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424 results for “gravity”
Data for model analysis in "Beyond the growth rate of cosmic structure: Testing modified gravity models with an extra degree of freedom", arXiv:1502.03710
<p>SQLite databases containing theoretical predictions for the model comparison in arXiv:1502:03710.</p>
Impact of a gravity wave process on the upper stratospheric ozone valley on the Qinghai-Tibetan Plateau
<p>The data sets are the results of WRF simulation and are used to plot the figures in this paper (Figure 8 to 11)</p>
Gravity and magnetotelluric data in the Asal Rift, Republic of Djibouti
<p>Gravity data were obtained from a gravimetric survey carried out by BRGM between 1979 and 1980. Magnetotelluric data was produced in 2007 (Árnason et al, 2008, Khodayar, 2008) in collaboration with Iceland Geosurvey (ISOR), Reykjavik Energy Invest (REI) and the Centre d'Etude et de Recherche de Djibouti (CERD).</p>
Dataset for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
Open the record for dataset details and reuse information.
Free-air gravity anomaly data at the continental margin and oceanic basin north of the Daimao Seamount of the South China Sea
<p>This archive includes the free-air gravity anomaly data (in mGal) from the continental margin to the oceanic basin north of the Daimao Seamount in the South China Sea. The data was collected with the help of the Guangzhou Marine Geological Survey. </p>
Global Marine Gravity Gradient Tensor Inverted from Altimetry-derived Deflections of the Vertical: CUGB2023GRAD
<p>CUGB2023GRAD is a dataset consisting of all six components of Earth's gravity gradient tensor over the oceans. The gravity gradient tensor is inverted from 1 arc-minute grid of altimetry-derived north-south and east-west components of deflection of the vertical. CUGB2023GRAD has a longitudinal extent of 180° W ~180° E and a latitudinal extent of 80° S ~ 80° N.</p> <p>This version used a merge of deflections of the vertical (north_32.1.nc and east_32.1.nc) developed by Scripps Institution of Oceanography, and DTU21GRA-derived deflections of the vertical. DTU21GRA is a highly accurate gravity anomaly model developed by Technical University of Denmark. Both sets of deflections of the vertical were developed from multiple satellite altimetry observations which include: Jason-1, Jason-2, Cryosat-2, SARAL/AltiKa, and Sentinel-3A/B.</p>
Dataset for "Contributions of Parameterized Gravity Waves and Resolved Equatorial Waves to the QBO Period in a Future Climate of CESM2" by Lee et al. (2024)
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Supporting data for "A method for non-linear inversion of the stellar structure applied to gravity-mode pulsators"
<p>These are the inlist and run_star_extras required to reproduce the stellar and asteroseismic models presented in 'A method for non-linear inversion of the stellar structure applied to gravity-mode pulsators', run with MESA r22.05.1.</p>
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>
A possible mechanism for the stratospheric influence on tropical cyclone intensity: Downward control by gravity waves
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Seismic refraction, reflection and free-air gravity data of OBS2020-3 in the southwest sub-basin, South China Sea
<p>This dataset (OBS2020-3.Files.zip) contains SEGY files of the OBS2020-3 and the NW section of the MCS2020-3 profiles, as well as the free-air gravity anomaly data along the seismic profiles. The time-axis of the SEGY files for the ocean bottom seismometers are reduced by a reduction velocity of 6.0 km/s. </p>
Data for Paper: Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions
<p>Laboratory measurements of wind, waves, and airside static pressure under low to moderate wind forcing (U10 ~ 6 -16 m/s) collected in Oct 2022 in the SUSTAIN wind-wave facility at the University of Miami.</p> <p>This dataset includes 11 runs, all of which contain monochromatic waves generated by the wave paddles with various wind forcing exerted above. All data is in ".mat" formate readable via MATLAB.</p> <p>Experiment set up and positions of instruments are documented in more details in the manuscript Tan et al (2024): Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions.</p> <p> Fig_3: time series static pressure p sampled at 100 Hz and horizontal/vertical wind speed (u/w) sampled at 1000 Hz</p> <p>Fig_4: Frictional velocity u_star_Rn* obtained at differenet heights (h) using frictional velocity</p> <p>Fig_5: a folder that containes the phase-averaged, spline-interpolated static pressure (p2_total), X-coordinate (long-wave phase), Y coordinate (heights above the stationary water) and the u/w at respective heights to generate airflow streamlines</p> <p>Fig_6 and 7: NSS-based phase-averaged, spline-interpolated pressure (delta_P_new).</p> <p>Fig_8: phase-averaged form stress based on measurements and NSS for all 11 runs</p> <p>Fig_9: NSS-based form stress deviation from measured form stress (NSS miscal) against wind-steepness and wave age;</p> <p>Fig_10 and 11: wave growth rate (gamma) against wave age (Cp/ustar) and two other parameterization from Fig.10</p> <p>(The revised version contains the projection of Donelan (1999) and Yang et al. (2013)'s data to the U10/Cp parameterization in panel (b) per reviewer's suggestion);</p> <p>Fig_12: form stress values (tau_form) and form stress to total stress (tau_tot) ratio.