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168 results for “Explosion”
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>
Explosive networking: the role of adaptive host radiations and ecological opportunity in a species-rich host-parasite assembly
<p>Dataset for Cruz-Laufer et al. (2021) Explosive networking: the role of adaptive host radiations and ecological opportunity in a species-rich host-parasite assembly.</p> <p><strong>Abstract: </strong>Many species-rich ecological communities emerge from adaptive radiation events. The effects of this explosive speciation on community assembly remain poorly understood. Here, we explore the well-documented radiations of African cichlid fishes and their interactions with the flatworm gill parasites <em>Cichlidogyrus </em>spp., including 10529 reported infections and 477 different host-parasite combinations collected through a survey of peer-reviewed literature. We assess how evolutionary, ecological, and morphological parameters determine host-parasite meta-communities affected by adaptive radiation events through network metrics, host repertoire measures, and network link prediction. The hosts’ evolutionary history mostly determined host repertoires of the parasites. Ecological and evolutionary parameters determined host-parasite interactions. Generally, ecological opportunity and fitting have shaped cichlid-<em>Cichlidogyrus</em> meta-communities suggesting an invasive potential for hosts used in aquaculture. Meta-communities affected by adaptive radiations are increasingly specialised with higher environmental stability. These trends should be verified across other systems to infer generalities in the evolution of species-rich host-parasite networks.</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>
Emission line models for the lowest mass core-collapse supernovae - I. Case study of a 9 M⊙ one-dimensional neutrino-driven explosion
<p>Model spectra of the 9 Msun iron-core model, and the pure H toy model, 200-600d. Distance 10 Mpc assumed.</p>
Data and code for "Pollen wars: Explosive pollination removes pollen deposited from previously visited flowers
<p>This data consist of 02 data files, 01 code script, and this README document, with the following data and code filenames and variables</p> <p>Data files and variables<br>1. [red flower experiment.csv] [Date: the date the data was taken; Flower number: the flower identity; labelled Pollen count on beak: number of pollen grains placed on hummingbird’s bill; Total unlabelled pollen grains on beak: number of unlabelled pollen grains on the hummingbird’s bill after visit; labelled pollen on flower keel: number of pollen grains on flower keel after visit; labelled pollen on petals: number of labelled pollen on petals after visit; labelled pollen on flower hairs: number of labelled pollen on flower hairs after visit; Before or After treatment: whether the pollen grains were counted before or after floral visit; Treatment: whether the visit was done on triggered or untriggered flower; Beak Photo number: photo identity of the bill; Keel photo number: photo identity for the keel (none was taken); hair photo number: photo identity for the floral hairs (none was taken); comment: any observation on the experiment; Labelled grains transferred to stigma: number of labelled pollen grains on the stigma after explosion (only one data point); unlabelled grains transferred to stigma: number of unlabelled pollen grains on the stigma after explosion (only one data point)].</p> <p>2. 2. [explosion_data.csv] [Flower number: the flower identity; Before count: number of pollen grains before floral explosion; After Count: number of pollen grains after explosion; Before Minus after: the subtraction of the last two values; % pollen removed: percentage of pollen grains removed by the explosion; Proportion pollen removed: proportion of pollen grains removed by the explosion; % removed (arcsin root transformed): arcsin root transformation for the last values; Total unlabelled pollen grains on beak: total number of pollen grains counted on hummingbird’s bill; % removed (arcsin root transformed): arcsin root transformation for the percentage of pollen removed].<br> <br>Code scripts and workflow<br>[script_analysis_Hypenea.R: code for data analysis]<br>1. libraries used on the analysis;<br>2. data loading and processing for explosion analysis;<br>3. modelling; checking model adjustment; anova table; estimation of marginal means; getting predicted values by the model.