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Plots source data for the Communications Earth & Environment research article titled: Diurnal expansion and contraction of englacial fracture networks revealed by seismic shear wave splitting
<p>The source data for plotting figures presented in the main text of article titled: Diurnal expansion and contraction of englacial fracture networks revealed by seismic shear wave splitting.</p>
Nickel and chromium stable isotopic composition of ureilites: implications for the Earth's core formation and differentiation of the ureilite parent body
<p>We report the first Ni and Cr stable isotope data for ureilite meteorites that are the mantle residue of a carbon-rich differentiated planet. Ureilites have similar Ni stable isotope compositions as chondrites, suggesting the core-mantle differentiation of ureilite parent body did not fractionate Ni isotopes. Since the size of Earth is potentially larger than that of ureilite parent body (UPB; with diameter > 690 km), resulting in higher temperatures at the core-mantle boundary of Earth, it can be predicted that the terrestrial core formation may not directly cause Ni stable isotope fractionation. On the other hand, we also report high-precision Cr stable isotope composition of ureilites, including one ureilitic trachyandesite (ALM-A) that is enriched in lighter Cr stable isotopes relative to the main-group ureilites, which suggests the partial melting occurred on UPB. The globally heavy Cr in the UPB compared to chondrites can be caused by sulfur-rich core formation processes.</p>
Surface Deformations from Glacial Isostatic Adjustment Models with Laterally Homogeneous, Compressible Earth Structure
<p>The zipped file contains global 1-degree grid files of four different surface deformation parameters:</p> <ul> <li>Horizontal velocity, North component (vnorth), in mm/a</li> <li>Horizontal velocity, East component (veast), in mm/a</li> <li>Vertical velocity (vup), in mm/a</li> <li>Geoid change (dgeoid), in mm/a</li> </ul> <p>calculated from a set of 26 different glacial isostatic adjustment models applying 10 different radially varying (=layered) earth structures and 3 different global ice models.</p> <p>The different earth structures and the available combinations with ice models can be found in the readme.pdf.</p> <p>File naming is simply {ice model}_{earth model}_{parameter}.grd. For example, anu-ice_f72_vup.grd is the global vertical velocity grid file calculated with a GIA model with ANU-ICE ice history and 60 km lithospheric thickness, 4E20 Pa s upper mantle viscosity and 2E21 Pa s lower mantle viscosity. Grid files (NetCDF format) were generated with GMT6 (Wessel et al., 2019), thus can be directly used.</p> <p>Ice thickness histories of ICE-6G_C (Argus et al., 20214; Peltier et al., 2015) and ICE-7G_NA (Roy & Peltier, 2017) were downloaded from W. R. Peltier’s data website at the University of Toronto, Canada: <a href="https://www.atmosp.physics.utoronto.ca/~peltier/data.php">https://www.atmosp.physics.utoronto.ca/~peltier/data.php</a></p> <p>Note that the velocity field of ICE-6G_C(VM5a) can be compared to the one available from W. R. Peltier’s data website. The files provided here are not a substitute for the ones by W. R. Peltier and colleagues! Analyzing the difference between the files from this work and the ones available on the data website can help getting an error estimate from the two GIA model implementations (see further below). The user will find minor differences in the uplift component but larger ones in far field areas of the horizontal components.</p> <p>ICE-7G_NA(VM7) results are added as complement for interested users. However, note that ICE-7G_NA contains ice thickness history modifications in North America only and a fully global re-optimization of the ice thickness is warranted. Hence, excessive use and interpretation of these grid files, especially on global scale, should be avoided.