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372 results for “Waveforms”
Data release for paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models"
<p>This data release for the paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models" [<a href="https://arxiv.org/abs/2010.05830">arXiv:2010.2010.05830</a>] contains posterior samples for the GW190412 binary black hole merger event obtained from public GWOSC data with the parallel bilby Bayesian inference package, dynesty nested sampler and a set of waveforms from the "generation X" of phenomenological waveform models: IMRPhenomXAS, IMRPhenomXHM, IMRPhenomXP, IMRPhenomXPHM, IMRPhenomT and IMRPhenomTHM. The provided file is a "meta file" that can be read with the <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a> python package. The posterior samples included correspond to runs [2,6,10,12,14,26] in Table III of the paper (standard settings for each waveform, standar priors and sampler settings of Nlive=2048 and Nact=10 or 50). If you make use of these samples, please cite both this data release and the paper.</p>
VSR Databases used in article "Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition"
<p>This dataset contains required volcano-seismic waveform DBs (<em>dec.95M.16c</em> and <em>dec.09U.4c</em>) used in the article:</p> <p>"<em>Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition</em>",</p> <p>published in the Seismological Research Letters (<a href="https://doi.org/10.1785/0220180334">https://doi.org/10.1785/0220180334</a>). The authors want to thank everyone at the Instituto Andaluz of Geofísica (<a href="http://iagpds.ugr.es">http://iagpds.ugr.es</a>), precisely to Prof. Jesús Ibáñez and Dr. Javier Almendros, IPs of several research projects which </p> <p>have made possible the monitoring of Deception Island since early 1990s.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).</p>
Seismogram waveform datasets for ConvNetQuake_INGV
<p>This data archive contains the training, validation and test datasets for ConvNetQuake_INGV as presented in this article:</p> <p>Lomax, A., Michelini, A., & Jozinović, D. (2019). An Investigation of Rapid Earthquake Characterization Using Single‐Station Waveforms and a Convolutional Neural Network. Seismological Research Letters, 90(2A), 517–529. https://doi.org/10.1785/0220180311</p> <p>The training [validation] datasets consist of 15,200 [1773] event and 10,724 [1198] noise three-component waveforms and associated metadata from MedNet stations using events from 2010 to 2018 at 0°–180° with lower magnitude limits set as a function of event epicentral distance.<br> The test datasets consists of 1003 event and 621 noise three-component waveforms and associated metadata and an extended set of 4074 test events from 2007 to 2009, selected otherwise with the same criteria as for the training and validation datasets.<br> </p> <p>The easiest way to work with the hdf5 file is to use the python library h5py. See README.txt</p>
MetTLM TLM waveform set 1
<p>“MetTLM waveform set 1” is a data set consisting of a set of simple mathematically generated waveforms indicative of typical behaviour of TLM measured in the field, it should not be considered representative in any strict sense.</p> <p>The waveforms contained in this data set consists of distinct points placed at equal distance on a time axis, where each point is defined independently.</p> <p><sup>- </sup>The duration is 180 seconds consisting of 3 600 000 points, interspaced by 5·10<sup>-5</sup> s.</p> <p>- The two first rows give the time interval and between each datapoint and the number of points, as suggested in CIE TN 012 (Thorseth et al., 2021)</p> <p>- The first column is the time stamp in seconds,</p> <p>- Each of the consecutive 20 next columns are representing the amplitude of the waveforms.</p>
Final models for "Global-Scale Full-Waveform Ambient Noise Inversion" by Sager et al. (2020)
<p>The exodus model contains the inverted structure model and the source distribution can be found in the HDF5 file. Both can be visualized in ParaView. For the source model, we recommend opening it with the correspoding XDMF file (select "XDMF Reader" in the dialogue box).</p>
Waveform Data - F08_EV_PLC_Data_V1.csv
<p>Raw waveform data. A 2 second recording of mains voltage with EMI superimposed including a switching frequency of PV inverter (at frequency around 20 kHz) and emission of the PLC system (in the frequency range 35-90 kHz).</p>
Waveform Data -EVCharger_Lab
<p>Measurement of EV with the on-board charger connected to supply voltage in the laboratory. Waveform includes EMI with switching frequency of a charger (at around 27 kHz) and its 2<sup>nd</sup> harmonic.</p>
Waveform Data -BbVar_Syn
