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115 results for “Geophysical data”
Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters
<p>This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters.</p> <p>Contact: Yohei Yamada (yoheiy@jamstec.go.jp)</p>
Data sets for "MELISSA: System description and spectral features of pre‐ and post‐midnight F‐region echoes. Journal of Geophysical Research: Space Physics" by Rodrigues et al.
<p>Observations used in the study "Rodrigues, F. S., Zhan, W., Milla, M. A., Fejer, B. G., de Paula, E. R., Neto, A. C., et al ( 2019). MELISSA: System description and spectral features of pre‐ and post‐midnight <em>F</em>‐region echoes. <em>Journal of Geophysical Research: Space Physics</em>, 124. <a href="https://doi.org/10.1029/2019JA027445">https://doi.org/10.1029/2019JA027445</a>."</p> <p>The uploaded files include the RTI maps measured by the MELISSA radar system between 2014 and 2018 (.tif files). They also include values of SNR versus local time and height and the spectra presented in the manuscript (.mat files).</p> <p>Please, see README.txt for additional details.</p>
Aurora Subglacial Basin GlaDs inputs, outputs and geophysical data
<p>The GlaDS_ASB_outputs.txt file includes the following:</p> <p>Glacier Drainage System (GlaDS) model inputs: node easting (m), node northing (m), bed elevation (m), ice thickness (m), basal velocity (m/year) and basal water production (m/year). GlaDS model results for water pressure as a fraction of overburden (Pw/Pi) and water depth (m):base line model, high conductivity, low conductivity, static water and static velocity model runs. </p> <p>Specularity content data for Aurora Subglacial Basin as an xyz file called: filtered.spec.asb.xyz with easting (m), northing (m) and specularity content. </p> <p>The ICECAP basal interface specularity content profiles can also be found at the U.S Antarctic Program (USAP) Data Center: <a href="https://doi.org/10.15784/601371">https://doi.org/10.15784/601371</a></p>
Data in support of the publication "First Detection and Thermal Characterization of Terminator CO2 Ice Clouds with ExoMars/NOMAD" on Geophysical Research Letters
<p>This archive contains the data underlying the 3 Figures in the main manuscript, including the vertical profiles of retrieved aerosols, vertical profiles of CO2 saturation ratio and spatial distribution of the retrieved clouds.</p>
Data for the paper "Two small volcanoes, one inside the other: geophysical and drilling investigation of Bažina maar in western Eger Rift"
<p>This dataset consists of all data used in the manuscript of "Two small volcanoes, one inside the other: geophysical and drilling investigation of Bažina maar in western Eger Rift". </p> <p>The repository contains</p> <p>1. Gravity data after topographic correction - for details see README.TXT</p> <p>2. Magnetic data - for details see README.TXT</p> <p>3. Ground resistivity measurement using the multielectrode method - for details see README.TXT</p> <p>4. Lithology and logging data from the two boreholes S4 and S4 - file Stratigraphy_and_Logging_S4.pdf</p>
WRF output for the Geophysical Research Letters publication "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific"
<p>This dataset is the Weather Research and Forecasting (WRF) model output for the <em>Geophysical Research Letters</em> publication entitled "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific". The dataset includes the azimuthal-averaged parameters with 0.2°resolution, three-day forecast, and two experiments for all cases analyzed in the publication. Due to the data size, only the variables used in the figures are uploaded (i.e., relative humidity, relative vorticity, temperature, and water vapor mixing ratio). Detailed information, composite calculation, and model settings can be found in the publication.</p>
Codes and data related to the article: Renard et al. Floods and Heavy Precipitation at the Global Scale: 100-year Analysis and 180-year Reconstruction. Journal of Geophysical Research - Atmospheres.
