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424 results for “gravity”
Fatiando a Terra Data: Earth - Gravity grid at 10 arc-minute resolution
<p>Global 10 arc-minute resolution grids of gravity acceleration (gravitational and centrifugal) at 10 km geometric height.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Convert the grid from the ASCII format of ICGEM to CF-compliant netCDF. Add relevant metadata, including names, units, datum, etc. Fix grid coordinates to be generated by <code>numpy.linspace</code> instead <code>numpy.arange</code> (or the equivalent used by ICGEM internally) to guarantee equal spacing to a higher accuracy. Export to compressed netCDF.</p> <p><strong>Source: </strong><a href="https://doi.org/10.5880/icgem.2015.1">EIGEN-6C4</a> spherical harmonic model (generated by the <a href="http://icgem.gfz-potsdam.de/home">ICGEM calculation service</a>)</p> <p><strong>Source license: </strong><a href="https://doi.org/10.5880/icgem.2015.1">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/earth-gravity-10arcmin">https://github.com/fatiando-data/earth-gravity-10arcmin</a></p>
Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b
<p>This dataset contains machine learning and volunteer classifications from the <a href="https://www.zooniverse.org/projects/zooniverse/gravity-spy">Gravity Spy project</a>. It includes glitches from observing runs <a href="https://doi.org/10.7935/K57P8W9D">O1</a>, <a href="https://doi.org/10.7935/CA75-FM95">O2</a>, <a href="https://doi.org/10.7935/nfnt-hm34">O3a</a> and <a href="https://doi.org/10.7935/pr1e-j706">O3b</a> that received at least one classification from a registered volunteer in the project. It also indicates glitches that are nominally retired from the project using our default set of retirement parameters, which are described below. See more details in the <a href="https://ui.adsabs.harvard.edu/abs/2017CQGra..34f4003Z/abstract">Gravity Spy Methods paper</a>. </p> <p>When a particular subject in a citizen science project (in this case, glitches from the LIGO datastream) is deemed to be classified sufficiently it is "retired" from the project. For the Gravity Spy project, retirement depends on a combination of both volunteer and machine learning classifications, and a number of parameterizations affect how quickly glitches get retired. For this dataset, we use a default set of retirement parameters, the most important of which are: </p> <ol> <li>A glitches must be classified by at least 2 registered volunteers</li> <li>Based on both the initial machine learning classification and volunteer classifications, the glitch has more than a 90% probability of residing in a particular class</li> <li>Each volunteer classification (weighted by that volunteer's confusion matrix) contains a weight equal to the initial machine learning score when determining the final probability</li> </ol> <p>The choice of these and other parameterization will affect the accuracy of the retired dataset as well as the number of glitches that are retired, and will be explored in detail in an upcoming publication (Zevin et al. in prep). </p> <p>The dataset can be read in using e.g. Pandas: <br> ```<br> import pandas as pd<br> dataset = pd.read_hdf('<a href="https://zenodo.org/api/files/512bfa79-dfbc-4af0-b563-9fdd06edcb16/retired_fulldata_min2_max50_ret0p9.hdf5?versionId=7f568823-76d9-4452-8553-c1eee5993f81">retired_fulldata_min2_max50_ret0p9.hdf5</a>', key='image_db')<br> ```<br> Each row in the dataframe contains information about a particular glitch in the Gravity Spy dataset. </p> <p><strong>Description of series in dataframe</strong></p> <ul> <li>['1080Lines', '1400Ripples', 'Air_Compressor', 'Blip', 'Chirp', 'Extremely_Loud', 'Helix', 'Koi_Fish', 'Light_Modulation', 'Low_Frequency_Burst', 'Low_Frequency_Lines', 'No_Glitch', 'None_of_the_Above', 'Paired_Doves', 'Power_Line', 'Repeating_Blips', 'Scattered_Light', 'Scratchy', 'Tomte', 'Violin_Mode', 'Wandering_Line', 'Whistle'] <ul> <li>Machine learning scores for each glitch class in