<br>4. plotting figure;<br>5. data loading and processing for pollen removal;<br>6. modelling; checking model adjustment; anova table; getting predicted values by the model.<br>7. plotting figure; </p> <p>SOFTWARE VERSIONS</p> <p>All the statistical analyses were run in R environment version 4.3.1 (R Development Core Team, 2023) using the default and the following packages: glmmTMB (Brooks et al., 2017), emmeans (Russell, 2022) and car (Fox & Weisberg, 2019). Residual dispersion around the fitted models was checked using Dharma package (Hartig, 2022).</p> <p><br>REFERENCES<br>Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Mächler, M., and Bolker, B. M. 2017. glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal, 9(2), 378-400. http://dx.doi.org/10.32614/RJ-2017-066 </p> <p>Fox, J., and Weisberg, S. 2019. An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. URL: https://socialsciences.mcmaster.ca/jfox/Books/Companion/</p> <p>Hartig, F. 2022. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. URL https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html </p> <p>R Development Core Team. 2023. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. URL https://www.r-project.org/ </p> <p>Russell, V. L. 2022. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.7.4-1. https://CRAN.R-project.org/package=emmeans</p>
Dataset for "Yield Estimation of the August 2020 Beirut Explosion by Using Physics-Based Propagation Simulations of Regional Infrasound"
<p>Dataset for “Yield Estimation of the August 2020 Beirut Explosion by Using<br> Physics-Based Propagation Simulations of Regional Infrasound”</p> <p>Authors: Keehoon Kim and Michael E. Pasyanos</p> <p>Lawrence Livermore National Laboratory, Livermore, CA, USA</p> <p>Description<br> This datset includes the infrasound waveform data recorded by the array at the<br> Mt. Meron (IMA) in Israel. A five-element array deployed by the National Data<br> Center of Israel (Fee et al., 2013), and all stations had Martec Tekelec MB2005<br> sensors which have a flat frequency response in the infrasound band (0.1–20Hz)<br> (Ponceau and Bosca, 2010). The infrasound data was provided by the National<br> Data Center of Israel, Soreq Nuclear Research Center, and the pressure<br> recordings only relevant to the 2020 Beirut explosion were uploaded to the<br> public repository (https://zenodo.org).</p> <p>File Description<br> IMA_array_infrasound_waveform.txt Pressure values in ASCII format for 5 stations</p> <p>Acknowledgments<br> This research was performed by the support from the U.S. Department of Energy,<br> National Nuclear Security Administration, Office of Defense Nuclear<br> Nonproliferation, Research and Development under the auspices of the U.S.<br> Department of Energy by the Lawrence Livermore National Laboratory under<br> Contract Number DE-AC52-07NA27344. This is LLNL Contribution LLNL-JRNL- 839419</p> <p>References<br> Fee, D., Waxler, R., Assink, J., Gitterman, Y., Given, J., Coyne, J., ... &<br> Grenard, P. (2013). Overview of the 2009 and 2011 Sayarim infrasound<br> calibration experiments. Journal of Geophysical Research Atmospheres, 118(12),<br> 6122-6143.</p> <p>Ponceau, D., & Bosca, L. (2010). Low-noise broadband microbarometers. In<br> Infrasound monitoring for atmospheric studies (pp. 119-140). Springer,<br> Dordrecht.</p>
Earthquake catalogs for: A specific earthquake processing workflow for studying long-lived explosive volcanic eruptions with application to the 2008 Okmok eruption
<p>Repository for the seismic catalogs from Garza-Giron et al. (2023a,b). These include the catalog with absolute locations using NonLinLoc (Lomax et al., 2001; Lomax and Curtis, 2001), and the relocated catalogs using hypoDD (Waldhauser and Ellsworth, 2000) and GrowClust (Trugman and Shearer, 2017).</p> <p>The header of the CSV files is as follows:</p> <p><strong>Date</strong> (year/month/day), <strong>Time</strong> (hr:min:sec:msec), <strong>Latitude</strong> (decimal degrees), <strong>Longitude</strong> (decimal degrees), <strong>Depth</strong> (km), <strong>Magnitude</strong> (Ml calculated for this study), <strong>Event_type</strong> (VT:vulcano-tectonic;LP:long-period), <strong>Number of stations</strong> where the event was detected, <strong>ID</strong></p> <p>References:</p> <div>Garza‐Giron, R., Brodsky, E. E., Spica, Z. J., Haney, M. M., & Webley, P. W. (2023a). A specific earthquake processing workflow for studying long‐lived, explosive volcanic eruptions with application to the 2008 Okmok Volcano, Alaska, eruption. <em>Journal of Geophysical Research: Solid Earth</em>, e2022JB025882.