<p>This package contains R codes and data related to the article:</p> <p>B. Renard, D. McInerney, S. Westra, M. Leonard, D. Kavetski, M. Thyer and J.-P. Vidal. Floods and Heavy Precipitation at the Global Scale: 100-year Analysis and 180-year Reconstruction. <em>Journal of Geophysical Research - Atmospheres</em>. DOI: <a href="https://doi.org/10.1029/2022JD037908">10.1029/2022JD037908</a></p> <p><strong>Analyses</strong></p> <p>This folder contains the R scripts used to set up models, analyse results and prepare figures. See README file for details.</p> <p><strong>ShinyApp</strong></p> <p>This folder contains an interactive Shiny App to explore the data and the results from the article.</p> <p>An online version can be found at <a href="https://hydroapps.recover.inrae.fr/HEGS-paper">https://hydroapps.recover.inrae.fr/HEGS-paper</a></p> <p> </p>
Geophysical and geochronological data files from the Cocos Plate
<p><strong>Supporting materials: Geophysical and geochronological data files from Cocos Plate study. </strong>Figure numbers refer to those published in Naif et al. (2023).</p> <p><em>SRTM_residual_bathymetry_Fig2b.grd</em> contains residual bathymetry data and metadata used to produce Fig. 2b. </p> <p><em>MGL0807-01.pstm-v2d.0.segy</em> contains multi-channel seismic data and metadata used to produce Fig. 4. </p> <p><em>s04_CSEM_Fig4b.mat</em> and <em>s10_CSEM_Fig4c.mat</em> contain CSEM data and metadata used to produce Fig. 4b and Fig. 4c.</p> <p>The following files contain CSEM inversion model used to produce Fig. S1 (the model can be plotted using the MARE2DEM code):</p> <ul> <li><em>lessLEM_corr159km.data</em></li> <li><em>perRxTxAll.15.resistivity</em></li> <li><em>perRxTxAll.15.resp</em></li> <li><em>perRxTxAll.poly</em></li> </ul> <p>The following files contain pdf figures, data and metadata from Ar/Ar geochronological measurements on core samples of gabbro sills from ODP Leg 205, Hole 1253A (used to produce Fig. S2): </p> <ul> <li><em>40Ar-39Ar Full raw data.xlsx</em></li> <li><em>40Ar-39Ar Full raw data.txt</em></li> <li><em>40Ar-39Ar_StepHeating-Run_30995-01_ODP-205-1253A-10R_FigS2a.pdf</em></li> <li><em>40Ar-39Ar_Isochron_Run_30995-01_ODP-205-1253A-10R_FigS2b.pdf</em></li> <li><em>40Ar-39Ar_StepHeating-Run_30998-01_ODP-205-1253A-34R_FigS2c.pdf</em></li> <li><em>40Ar-39Ar_Isochron_Run_30998-01_ODP-205-1253A-34R_FigS2d.pdf</em></li> </ul> <p><em>Residual_GEBCO2021_bathymetry_FigS3a.grd</em> and <em>Residual_v30_Gravity_FigS3b.grd</em> contain residual bathymetry and residual gravity data and metadata used to produce Fig. S3.</p>
Digitized geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy) collected from public sources.
<p>The database is a supplementary material of:</p><p>Livani, M., Petracchini, L., Benetatos, C., Marzano, F., Billi, A., Carminati, E., Doglioni, C., Petricca, P., Maffucci, R., Codegone, G., Rocca, V., Antoncecchi, I. (2023). Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy). Earth System Science Data Discussions, 15, n. 9, 4261-4293, doi: 10.5194/essd-15-4261-2023</p><p> </p><p>This database comprises subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy). We realized the database by collecting, revising, and digitizing data, originally in raster format, from public sources. These data have been then used to reconstruct the overall subsurface 3D architecture and to extract the physical properties of the subsurface geological units.</p><p>The data have a common geographical system: WGS 84/UTM zone 32N; EPSG: 32632.</p><p>The database contains borehole information from 160 deep wells (i.e., wellhead coordinates, rotary table elevation, measured depth, true depth, total depth and deviation survey) and digitized Spontaneous Potential, Gamma Ray, and Sonic logs. Five horizons were digitized from 61 geological cross-sections and from 10 isobath maps that roughly correspond to the boundaries of units showing different lithological properties and with different mechanical properties. The horizons are, from the oldest to the youngest: the top of the magnetic basement, the top of the carbonate succession, the base of the Pliocene, the base of the Calabrian and the base of recent continental deposits. In addition, the gridded surfaces of the 3D geological model are available.</p><p>We organized the database into two groups: "primitive data" and "derived data".