</p> <p>ANU-ICE is a global 1-degree ice thickness model merged from several regional models (Lambeck, 1995; Fleming & Lambeck, 2004; Lambeck et al., 2010; 2014; 2017) kindly provided by Anthony Lambert and Kurt Lambeck, ANU, Canberra, Australia. The regional models contain differing spatial and temporal resolutions that were unified to fit the global 1-degree spatial resolution at mainly common time steps (500–1000 years). The Antarctic Ice Sheet part contains changes in the last time steps, thus some larger changes in the velocities can be found there.</p> <p>The software ICEAGE (Kaufmann, 2004) is used for calculating the grids, which applies the viscoelastic normal-mode method (Peltier, 1974; Wu, 1978). The sea-level equation is solved in a pseudo-spectral approach (Mitrovica et al., 1994; Mitrovica & Milne, 1998) in an iterative procedure in the spectral domain. See further details in Kaufmann and Lambeck (2000; 2002). The spherical harmonic expansion in the spectral domain is truncated at degree 192, which corresponds to ~1° spatial resolution. The models are spherically symmetric (1D), compressible, with Maxwell-viscoelasticity, rotational feedback, and time-dependent coastlines. The Earth’s core is, as assumed to be inviscid, incorporated as lower boundary condition. Rheological parameters such as depth-dependent density, Young’s modulus, etc., are taken from PREM (Preliminary Reference Earth Model; Dziewonski & Anderson, 1981).</p> <p><strong>Acknowledgments</strong></p> <p>HS would like to thank Jeff Freymueller for discussions on model selection.</p> <p><strong>References</strong></p> <p>Argus, D. F., Peltier, W., Drummond, R., Moore, A.W. 2014. The Antarctica component of postglacial rebound model ICE-6G_C (VM5a) based on GPS positioning, exposure age dating of ice thicknesses, and relative sea level histories. Geophysical Journal International 198, 537–563, doi:10.1093/gji/ggu140.</p> <p>Dziewonski, A. M., Anderson, D. L. 1981. Preliminary reference Earth model. Physics of the Earth and Planetary Interiors 25, 297–356, doi:10.1016/0031-9201(81)90046-7.</p> <p>Fleming, K., Lambeck, K. 2004. Constraints on the Greenland ice sheet since the Last Glacial Maximum from sea-level observations and glacial-rebound models. Quaternary Science Reviews 23, 1053–1077, doi:10.1016/j.quascirev.2003.11.001.</p> <p>Kaufmann, G. 2004. Program Package ICEAGE, Version 2004. Manuscript. Institut für Geophysik der Universität Göttingen.</p> <p>Kaufmann, G., Lambeck, K., 2000. Mantle dynamics, postglacial rebound and the radial viscosity profile. Physics of the Earth and Planetary Interiors 121, 301–324, doi:10.1016/S0031-9201(00)00174-6.</p> <p>Kaufmann, G., Lambeck, K., 2002. Glacial isostatic adjustment and the radial viscosity profile from inverse modeling. Journal of Geophysical Research Solid Earth 107, ETG 5-1-ETG 5-15, doi:10.1029/2001JB000941.</p> <p>Lambeck, K. 1995. Late Devensian and Holocene shorelines of the British Isles and North Sea from models of glacio-hydro-isostatic rebound. Journal of the Geological Society London 152, 437–448, doi:10.1144/gsjgs.152.3.0437.</p> <p>Lambeck, K., Purcell, A., Zhao, J., Svensson, N.-O. 2010. The Scandinavian ice sheet: from MIS 4 to the end of the last glacial maximum. Boreas 39 (2), 410–435, doi:10.1111/j.1502-3885.2010.00140.x.</p> <p>Lambeck, K., Rouby, H., Purcell, A., Sun, Y., Sambridge, M. 2014. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. Proceedings of the National Academy of Sciences of the United States of America 111 (43), 15296–15303, doi:10.1073/pnas.1411762111.</p> <p>Lambeck, K., Purcell, A., Zhao, J. 2017. The North American Late Wisconsin ice sheet and mantle viscosity from glacial rebound analyses. Quaternary Science Reviews 158, 172–210, doi:10.1016/j.quascirev.2016.11.033.</p> <p>Mitrovica, J. X., Davis, J. L., Shapiro, I. I. 1994. A spectral formalism for computing three–dimensional deformations due to surface loads: 1. Theory. Journal of Geophysical Research Solid Earth 99(B4), 7057–7073, doi:10.1029/93JB03128.</p> <p>Mitrovica, J. X., Milne, G. A. 1998. Glaciation-induced perturbations in the Earth’s rotation: a new appraisal. Journal of Geophysical Research Solid Earth 103, 985–1005, doi:10.1029/97JB02121.