Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
364
datasets available to search
ShareScore release 0.9.0
Dataset results
364 results for “Convection”
Data from: A porous convection model for small-scale grass patterns
Open the record for dataset details and reuse information.
Data from: Effects of soil particles and convective transport on dispersion and aggregation of nanoplastics via small-angle neutron scattering (SANS) and ultra SANS (USANS)
Open the record for dataset details and reuse information.
Planetary core-style rotating convective flows in paraboloidal laboratory experiments
Open the record for dataset details and reuse information.
SuperDARN grid files used to create the Thomas and Shepherd [2018] statistical convection model
<p>Daily Northern Hemisphere SuperDARN grid files in DataMap format for the years 2010-2016 (inclusive) used to create the <em>Thomas and Shepherd</em> [2018] statistical convection model. Grid files were processed using a modified copy of v4.2 of the Radar Software Toolkit (RST) where the average slant range associated with each grid vector is stored in the 'pwr.median' field.</p> <p>References:</p> <ul> <li>Thomas, E. G., and S. G. Shepherd (2018), Statistical patterns of ionospheric convection derived from mid-latitude, high-latitude, and polar SuperDARN HF radar observations, J. Geophys. Res. Space Physics, 123, 3196-3216, doi:10.1002/2018JA025280.</li> <li>SuperDARN Data Analysis Working Group. Participating members: Thomas, E. G., P. V. Ponomarenko, D. D. Billett, E. C. Bland, A. G. Burrell, K. Kotyk, A. S. Reimer, M. Schmidt, S. G. Shepherd, K. T. Sterne, and M.-T. Walach, (2018), SuperDARN Radar Software Tookit (RST) v4.2, Zenodo, doi:10.5281/zenodo.1403226.</li> </ul>
Data accompanying the paper: Can a combination of convective and magmatic heat transport in the mantle explain Io's volcanic pattern?
<p>Data accompanying the publication Steinke et al. 2020 - "Can a combination of convective and magmatic heat transport in the mantle explain Io's volcanic pattern?", JGR Planets. For more information see the README. Please contact Teresa Steinke with any questions.</p> <p>PART A</p> <p>Two sets including Io's volcanic features locations adopted from previous publications, fitted and filtered in order to be suitable for the comparision with Io's interior dynamics.</p> <p>PART B</p> <p>Solution spaces in three-dimensional parameter space spanned by the viscosity, the thickness and the heat flux fraction of Io's upper convective layer (Figure 3, 4, 5, and A1)</p> <p> </p>
Convective Differential Rotation in Stars and Planets II: Observational and Numerical Tests - Supporting Data
<p>Contains all scripts and data used in the analysis in the paper 'Convective Differential Rotation in Stars and Planets II: Observational and Numerical Tests'. Includes inputs to and output from MESA models.</p>
Convective invigoration traced to warm-rain microphysics (manuscript data)
<p>Data and scripts used to generate the results in the manuscript "Convective invigoration traced to warm-rain microphysics (manuscript data)" by Xin Rong Chua and Yi Ming.</p>
Is shallow convection sensitive to environmental heterogeneities?
This dataset contains the data files that underlie the four figures that are included in the paper entitled "Is shallow convection sensitive to environmental heterogeneities?" (please see the "Related Links" section to access the paper). The data represents analysis of 3D large eddy simulation. These are either profiles (e.g., the cloud fraction) or evolutions (e.g., the mean liquid water path) of various model-predicted quantities. These typically include several dozen levels each. One figure includes 2D cross sections of predicted cloud water mixing ratio. The abstract of the paper is included as follows: "The key assumption underlying convection parameterizations is that rising plumes develop in a horizontally-homogeneous environment. With this in mind, we investigate the impact of environmental cloud-layer heterogeneities on shallow convection using large-eddy simulation that applies a master-slave methodology. In the master-slave approach, two independent sets of thermodynamic variables (master and slave) are driven by one dynamics (coupled with the master) to remove the impact of internal variability of the dynamical system considered. The two thermodynamic sets include either a realistic heterogeneous environment or an environment that is homogenized outside clouds. It is important that homogenization also excludes subsiding shells surrounding cumulus clouds. The results show a small impact of the homogenization since the cloud field properties differ little between the master and slave thermodynamic sets. The physical explanation highlights the role of the subsiding shell shielding a shallow cumulus cloud from its environment."
