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245 results for “geophysical”
Crustal heterogeneity onshore central Spitsbergen: insights from new gravity and vintage geophysical data (digital appendix)
<p>This is a digital appendix with data sets related to a scientific paper in G-cubed. It contains gravity, GPR and positioning data from Svalbard.</p> <p> </p> <p><span>Crustal heterogeneity onshore central Spitsbergen: insights from new gravity and vintage geophysical data </span></p> <p><span>Kim Senger<sup>1,2*, </sup>Fenna Ammerlaan<sup>1,3</sup>, Peter Betlem<sup>1,</sup> <sup>†</sup>, Marco Brönner<sup>4</sup>, Marie-Andrée Dumais<sup>4</sup>, Jomar Gellein<sup>4</sup>, Tormod Henningsen<sup>5</sup>, Julian Janocha<sup>1,6</sup>, Erik P. Johannessen<sup>7</sup>, Jonas Liebsch<sup>1,8</sup>, Jakob Machleidt<sup>9,1</sup>, Tereza Mosočiová<sup>1,10,11</sup>, Snorre Olaussen<sup>1</sup>, Bo Olofsson<sup>12</sup>, Nil Rodes<sup>1</sup>, Sofia Rylander<sup>12,1</sup>,<span> </span>Grace E. Shephard<sup>13,14</sup>, Aleksandra Smyrak-Sikora<sup>15</sup>, Juan D. Solano-Acosta<sup>16,1 </sup>and Anna Sterley<sup>11,1</sup></span></p>
supplementary material for "Analyzing the joint effect of volcanic carbon and sulfur emissions on the marine carbon cycle", for Geochemistry, Geophysics, Geosystems
<p>We provide our LOSCAR output and eruption history data files, along with IPython Jupyter notebook code to plot the various main text and supplementary figures.</p> <p>Please see README.txt for more information.</p> <p> </p>
Geophysical data from: "Lasting effects of soil compaction on soil water regime confirmed by geoelectrical monitoring"
<p>The geophysical data presented herein were collected in the Soil Structure Observatory (see, Keller et. al. 2017) and were used in the manuscript titled: "Lasting effects of soil compaction on soil water regime confirmed by geoelectrical monitoring". The data set includes:</p> <p> </p> <p>1. Seasonal and bi-hourly DC-resistivity data:</p> <p>DC-res.ods</p> <p> </p> <p>2. Standard deviation of seasonal and bi-hourly DC-resistivity data:</p> <p>DC-res-std.ods</p> <p> </p> <p>3. Volumetric water content data inferred from TDR data using Topp's equation at 10, 20, 40 and 70 cm.</p> <p>Water_content.ods</p> <p> </p> <p>4. Temperature data at 10, 20, 40 and 70 cm.</p> <p>Temperature.ods</p> <p> </p> <p>5. Meteorological data from 2013 to 2018 collected at the SSO:</p> <p>meteo.ods</p> <p> </p> <p>Data sets 1, 2, 3 and 4 were collected at the compacted ley (CL), non-compacted ley (NL) and compacted bare soil (CB) and non-compacted ley (NB)</p> <p> </p> <p> </p> <p> </p> <p>Keller, T., Colombi, T., Ruiz, S., Manalili, M. P., Rek, J., Stadelmann, V., ... & Schymanski, S. (2017). Long-Term Soil Structure Observatory for Monitoring Post-Compaction Evolution of Soil Structure. Vadose Zone Journal, 16(4).</p>
Geophysical and geochemical evidence for a new mafic magmatic province within the Northwest Shelf of Australia: Supplementary material
<p>Buried magmatic provinces are rarely identified and difficult to classify. The Northwest Shelf of Australia contains large volumes of potentially interconnected mafic igneous material across several sedimentary basins. However, limited study and a lack of surface exposure have prevented detailed description and classification of these rocks. In this study, the distribution and composition of these mafic igneous rocks is described using an integrated geophysical and geochemical approach, which included over 10,000 km of 2D historical seismic data, well log data and chemical analysis of samples from 14 wells across the Browse, Roebuck, Canning and North Carnarvon basins. Using this combined dataset, we demonstrate interconnectivity of buried mafic igneous rocks across the Northwest Shelf and calculate for these mafic rocks a total surface area exceeding 280,000 km<sup>2</sup> and a cumulative minimum volume of ~140,000 km<sup>3</sup>. Petrology and geochemistry of samples indicate they are basaltic and doleritic with alkaline and sub-alkaline compositions and formed in a continental rift setting. Collectively, the igneous rocks meet the criteria for classification as a mafic magmatic province (MMP) and closely match the criteria required for classification as a large igneous province (LIP). The interconnected mafic igneous rocks found across the Northwest Shelf of Australia are here formally defined as an MMP. Emplacement of the Northwest Shelf MMP may represent hotspot magmatism that could have initiated rifting of the Cimmerian Block from NW Australia near the end Permian, and may have broad implications for petroleum prospectivity, CO<sub>2</sub> sequestration and new resource opportunities in the Northwest Shelf.</p>