</p> <p>Peltier, W. R. 1974. The impulse response of a Maxwell Earth. Reviews of Geophysics and Space Physics 12(4), 649–669, doi:10.1029/RG012i004p00649.</p> <p>Peltier, W., Argus, D., Drummond, R. 2015. Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model. Journal of Geophysical Research Solid Earth 120, 450–487, doi:10.1002/2014JB011176.</p> <p>Roy, K., Peltier, W. R. 2017. Space-geodetic and water level gauge constraints on continental uplift and tilting over North America: regional convergence of the ICE-6G_C (VM5a/VM6) models. Geophysical Journal International 210(2), 1115-1142, doi:10.1093/gji/ggx156.</p> <p>Wessel, P., Luis, J. F., Uieda, L., Scharroo, R., Wobbe, F., Smith, W. H. F., Tian, D. 2019. The Generic Mapping Tools version 6. Geochemistry, Geophysics, Geosystems 20, 5556–5564, doi:10.1029/2019GC008515.</p> <p>Wu P. 1978. The response of a Maxwell earth to applied surface mass loads: glacial isostatic adjustment. MSc thesis, University of Toronto, Toronto, Ontario, Canada.</p>
01. Sun, Earth [Milky way Planets]
<p>01. Sun, Earth [Milky way Planets]</p> <p>In this video, visualization of Sun and Earth is carried out of various positions of Sun and Earth in the space.</p>
pypsa-earth_tutorial_networks
<p>Unsolved and solved networks for the tutorial config of pypsa-earth.</p>
Dataset for "Constraints on magma properties at fragmentation during the 2011 sub-Plinian eruptions of Kirishima Shinmoe-dake volcano, Japan" by Kozono, T. and Okumura S. (submitted to JGR-Solid Earth)
<p>This dataset includes petrological data of pumices from the 2011 sub-Plinian eruptions at Kirishima Shinmoe-dake volcano, Japan, used in "Constraints on magma properties at fragmentation during the 2011 sub-Plinian eruptions of Kirishima Shinmoe-dake volcano, Japan" by Kozono, T. and Okumura S. (submitted to JGR-Solid Earth).</p>
GPS data set used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations"
<p>Data set of continuous GPS observations at CVAN site in Gran Canaria (Canary Islands, Spain). The period of observation spans from July 3, 2013 to November 30, 2015. Data was acquired during the execution of the research project GCL2011-25494 of the Spanish Research Agency. </p> <p>This data set belongs to the Research Group ‘Geodesia’ of the University Complutense of Madrid, Spain, and has been used in the paper "Anelastic response of the Earth's crust underneath the Canary Islands revealed from ocean tide loading observations", by Jose Arnoso, Machiel S. Bos, Maite Benavent, Nigel T. Penna, Sergio Sainz-Maza, submitted to Geophysical Journal International, 2022.</p>
Data to reproduce the results presented in Sehgal et al. 2022. Journal of Geophysical Research Earth Surface, https://doi.org/10.1029/2022JF006838 ("A Generic Relation Between Turbidity, Suspended Particulate Matter Concentration, and Sediment Characteristics")
<p>This repository consists data to reproduce results as presented in: "A Generic Relation Between Turbidity, Suspended Particulate Matter Concentration, and Sediment Characteristics", Journal of Geophysical Research Earth Surface. Kindly refer to the readme.text file to navigate through the dataset.</p>
Core Origin of Seismic Velocity Anomalies at the Earth's Core-Mantle Boundary
<p>Datasets supporting the findings in the article "Core Origin of Seismic Velocity Anomalies at the Earth's Core-Mantle Boundary"</p>
Machine learning reveals a taxonomy of bacterial sensors in Earth ecosystems
<p>Datasets and FASTA files used for the manuscript titled: "Machine learning reveals a taxonomy of bacterial sensors in Earth ecosystems." Description.ipynb contains descriptions and first 5 lines of each file.</p>
Supplementary Data for "Earth's Volatile Depletion Trend Consistent with a High-Energy Moon-Forming Impact"
<p>Vaporization statistics for pyrolite melts containing In, Cd, S, Zn and Hg. Please refer to "Computational Methods" of the main text for details of the simulations and Supplementary Table 2 for the stoichiometry of the melts.</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Supplementary Data for "The Phosphorus Budget of the Silicate Earth Based on an Updated Estimate of the P/Nd Ratio" in JGR-Solid Earth.
