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245 results for “geophysical”
Dataset for Rastogi et al., 2022 Geophysical Research Letters
<p>This spreadsheet contains measurements and inferred quantities that are reported in Rastogi et al., 2022 (GRL).</p>
Datasets used for Publication "Contribution of Non-Water Ices to Salinity and Electrical Conductivity in Ocean Worlds" - Geophysical Research Letters
<p>These files father the output data used in the figures published in the Geophysical Research paper:</p> <p><strong>Contribution of Non-Water Ices to Salinity and Electrical Conductivity in Ocean Worlds</strong></p> <p>One output file may be used for multiple figures, as indicated in the name of the Excel spreadsheet.</p>
Model and observations used for Sigmond, M. et al, 'Large contribution of ozone-depleting substances to global and Arctic warming in the late 20th century", Geophysical Research Letters
<p>This dataset contains all the data used for the study "Large contribution of ozone-depleting substances to global and Arctic warming in the late 20th Century" by Sigmond et al, which was submitted to Geophysical Research Letters on July 26, 2022. It contains the following directories:</p> <p>- AR6: csv data files containing timeseries of Effective Radiative Forcing (ERF) as assessed by the IPCC Sixth Assessment Report (AR6).</p> <p>- CanESM5/DATA_siconc_canesm5_past_[expname] contain timeseries of September Sea Ice Extent (SSIE), where expname=historical (all forcing) or hist-noXXX (as historical, except that XXX is fixed to 1955 values)</p> <p>- CanESM5/DATA_tas_canesm5_past_[expname] contains timeseries of surface air temperature (am_: annual mean, arcam_: Arctic and annual mean, gmam_: Global and annual mean). expname as above </p> <p>- CanESM5/IN_TOA_COUPLED_CanESM5 contain timeseries of the global mean top of atmosphere net raditative flux. his=historical (all forcing) run, rms0056=fixed CFC run, rms0058=fixed CFC+stratospheric ozone run, rms0065: fixed CO2 run, and rms0066=fixed aerosol run.</p> <p>- CanESM5_uncoupled/DATA_rtmt_canesm5_YYYY: contains timeseries of RTMT (top of atmosphere radiative imbalance) in the uncoupled (fixed 1955 SST/SI) CanESM5 simulations. YYYY=fall: historical, faer: fixed aerosols, fco2: fixed CO2, fods: fixed CFC runs</p> <p>- OBSERVATIONS: contains timeseries of observational September Sea Ice Extent and surface air temperature used in the study</p> <p>- data_GSAT+ASAT+ERF+efficacies_2001-2005min1955.txt: contains the most import post-processed data </p> <p>Python code used to create these files from the raw model output are available at https://gitlab.com/michael.sigmond/cfc/</p>
The Dataset for the Joint geodynamic-geophysical inversion reveals passive subduction and accretion of the Ontong Java Plateau
<p>The Dataset for the Joint geodynamic-geophysical inversion reveals passive subduction and accretion of the Ontong Java Plateau</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>
HVSR and TDEM geophysical surveys at Jangany, Madagascar.
<p>This dataset is related to the scientific publication Paschetto et al., 2024. Lowcost and portable geophysical solutions for water resources investigation in arid regions. (Submitted to Journal of Applied Geophysics).</p> <p>It contains raw and processed geophysical TDEM and HVSR data for groundwater exploration in a arid environment, with a geological setting composed of layers of laterite and saprolite.</p> <p>For more information, see the readme file and the above mentioned publication. </p>
Data used in manuscript "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning"
<p>Data used in the study titled "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning" published in Environmental Research Letters</p>
Geophysical data from: "A review of geophysical methods for soil structure characterization"
<p>The geophysical data presented herein was collected in the Soil Structure Observatory (see, Keller et. al. 2017) and were used to create Figures 4b, 5a, 5b, 9a and 9b for the manuscript entitled: "A review of geophysical methods for soil structure characterization" submitted to reviews of geophysics. The data set includes:<br> <br> GPR data collected from South to North in the bare soil of block C before and after compaction<br> GPR_800Mhz_BSbeforecompaction.DZW<br> GPR_800Mhz_BSbeforecompaction.DZT<br> GPR_800Mhz_BSaftercompaction.DZW<br> GPR_800Mhz_BSaftercompaction.DZT<br> <br> ERT data collected from South to North in the bare soil of block B before and after compaction<br> ERT_B2_14-03beforecompaction.bin<br> ERT_B2_14-04aftercompaction.bin<br> <br> Time-lapse ERT data collected in block A in September of 2017:<br> timelapseERT2017.zip<br> The date and time in the files names indicate the time in which the resistivimeter started collecting data plus two hours.<br> e.g. for 2017-09-11-18-01-41.bin, the first data point was collected at 14:01 on the 11th of September of 2017.<br> The first 464 data points correspond to bare soil collected from North to South<br> The following 464 data points correspond to grassy soil from South to North<br> <br> Meteorological data from 2017<br> meteodata_Reckenholz2017.xls<br> <br> Time domain reflectometry data from September of 2017 for block A (10, 20, 40 and 70cm depth). Two land covers (bare soil and grassy soil) and two compaction treatments (full compacted and non compacted) are included.<br> TDR_A1_Sept2017.xlsx</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. <em>Vadose Zone Journal</em>, <em>16</em>(4).</p>