</p><p>Primitive data are the result of the digitization of public data. The database of the primitive data is formed by the main horizons reported in the geological cross-sections, the isobaths of the main geological surfaces, the well locations, comprised their trajectory along depth, lithological and stratigraphical information, and geophysical logs from composite well logs. Well data include specific sets of well logs aimed to geological/mechanical characterization of the geological units (e.g., Spontaneous Potential log, Resistivity log, Gamma Ray log, and Sonic log).</p><p>Derived data consist of two datasets: i) the primitive isobaths maps and geological cross-sections data that have been filtered and verified after a data accuracy analysis performed to unravel discrepancies in the interpretation of the subsurface geological horizons; ii) a set of regional surfaces of the main geological units of the Po Plain subsurface. These surfaces were generated by interpolating the filtered primitive data and without considering the fault occurrence/displacements.</p><p>Primitive and derived data are provided in delimited text file format organized according to the data type (i.e., well, geological cross-section, map and gridded surface). The format and the organization of data are explained in the related "readme" file presents within each data folder.</p><p>Our database represents a collection of the main published works regarding the Po Plain. Detailed studies related to specific sectors of the Po Plain might not be present in our database.</p><p>Further details about the data processing and organization are given in the related manuscript.</p><p> </p>
Supplementary Data to: "Quantifying sub-seasonal growth rate changes in fossil giant clams using wavelet transformation of daily Mg/Ca cycles" in Geochemistry, Geophysics, Geosystems
<p>El/Ca data and high resolution images of 3 laser-ablation tracks on a fossil giant calm. The following Isotopes were monitored <sup>11</sup>B, <sup>23</sup>Na, <sup>24</sup>Mg, <sup>27</sup>Al, <sup>43</sup>Ca, <sup>88</sup>Sr, <sup>89</sup>Y and <sup>138</sup>Ba. The data was measured with laser-ablation inductively coupled plasma mass spectrometry (LA-ICPMS) using a 3 x 33 µm laser slit. El/Ca ratios were calibrated using NIST SRM 612 as bracketing external standard (Jochum et al., 2011) with updated Mg values from Evans & Müller (2018) and <sup>43</sup>Ca as the internal standard; data quantification follows Longerich et al. (1996) and was performed using the software iolite 4 (Paton et al., 2011). For details see main text.</p> <p>References:</p> <p>Evans, D., & Müller, W. (2018). Automated Extraction of a Five-Year LA-ICP-MS Trace Element Data Set of Ten Common Glass and Carbonate Reference Materials: Long-Term Data Quality, Optimisation and Laser Cell Homogeneity. <em>Geostandards and Geoanalytical Research</em>, <em>42</em>(2), 159–188. https://doi.org/10.1111/ggr.12204</p> <p>Jochum, K. P., Weis, U., Stoll, B., Kuzmin, D., Yang, Q., Raczek, I., Jacob, D. E., Stracke, A., Birbaum, K., Frick, D. A., Günther, D., & Enzweiler, J. (2011). Determination of Reference Values for NIST SRM 610–617 Glasses Following ISO Guidelines. <em>Geostandards and Geoanalytical Research</em>, <em>35</em>(4), 397–429. https://doi.org/10.1111/j.1751-908X.2011.00120.x</p> <p>Longerich, H. P., Jackson, S. E., & Günther, D. (1996). Inter-laboratory note. Laser ablation inductively coupled plasma mass spectrometric transient signal data acquisition and analyte concentration calculation. <em>Journal of Analytical Atomic Spectrometry</em>, <em>11</em>(9), 899–904. https://doi.org/10.1039/JA9961100899</p> <p>Paton, C., Hellstrom, J., Paul, B., Woodhead, J., & Hergt, J. (2011). Iolite: Freeware for the visualisation and processing of mass spectrometric data. <em>Journal of Analytical Atomic Spectrometry</em>, <em>26</em>(12), 2508–2518. https://doi.org/10.1039/C1JA10172B</p>
Synthetic data for the solution of geophysical inverse problems
<p>The synthetic LAB used to define an inverse problem is discretized in different number of parameters. Each "LAB" file is one specific discretization and contains the coordinatates X and Y and the value of the LAB at each position</p> <p>The reference velocity fields obtained from solving the Stokes equation with the synthetic LABs are found in "velo" files. They contain, X, Y, and Z and the field Vx, Vy and Vz.</p> <p>Another synthetic data set represents the african lithosphere. The first two columns of its LAB file contain the longitude and latitude of each point and the third column is the value of the LAB. The reference velocity field obtained from solving Stokes is found in the corresponding "velo" file.</p>