<p>Supplementary data tables for Ma et al. (2023) associated with the paper entitled "The Phosphorus Budget of the Silicate Earth Based on an Updated Estimate of the P/Nd Ratio" published in <em>JGR-Solid Earth</em>.</p>
Dataset for "Parametrization of energetic ion and electron fluxes in the near-Earth magnetotail" draft
<p>This archive contains four dataset of electron and ion fluxes measured by MMS, THEMIS, and GOES mission in the near-Earth plasma sheet. We used three THEMIS inner probes measurements from 2008-2020, GOES-13 measurements from 2011-2017, GOES-14 measurements from 2018-2019, GOES-15 measurements from 2011-2012, and MMS-1 measurements from 2017-2019. We implemented the following selection criteria to identify near-Earth plasma sheet observations: (1) X < -6RE, |Y| < |X|/2, Bxy < 20nT for THEMIS; (2) X<0, |Y| < |X|, Bxy < 2Bz for GOES; (3) X < -6RE, |Y| < |X|/2, Bxy < 2Bz, Rxy < 12RE for MMS. Fluxes are given in units of energy flux, keV/cm^2/s/sr/keV. </p> <p>*THEMIS_Electron_DATA.dat: Bz, AE, fluxes of 75keV, 20keV, 150keV, and 300keV electrons</p> <p>*THEMIS_Ion_DATA.dat: fluxes of 75keV, 20keV, 150keV, and 300keV ions</p> <p>*MMS_Electron_DATA.dat: Bz, AE, fluxes of 75keV electrons</p> <p>*GOES_Electron_DATA.dat: Bz, AE, fluxes of 75keV electrons</p>
Globally Distributed Subducted Materials along the Earth's Core-Mantle Boundary: Implications for Ultra-low Velocity Zones
<p>Supplemental movie files related to the geodynamic models in our manuscript "Globally Distributed Subducted Materials along the Earth’s Core-Mantle Boundary: Implications for Ultra-low Velocity Zones"</p>
A global data set of realized treelines sampled from Google Earth aerial images
<div> <span>We </span><span>sampled</span><span> Google Earth aerial images</span><span> to get a representative and globally distributed dataset of treeline locations</span><span>. </span><span>Google Earth images</span><span> are available to everyone, but may not be automatically downloaded and processed according to Google's license terms. Since we only wanted to detect tree individuals, we evaluated the aerial images manually by hand.</span> </div> <div> </div> <div> <span>Doing so, we scaled Google Earth's GUI interface to a buffer size of approximately 6000 m from a perspective of 100 m (+/- 20 m) above Earth's surface. Within this buffer zone, we took coordinates and elevation of the highest </span><span>realized </span><span>treeline locations. In some remote areas of Russia and Canada, individual trees were not identifiable due to insufficient image resolution. If this was the case, no treeline was sampled, unless we detected another visible treeline within the 6,000 m buffer and took this next highest treeline</span><span>. We did not ap</span><span>p</span><span>ly an automated image processing approach. </span><span>We calculated mass elevation effect as the distance to the nearest mountain chain limits. Continentality was assessed by the distance to the nearest coastline. Isolation was calculated by the nearest distance of a mountain chain to another mountain chain within a comparable elevational band. </span> </div>
data for the submission in JGR: Earth Surface