<p>Synthetic waveform with EMI varying in magnitude and frequency. File RefValuesBb_Var_Data.csv contains the reference values as the time domain rms values of each frequency component. </p>
Waveform Data -BbConst_Syn
<p>Synthetic waveform with EMI constant in magnitude, varying in frequency. File RefValuesBb_Const_Data.csv contains the reference values as the time domain rms values of each frequency component. </p>
Data for Fast EMRI Waveforms
<p>This data is required for the Fast EMRI Waveform package (the code can be found <a href="https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms">here</a>). The user does not need to download this data from here. The data will automatically download from the <a href="https://download.bhptoolkit.org/few/">BHPT download server</a> when the code requires it.</p> <p>If you use this data, please follow citation guidance found at the <a href="https://github.com/BlackHolePerturbationToolkit/FastEMRIWaveforms">code repository</a>. </p>
Dataset and supplementary files - Behavioral response of chub (Squalius cephalus), barbel (Barbus barbus) and brown trout (Salmo trutta) to pulsed direct current electric fields and resulting optimal waveform for use at electrified bar racks
<p><strong>Behavior Library.zip: </strong>For each species and behavior observed during the experiments an exemplary video is provided. </p><p><strong>Behavior_all.pdf: </strong>Additional plots showing the thresholds for the first time each individual behavior was observed for all fish species and tested waveforms</p><p><strong>Species.pdf: </strong>Additional plot allowing direct comparison of observed thresholds for the tested species when subjected to different waveforms. </p><p><strong>data.csv:</strong> All data necessary to reevaluate the conducted experiments. The dataset consists of</p><ul><li>Experiment ID</li><li>waveform - indicating the set of electrical parameters used</li><li>fish species and fish id </li><li>behavior - observed behavior</li><li>time from and time to - time in s after the start of the experiment that a behavior was started and ended respectively</li><li>type - point or interval referring to whether a behavior is considered instantaneous or continuous</li><li>voltage - applied voltage at the start of the given behavior</li><li>experiment_timestamp - date and time of the start of the experiment</li><li>breathing rate start - breathing rate at the start of the experiment</li><li>water conductivity - water conductivity at a reference temperature of 25°C [muS/cm]</li><li>water temperature [°C]</li><li>breathing rate end - breathing rate at the end of the experiment</li><li>meta behavior - assigned category of meta behavior based on the observe behavior category</li><li>standard length, total length and height - standard length, total length and height of the tested fish in [mm]</li><li>volume - calculated fish volume based on the measured length and height and an assumed elliptical form of the fish</li><li>Fangdatum - Date of catch</li><li>t.Pulse - pulse length of the tested waveform [ms]</li><li>Frequency - Frequency of the tested waveform</li><li>N.Pulses.Group - Number of pulses per group of pulses for the waveform pattern</li><li>t.Gap - time between two pulses within a group of pulses [ms]</li><li>DutyCycle - Percentage of time current is flowing for a given waveform. Calculated based on the waveform parameters</li><li>usage - first, second or third time a fish was used in the experiments. </li><li>field strength - field strength at the time of this behavior calculated based on the applied voltage</li><li>c_w ambient water conductivity [muS/cm]</li><li>p_d - power density calculated based on the field strength and the ambient water conductivity</li><li>p_t - power transferred to the fish calculated based on the field strength, the ambient water conductivity and an assumed conductivity of the fish of 115 muS/cm</li></ul><p> </p><p> </p>
WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data
<p>WINTERC-G: A global, temperature and compositional model of the lithosphere<br> and upper mantle.<br> Version: v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli<br> <br> Contact: Javier Fullea (jfullea@ucm.es)<br> Facultad de Fisica,<br> Universidad Complutense de Madrid (UCM),<br> Spain<br> ////////<br> Geophysics Section,<br> Dublin Institute for Advanced Studies<br> Dublin, Ireland<br> </p> <p>TYPE:<br> This contains files with:<br> i) the model directly on the triangular grid solved for in the surface wave inversion.</p> <p> ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion<br> </p> <p>If you have any questions regarding the methodology or the construction<br> of the model, please contact the authors. If you use the model, we would<br> request that you cite the reference indicated below, and appreciate<br> your feedback regarding the model and its application.