</div> <div> </div> <div> <div>Garza‐Girón, R., Brodsky, E. E., Spica, Z. J., Haney, M. M., & Webley, P. W. (2023b). Earthquakes record cycles of opening and closing in the enhanced seismic catalog of the 2008 Okmok Volcano, Alaska, eruption. <em>Journal of Geophysical Research: Solid Earth</em>, <em>128</em>(7), e2023JB026893.</div> <div> </div> </div> <p>Lomax A, Curtis A (2001) Fast, probabilistic earthquake location in 3-D models using oct-tree importance sampling. Geophys Res Abstracts, 3:955.</p> <p>Lomax, A., Zollo, A., Capuano, P., and Virieux, J. (2001). Precise, absolute earthquake location under Somma‐Vesuvius volcano using a new 3D velocity model.Geophysical Journal International,146, 313–331.</p> <p>Trugman, D. T., and Shearer, P. M. (2017). GrowClust: A hierarchical clustering algorithm for relative earthquake relocation, with application to the Spanish Springs and Sheldon, Nevada, earthquake sequences. Seismological Research Letters, 88(2A), 379-391.</p> <p>Waldhauser, F., and Ellsworth, W. L. (2000). A double-difference earthquake location algorithm: Method and application to the northern Hayward fault, California. Bulletin of the Seismological Society of America, 90(6), 1353-1368.</p>
Forecasting and Tracking Volcanic Explosions using Shannon Entropy at Volcán de Colima
<pre><strong>Forecasting and Tracking Volcanic Explosions using Shannon Entropy at Volcán de Colima</strong>. by: Pablo Rey-Devesa (1,2),*, Janire Prudencio (1,2), Carmen Benítez (3), Mauricio Bretón (4), Imelda Plasencia (4), Zoraida León (4), Félix Ortigosa (4), Ligdamis Gutiérrez (1,2), Raúl Arámbula (4) and Jesús M. Ibáñez (1,2). <strong>Institutions associated</strong>: (1) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain. (2) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain. (3) Department of Signal Theory, Telematics and Communication. University of Granada. Informatics and Telecommunication School. 18071. Granada. Spain. (4) Centro Universitario de Estudios Vulcanológicos (CUEV), Observatorio Vulcanológico, Universidad de Colima, Colima, México <strong>Acknowledgment</strong>: a) This study was partially supported by the Spanish FEMALE (PID2019-106260GB-I00) and PROOF-FOREVER (EUR2022.134044) projects. P. Rey-Devesa was funded by the Ministerio de Ciencia e Innovación del Gobierno de España (MCIN), Agencia Estatal de Investigación (AEI), Fondo Social Europeo (FSE) and Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+I Ayudas para contratos predoctorales para la formación de doctores 2020 (PRE2020-092719). b) To the Visual Monitoring and Seismicity Monitoring projects, both from the Center for Volcanological Studies of the Colima University. <strong>Data availability statemen</strong>t: Seismic data from Volcán de Fuego de Colima. <strong>Contents</strong>: Seismic Data from Volcán de Fuego de Colima recorded at stations INCA and SOMA. The data represent the vertical component of the seismic signal, associated to the period analyzed in the study: "<em>Forecasting and Tracking Volcanic Explosions using Shannon Entropy at Volcán de Colima</em>" Data available between January 2015 and May 2017.</pre>
UV-Vis absorption data for five common crystalline explosives
<p>Absolute spectral absorption data for PETN, RDX, HMX, HMS and Cl-20. DOI will be referenced from a journal article so that readers can have access to the numerical data used in one of the plots.</p>
Towards a Realistic Explosion Landscape for Binary Population Synthesis
<p>Data from:</p> <p> Towards a Realistic Explosion Landscape for Binary Population Synthesis</p> <p> by</p> <p> Patton, Rachel A. and Sukhbold, Tuguldur<br> (2020).</p> <p> (Submitted to MNRAS)</p> <p>- Detailed descriptions of calculations are provided in the paper, but<br> please feel free to get in touch with any of us with questions, or<br> if you need something that is not provided here.</p> <p>Contacts:<br> Rachel Patton = patton[dot]502[at]osu[dot]edu<br> Tuguldur Sukhbold = tuguldur[dot]s[at]gmail[dot]com<br> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -</p> <p>Below are brief descriptions of each dataset. More details can be found in the paper.</p> <p>kepler_table.tar.gz (1.0GB)<br> -These are tables for our entire set of 3496 KEPLER CO-core models, containing the final presupernova structure and composition quantities for each zone. Core masses range from 2.5 to 10.0 in increments of 0.1 Msun and the initial composition ranges from 5% carbon and 95% oxygen to 50% carbon and oxygen, in incrmeents of 1%. Each file has the naming convention "mA_B.ktbl" where A is the core mass in Msun (2.5, 2.6, etc.) and B represents the initial carbon mass fraction as the number of increments of 0.01 taken from 0.05 (5%). For example, the file 'm8.0_27.ktbl' would be for an 8 Msun core with an initial carbon mass fraction of 0.32 (= 0.05 + 27*0.01).