the trained model, which for a particular glitch will sum to unity</li> </ul> </li> <li>['ml_confidence', 'ml_label'] <ul> <li>Highest machine learning confidence score across all classes for a particular glitch, and the class associated with this score</li> </ul> </li> <li>['gravityspy_id', 'id'] <ul> <li>Unique identified for each glitch on the Zooniverse platform ('gravityspy_id') and in the Gravity Spy project ('id'), which can be used to link a particular glitch to the <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">full Gravity Spy dataset</a> (which contains GPS times among many other descriptors)</li> </ul> </li> <li>['retired'] <ul> <li>Marks whether the glitch is retired using our default set of retirement parameters (1=retired, 0=not retired)</li> </ul> </li> <li>['Nclassifications'] <ul> <li>The total number of classifications performed by registered volunteers on this glitch</li> </ul> </li> <li>['final_score', 'final_label'] <ul> <li>The final score (weighted combination of machine learning and volunteer classifications) and the most probable type of glitch</li> </ul> </li> <li>['tracks'] <ul> <li>Array of classification weights that were added to each glitch category due to each volunteer's classification</li> </ul> </li> </ul> <p> </p> <p>```<br> For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see <a href="https://zenodo.org/record/5649212#.YfLNjVjMLzc">Gravity Spy Machine Learning Classifications</a> on Zenodo</p> <p>For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see <a href="https://zenodo.org/record/1486046#.YZfcar3MJqs">Gravity Spy Training Set</a> on Zenodo.</p> <p>For detailed information on the training set used for the original Gravity Spy machine learning paper, please see <a href="https://zenodo.org/record/1476156#.YZfchL3MJqs">Machine learning for Gravity Spy: Glitch classification and dataset</a> on Zenodo. </p>
Fatiando a Terra Data: Bushveld, Southern Africa - Observed and preprocessed gravity
<p>This dataset contains ground gravity observations over the area that comprises the Bushveld Igenous Complex in Southern Africa, including preprocessed gravity fields such as the <em>gravity disturbance</em> and the <em>bouguer gravity disturbance</em> (topography-free gravity disturbance). In addition, the dataset contains the heights of the observation points referenced on the WGS84 reference ellipsoid and over the mean sea-level (what can be considered to be the geoid). This dataset was built upon a portion of the Southern Africa gravity compilation available through <a href="https://www.ngdc.noaa.gov/mgg/gravity/">NOAA NCEI</a>.<br> <br> <strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.<br> <br> <strong>Changes made: </strong></p> <ul> <li>The original data were cropped to a region bounded by 25 and 32 degrees on longitude and -27 and -23 degrees on latitude.</li> <li>Geometric observation heights were obtained by adding geoid heights to the original observation heights referenced on the mean sea-level. The geoid heights on each observation point were obtained by interpolation of the geoid available in doi: <a href="https://doi.org/10.5281/zenodo.5882205">10.5281/zenodo.5882205</a>.</li> <li>Gravity disturbances were computed by removing the normal gravity of the WGS84 ellipsoid computed through <a href="https://www.fatiando.org/boule">Boule</a>.</li> <li>Bouguer gravity disturbances were computed by forward modelling the topography using <a href="https://www.fatiando.org/harmonica">Harmonica</a> starting from the topography grid provided in doi: <a href="https://doi.org/10.5281/zenodo.6481379">10.5281/zenodo.6481379</a> and using densities of 2670 kg/m³ above the ellipsoid and 1040 - 2670 kg/m³ below the ellipsoid.</li> </ul> <p><strong>Source: </strong><a href="https://www.ngdc.noaa.gov/mgg/gravity/">NOAA NCEI</a> (gravity) and <a href="https://doi.org/10.7289/V5C8276M">ETOPO1</a> (topography)</p> <p><strong>Source license: </strong><a href="https://ngdc.noaa.gov/ngdcinfo/privacy.html">public domain</a> (gravity) and <a href="https://ngdc.noaa.gov/mgg/global/dem_faq.html#sec-2.4">public domain</a> (topography)</p> <p><strong>Repository</strong>: <a href="https://github.com/fatiando-data/bushveld-gravity">https://github.com/fatiando-data/bushveld-gravity</a></p>
Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations
<p>The data for the article "Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations" (GWTA). </p> <p> </p> <ol> <li>"Titan_CJP_std_chem.dat" is the background atmosphere data of Titan's atmosphere from Strobel's model. It is used in Figure 1 of the article.</li> <li>"HASI_T_p_rho_vsZ_2008.dat" is the data for Cassini-Huygens observations in Titan's atmosphere. It is used in Figure 1 of the article.</li> <li>"Mma-Program-for-GW-on-Titan.txt" is the main Mathematica program to simulate the gravity waves on Titan.</li> <li>"solutions-fun.rar" is the simulation result. This RAR file includes 174 gravity wave samples simulated with different periods and horizontal wavelengths (can be read from the subfile names after uncompressing). These gravity wave solutions are stored as InterpolatingFunction of Mathematica. The solution describes the gravity wave temperature, velocity, and density perturbations profiles from altitude 300km to 2000km. However, they are plain texts and can easily be read by any software. Figures from 2-10 are based on these data.</li> </ol> <p> </p>
Dataset for paper "Towards "Matter matters" in spin foam quantum gravity"
<p>This is a dataset and Julia tools for evaluation / presentation corresponding to the article: https://arxiv.org/abs/2206.04076</p> <p>The data correspond to samples of a coupled spin foam / matter system (via a Markov Chain Monte Carlo algorithm) for different lattice sizes. We also add an IJulia file and to generate the plots from the data and eventually modify them to study also other aspects.</p> <p>The code to generate these samples can be found at: https://github.com/amoosam/CuboidSpinfoamScalarField</p>
Corresponding Dataset for Gravity Field of Ganymede after the Juno Extended Mission
<p>Corresponding Dataset for Gravity Field of Ganymede after the Juno Extended Mission</p> <p> Luis Gomez Casajus August 2, 2022</p> <p>=============================================================================<br> Introduction<br> =============================================================================</p> <p> This dataset contains the estimated gravity field and its corresponding<br> full covariance matrix. This dataset is provided in order to <br> supplement the submitted article to the "Geophysical Research Letters"<br> journal:</p> <p> Gravity Field of Ganymede after the Juno Extended Mission.<br> L. Gomez Casajus (*), A. I. Ermakov, M. Zannoni, J. T. Keane, <br> D. Stevenson, D. R.Buccino, D. Durante, M. Parisi, R. S. Park, <br> P. Tortora and S. J. Bolton<br> <br> - (*) luis.gomezcasajus@unibo.it</p> <p>=============================================================================<br> File description<br> =============================================================================</p> <p> This archive contains two files within the root directory.<br> <br> ROOT<br> -SOI_gravity_field.txt</p> <p> This file contains the estimated normalized spherical harmonics <br> coefficients of the Ganymede gravity field.<br> The file contains a header row which provides a description of <br> the file.</p> <p> -SOI_cov_matrix.txt</p> <p> This file contains the estimated full covariance matrix (32x32) <br> of the normalized spherical harmonics coefficients of the <br> Ganymede gravity field. The file contains a header row which <br> provides a description of the file. The matrix is represented as<br> a 2 dimensional array whose coefficients follow the following <br> order scheme: C20 C21 S21 C22 S22 C30 C31 ...