Data Tables from contribution titled "Joint geophysical-petrological modeling on the Ivrea geophysical body beneath Valsesia, Italy: Constraints on the continental lower crust" published in Geochemistry, Geophysics, Geosystems
<p><strong>TABLE CAPTIONS</strong></p> <p><strong>Table 1: </strong>Average and specific bulk compositions of the representative exposed primitive rocks of the deep lower crust and upper mantle of the IVZ (Balmuccia and Lower Mafic Complex in Valsesia, Finero in Val Cannobina, and Sessera river in Val Sessera) used for the Perple_X calculations using the two thermodynamic models STX11 and HP02. Mn, Ni, P, and Cr were not used for the Perple_X calculations. Mg# is calculated as Mg / (Mg + Fe) (mole fractions). Symbols in the last row indicate the rock compositions that are reported (✓) and not reported (✗) in Figure 5. Average compositions are normalized to 100 wt.% on a volatile-free basis; specific compositions report original totals and H<sub>2</sub>O contents estimated based on the volumetric proportions hydrous minerals (amphibole and phlogopite) at the scale of the bulk rock. References: 1) Voshage et al. (1990); 2) Voshage et al. (1990), Shervais and Mukasa (1991), Hartmann and Wedepohl (1993), Rivalenti et al. (1995), and Mukasa and Shervais (1999); 3) Pin and Sills (1986); 4) Pin and Sills (1986), Shervais and Mukasa (1991), Hartmann and Wedepohl (1993), Rivalenti et al. (1995), and Mukasa and Shervais (1999); 5) Shervais and Mukasa (1991) and Mazzucchelli et al. (2009); 6) Sinigoi et al. (1991; 2011); 7) Hartmann and Wedepohl (1993); 8) Coltorti and Siena (1984); 9) Siena and Coltorti (1989), Lu et al. (1997a; b), and Stähle et al. (2001).</p> <p> </p> <p><strong>Table 2: </strong>Summary of <em>V<sub>P</sub></em> and <em>r</em> from the thermodynamic calculations based on representative rock compositions listed in Table 1. Mineral abbreviations: Pl = plagioclase, Spl = spinel, Grt = garnet, Ky = kyanite, Px = pyroxene, Cr-Di = Cr-diopisde, Am = amphibole, Phl = phlogopite.</p>
3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)
<p>3-D geological and petrophysical models with synthetic geophysics based on data from the Hamersley region (Western Australia)</p> <p>M. Jessell<sup>1,2</sup>, J. Giraud<sup>1,2</sup>, M. Lindsay<sup>1,2 </sup></p> <p><sup>1</sup>Centre for Exploration Targeting (School of Earth Sciences), University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia</p> <p><sup>2</sup>Mineral Exploration Cooperative Research Centre, School of Earth Sciences, University of Western Australia, 35 Stirling Highway, WA Crawley 6009, Australia</p> <p>Contact author: Jeremie Giraud (jeremie.giraud@uwa.edu.au)</p> <p>Companion dataset to the paper:</p> <p>Structural, petrophysical and geological constraints in potential field inversion using the Tomofast-x open-source code, J. Giraud, V. Ogarko, R. Martin, M. Lindsay, M. Jessell, Geoscientific Model Development Discussions.</p> <p>This dataset contains models and data shown in the paper, in both 2D and 3D:</p> <p>1. Geological model</p> <ul> <li>Reference lithology voxet:</li> </ul> <p>The reference geological model was obtained using public data from the Geological Survey of Western Australia and modified subsequently (stretched vertically and flattened at surface level) for the purpose of this study.</p> <ul> <li>Probability voxet<br> The lithology probability voxet was derived using Monte Carlo simulations for uncertainty estimation as mentioned in the paper.</li> </ul> <p>2. True and inverted models for density and magnetic susceptibility</p> <p>Derivation is detailed in the paper; it uses fictitious density and magnetic susceptibility values.</p> <p>3. Bouguer and total magnetic field anomaly</p> <p>Calculation is detailed in the paper.</p> <p>The authors are supported, in part, by Loop – Enabling Stochastic 3D Geological Modelling (LP170100985) and the Mineral Exploration Cooperative Research Centre (MinEx CRC) whose activities are funded by the Australian Government's Cooperative Research Centre Program. This is MinEx CRC Document 2021/3. Mark Lindsay acknowledges funding from the ARC and DECRA DE190100431.</p> <p>It is a companion dataset to: <br> Vitaliy Ogarko, Jeremie Giraud, & Roland. (2021, February 5). Tomofast-x v1.0 source code (Version 1.0). Zenodo. <a href="http://doi.org/10.5281/zenodo.4452620">http://doi.org/10.5281/zenodo.4452620</a></p>