Cross-correlation coefficient maps generated in the paper "Correlation of Venusian Mesoscale Cloud Morphology Between Images Acquired at Various Wavelengths" by Narita et al. published in Journal of Geophysical Research - Planets
<p>This data archive contains the cross-correlation coefficient maps. Unzipping the compressed file, the following directories corresponding to different wavelength pairs appear. </p> <p> IR1_IR2/ : 0.9 micron & 2.02 micron<br> UVI283_UVI365/ : 283 nm & 365 nm<br> IR2_UVI365/ : 2.02 micron &. 365 nm<br> IR2_UVI283/ : 2.02 micron & 283 nm<br> IR2_LIR/ : 2.02 micron & 10 micron</p> <p>If usual unzip tools do not work, the use of 7zip is recommended:<br> https://www.7-zip.org/download.html</p> <p>Each directory contains CSV files for the longitude-latitude distribution of the correlation coefficient. The 2880 longitude grids cover the longitude range of 0 - 360 degrees, and the 1440 latitude grids cover the latitude range of -90 - +90 degrees, with a pixel resolution of 0.125 degree/pixel. Invalid regions are filled with the value of 1.1.</p> <p>Each filename is composed of the date, the instrument (wavelength), and the time. For example, for the file "20160720_ir2_150821_hp6_IR1_150209_hp6_sb24.csv":</p> <p> 20160720 : July 20, 2016<br> ir2 : 2.02 micron filter of IR2 camera<br> 150821 : IR2 exposure at 15:08:21<br> hp6: High-pass filtering size is 6 deg x 6 deg<br> IR1 : 0.9 micron filter of IR1 camera<br> 150209 : IR1 exposure at 15:02:09<br> sb24 : Sliding box size is 24 deg x 24 deg</p>
Acoustic geophysical data over the site of the proposed Mahuika Crater on the continental shelf southwest of New Zealand
<p>This dataset contains acoustic geophysical data used to question the existence of the Mahuika Crater: a proposed impact depression 20.2 km wide lying on the continental shelf ~250 km SW of New Zealand. Multibeam echo sounder and sub-bottom profiler data, provided in this dataset, across the published location of the crater was collected and synthesised with existing bathymetry data for the wider region to assess the validity of such a claim. We conclude that there is no geophysical basis for the existence for the Mahuika impact crater.</p> <p>The multibeam echo sounder data was collected using a Kongsberg EM302 MBES. These data are provided in the Kongsberg raw *.all file format. </p> <p>Sub-bottom acoustic data was collected by a 3.5 kHz Knudsen chirp sub-bottom profiler. These data are provided in the Knudsen raw *.keb format and have also been converted to a *.sgy format using the Knudsen Conversion Utility software.</p>
The datasets for the paper "Spatial and temporal distribution of lobate scarps in the lunar south polar region: Evidence for latitudinal variation of scarp geometry, kinematics and formation ages, continuous tectonic activity in the last 100 million years and seismically safe south pole Artemis human landing site" Geophysical Research Letters.
<p>This dataset provides the original data that were used for preparing the illustrations, figures and tables.</p>
Dataset for "Multidecadal regime shifts in North Pacific subtropical mode water formation in a coupled atmosphere-ocean-sea ice model" by Kim et al., 2022 in Geophysical Research Letters
<p>Kiel Climate Model pre-industrial simulation data used in the Geophysical Research Letters publication titled “Multidecadal regime shifts in North Pacific subtropical mode water formation in a coupled atmosphere-ocean-sea ice model” by Kim et al., 2022</p>
Dataset related to the paper submitted in Journal of Geophysical Research : Solid Earth, named : A Controlled-Source Physical Model for Long Period Events
Open the record for dataset details and reuse information.