Buoyancy in Deep Convection Simulations
The dataset contains some of the data from numerical simulations investigating deep convection dynamics, the impact of in-cloud supersaturations on convective updraft strength in particular. It includes simple fortran codes to read the data and write some of them into a data files (standard write from fortran), the data files, and a README file that explains how the data was written and how to read it.
Model dataset for Morrison et al. (2020) "Comparing growth rates of moist and dry convective thermals" submitted to JAS
<p>This model generated dataset includes simulation data and model files for work described in the paper "Comparing growth rates of moist and dry convective thermals" by Morrison et al., submitted to the Journal of the Atmospheric Sciences. Model data comes from the CM1 atmospheric model maintained by Dr. George Bryan at NCAR. The model data are idealized high resolution model runs. We are making this request through DASH because inclusion of these data in a public repository is now mandatory for AMS publications.</p>
E3SM output for tracking mesoscale convection systems in the US
<p>E3SMv1 output for tracking mesoscale convection systems in the US.</p>
Dataset for evolution characteristics of convective clouds over south China
Open the record for dataset details and reuse information.
SuperDARN convection data used in the study of "Relation between Magnetopause Position and Reconnection Rate under Quasi-Steady Solar Wind Dynamic Pressure"
<p>Convection velocity data is available in the netCDF file format.</p>
Data produced and studied in "What controls local entrainment and detrainment rates in simulated shallow convection?"
<p>The data stored here has been produced and used in the paper entitled "What controls local entrainment and detrainment rates in simulated shallow convection?", submitted to the Journal of Atmospheric Sciences on the 27th of January, 2022.</p> <p>The uploaded repository contains 14 files, and is composed of 3 two-dimensional fields, 10 one-dimensional profiles, and a README file. The 2D fields were used to identify individual cloud objects and extract cloud dependent entrainment-detrainment rate statistics. The profiles were used to calculate horizontally averaged entrainment-detrainment rates and their respective contributions.</p>
Data for "Impact of warmer sea surface temperature on the global pattern of intense convection: insights from a global storm resolving model"
<p>Data relevant to a manuscript on X-SHiELD, for submission to GRL.</p>
Machine-learning convection model output file for use in convection pattern estimation.
<p>Output file from the model described in:</p> <p>Bristow, W. A., Topliff, C. A., & Cohen, M. B. (2022). Development of a high-latitude convection model by application of machine learning to SuperDARN observations. <em>Space Weather</em>, 20, e2021SW002920. <a href="https://doi.org/10.1029/2021SW002920">https://doi.org/10.1029/2021SW002920</a></p> <p>The file provides model convection patterns for the period 0000 UT to 2359 UT on 26 March 2014</p> <p> </p>
Exploring the Carbon Simmering Phase: Reaction Rates, Mixing, and the Convective Urca Process
<p>MESA inlists associated with <a href="http://adsabs.harvard.edu/abs/2017ApJ...851..105S">Schwab et al. (2017)</a>. MESA version r10108.</p>
Convection Destroys the Core/Mantle Structure in Hybrid C/O/Ne White Dwarfs
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017ApJ...834L...9B">Brooks et al. (2017)</a>. MESA version 5118.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/2041-8213/834/2/L9">10.3847/2041-8213/834/2/L9</a></p>
Stellar models with calibrated convection and temperature stratification from 3D hydrodynamics simulations
<p>MESA T-tau data file associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.478.5650M/abstract">Stellar models with calibrated convection and temperature stratification from 3D hydrodynamics simulations</a></p>
Data for Global Convection-Permitting Model Improves Subseasonal Forecast of Plum Rain around Japan
<p>Data and plot scripts for this manuscript <strong>"<span>Global Convection-Permitting Model Improves Subseasonal Forecast of</span><span><span> </span></span><span><span>Plum Rain around Japan".</span></span></strong></p> <blockquote> <p><span><span>The md5 value is 7bf40160a0f97e94f048ff68048dee02</span></span></p> </blockquote>
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.