</p> <p>(The revised version contains U10 per reviewer's suggesion).</p> <p>This project was Funded in part by Office of Naval Research/Naval Research Laboratory base program unit 73-1Y91.</p> <p>Please cite our JGR: Oceans paper "Wind-wave momentum flux in steep, strongly forced,1 surface gravity wave conditions" if you were to use our dataset.</p> <p>Contact: Peisen Tan <pxt254@miami.edu> for different levels of raw data collected in this experiment.</p> <p>We kindly ask the readers who use our dataset to cite our paper:</p> <p><span>Tan, P.</span><span>, </span><span>Savelyev, I.</span><span>, </span><span>Laxague, N. J. M.</span><span>, </span><span>Haus, B. K.</span><span>, </span><span>Curcic, M.</span><span>, </span><span>Matt, S.</span><span>, et al. (</span><span>2025</span><span>). </span><span>Wind-wave momentum flux in steep, strongly forced, surface gravity wave conditions</span><span>. </span><em>Journal of Geophysical Research: Oceans</em><span>, </span><span>130</span><span>, e2024JC021616. </span><a href="https://doi.org/10.1029/2024JC021616">https://doi.org/10.1029/2024JC021616</a></p> <p>We would also appreciate if you can send us a copy of your manuscript if you have used our data. Thank you!</p>
Supporting material for Petricca et al. (2024), "Gravity and Radio Science Investigation at the Moons of Uranus to Reveal Subsurface Oceans and Characterize Interior Structures", JGR: Planets
<p>This archive contains the supplementary material for the paper "Gravity and Radio Science Investigation at the Moons of Uranus to Reveal Subsurface Oceans and Characterize Interior Structures", JGR: Planets</p> <p>Content of the dataset:</p> <ol> <li>Synthetic gravity fields for Ariel and Titania generated in the study</li> <li>SPICE kernels of the trajectory of the Uranus Orbiter and Probe designed at JPL</li> </ol> <p> </p> <p>---------------------------------------------------------------</p> <p>Synthetic gravity fields</p> <p>---------------------------------------------------------------</p> <p>The gravity fields are generated following the procedures described in Section 2.1.2 of the main paper. The hydrosphere thickness is assumed to be 190 km and 220 km for Ariel and Titania, respectively. The ocean density is fixed at 1050 kg/m^3. The syntethic topography is generated with pyshtools (Wieczorek and Meschede, 2018). The label of the file indicates the amplitude of the topography of each interface (ice shell or ocean floor) and the maximum degree of the spherical harmonics expansion. The files are formatted according to the Spherical Harmonics ASCII Data Record (SHADR) standard.</p> <p>The header of each file contains: reference radius (km), GM (km^3 / s^2), uncertatinty in the GM (not used and set to zero), maximum degree <em>l </em>of the<em> </em>expansion, maximum order<em> m </em>of the expansion, normalization (0 for unnormalized, 1 for 4pi normalization), reference latitude, reference longitude</p> <p>The columns contain: degree <em>l</em>, order <em>m</em>, coefficient C_<em>lm</em>, coefficient S_<em>lm</em></p> <p>---------------------------------------------------------------</p> <p>UOP trajectories</p> <p>---------------------------------------------------------------</p> <p>The reference positions and velocities of the UOP were generated by Damon Landau (JPL) as part of an internal study at JPL. These initial positions and velocities were numerically integrated by Flavio Petricca (JPL) using the dynamical models described in the main paper. For this reason, the trajectories only cover +- 8 hours from closest approach with each moon and not the entire tour.</p> <p>The ID of the spacecraft is set to -999. The simple text kernel provided here (id_name_map.txt) can be loaded in the kernel pool to associate the ID code with the SPICE names 'URANUS ORBITER PROBE' and 'UOP' for a more explicit and user-friendly access to the trajectories.</p>
WRF Output for 2 km Simulation of Spiral Gravity Waves
<p>WRF output file for the 2km Spiral Gravity Wave simulation of Nolan and Onderlinde (2022).</p>
WRF Output for 0.5 km Simulation of Spiral Gravity Waves
<p>WRF Output for 0.5 km Simulation of Spiral Gravity Waves</p>
WRF Output for 1 km Simulation of Spiral Gravity Waves with 25 Levels
<p>WRF Output for 1 km Simulation of Spiral Gravity Waves with 25 Levels</p>
WRF Output for 1.5 km Simulation of Spiral Gravity Waves
<p>WRF Output for 1.5 km Simulation of Spiral Gravity Waves</p>
WRF Output for 1 km Simulation of Spiral Gravity Waves
<p>WRF Output for 1 km Simulation of Spiral Gravity Waves, gzipped.</p>
La Soufriere volcanic eruptions launched gravity waves into Space
<p>The GNSS filtered TEC observations with bandpass filtering of 10-30 min for the study of "<strong>La Soufriere volcanic eruptions launched gravity waves into Space" </strong>by Yue et al.</p>
Data for "Machine Learning Parameterization of Subgrid-Scale Orographic Gravity Wave Drag in a Middle-Atmosphere General Circulation Model" by Lu et al., submitted to JAMES, 2022.
<p>The NetCDF data file involving the decision tree strucutre attributes of the random forest emulator.</p> <p>gcm_regressors/<br> The data file involving the decision tree strucutre attributes (in NetCDF format)</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.