</p> <p>Citation:</p> <p>Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191.</p> <p>*******************************<br> Summary: construction of the model.<br> WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements<br> from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at<br> global scale. WINTERC-G is based on upon the integrated geophysical-petrological<br> approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all<br> relevant mantle rock physical properties modelled (seismic velocities and density) are<br> computed within a thermodynamically self-consistent framework allowing for a direct<br> parameterization in terms of the temperature and composition of the lithosphere-upper<br> mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love<br> fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion,<br> along with surface heat flow and elevation (isostasy) for temperature and crustal structure<br> using a point-wise, non-linear, gradient-search inversion<br> over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we<br> use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in<br> order to refine the initial crustal density and mantle composition distributions from the step 1<br> for a fixed temperature field.</p> <p>The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)<br> and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary,<br> the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different<br> equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and<br> the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330,<br> and 400 km depths.</p> <p>The parameter space in step 2 is defined by the average crustal density, and the<br> mantle composition in the lithosphere and sublithosphere.<br> We use the output crustal density from step 1 as the<br> initial value in step 2 inversion. Mantle densities are derived based on the output temperature<br> field from step 1 (kept fixed) and the bulk mantle composition inversion variables.</p> <p> </p> <p> </p> <p>*******************************</p> <p>This archive contains the following files:<br> README (this file)<br> WINTERC-G_Vp-Vs.lis (triangular grid)<br> WINTERC-G_rad_anis_Vs.lis (triangular grid)<br> WINTERC-G_Temperature.lis (triangular grid)<br> WINTERC-G_Density.lis (triangular grid)<br> WINTERC-G_LAB.lis (triangular grid)<br> WINTERC_T_rho_1D.z (1D average model of temperature and density)<br> rho_*_out.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)<br> Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field)</p> <p><br> Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points):</p> <p>* WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)<br> 5640 93.72 4.135 -5.0 3.91 2.11</p> <p><br> * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) anisotropy (%)</p> <p>* WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth (km, <0 downwards) T (ºC) dT (%) dT(K) <br> 6437 297.20 -2.524 -259.000 1431.9 -1.91 -27.9<br> The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2).</p> <p>* WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)<br> The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3).</p> <p>* WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km <br> 5.00000000 0.0000000000000000 6.0259973839110526<br> 3.00000000 0.0000000000000000 38.960571309690394<br> 1.00000000 0.33634006819423840 174.42296045978722<br> -1.00000000 3.8888495253719624 1692.8437489147236<br> -3.00000000 23.974111923225379 1863.8834351235944<br> -5.00000000 47.727920701943034 2568.2414495590924<br> -7.00000000 89.633398074381162 2819.8386016341910<br> -9.00000000 137.01489361657013 2839.5325893195904<br> -11.0000000 182.35233447017222 2897.6600872935287<br> -13.0000000 224.46247069572485 2945.2036923862997<br> -15.0000000 260.63395547331390 3069.6809340323475<br> -17.0000000 292.28175449521456 3132.4574175461721<br> -19.0000000 322.29571965406632 3145.5747337463940<br> -21.0000000 351.58698283375054 3157.2401512748038<br> -23.0000000 380.30002225705056 3177.0000420059773<br> -25.0000000 408.50259805632055 3183.6651032398490<br> -27.0000000 436.22632217636487 3190.9586785996116<br> -29.0000000 463.48733903170023 3198.9369509456310<br> -31.0000000 490.29841705549831 3209.7229872383764<br> -33.0000000 516.71149258457456 3221.6329506091679<br> ...</p> <p>Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:</p> <p> * rho_c_out.xyz: average crustal density<br> * rho_submoho_out.xyz: mantle density below the Moho discontinuity<br> * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.