</p> <p>=============================================================================<br> ACKNOWLEDGMENTS<br> =============================================================================</p> <p> The authors are grateful to William Folkner, to the entire Solar System <br> Dynamics Group and to Robert Haw, former Galileo navigator, for the useful <br> discussions and suggestions regarding the procedures for Galileo data <br> analysis. L.G.C., M.Z., and P.T. are grateful to the Italian Space Agency <br> (ASI) for financial support through Agreement No. 2017-40-H.1-2020, and its <br> extension2017-40-H.02020-13-HH.0, for ESA’s BepiColombo and NASA’s Juno radio<br> science experiments. L.G.C., M.Z., and P.T. acknowledge Caltech and the Jet <br> Propulsion Laboratory for granting the University of Bologna a license to an <br> executable version of MONTE Project Edition S/W. JTK and AIE acknowledge <br> support from the Juno participating scientist program. The work of RP, DB, <br> JTK, and MP was carried out at the Jet Propulsion Lab, California Institute<br> of Technology, under a contract with the National Aeronautics and Space <br> Administration (80NM0018D0004). Government sponsorship acknowledged. </p>
Global characterization of the ocean's internal gravity wave vertical wavenumber spectrum from Argo float profiles
<p>Oceanic internal gravity wave energy levels E (m^2/s^2), vertical wavenumber spectral slopes s, and vertical wavenumber scale m* (1/m) estimated by fitting the Garrett Munk model vertical wavenumber shape function to strain spectra obtained from Argo float hydrographic profiles based on the finestructure method, as discussed in Pollmann (2020): "Global Characterization of the Ocean’s Internal Wave Spectrum" (<em>Journal of Physical Oceanography</em> 50.7: 1871-1891). The paper and hence this dataset are a contribution to the Collaborative Research Centre TRR181 ‘Energy Transfers in Atmosphere and Ocean’ funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Projektnummer 274762653. The hydrographic profiles used in this study were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System.</p> <p>Please cite Pollmann (2020) when using this dataset.</p> <p>This dataset includes:</p> <p>a) energy density (m^2/s^2) binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>b) vertical wavenumber spectral slopes binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>c) vertical wavenumber scale m* (1/m) binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>d) latitude and longitude, defined such that, e.g., E(10,10) represents energy levels in the bin bounded by lat(10), lat(11) as well as lon(10), lon(11)</p>
Dataset: Gravity Co., Ltd. (GRVY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
A new global marine gravity model NSOAS24 derived from multi-satellite sea surface slopes
<p><span><span><span>NSOAS24 is the global marine gravity anomaly model on a grid of 1′×1′, which is derived based on sea surface slopes from multi-satellite altimetry missions. </span></span></span><span><span><span>Its spatial coverage is 80°S-80°N.</span></span></span></p>
Analysing the intra and interregional components of spatial accessibility gravity model to capture the level of equity in the distribution of hospital services: does they influence patient mobility?
<p>aggregated_data_age55+.csv and distance_matrix_age55+.csv have been included in the second version of the dataset as the reference population is limited to resident with 55 years old or more.</p>
Yerrida Basin 3D geological model and gravity inversion results
<p>This dataset contains an archive for an implicit 3D geological model of the Yerrida Basin, southern Capricorn region, Western Australia.</p> <p><strong><em>Yerrida_Basin_3D.zip </em></strong>is a GeoModeller three dimensional geological model. Also included are 2D and 3D voxets resulting from inversion of gravity data using the geological model as a constraint. Geomodeller software is available from here: <a href="https://www.intrepid-geophysics.com/ig/index.php?page=downloads">https://www.intrepid-geophysics.com/ig/index.php?page=downloads</a></p> <p>This is a companion dataset for the paper submitted to the scientific journal Solid Earth: Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin.<em> </em>Mark D Lindsay, Sandra Occhipinti, Crystal LaFlamme, Alan Aitken, Lara Ramos.</p> <p> </p>