Zonal mean of primary production and carbon export in the Southern Ocean (Huang and Fassbender, 2023, Geophysical Research Letters)
<p>This dataset contains zonal means of primary production, export potential of distinct biogenic carbon pools, particle sinking flux, and particle export efficiency in the Southern Ocean. See more details in the relevant publication of Huang and Fassbender (2023, Geophysical Research Letters).</p> <p> </p>
Investigating solute transport and reaction using a mechanistically coupled geochemical and geophysical modeling approach
<p>This repository encompasses the acquired data for the five experiments of reactive percolation in a column with geoelectrical monitoring. The input and output files of the reactive transport simulations are also given. In addition, five movies of the reaction rate evolving with time are also part of this dataset. The files are:</p> <ul> <li><strong>physicochemical_data.xlsx</strong>: the ionic concentrations, alkalinity, pH, and electrical conductivity from the collected water samples, the pH monitored at two locations in the column, and the inlet and outlet water conductivity and temperature. Porosity and formation factor calculated from the measured calcium concentration are also given.</li> <li><strong>sp-data-exp1-exp2-exp3.xlsx</strong>: the self-potential (SP) method has been used to monitor the reactive percolation for experiments 1, 2, and 3.</li> <li><strong>sip-data-exp4.xlsx </strong>and <strong>sip-data-exp5.xlsx</strong>: the spectral induced polarization (SIP) method has been used to monitor the reactive percolation for experiments 4 and 5.</li> <li><strong>output_exp</strong><em><strong>X</strong></em><strong>.zip</strong>: compressed folders of all output files for the reactive transport simulations using Crunchflow software.</li> <li><strong>1DcalciteFT_ex<em>X</em>.in</strong>: input files for the reactive transport simulation using CrnchFlow software.</li> <li><strong>OldRifleDatabaseLiLi_nicole.dbs</strong>: database for the reactive transport simulation using CrnchFlow software.</li> <li><strong>video-rate-exp</strong><em><strong>X</strong></em><strong>.avi</strong>: movies of the reaction rate along the column and evolving with time for the complete duration of each experiment.</li> </ul>
Angular momentum estimates for global geophysical fluids, 1995--2015
<p>Data supplement for manuscript Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90, <a href="https://doi.org/10.5194/esd-16-75-2025"> https://doi.org/10.5194/esd-16-75-2025</a>.</p> <p> </p> <div> <p>Provided are the following monthly angular momentum time series, 1995-2015:</p> <ul> <li>Atmospheric angular momentum: AAM_ERA_Int_monthly_1995-2015.aam</li> <li>Oceanic angular momentum: <ul> <li>OAM_OCCIPUT_EnsMean_monthly_1995-2015.oam</li> <li>OAM_OCCIPUT_ens*_monthly_1995-2015.oam</li> </ul> </li> <li>Hydrologic angular momentum: HAM_SLR_DORIS_monthly_1995-2015.asc</li> <li>Cryospheric angular momentum: <ul> <li>Cryo_AM_Greenland_SLR_DORIS_monthly_1995-2015.asc</li> <li>Cryo_AM_Antarctica_SLR_DORIS_monthly_1995-2015.asc</li> </ul> </li> <li>Gravitational attraction and loading angular momentum: GAL_SLR_DORIS_monthly_1995-2015.asc</li> </ul> </div> <p> </p> <p>For content see <em>ReadMe.txt</em>.</p> <p> </p> <p>Terms of usage:</p> <p>If you use the OAM time series, please cite: </p> <p>Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90, <a href="https://doi.org/10.5194/esd-16-75-2025"> https://doi.org/10.5194/esd-16-75-2025</a>.</p> <p>Bessières, L., Leroux, S., Brankart, J.M., Molines, J.M., Moine, M.P., Bouttier, P.A., Penduff, T., Terray, L., Barnier, B., Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: Application at eddying resolution. Geosci. Model Dev. 10, 1091–1106. doi:10.5194/gmd-10-1091-2017.</p> <p>Hogg, A.M., Penduff, T., Close, S.E., Dewar, W.K., Constantinou, N.C., Mart ́ınez-Moreno, J., 2022. Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics. J. Geophys. Res. Oceans 127, e2022JC018440. doi:10.1029/2022JC018440.</p> <div>Penduff, T., Bernier, B., Terray, L., Bessières, L., Sérazin, G., Gregorio, S., Brankart, J.M., Moine, M.P., Brankart, J.M., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate. CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling 19, 26–29.</div> <div> </div> <div> </div> <div> <p>If you use the angular momentum estimates of the other geophysical fluids, please cite: </p> <p>Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90, <a href="https://doi.org/10.5194/esd-16-75-2025"> https://doi.org/10.5194/esd-16-75-2025</a>.</p> </div> <p> </p> <p>Contact: L. Börger (lboerger@igg.uni-bonn.de)</p>
Model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"