</p> <p> Format for the density files:<br> # longitude latitude density (kg/m3)<br> <br> Files containing layer discontinuities:</p> <p> * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)</p> <p> * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)</p> <p> Format for the discontinuity files:<br> # longitude latitude depth (km)<br> <br> <br> The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization<br> in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius:</p> <p>1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically)</p> <p>2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz with rho=910 kg/m3 (constant vertically)</p> <p>3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically)</p> <p>4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom)</p> <p>5/ 20km-36km: from z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km) with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom)</p> <p>6/ 36km-56km: from z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km) with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom)</p> <p>7/ 56km-80km: from z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom)</p> <p>The next 6 layers are computed using the constant radius option:</p> <p>8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom)</p> <p>9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom)</p> <p>10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom)</p> <p>11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom)</p> <p>12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom)</p> <p>13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)</p> <p> </p> <p> </p>
Combining Horizontal Strain DAS and Local Seismic Stations in a Full Waveform Attribute Stacking Detector/Locator Algorithm: Verification Test for the Thorbjörn, Iceland, 2020 Unrest Episode
<p>We present a waveform stacking-based earthquake catalog of the seismicity unrest episode in the Svartsengi fissure swarm close to Mt. Thorbjörn, SW Iceland, which started in January 2020 and was still ongoing in January 2021. The magmatic unrest produced more than 5 earthquake swarms comprising thousands of individual events each. We were able to combine local and regional seismic networks with 6 months recording of a 17 km long distributed acoustic sensing (DAS) fibre optical cable with a channel resolution of 4 m. The kHz DAS data were downsampled to 200 Hz and stacked every 64 m. The catalog is based on a migration-based detector / locator technique as for instance implemented in Lassie (Pyrocko). In the accompanying we demonstrate the robustness in a wide variety of applications in seismology. For this dataset, we have extended Lassie to efficiently combine linear ultra-dense sensor arrays with sparse seismological networks.</p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>The dataset includes waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo and a 3-D Earth model in the Japanese islands. The data are provided as Green's strains at the maximum-likelihood location (indicated in the title of each text file) for all study events inverted at different periods. Inversion period is also indicated in the title. All the data are filtered between 15 s and 80 s. Additionally we provide a Python code to obtain displacement from strains given a moment tensor.</p>
NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>The purpose of this data set is to enable users working with NASA’s ATM airborne lidar waveform data to estimate their own range calibration if using range tracking methods different from the centroid estimate included in the ATM data files (Studinger <em>et al.</em>, 2022; <a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>). The airborne data are freely available from the National Snow and Ice Data Center (<a href="https://nsidc.org/home">NSIDC</a>) at the links listed in the table below. </p> <p>In pressurized aircraft the transmitted laser pulse travels through the aircraft’s optical window close to the scan mirror. Backscatter from both the scan mirror and the aircraft’s optical window in the fuselage are close in time to the transmitted laser pulse and partially overlap with the transmit waveform recorded by the ATM’s optical receiver. To record a “clean” transmit waveform the transmit pulse is sampled from behind a translucent beam splitter and subsequently injected into a multimode fiber-optic cable to provide a fixed optical delay that results in temporal separation between the recorded transmit pulse and contamination from backscattered photons from the scan mirror and the aircraft’s optical window. The delay due to the optical fiber and other system components introduce a laser time-of-flight range bias. The signal strength can also affect the calculated range in a way that depends on the waveform tracking algorithm. This variable influence, known as range walk, and the bias are determined from ground calibration measurements in which ATM data is collected from a stationary target at known range (true range) while varying the return intensity from signal extinction to detector saturation. The resulting signal-dependent deviation of measured range from the true range combine the system delay and range walk correction and are subtracted from the uncalibrated ranges to yield the calibrated range estimates "/laser/calrng" in the airborne waveform files (Studinger <em>et al.