Surface Gravity Waves in the Gulf of Mexico
<p>This dataset includes the results of a coupled SWAN-ROMS simulation over the Gulf of Mexico between 2001 and 2010. Published at https://doi.org/10.1029/2018JC014820 (JGR: Oceans)</p>
Ocean and ice with waves data for role of surface gravity waves in aquaplanet ocean climates
<p>This data corresponds to the runs analysed in the manscript: Role of Surface Gravity Waves in Aquaplanet Ocean Climates (JAMES, 2021).</p> <p>In this work, we present a set of idealised numerical experiments that demonstrate the thermodynamic and dynamic implications of surface gravity waves for the oceanic climate of an aquaplanet. We study the impact of accounting for modulations by such waves upon air-sea momentum fluxes, Langmuir circulation and the Stokes-Coriolis force.</p> <p>This dataset is made up of atmospheric, oceanic and surface gravity wave simulations. When uncompressed the total dataset is 1.6 TB, the ocean and ice with waves component is 564 GB. See below for further details.</p> <p>See the related works section for the corresponding datasets.</p>
Ocean and ice without waves data for role of surface gravity waves in aquaplanet ocean climates
<p>This data corresponds to the runs analysed in the manscript: Role of Surface Gravity Waves in Aquaplanet Ocean Climates (JAMES, 2021).</p> <p>In this work, we present a set of idealised numerical experiments that demonstrate the thermodynamic and dynamic implications of surface gravity waves for the oceanic climate of an aquaplanet. We study the impact of accounting for modulations by such waves upon air-sea momentum fluxes, Langmuir circulation and the Stokes-Coriolis force.</p> <p>This dataset is made up of atmospheric, oceanic and surface gravity wave simulations. When uncompressed the total dataset is 1.6 TB, the ocean and ice without waves component is 484 GB. See below for further details.</p> <p>See the related works section for the corresponding datasets.</p>
Linking detected gravity modes to axisymmetric internal magnetic fields
<p>The spectropolarimetric techniques used to characterize surface magnetic fields do not probe the stellar interior, and therefore cannot be used to constrain internal magnetic fields. The most promising way to detect/characterize such fields is to consider their effect on stellar oscillations that probe deep stellar layers, the gravity modes. Conventional asteroseismic modelling ignores any effect of a (internal) magnetic field, however, Prat et al. (2019) developed a formalism that takes the perturbative effect into account for axisymmetric, dipolar, fossil magnetic fields confined to the stellar interior, allowing one to perform magneto-asteroseismic modeling for the first time. Most detected gravity modes in intermediate-mass main-sequence pulsating stars are dipolar, and propagate in the direction of rotation (e.g. Li et al. (2020)). In this talk I will discuss the typical signatures expected in period spacing patterns of dipolar gravity modes, according to the Prat et al. (2019) formalism, for a range of stellar models covering fundamental parameters typical for this mass range. We vary the initial metallicity and mass, the magnetic field strength, the rotation rate, and the degree of mixing in the envelope and near-core regions, throughout the evolution of the main sequence. We find detectable signatures that are significantly different from those due to rotation in the period spacing patterns of gravity modes in terminal age main sequence models if the near-core magnetic field strength is higher than 10^5 Gauss. Such signatures can be used in future magneto-asteroseismic modeling based on photometry from space missions such as Kepler (Borucki et al. (2010)), TESS (targets in the continuous viewing zone; Ricker et al. (2015)) and PLATO (Rauer et al. (2014)).</p>
Internal gravity waves generated by subglacial discharge: Implications for tidewater glacier melt.