Data set of manuscript entitled "Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense GNSS network during 2013–2016" submitted to the Journal of Geophysical Research: Solid Earth
<p>This data set was used for manuscript entitled “Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense Global Navigation Satellite Systems (GNSS) network during 2013–2016” submitted to the Journal of Geophysical Research: Solid Earth. This data set includes 1 figure file, 1 station list and 26 numerical data files. Figure and numerical data are locations of GNSS stations and GNSS time series used in our submitted manuscript, respectively.</p> <p>Figure file maned “location_of_station.png” shows locations of GNSS stations used in our submitted manuscript. Blue dots denote a continuous GNSS network named GEONET was installed by the Geospatial Information Authority of Japan, and red triangles denote continuous GNSS stations constructed by the Japanese University Consortium for GPS Researchers (JUNCO) and operated by the Earthquake Research Institute at the University of Tokyo and allied universities.</p> <p>The coordinates of JUNCO station are collected in a file named “site_junco.bl”. Description of each column is as follows:</p> <p>1. Column 1: Longitude in degree.</p> <p>2. Column 2: Latitude in degree.</p> <p>3. Column 3: Station name.</p> <p>Numerical data is GNSS time series, corresponds to the corrected time series in our submitted manuscript, observed for the period between 1 January 2013 and 31 January 2016. A complete description of data set is found in our submitted manuscript. Description of each column is as follows:</p> <p> </p> <p>1. Column 1: Days since 31 December 2012.</p> <p>2. Column 2: East displacement in cm</p> <p>3. Column 3: North displacement in cm</p> <p>4. Column 4: Vertical displacement in cm</p> <p>5. Column 5: Standard deviation of east displacement in cm</p> <p>6. Column 6: Standard deviation of north displacement in cm</p> <p>7. Column 7: Standard deviation of vertical displacement in cm</p> <p> </p> <p>The numerical data in this data set includes only the 26 JUNCO stations data. Numerical data files are named by the regularity of the combination of the 4 characters station name and extension “.dat”.</p>
Upscaling of elastic properties in carbonates: a modeling approach based on a multi-scale geophysical dataset
<p>Dataset for the article "Upscaling of elastic properties in carbonates: a modeling approach based on a multi-scale geophysical dataset"<br> by Bailly C., Fortin J., Adelinet M., Hamon Y.</p> <p>submitted to Journal of Geophysical Research: Solid Earth.</p> <p>Please refer to the ReadMe file for more details.</p>
Open Datasets for the Manuscript Submitted to Geophysical Research Letters
<ol> <li>Hourly land surface temperature data derived from GOES-R</li> <li>Hourly land surface temperature data derived from Himawari-8</li> <li>MODIS land surface temperature</li> <li>Urban-rural delineation</li> <li>Annual temperature cycle modeling results</li> </ol>
Geophysical Signals from Magma Propagation: Experimental and Processed Data
<p>This repository contains the experimental data analyzed and interpreted in the manuscript titled "<em>Geophysical signals induced by magma propagation: Insights from analog experiments</em>" by S. Furst, J. Vandemeulebrouck, and V. Pinel. It includes video recordings, timelapse photos, accelerometer data, and deformation data. Additionally, there are three MATLAB scripts for post-processing the timelapse photos following the approach described in the manuscript. The results of the MFP analysis on the accelerometer data, as well as the outcomes from the COMSOL Multiphysics simulations, are also included in the repository.</p>
Applied Geophysics - Magnetic Anomaly Data Mosaic of the Socorro-Guaxupé Geological Area (Brazil)
<p>The Socorro-Guaxupé area is a complex structural environment formed during Proterozoic times, associated with the formation of the Gondwana Supercontinent. The notable magnetic signatures of these geological areas were selected for an Applied Geophysical project for undergraduates in the Geology Course at the University of São Paulo in 2024.</p> <p>This grid mosaic encompasses airborne magnetic data from areas 15, 14, 7, and 2 of the CODEMIG repository (<a href="http://www.codemig.com.br/" target="_new" rel="noopener">http://www.codemig.com.br/</a>) as well as data from areas 1105, 1117, 1039, 4099, 4112, and 4210 from the Geological Survey of Brazil (SGB-CPRM). The surveys were gridded with cell sizes of 1/4 of the flight line spacing, using the minimum curvature method. Additionally, they were continued to a nominal altitude of 700 m for decreasing noise signal, due to the variable spatial resolution and acquisition techniques. Grid stitching was performed using pairs of grids, with the stitching method implemented in Oasis Montaj software. The locations of all surveys are presented in the figure "socorro-guaxupe_surveys.tif."</p> <p>We would like to thank the team at the Laboratory for Research in Applied Geophysics (LPGA) and the professors from the Rio de Janeiro State University (UERJ) and GeoAtlantico Institute for their technical support in assembling this data.</p> <p>This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 (L. Szameitat 88887.798323/2022-00)</p> <p> </p>