</em>, 2022; Appendix B3; <a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>).</p> <p>This data set includes ATM ground test waveform data from the wide and narrow scanner, the true ranges, as well as the range calibration tables (caltables) used for processing the airborne data products. A MATLAB® function to read the ground test waveform data is available at: <a href="https://doi.org/10.5281/zenodo.6248436">https://doi.org/10.5281/zenodo.6248436</a></p> <p> </p> <table> <tbody> <tr> <td> <p><strong><strong>ATM Data Product ID at NSIDC</strong></strong></p> </td> <td> <p><strong><strong>Temporal Coverage</strong></strong></p> </td> </tr> <tr> <td> <p><strong><a href="https://nsidc.org/data/ilatmw1b">https://nsidc.org/data/ilatmw1b</a></strong></p> </td> <td> <p><strong>17 July 2017 - 20 November 2019</strong></p> </td> </tr> <tr> <td> <p><strong><a href="https://nsidc.org/data/ilnsaw1b">https://nsidc.org/data/ilnsaw1b</a></strong></p> </td> <td> <p><strong>29 October 2017 - 20 November 2019</strong></p> </td> </tr> </tbody> </table> <p>The file name of the groundtest calibration table that was used to process the airborne data is stored in the field "/ancillary_data/documentation/header_text" of each airborne data file. The corresponding groundtest waveform file has the same time tag and data set identifier as the calibration table file. E.g., the corresponding ground test waveform file for the calibration table “caltable_20170628_193814.atm6AT5.binned_data.txt” is “ILATMW1B_20170628_193814.atm6AT5.h5”. The table below lists the ground test waveform files that should be used for each campaign and instrument:</p> <table> <tbody> <tr> <td> <p><strong><strong>Year</strong></strong></p> </td> <td> <p><strong><strong>Campaign</strong></strong></p> </td> <td> <p><strong><strong>Data Set</strong></strong></p> </td> <td> <p><strong><strong>Groundtest Waveform Data</strong></strong></p> </td> </tr> <tr> <td> <p><strong><strong>2017</strong></strong></p> </td> <td> <p><strong>17-JUL-2017 25-JUL-2017</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20170628_193814.atm6AT5.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>29-OCT-2017 25-NOV-2017</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20171017_141954.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>29-OCT-2017 25-NOV-2017</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20171208_122808.atm6BT7.h5</strong></p> </td> </tr> <tr> <td> <p><strong><strong>2018</strong></strong></p> </td> <td> <p><strong>22-MAR-2018 01-MAY-2018</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20180309_110944.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>10-OCT-2018 16-NOV-2018</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20181002_151220.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>22-MAR-2018 01-MAY-2018</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20180301_115659.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>10-OCT-2018 16-NOV-2018</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20181002_160127.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> <p><strong><strong>2019</strong></strong></p> </td> <td> <p><strong>03-APR-2019 16-MAY-2019</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20190321_102239.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>03-SEP-2019 16-SEP-2019</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20190817_132201.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>23-OCT-2019 20-NOV-2019</strong></p> </td> <td> <p><strong>ILATMW1B</strong></p> </td> <td> <p><strong>ILATMW1B_20191017_121209.atm6AT6.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>03-APR-2019 16-MAY-2019</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20190327_105730.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>03-SEP-2019 16-SEP-2019</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20190817_131026.atm6DT7.h5</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>23-OCT-2019 20-NOV-2019</strong></p> </td> <td> <p><strong>ILNSAW1B</strong></p> </td> <td> <p><strong>ILNSAW1B_20191017_122059.atm6DT7.h5</strong></p> </td> </tr> </tbody> </table> <p> </p> <p><strong><strong>See also:</strong> </strong></p> <p>User guide for NASA's Airborne Topographic Mapper HDF5 Waveform Data: Products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></p> <p>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></p> <p>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p>
Final model for "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018)