<p><strong>Data explanation</strong></p> <p>The dataset includes the major data for the manuscript: ‘Modeling Multi-Decadal Morphological Evolution of the Radial-Shaped Sand Ridges in the Southern Yellow Sea, China’.</p> <p> </p> <p>There are 7 data structures in the mat file:</p> <ol> <li>Elevation_SLRscenarios: this includes the elevation data for the sea level rise scenarios at year 2059, including the base scenario (noSLR), SLR1 (3 mm/yr), and SLR2 (11 mm/yr). Besides, the spatial info for the data and shown as X and Y, and the variable area is the area of the grid.</li> <li>Elevation_tide_ww_2017: this includes the elevation data at 2017 for two cases: tide-driven model (elevation_tide), and the tide, wind and waves driven model (elevation_tww). The spatial info is also included.</li> <li>Grainsize: this includes sand and fine sediment percentages for initial condition (RSR_fine_ini, RSR_sand_ini), at 2017 from the tide-driven model (RSR_fine_tide_2017, RSR_sand_2017), and at 2017 from the tide, wind and waves driven model (RSR_fine_tww_2017, RSR_sand_tww_2017). The spatial info is also included.</li> <li>Residual1979: this includes the residual current and residual sediment flux calculated for 7 tidal cycles at 1979. The spatial info is also included.</li> <li>Residual2017: this includes the residual current and residual sediment flux calculated for 7 tidal cycles at 2017. The spatial info is also included.</li> <li>Residual2059: this includes the residual current and residual sediment flux calculated for 7 tidal cycles at 2059. The spatial info is also included.</li> <li>Sedimentflux_profiles: this includes the net sediment flux across the profiles between the radial sand ridge system (RSR) and the abandoned yellow river delta (AYRD) (p1), and the RSR and the Yangtze River Delta (YRD) (p2). The variable distance_p1 and distance_p2 show the distance from coastline for P1 and P2 profiles, respectively. And the variables with ‘tide’ show the sediment flux from the tide-driven model at 2017, while the variables with ‘tww’ show the sediment flux from the tide, wind and waves driven model at 2017.</li> </ol>
Supplementary tables for "Impurity resistivity of the Earth's inner core"
<p>Supplementary tables for "Impurity resistivity of the Earth's inner core" contain the results of first-principles calculations on the electrical resistivity of 4- and 6-component hcp Fe alloys at the Earth's inner core pressure.</p>
Can Archean Impact Structures be Discovered? A Case Study from Earth's Largest, Most Deeply Eroded Impact Structure. Additional Data.
<p>Modeling inputs: Input files for iSALE. </p> <p>Code: Files for Matlab. </p> <p>Out: Graphs and tabulated results. </p>
Differences in Scattering Properties of the Shallow Crusts of Earth, Mars, and the Moon Revealed by P-Wave Receiver Functions - Dataset
<p>This version includes three parts. The first part is the seismic data set of Earthquakes, Marsquakes, and Moonquakes used in "Differences in Scattering Properties of the Shallow Crusts of Earth, Mars, and the Moon Revealed by P-Wave Receiver Functions" by Shi et al. (2023). In the data set of Earthquakes and Marsquakes, the direct P wave starts after 120 s. In the data set of Moonquakes, the direct P wave starts after 50 s. The second part is the velocity models used in the article for the Earth, Mars, and Moon. The third part is the code used to reproduce all the figures in the main text.</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.