p>This dataset is part of a larger repository (DOI: 10.5281/zenodo.5248082) which houses links to the data used in the publication "Shear Shock Waves Mediate Haptic Holography via Focused Ultrasound" (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">Reardon et al., 2023</a>). If you use these data please cite our publication (<a href="http://www.science.org/doi/10.1126/sciadv.adf2037">http://www.science.org/doi/10.1126/sciadv.adf2037</a>).</p> <p>This dataset contains the in vivo response of a single participant's hand to focused ultrasound (UHEV1, Ultrahaptics) scanned in a zigzag path from the wrist to the distal end of digit 2 (and vice-versa). The data is provided as .mat files. The files are separated via longitudinal scanning speed, <em>v<sub>l</sub></em> = 1, 2, 4, 7, 11 m/s. At all speeds, the ultrasound focus was modulated transverse to its primary motion direction at a speed, <em>v<sub>mod</sub></em> of +-2.5 m/s yielding a zigzag path (2 cm path width). The longitudinal speed is designated in the filename. The direction of scanning - either from the wrist to the distal end of digit 2 (Distal direction) or from the distal end of digit 2 to the wrist (Proximal direction) - is also designated in the filename. Written, informed consent was gathered from the participant in this study, and the protocol was approved by the human subjects committee of our institution. Details about our experimental procedure can be found in our publication.</p> <p>IMPORTANT - The data is the unprocessed output from a laser doppler vibrometer (PSV-500, Polytec). The data is NOT time-aligned and must be reconstructed using the reference signal and the map of the measurement locations.</p> <p> </p> <p><strong>Data Fields</strong></p> <p><strong>y</strong> (NxMx2) - 3D array containing the skin velocity normal to the laser doppler vibrometer (in m/s) at N measurement locations for M timepoints and 2 repetitions</p> <p><strong>ref</strong> (NxMx2) - 3D array containing a reference voltage signal taken from the ultrasound phased array. The beginning of the reference signal can be used to time-align each of the measurements and repetitions</p> <p><strong>fs</strong> - Laser doppler vibrometer sampling rate (in Hz)</p> <p><strong>measurementLocations</strong> (Nx3) - 3D locations on the hand (x,y,z; in m) for each of N measurement locations<br> <br> </p> <p> </p> <p><strong>BehavioralDataset.zip</strong></p> <p>Contains the responses from three different perception experiments on tactile motion direction discrimination. The experiments are provided in three separate files; the results are provided as a MATLAB table. Written, informed consent was gathered from all participants in this study, and the protocol was approved by the human subjects committee of our institution. Details about our experimental procedure can be found in our publication.</p> <p>In the first experiment, SSW_PrimaryDataset.mat, participants (N=12) identified the direction of the focused ultrasound as either moving from the wrist to the end of digit 2 (Distal direction) or from the end of digit 2 to the wrist (Proximal direction).</p> <p>The second experiment, SSW_SecondaryDataset-Zigzag.mat, was nearly identical to the first experiment, except we cyclically repeated the stimuli such that the total integrated time in which the stimulus was applied to the skin was approximately constant between all of the different scan speeds. The participants (N=3) identified the motion direction of the focused ultrasound as either "Distal" or "Proximal" under two conditions - one in which there was no delay between our cyclical repeats (No Delay condition) and a second in which there was a 500 ms time delay between subsequent repetitions (With Delay condition).</p> <p>The third file, SSW_SecondaryDataset-Circle.mat, presents the pilot results (N=1) of a similar tactile motion experiment, except with circular trajectories (radius 2.8 cm) drawn on the palm of the hand in either a clockwise or counterclockwise direction. The stimuli were also repeated cyclically (with and without delay between repetitions), similar to experiment two.</p> <p> </p> <p> </p> <p><strong>Table Fields - SSW_PrimaryDataset.mat</strong></p> <p><strong>Participant</strong> - Participant label</p> <p><strong>Speed</strong> - Longitudinal speed, *v<sub>l</sub>*, of the focused ultrasound stimulus (in m/s)</p> <p><strong>Response</strong> - Participant response as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>Direction</strong> - True direction of the stimulus as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>isCorrect</strong> - Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition</strong> - Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Distal" or "Proximal"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Distal" or "Proximal"</p> <p><strong>Plays</strong> - Number of times the participant felt the stimulus before selecting a response</p> <p> </p> <p><strong>Table Fields - SSW_SecondaryDataset-Zigzag.mat</strong></p> <p><strong>Participant</strong> - Participant label</p> <p><strong>Speed </strong>- Longitudinal speed, *v<sub>l</sub>*, of the focused ultrasound stimulus (in m/s)</p> <p><strong>Response </strong>- Participant response as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>Direction </strong>- True direction of the stimulus as a binary 0 (Distal direction) or 1 (Proximal direction)</p> <p><strong>isCorrect </strong>- Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition </strong>- Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Distal" or "Proximal"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Distal" or "Proximal"</p> <p><strong>Condition </strong>- Indicates the experimental condition ("NoDelay" or "WithDelay")</p> <p> </p> <p><strong>Table Fields - SSW_SecondaryDataset-Circle.mat</strong></p> <p><strong>Participant </strong>- Participant label</p> <p><strong>Speed </strong>- Linear speed of the focused ultrasound stimulus along the circular trajectory (in m/s)</p> <p><strong>Response </strong>- Participant response as a binary 0 (Counterclockwise direction) or 1 (Clockwise direction)</p> <p><strong>Direction </strong>- True direction of the stimulus as a binary 0 (Counterclockwise direction) or 1 (Clockwise direction)</p> <p><strong>isCorrect </strong>- Indicates whether the participant's response matches the true stimulus direction</p> <p><strong>Repetition </strong>- Stimuli were block randomized and "Repetition" refers to how many times the participant has seen that specific stimulus</p> <p><strong>ResponseLabel </strong>- Participant response as either "Counterclockwise" or "Clockwise"</p> <p><strong>DirectionLabel </strong>- True label of the stimulus as either "Counterclockwise" or "Clockwise"</p> <p><strong>Condition </strong>- Indicates the experimental condition ("NoDelay" or "WithDelay")</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.