</p> <p>kepler_datatset.tar.gz (30.4KB)<br> -This folder contains four tables listing the presupernova parameters evaluated from all of our KEPLER CO-cores: M_fe, the iron core mass, M4, the mass enclosed where the entropy per baryon reaches 4 Kb, mu_4, the radial gradient of mass at the location of M4, and xi_2.5, the compactness parameter. See section#3 of our paper for details on how these were evaluated. Columns represent each CO-core mass and rows represent starting carbon mass fraction.</p> <p>inlist (2.8KB)<br> -This is the inlist used to create 760 MESA (version 7624) CO-core models, presented in section#4.1. The only thing that changes between models is the CO-core mass, set by 'initial_mass' and the composition, set by 'accretion_species_xa(1)' and 'accretion_species_xa(2)'. In our grid, we covered the same mass and composition range as the KEPLER models, keeping the same mass increment but increasing the carbon mass increment from 0.01 to 0.05.</p>
Data from: Explosion-generated infrasound recorded on ground and airborne microbarometers at regional distances
Recent work in deploying infrasound (low frequency sound) sensors on aerostats and free flying balloons has shown them to be viable alternatives to ground stations. However, no study to date has compared the performance of surface and free floating infrasound microbarometers with respect to acoustic events at regional (100s of kilometers) range. The prospect of enhanced detection of aerial explosions at similar ranges, such as those from bolides, has not been investigated either. We examined infrasound signals from three 1 ton TNT equivalent explosions using microbarometers on two separate balloons at ranges of 280 to 400 km and ground stations at ranges of 6.3 to 350 km. Signal celerities were consistent with acoustic waves traveling in the stratospheric duct. However, significant differences were noted between the observed arrival patterns and those predicted by an acoustic propagation model. Very low background noise levels on the balloons were consistent with previous studies that suggest wind interference is minimal on freely drifting sensors. Simulated propagation patterns and observed noise levels also confirm that balloon-borne microbarometers should be very effective at detecting explosions in the middle and upper atmosphere as well as those on the surface.
Dataset for: "Gas buffering of magma chamber contraction during persistent explosive activity at Mt. Etna volcano"
<p>Data used for generating the figures in the paper "Gas buffering of magma chamber contraction during persistent explosive activity at Mt. Etna volcano", accepted for publication in Nature Communications Earth & Environment.</p>
Explosive hardening of 7075-T651 FSW joints (selected results)
<p>This work was financially supported by the National Science Centre (NCN) in Poland, Miniatura 5 no. 2021/05/X/ST8/01480.</p>
Identification of Ionospheric Acoustic Wave Signatures from Conventional Surface Explosions Using MF/HF Doppler Sounding
<p>These HDF5 files contain complex time series data from HF receptions of a Digisonde Portable Sounder 4D (DPS4D). Each data point is the phase and amplitude of a decoded Sky Map mode pulse. </p>
Infrasonic Early Warning System for Explosive Eruptions
<p>The two datasets 2018JB015561-ds01 and 2018JB015561-ds02 provide data used to create most of the figures and to reach most of the findings of the manuscript <em>[Ripepe, M.; Marchetti, E.; Delle Donne, D.; Genco, R.; Innocenti, L.; Lacanna, G.; Valade, S. Infrasonic Early Warning System for Explosive Eruption. J. Geophys. Res. Solid Earth <strong>2018</strong>, 123, 9570–9585].</em></p> <p>2018JB015561-ds01 is a matlab (,mat) file containing a sample of infrasound detections (December, 2nd, 2013) between 09:00 and 24:00 UTC. This dataset is used to create part of Figure 3 of the manuscript. Infrasound detections are obtained from raw infrasound data as discussed in Section 4. Infrasound detections (evaluated every 5 seconds) are used to create the IP, which is evaluated every minute as discussed in detail in Section 5.