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”
Pertubation Profiles Dataset used for "Convection-generated gravity waves in the tropical lower stratosphere from Aeolus wind profiling and ERA5 reanalysis"
<p>These are the perturbation profiles, from 5km to 29.5km, with a 500m grid. In the study, we picked up the data between tropopause-1km to 22km, which was then squared, smoothed, and averaged into one value. We used a 14 points moving average for the smoothing.</p> <p>The data is from 2018-09 to 2022-09, based on the Aeolus L2B Rayleigh clear wind, using only quality flag 1 data.</p> <p>Please email me at mathieu.ratynski@estaca.eu if you're interested in the 100m resolution version, used in the final version of the manuscript.</p>
MESA (r11701) models with convective turnover times
<p>$\texttt{MESA r11701}$ (<a title="r11701 release paper" href="https://ui.adsabs.harvard.edu/abs/2019ApJS..243...10P/abstract" target="_blank" rel="noopener">Paxton et al. 2019</a>) stellar models in the range 0.08 - 1.3 $\rm M_{\odot}$ with a metallicity of $\rm Z_{\odot}$ (as according to <a href="https://ui.adsabs.harvard.edu/abs/2009ARA%26A..47..481A/abstract" target="_blank" rel="noopener">Asplund et al. 2009</a>) and no rotation. These models are part of a study on convective turnover times accepted to ApJ and available to read here: <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241020000G/abstract" target="_blank" rel="noopener">Gossage et al. 2024</a>.</p> <p> </p> <ul> <li>$\texttt{MESA r11701}$ may be downloaded here: <a href="https://zenodo.org/records/2665077" target="_blank" rel="noopener">https://zenodo.org/records/2665077</a></li> <li>The associated $\texttt{MESA SDK}$ may be found here: <a href="http://user.astro.wisc.edu/~townsend/static.php?ref=mesasdk-old#linux-download" target="_blank" rel="noopener">old release archive</a> (compiled under $\texttt{GCC version 8.3.0}$)</li> </ul> <p> </p> <h3><strong>Models and inlists</strong></h3> <p>The compressed archive files $\texttt{MESA_run_directories_to_XGyr.tar.gz}$ contain run directories for our models evolved up to $\texttt{X}$ Gyrs (1, 5, or 14). Each directory within has a name corresponding to the model's initial mass (e.g., with $\texttt{00101M_dir}$ corresponding to a 1.01 $\rm M_{\odot}$ model) and contains the inlist (called $\texttt{inlist_project}$) used for that run. Each directory contains a subdirectory called $\texttt{LOGS}$ that contains the run's output (stellar profiles and histories in this case). The stellar profiles are available at approximately 1 Myr, 1 Gyr, 5 Gyr and 14 Gyr, when possible. Every $\texttt{LOGS}$ directory should contain a $\texttt{final_profile.data}$ file which is the stellar profile at the final simulation step of that run. These models were produced to study the variation of the convective turnover time, according to mixing length theory (as according to <a href="https://ui.adsabs.harvard.edu/abs/1965ApJ...142..841H/abstract" target="_blank" rel="noopener">Henyey et al. 1965</a>) in 1D stellar evolution.</p> <p>In the history files, the convective turnover times provided are (in units of seconds):</p> <ol> <li>$\texttt{conv_env_turnover_time_l_hp}$, calculated one half of a (local) pressure scale height from the bottom of the convection zone (BCZ), or core in fully convective stars. This calculation corresponds to the values cited in <a href="https://arxiv.org/abs/2410.20000" target="_blank" rel="noopener">Gossage et al. 2024</a>.</li> <li>$\texttt{conv_env_turnover_time_l_hp_mid}$ at one pressure scale height from the BCZ</li> <li>$\texttt{conv_env_turnover_time_l_hp_hi}$ at two pressure scale heights from the BCZ</li> <li>$\texttt{conv_env_turnover_time_l_hp_hi2}$ at four pressure scale heights from the BCZ</li> <li>$\texttt{conv_env_turnover_time_l_hp_hi3}$ at eight pressure scale heights from the BCZ</li> <li>$\texttt{conv_env_turnover_time_l_b}$ at half pressure scale height from the BCZ, as calculated and adopted in <a href="https://ui.adsabs.harvard.edu/abs/1985ApJ...299..286G/abstract" target="_blank" rel="noopener">Giliand et al. 1985</a></li> <li>$\texttt{conv_env_turnover_time_l_t}$ at one pressure scale height from the BCZ (calculated in same manner as <a href="https://ui.adsabs.harvard.edu/abs/1985ApJ...299..286G/abstract" target="_blank" rel="noopener">Giliand et al. 1985</a>)</li> <li>$\texttt{conv_env_turnover_time_g}$ a "global" convective turnover time, calculated as a running sum of local distances divided by convective velocities (computed cell-wise) through the outer convection zone of the model</li> </ol> <p>The quantities 1-5 above are scaled by the $\texttt{inlist}$ parameter $\texttt{x_ctrl(15)}$, which is set to 0.5 by default.