Data supplementing the article: Schultz, N.M., Lawrence P.J, Lee X., Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. Journal of Geophysical Research - Biogeosciences
<p>These data supplement the article: Schultz, N.M., Lawrence P.J, Lee X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation, under review at the Journal of Geophysical Research - Biogeosciences.</p> <p>contact: Natalie M. Schultz, natalie.schultz@yale.edu</p> <p>Below are descriptions of the data files included here:</p> <p><br> (1) Global LST data: globalLST_Forest_Open.YYYY.nc [2003-2013]</p> <p>- DayLST1/NightLST1 and DayLST2/NightLST2 are the final (after the DEM correction) LST values for forest, and open land cover classes, respectively.<br> - Count variables show the number of pixels of each land cover class in each 0.5 degree grid<br> - The average elevation of each class is given by the DEM1 and DEM2 vars<br> - The DEM correction is the dLSTdDEM vars</p> <p>(2) Global fluxes data: globalFluxes_Forest_Open.YYYY.nc [2003-2013]<br> - Again, forest class = var1, open class = var2<br> - SWRABS is absorbed solar radiation<br> - LE is the latent heat flux<br> - HP is the heating potential term, as defined in the manuscript<br> - As described for the LST data, class pixel counts and DEM data are included</p> <p>(3) climzones3.nc<br> - The delineation of the three climate zones defined in this paper</p> <p>(4) MERRA inversion data: MERRA_11yr_TS_T10M.mat [2003-2013]<br> - 11 years of daily 1am local data averaged over 8-day intervals for 2003-2013<br> - TS = surface temperature<br> - T10M = 10M air temperature (above d)</p> <p> </p> <p> </p>
Geophysical data
<p>This publication contains lithological units (bodies numbered 31, 35, 61, 62, 63, 64, 90, 99), LitMod files (.out, .inp) as well as used by tAo to compute plate deflection (.PRM). We also added gridded data (netcdf grid format) and Bouguer and topographic profiles (.dat) used in Pajang et al. as well as Github link preseting our to install and run LitMod (ajay-Litmod_github.docx). </p> <p>gridded data are : </p> <p>1) crustal model - EPcrust.grd</p> <p>2) bouguer gravity data - grd_Aq_points.grd</p> <p>2) Vp and Vs data - pm_16km.grd and sm_16.grd</p> <p>3) LAB models - SL2013sv.grd and LITHO1_EU.grd</p>
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
Data for Jucker and Goyal, Ozone-forced Southern Annular Mode during Antarctic Stratospheric Warming Events, Geophysical Research Letters 2022
<p>Data from climate model simulations and reanalysis which were analysed in associated paper.</p> <p>Read README.md file for detailed information on each file.</p>
Multiple geophysical data in the Shuangjianzishan ore district, NE China
<p>The dataset consists of gravity, magnetic, magnetotelluric and seismic data in the Shuangjianzishan Ag-Pb-Zn distrcit, NE China.</p>
Data supplementing article "Tidal response to sea-level rise in different types of estuaries: the importance of length, bathymetry, and geometry" submitted to Geophysical Research Letters
<p>These data supplement the article "Tidal response to sea-level rise in different types of estuaries: the importance of length, bathymetry, and geometry". </p> <p>contact: Jiabi Du, jiabi.du@gmail.com</p> <p>Below are descriptions of the data files included here:</p> <p>1. ReadMe.txt</p> <p>- the description of each model configuration is listed in this file, inlcuding the estuarine geometry type, length, and bathymetry type. </p> <p>2. model grid and surface elevation output</p> <p>- In each directory named after the grid_name, a grid file with .gr3 format is included. <br> The .gr3 file contains two parts: the location of each node (first part), and the node number for each triangle grid (second part).</p> <p>- Two sub_directory name 'base' and 'slr' contains the time series of water level. </p> <p>3. MatlaScript ExtractWL</p> <p>- a function named "f_extract_wl.m" are used to extract the station output</p> <p> </p>
Recovering data of an observation of the Leonids in 1869 from the Lisbon Geophysical Observatory (Portugal)
<p>Number of meteors observed every five minutes by the staff of the Geophysical Observatory of Lisbon during the night from November 13 to 14, 1869.</p>
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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.