<p>This folder contains the model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"</p> <p>The code for plotting the figures is the notebook Plot_figures.ipynb</p> <p>Fig1/simulation_output/ : Model output necessary for plotting the first figure </p> <p>The last timestep of each simulation is provided. There is one file for 1D variables (ice volume, ice volume above flotation), and one file for 2D variables (ice sheet thickness for instance).</p> <ul> <li><span>melt_insoPI_output/ : melt branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>growth_insoPI_output/ : growth branch, pre-industrial insolation. Results for different CO2 levels</span></li> <li><span>melt_insoMAX_output/ : melt branch, maximum insolation. Results for different CO2 levels</span></li> <li><span>growth_insoMIN_output/ : growth branch, minimum insolation. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig1/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p>Fig2/simulation_output/ : Model output necessary for plotting the second figure </p> <p>The last timestep of each simulation is provided. </p> <ul> <li><span>melt_insoPI_enhancedmelt_albfb/ : melt branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_albfb/ : growth branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</span></li> <li><span>melt_insoPI_enhancedmelt_fixedalb/ : melt branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> <li><span>growth_insoPI_enhancedmelt_fixedalb/: growth branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</span></li> </ul> <p><span>compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output</span></p> <p><span>Fig2/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level</span></p> <p> </p> <p><span>Fig3/simulation_output/ : Model output necessary for plotting the third figure </span></p> <ul> <li><span>1xCO2_nocoupling/ : simulation with pre-industrial CO2 levels and insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_nocoupling/ : simulation with 8xpiCO2 (pre-industrial CO2) levels, pre-industrial insolation and no coupling to the ice sheet model</span></li> <li><span>8xCO2_transient_albfb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model </span></li> <li><span>8xCO2_transient_fixedalb/ : quasi transient simulation, 8xpiCO2 levels, pre-industrial insolation, coupling with the ice sheet model excluding the albedo-melt feedback</span></li> </ul> <p> </p>
Data supplementing article "Role of baroclinic processes on flushing characteristics in a highly stratified estuarine system, Mobile Bay, Alabama" submitted to Journal of Geophysical Research: Oceans
<p>The dataset uploaded includes the model inputs and outputs, as well as the time-series of dye mass for each of the 16 numerical experiments that are tested in this study. </p>
Data supplementing article "Estuarine circulation in a shallow but stratified estuary: Different responses to river discharge between deep ship channel and shoals" submitted to Journal of Geophysical Research: Oceans
<p>The dataset uploaded includes the measured salinity and velocity at two monitoring stations (one at the lower Mobile Bay and the other at the eastern edge of ship channel in middle Mobile Bay) and from multiple ship cruises crossing the lower, middle, and upper Mobile Bay. </p> <p>Detail information on the measurement frequency, date, and location can be found in the mat files. Records with bad quality are filled with NaN values. </p> <p> </p> <p> </p>
Data associated with a manuscript submitted to Journal of Geophysical Research - Oceans (Wave generation, dissipation, and disequilibrium in an embayment with complex bathymetry
<p>Observations and Model setup associated with a manuscript submitted to Journal of Geophysical Research - Oceans (Wave generation, dissipation, and disequilibrium in an embayment with complex bathymetry</p>
Datasets for Journal of Geophysical Research-Oceans submission
<p>These Datasets are used in a manuscript submitted to JGR-Oceans. Datasets A1 and A2 include the data from the Acoustic Doppler Current Profiler, CTD cast (with a built-in OBS 3+ sensor), and echo sounder.</p> <p>Dataset A1. This dataset includes profiles of velocity, salinity, temperature, and suspended sediment concentration (SSC).</p> <p>Dataset A2. This dataset includes echosounder signal used for image plots in the manuscript.</p>
Dataset used in "Urban Seismic Site Characterization by Fiber-Optic Seismology" by Spica et al. in Journal of Geophysical Research: Solid Earth
<p>Spica et al. (2019, Journal of Geophysical Research: Solid Earth). This repository contains continuous waveforms from DAS for one day and for the stations mentioned in the article (Fig. 8).</p> <p>Files: all traces in miniseed format. Traces stats in the headers and in the article.</p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF) and Convolutional Neural Network (CNN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <div>[1] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</div> <div>[2] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Lancaster Priory Geophysics Report (Data)
<p>Data for report on ground penetrating radar (GPR) surveys conducted in the grounds of Lancaster Priory (2021-2022)</p>
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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.