</p> <h3><strong>$\texttt{MESA src (run_star_extras.f90)}$ and other files </strong></h3> <p>Our $\texttt{run_star_extras.f90}$ file is provided as well. Several additional files may be needed, such as reaction networks (these are as in $\texttt{MIST v1.2}$, <a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...823..102C/abstract" target="_blank" rel="noopener">Choi et al. 2016</a>) that may be downloaded here: <a href="https://waps.cfa.harvard.edu/MIST/resources.html" target="_blank" rel="noopener">https://waps.cfa.harvard.edu/MIST/resources.html</a>. Our $\texttt{run_star_extras.f90}$ file is also based on that used in the $\texttt{MIST v1.2}$ models, but with some additions. </p> <h3><strong>Observational Data</strong></h3> <p>The compiled observational data used in our study is recorded in literature_sample.csv. This data is comprised of observations from several sources:</p> <ul> <li><a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...967L..36S/abstract" target="_blank" rel="noopener">Stassun & Kounkel 2024</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2011ApJ...743...48W/abstract" target="_blank" rel="noopener">Wright et al. 2011</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...931...45N/abstract" target="_blank" rel="noopener">Nunez et al. 2022</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2024A%26A...684A...9S/abstract" target="_blank" rel="noopener">Shan et al. 2024</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2019A%26A...628A..41P/abstract" target="_blank" rel="noopener">Pizzocaro et al. 2019</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2022AN....34320049M/abstract" target="_blank" rel="noopener">Magaudda et al. 2022</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2012A%26A...546A.117G/abstract" target="_blank" rel="noopener">Gondoin 2012</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.2351W/abstract" target="_blank" rel="noopener">Wright et al. 2018</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2013A%26A...556A..14G/abstract" target="_blank" rel="noopener">Gondoin 2013</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2016Natur.535..526W/abstract" target="_blank" rel="noopener">Wright & Drake 2016</a></li> </ul> <p>The csv contains the cross-match with Gaia DR3 (<a href="https://ui.adsabs.harvard.edu/abs/2016A%26A...595A...1G/abstract" target="_blank" rel="noopener">Gaia Collaboration et al. 2016</a>; <a href="https://ui.adsabs.harvard.edu/abs/2023A%26A...674A...1G/abstract" target="_blank" rel="noopener">Gaia Collaboration et al. 2023</a>), and we removed duplicated sources which had the same X-ray measurement. </p>
Processed ERA5, IMERG and TRMM PR/GPM DPR precipitation data for Nicolas & Boos - "Understanding the spatiotemporal variability of tropical orographic rainfall using convective plume buoyancy."
<p>The dataset contains processed data from large datasets that are freely available online. <br>All data cover the period 01/2001 - 12/2020. The file names describe the months & region that each file contains. Variable codes for ERA5 data (all files starting in e5.) are:</p><p> - 228_246_100u : 100m u-wind<br> - 228_247_100v : 100m v-wind<br> - qL : 900-600hPa averaged specific humidity<br> - thetaeb : surface - 900hPa averaged equivalent potential temperature<br> - thetaeL : 900-600hPa averaged equivalent potential temperature<br> - thetaeLstar : 900-600hPa averaged saturation equivalent potential temperature<br> - tL : 900-600hPa averaged temperature<br> - uBL : surface - 900hPa averaged u wind<br> - vBL : surface - 900hPa averaged v wind<br> - 128_034_sstk : sea surface temperature<br> - 162_071_viwve : eastward component of vertically integrated water vapor transport<br> - 162_072_viwvn : northward component of vertically integrated water vapor transport</p><p> </p>
Video Supplement for "Understanding the dependence of mean precipitation on convective treatment and horizontal resolution in tropical aquachannel experiments"
<p>A time series of snapshots of precipitable water (shading) and rainfall rate (contour) in the tropical aquachannel simulations. </p>
Convective systems associated with Amazon coastal squall lines using ForTraCC outputs
<p>List of convective systems tracked with ForTraCC that are associated with Amazon coastal squall lines during the period between July and October 2020. Contains information on the date, duration, displacement inland, maximum size, classification according to ForTraCC criteria and according to Cohen et al. (1995).</p> <p> </p>
Data for JGR-Atmospheres Paper: Stratospheric Hydration Processes in Tropopause-Overshooting Convection Revealed by Tracer-Tracer Correlations from the DCOTSS Field Campaign
<p>Airborne 1-second data merger of observations from the NASA Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign. This merger includes subjective feature identifications analyzed in the paper referenced in the title. </p>
Codes and Data for "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy"
<p>This file contains the analysis code and a condensed version of data to reproduce figures in the paper "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy". </p>
A convection-permitting and limited-area model hindcast driven by ERA5 data: BOLAM precipitation daily data for the period 1979-2019
<p>This dataset represents a hindcast of daily total precipitation for the period 1979-2019. Data were obtained using the BOLAM model fed by ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: -26 to 53.121 by 0.089 degrees_east; latitude: 25.035 to 58.705 by 0.07 degrees_north (the Mediterranean Sea and nearby areas)";</p> <p>Grid spacing = "7 km";</p> <p>Grid = "890x482"</p>
Cloud Feedbacks and Organized Convection in Radiative-Convective Equilibrium