Data to support "Reduced winter-time Clear Air Turbulence in the trans-Atlantic region under Stratospheric Aerosol Injection by Katie L Barnes, Anthony C Jones, Paul D Williams and James M Haywood, Submitted to Geophysical Research Letters, November 2024"
<p>This dataset contains all the UKESM1 climate model output needed to reproduce Figures 1-4 in the paper "Reduced winter-time Clear Air Turbulence in the trans-Atlantic region under Stratospheric Aerosol Injection by Katie L Barnes, Anthony C Jones, Paul D Williams and James M Haywood, Submitted to Geophysical Research Letters, November 2024". UKESM1 is the Met Office Earth System Model (N96 resolution) with simulations using a fully-coupled ocean-atmosphere following CMIP6 protocol.</p> <p>All files are CF-1.7 compliant and in NetCDF format with appropriate metadata. Each file contains 6 hourly data for the 30 simulated years used in the paper (50 for the pre-industrial control period).</p> <p>The following simulations are included, following ScenarioMIP and GeoMIP specifications:</p> <p>Folder | Simulation name | Time period<br>-------------------------------------------------------------<br>piControl | PiControl | 2250-2299<br>historical | Historical | 1985-2014<br>ssp245 | SSP2-4.5 | 2070-2099<br>ssp585 | SSP5-8.5 | 2070-2099<br>g6sulfur | G6sulfur | 2070-2099</p> <p>The files use CMIP-like naming conventions.</p> <p>VARIABLE_LEVEL_TIMEPERIOD_MODEL_EXPERIMENT_STARTDATE-ENDDATE_SEASON.nc</p> <p>The variables comprise:</p> <p>Short name | Description (units, if any)<br>-------------------------------------------------------------<br>ua | zonal wind (m.s-1)<br>va | meridional wind (kg.m-3)<br>ta | air temperature (K)</p> <p>All figures in the paper were produced using Python (3.11) and Iris scientific analysis software.</p> <p>All data is Crown Copyright, Met Office, and is made available under the terms of the Non-Commercial Government Licence: http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/</p> <p>Description of the UKESM1 model is provided in Sellar, A. A., et al.: UKESM1: Description and evaluation of the U.K. Earth System Model. Journal of Advances in Modeling Earth Systems, 11, 4513–4558. https://doi.org/10.1029/2019MS001739, 2019.</p> <p>Description of the ScenarioMIP specifications are provided here: O'Neill, B. C., et al.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.</p> <p>Description of G6sulfur is provided here: Kravitz, B., et al. : The Geoengineering Model Intercomparison Project Phase 6 (GeoMIP6): simulation design and preliminary results, Geosci. Model Dev., 8, 3379–3392, https://doi.org/10.5194/gmd-8-3379-2015, 2015. </p>
Dataset_Guinoiseau_et_al_2021_Geophysical_Research_Letters
<p>The Excel spreadsheet reports the dataset associated with the article entitled "Tropical weathering history recorded in the silicon isotopes of lateritic weathering profiles" and published in Geophysical Research Letters by Guinoiseau and coauthors. </p>
Mauna Kea permafrost survey, Geophysical Data
<p>Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) Survey raw data at Puu Wekiu, Puu Hau Kea, Puu Waiau, and Puu Pohaku, Mauna Kea, Hawaii. See README.txt for more information and cited references for context and results.</p>
Sensing a priori constraints in DNNs for solving geophysical problems
<p>Original data and associated results for the paper "Sensing a priori constraints in DNNs for solving geophysical problems" submitted to PNAS on Nov. 16th, 2022.</p>
Dataset to manuscript "The role of weathering on morphology and rates of escarpment retreat of the rift margin of Madagascar" by Wang et al. (2023) submitted to Journal of Geophysical Research-Earth Surface.
<p>The dataset includes the relevant raw chemical element content data and the chemical weathering condition analysis of river sediment samples of Madagascar. The dataset is a supplement to the manuscript "The role of weathering on morphology and rates of escarpment retreat of the rift margin of Madagascar", by Wang et al. (2023) submitted to the Journal of Geophysical Research-Earth Surface. Commercially sensitive data is hidden but is available by request directly to the corresponding author. The data should <strong>NOT</strong> be used commercially.</p> <p>A MATLAB code for the weathering indices calculation is open-access on GitHub (https://github.com/yanyanwangesd/chemical_weathering). Please contact the corresponding author for more info or technical support on using the code. </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.