<p>The HDF5 file contains the final model of the paper "Automated Large-Scale Full Seismic Waveform Inversion for North America and the North Atlantic" by Krischer et al. (2018), soon to be published in the Journal of Geophysical Research - Solid Earth.</p> <p>The "coordinates_0", "coordinates_1", and "coordinates_2" data sets are the coordinates along each dimension, here colatitude in degree, longitude in degree, and radius in meter, respectively. The regularly sampled data is available in five 3D-arrays in the "data" group: "vp", "vsv", "vsh", "rho", and "Q". Velocities are defined at 1 Hertz and are given in km/s, the density in kg/m^3. Q is Q_mu.</p> <p>The coordinates have to be rotated to yield true spherical Earth coordinates. They have to be rotated around on axis vector of 0.766044443118978/0.6427876096865393/0.0 in cartesian x/y/z coordinates by -30.0 degrees. Conversion of spherical to cartesian coordinates happens with the standard convention:</p> <p>x = r sin(theta) cos(phi)<br> y = r sin(theta) sin(phi)<br> z = r cos(theta)</p>
Reproduction package for: 'Exploring Waveform Variations among Neutron Star Ray-tracing Codes for Complex Emission Geometries'
<p>Data files, python scripts and notebooks to reproduce the code output comparisons performed in "Exploring Waveform Variations among Neutron Star Ray-tracing Codes for Complex Emission Geometries" by Choudhury et al. (2024; <a href="https://doi.org/10.3847/1538-4357/ad7255" target="_blank" rel="noopener"><em>ApJ</em> <strong>975</strong> 202</a>, <a href="https://doi.org/10.48550/arXiv.2406.07285" target="_blank" rel="noopener">arXiv.2406.07285</a>).</p> <p>Please refer to the README for detailed information.</p> <p>N.B. The neutral hydrogen column density (${\rm N}_{\rm H}$) value is mentioned in the paper to be $0.2 \times 10^{20} {\rm cm}^{-2}$, whereas all the analyses in the paper, as reflected in this Zenodo package, actually uses ${\rm N}_{\rm H} = 2 \times 10^{20} {\rm cm}^{-2}$.</p>
Calculation of Ferrite Core Losses with Arbitrary Waveforms using the Composite Waveform Hypothesis: Reproducibility Dataset
<p><strong>Paper</strong></p> <p>This package contains the datasets used in the following paper:</p> <ul> <li><strong>Calculation of Ferrite Core Losses with Arbitrary Waveforms using the Composite Waveform Hypothesis</strong></li> <li><strong>Thomas Guillod, Jenna S. Lee, Haoran Li, Shukai Wang, Minjie Chen, and Charles R. Sullivan</strong></li> <li><strong><a href="https://doi.org/10.1109/APEC43580.2023.10131348">https://doi.org/10.1109/APEC43580.2023.10131348</a></strong></li> <li><strong>IEEE APEC 2023, Orlando, Florida, USA</strong></li> </ul> <p><strong>Datasets</strong></p> <p>The EPCOS TDK N87 datasets used in this paper are part of the MagNet initiative (<a href="http://mag-net.princeton.edu">https://mag-net.princeton.edu</a>). MagNet is an openly available large-scale dataset including measurements of several core materials under various operating conditions. MagNet is a joint project between Princeton University, Dartmouth College, and Plexim GmbH.</p> <p>This package includes the two datasets used in the paper:</p> <ul> <li>"N87_ambient_temperature" - Loss dataset for EPCOS TDK N87 at ambient temperature (measured on a R22.1X13.7X7.9, 2022-02-01).</li> <li>"N87_variable_temperature" - Loss dataset for EPCOS TDK N87 at different temperatures (measured on a R34.0X20.5X12.5, 2022-07-14).</li> </ul> <p>It should be noted that the datasets contains more measurements than used in the paper:</p> <ul> <li>The measurements where the iGCC can be evaluated without extrapolation are used in the paper.</li> <li>The measurements where the iGCC require an extrapolation of the loss data are not used in the paper.</li> <li>A flag in the dataset indicates in which category a measurement belongs.</li> </ul> <p>More details about the measurement setup can be found on the MagNet website (<a href="http://mag-net.princeton.edu">https://mag-net.princeton.edu</a>).</p> <p>More details about the iGCC method can be found on GitHub (<a href="https://github.com/otvam/magnet_webinar_eqn_models">https://github.com/otvam/magnet_webinar_eqn_models</a>).</p> <p><strong>File Formats</strong></p> <p>The datasets are available in three different formats:</p> <ul> <li>CSV (text files).</li> <li>MATLAB tables (MAT v7.3 binary files, exported with MATLAB 2021a).</li> <li>Pandas dataframes (HDF5 binary files, exported with Python 3.10.6 and Pandas 1.3.5).</li> </ul> <p>The file "dataset_metadata.csv" contains the description of the different variables.<br>The file "test_matlab.m" is a MATLAB test file for loading the MATLAB tables.<br>The file "test_python.py" is a Python test file for loading the Pandas dataframes.</p>
Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data for April-July 2019 and 2020. We refer to the original research article below for further information. The footprint data were filtered with respect to predictive uncertainty and MODIS non-vegetated probability.</p><p>The unfiltered data organized in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data is available here:</p><p>April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a></p><p>April-July 2020: <a href="https://doi.org/10.5281/zenodo.7737869">https://doi.org/10.5281/zenodo.7737869</a></p><p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p><p><strong>Citation:</strong></p><p>Use of these data require citation of this dataset:</p><p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020 (1.0) [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7737946">https://doi.org/10.5281/zenodo.7737946</a></p><p>Original research article:</p><p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <i>Remote Sensing of Environment</i>, <i>268</i>, 112760.</p><p>This filtered dataset (2019 and 2020) was used to develop the global canopy height model fusing Sentinel-2 and GEDI that is presented in:</p><p>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, <a href="https://doi.org/10.1038/s41559-023-02206-6">https://doi.org/10.1038/s41559-023-02206-6</a></p>
Data release for paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method"
<p>This is the data release for the paper "Waveform systematics in identifying gravitationally lensed gravitational waves: Posterior overlap method", which is available on https://arxiv.org/abs/2306.12908.</p> <p>These results are derived from the gravitational-wave parameter-estimation results by the LIGO-Virgo-KAGRA Collaboration, released with the GWTC-1, GWTC-2, GWTC-2.1, and GWTC-3 catalogs under the following links:</p> <ul> <li> https://dcc.ligo.org/P1800370-v5/public</li> <li> https://dcc.ligo.org/P2000223-v7/public</li> <li> https://doi.org/10.5281/zenodo.6513631</li> <li> https://doi.org/10.5281/zenodo.5546663</li> </ul> <p>For the lensed-unlensed hypothesis test posterior overlap Bayes factors, we provide the following files for event pairs from within each observing run:</p> <ul> <li> blu_all_pairs_O1.txt</li> <li> blu_all_pairs_O2.txt</li> <li> blu_all_pairs_O3.txt</li> </ul> <p>In each file, the column "event_pair" contains the names of the two events from the pair sorted chronologically, the column "data_releases" contains the names of the data releases from which the posterior samples of each event were taken, the column "waveform" contains the name of the waveform model used in the parameter estimation for both sets of posteriors, and the column "log10blu" contains the log10 of the Bayes factors.</p> <p>The differences between runs for the same event pair, only including O1-O1, O2-O2, O3-O3 pairs, where at least one run gave log10blu>0, are also given in the file "blu_differences_pairs_with_log10blu_pos.txt". The column "event_pair" contains the event pairs, the columns "waveform_{1,2}" contain the names of the waveform models used in the parameter estimation for both sets of posteriors, the columns "data_releases_{1,2}" contain the the data releases from which the posterior samples of each event were taken, the columns "log10blu_{1,2}" contain the log10 Bayes factors, and the column "difference" contains the difference between "log10blu_1" and "log10blu_2".</p> <p>We also provide the following files corresponding to the appendix of the paper, analyzing overlaps between posterior samples for individual events:</p> <ul> <li> overlap_different_runs.txt</li> <li> overlap_same_run.txt</li> <li> rescaled_difference_single_event.txt</li> </ul> <p>The file "overlap_different_runs.txt" contains Bayes factors for a single event, but comparing the posteriors from different runs. The file "overlap_same_run.txt" contains Bayes factors for the overlap of a single run on a single event with itself. The file "rescaled_difference_single_event.txt" contains the difference between the results contained in the file overlap_different_runs.txt and the results in overlap_same_run.txt, taking the ones that produce the biggest difference, as per equation (A.1) in the paper.</p> <p>In these files, the column "event_name" is the name of the event, the column "data_release" or "data_releases" contains the name(s) of the data release(s) from which the posterior samples of each run were taken, the column "waveform" or "waveform_pair" contains the name(s) of the waveform model(s) used, and the column "log10blu" is the log10 Bayes factor obtained. In the file "rescaled_difference_single_event.txt", the columns "max_run_waveform" and "max_run_data_release" identify an entry from the "overlap_same_run.txt" file from which we use the "log10blu" to compute the value listed in the "difference" column using equation (A.1).<br> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.