<p>Additional data used in the paper titled "Internal gravity waves generated by subglacial discharge: implications for tidewater glacier melt", submitted to Geophysical Research Letters, that is not already contained in the GitHub repository.</p>
Data files for Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC)
<p>The files in this set are data obtained from the NIRAC airglow imager on the International Space Station. The files are named for a JGR paper by J. Hecht et al. entitled Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC). These files are for plots in Figures 5,7,10,11,17,18, and 19 in the submitted paper. The files are published here so as to be available for review. This paper should appear in JGR Atmospheres sometime in late 2023 or early 2024. The files that are text files are meant to be read with IDL as discussed in the readme file. </p>
Insights into the Magmatic Feeding System of the 2021 Eruption at Cumbre Vieja (La Palma, Canary Islands) Inferred from Gravity Data Modeling. Remote Sens. 2023, 15, 1936. https://doi.org/10.3390/rs15071936
<p>Paper: Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling <br> F. G. Montesinos1,7, S. Sainz-Maza2,7, D. Gómez-Ortiz3, J. Arnoso4,7, I. Blanco-Montenegro5,7, M. Benavent1,7 E. Vélez4,7, N. Sánchez6 and T. Martín-Crespo3</p> <p>1 Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Plaza de Ciencias 3, 28040 Madrid, Spain.<br> 2 Observatorio Geofísico Central (IGN). C/ Alfonso XII, 3. 28014 Madrid, Spain.<br> 3 Dpt. Biología y Geología, Física y Química Inorgánica, ESCET, Universidad Rey Juan Carlos. C/Tulipán s/n, 28933 Móstoles, Madrid, Spain.<br> 4 Instituto de Geociencias (IGEO), CSIC-UCM. C/ Doctor Severo Ochoa, 7. 28040 Madrid, Spain.<br> 5 Departamento de Física, Escuela Politécnica Superior, Universidad de Burgos. Avda. de Cantabria s/n, 09006 Burgos, Spain.<br> 6 Instituto Geológico y Minero de España (IGME, CSIC), Unidad Territorial de Canarias, Alonso Alvarado, 43, 2A, 35003 Las Palmas de Gran Canaria, Spain.<br> 7 Research Group ‘Geodesia’, Universidad Complutense de Madrid, Spain.</p> <p><br> Corresponding author: Fuensanta G. Montesinos (fuensant@ucm.es)</p> <p>This research is supported by the project PID2019-104726GB-I00/AEI/10.13039/501100011033 funded by the Spanish Research Agency. Further, the University Complutense of Madrid (grants Financiación Grupos 2021, UCM 2022-GRFN14/22) and the Spanish Ministry of Science and Innovation (RD 1078/2021, funding for research activities of the CSIC-PIE project CSIC-LAPALMA-07) supported this research.</p> <p>------------------------------------------------------------------------------------------------</p> <p>Responsible Researchers:<br> - Fuensanta González Montesinos, Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Spain<br> fuensant@ucm.esResponsible Researchers: </p> <p>- José Arnoso Sampedro, Instituto de Geociencias (CSIC-UCM), Spain<br> jose_arnoso@csic.es</p> <p> </p> <p><br> >> The use of this data set is limited to academic or research purposes and it have to be referenced</p> <p><br> Zone:Cumbre Vieja (La Palma Island, Spain)<br> Geodetic Coordinates Datum WGS84<br> Gravity(mGal) and Bouguer Gravity anomaly GRS80 (mGal)(Terrain density 2450 kg/m3)</p> <p>The file GravityCumbreVieja_FGMontesinos_et_al.dat includes the values of gravity and complete Bouguer gravity anomaly (GRS80) calculated for the land gravity stations at the Cumbre Vieja area (La Palma Island, Spain). The gravity values were observed in 142 land gravity stations (Figure 3 in the manuscript) by our group in 2005 and 2021 surveys The positions of the stations were selected to cover most of the Cumbre Vieja area, and the coordinates were obtained by differential GPS (WGS84 Datum). The gravity observations were processed taking into account the usual corrections (instrument height, drift, jumps, etc.). The tidal correction was calculated from gravity tide measurements made in several islands of the Canary Archipelago. All the gravity values referred to absolute gravity stations (Table S1). The procedure to obtain the terrain correction and the Bouguer anomaly map is explained in the manuscript and in the supporting information.</p>
Gravity modes on rapidly rotating accreting white dwarfs and their variation after dwarf novae
<p>MESA (r10398) inlist files and Gyre (5.2) files used in preparation of the paper "Gravity modes on rapidly rotating accreting white dwarfs and their variation after dwarf novae". More details for specific files are given in README.txt. </p>
A haptic illusion created by gravity - Data and Codes
<p>Dataset and code related to the study <em>A haptic illusion created by gravity</em> by Opsomer L, Delhaye BP, Théate V, Thonnard J-L, and Lefèvre P.</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.