</p> <p><strong>Data Set S1. </strong>2018JB015561-ds01: The file consists three variables: <em>t</em>, that corresponds to matlab time; <em>pr</em>, that corresponds to the acoustic pressure of the detection as recorded at the array; <em>az</em>, that corresponds to the infrasound back-azimuth and is used to limit the analysis to infrasound produced by the volcano. The data has a time stamp of 5 seconds.</p> <p>2018JB015561-ds02 is a matlab (.mat) file containing the infrasound parameter, between January, 1st, 2008 and December, 31st, 2016. The infrasound parameter is obtained from infrasound detections following the procedure described in Section 5, and used eventually to provide the automatic notification as described in section 6. This dataset is used to create Figure 5 of the manuscript.</p> <p><strong>Data Set S2. </strong>2018JB015561-ds02: It contains two variables: <em>t</em>, that corresponds to matlab time; <em>ip</em>, that corresponds to the infrasound parameter. The data has a time stamp of 1 minute.</p> <p>The movie shows an example of how the IP calculated with the ETN infrasound array is efficiently tracking the changes of eruptive activity at Etna and can be used to deliver an alert of ongoing volcanic activity.</p> <p><strong>Movie S1. </strong>2018JB015561-ms01: The movie shows the eruptive activity at Etna volcano recorded between 23:00 UTC of December, 3rd, 2015, and 12:00 UTC of December, 4th, 2015. It shows movies from a thermal camera synchronized with the corresponding values of seismic tremor and Infrasound Parameter. The Early Warning level is over imposed on the IP.</p> <p> </p>
Tests showing the projectile flight trajectory of 5, 7 and 8mm diameter steel spheres attached to either a detonator or a rear detonated C4 spherical explosive charge.
<p>These videos are the results of an experimental campaign carried out in the department of Propellant, Explosives and Blast Engineering of the Royal Military Academy in Brussels to study the flight trajectory of a projectile (steel sphere (5 or 7 or 8mm diameter)) attached to either a detonator or a rear detonated C4 spherical explosive charge. The projectile is accelerated by the blast wave generated by the explosive. The projectile initial trajectory is hidden by the flash of the detonation. After its passage through the opaque gas cloud, the first appearance of the projectile is captured and an almost straight trajectory can be tracked until the end of the field of view of the high speed camera. The test campaign reveals the capability of the projectiles to maintain an oriented path until impact on a target placed at 910mm stand of distance from the center of the explosion. </p>
Data from: Explosive diversification of marine fishes at the Cretaceous-Paleogene boundary
The Cretaceous–Palaeogene (K–Pg) mass extinction is linked to the rapid emergence of ecologically divergent higher taxa (for example, families and orders) across terrestrial vertebrates, but its impact on the diversification of marine vertebrates is less clear. Spiny-rayed fishes (Acanthomorpha) provide an ideal system for exploring the effects of the K–Pg on fish diversification, yet despite decades of morphological and molecular phylogenetic efforts, resolution of both early diverging lineages and enormously diverse subclades remains problematic. Recent multilocus studies have provided the first resolved phylogenetic backbone for acanthomorphs and suggested novel relationships among major lineages. However, these new relationships and associated timescales have not been interrogated using phylogenomic approaches. Here, we use targeted enrichment of >1,000 ultraconserved elements in conjunction with a divergence time analysis to resolve relationships among 120 major acanthomorph lineages and provide a new timescale for acanthomorph radiation. Our results include a well-supported topology that strongly resolves relationships along the acanthomorph backbone and the recovery of several new relationships within six major percomorph subclades. Divergence time analyses also reveal that crown ages for five of these subclades, and for the bulk of the species diversity in the sixth, coincide with the K–Pg boundary, with divergences between anatomically and ecologically distinctive suprafamilial clades concentrated in the first 10 million years of the Cenozoic.
Data for figures of explosive volcanic tsunami generation, propagation and inundation around Lake Taupō
<p>Data for figures in a planned publication of scenario-based study of explosive volcanic tsunami generation, propagation and inundation around Lake Taupō.</p>
Crankcase explosion models
<p>Dataset for crankcase explosion modelling.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.