<p>Derived data and scripts used in Stauffer and Wing (2024).</p> <p>The standardized RCEMIP output, including time and domain mean profiles, is hosted by the German Climate Computing Center (DKRZ) and is publicly available at <a href="http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e">http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e</a>.</p> <p>Descriptions of each of the data files and the variables contained within them are discussed in a README file. The jupyter notebook contains the scripts used to plot the figures used in Stauffer and Wing (2024).</p> <p>Updated 01 April 2025 to include corrected Index of Organization values.</p>
Dataset of steady magnetospheric convection events in Earth's magnetosphere from 1997 to 2013
<p>This dataset is a list of steady magnetospheric convection (SMC) events occurring in Earth's magnetosphere between 1997 and 2013. The criteria and method of SMC selection are documented in the following two papers:</p> <p>Kissinger et al. (2011), "Steady magnetospheric convection and stream interfaces: Relationship over a solar cycle", JGR, doi:10.1029/2010JA015763.<br> <br> Kissinger et al. (2012), "Diversion of plasma due to high pressure in the inner magnetosphere during steady magnetospheric convection", JGR, doi:10.1029/2012JA017579.</p> <p>The filename "smc_list_1997_2013.csv" is a list of every SMC selected during this time period. This is a comma-separated file with two columns: the first column is the timestamp indicating the start of the SMC event, and the second column is the timestamp indicating the end of the SMC event. </p> <p>The filename "smc_list_1997_2013_longerthan5hrs.csv" is a subset of the first list, filtered to only SMCs with a duration longer than 5 hours. The schema of this file matches the first one, with an additional third column showing the duration of the SMC in hours.</p>
Comparative analysis of Printed Circuit Boards with Surface and Embedded Components under Natural and Forced Convection
<p>Figures of heat distribution on PCB depending on the installation method (surface and embedded) and the speed of forced airflow.</p>
Xin_ACP_2021_Convection_Effect_data
<p>Core data used in <a href="https://github.com/zxdawn/Xin_ACP_2021_Convection_Effect.git">Xin_ACP_2021_Convection_Effect repository</a>.</p> <p>For more detail, please check the README.md file.</p>
Supporting information for "An Atlas of Convection in Main-Sequence Stars"
<p>The contents of 'code.zip' are the plotting and analysis scripts used in generating the plots in this work.</p> <p>The contents of 'atlas_Z_MW_time_2022_05_06_13_40_06_sha_94d5.zip' are the history files for the MESA runs which the plotting scripts analyze. Each file is in its own directory, labelled by the mass of the star in solar units.</p>
Turbulent Mechanisms for the Deep Convective Boundary Layer in the Taklimakan Desert
<p>The deep convective boundary layer (CBL) in the Taklimakan Desert plays an important role in the climate system in East Asia. Based on the observation experiment and large-eddy simulation, turbulent mechanisms for its formation were revealed in this study. This explained why the daily maximum CBL depth was independent of surface heating. In the late-morning, there was a weak temperature inversion and a near-neutral residual layer (RL) above the CBL. With the development of the CBL, stronger convection could penetrate the RL and even overshoot the top of the RL. The distinctive boundary-layer process entrained free-tropospheric air to warm the RL and then promoted the entrainment of the warmed air in the RL into the CBL. This extra energy supply effectively contributed to the growth of the CBL. With further positive feedback between the CBL and RL depths, a deeper CBL would form in consecutive fair-weather conditions.</p>
The Tracing Convective Momentum Transport in Complex Cloudy Atmospheres Experiment - Level 2
<p>The first field campaign from the Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment project (CMTRACE) took place in Cabauw, the Netherlands, between September 13th and October 3rd 2021. During this field campaign, two cloud radars and one wind lidar were operated with a similar scanning strategy for deriving wind speed and direction profiles from near the surface up to cloud tops. Here we provide the daily Level 2 data from the campaign. At this level, several processing steps were applied to the Level 1 data from each instrument to minimize the differences between the sampled volumes resampled and temporal and spatial resolution to generate merged profiles of wind speed and direction. The raw dataset is available for the users on request from the corresponding author.</p>
Datasets supporting the original submission of Harris et al., "A Global Survey of Rotating Convective Updrafts in the GFDL X-SHiELD 2021 Global Storm Resolving Model"
<p>Datafiles used in the analyses described by Harris et al, "A Global Survey of Rotating Convective Updrafts in the GFDL X-SHiELD 2021 Global Storm Resolving Model", to be submitted to the Journal of Geophysical Research.</p> <p>Model output was created by X-SHiELD 2021 <a href="http://doi.org/10.5281/zenodo.6941034">https://doi.org/10.5281/zenodo.6941034</a> described in the paper:</p> <p>Harris, L., Zhou, L., Lin, S.-J., Chen, J.-H., Chen, X., Gao, K., et al. (2020). GFDL SHiELD: A unified system for weather-to-seasonal prediction. <em>Journal of Advances in Modeling Earth Systems</em>, 12, e2020MS002223.<a href="https://doi.org/10.1029/2020MS002223"> https://doi.org/10.1029/2020MS002223</a></p> <p>GPM data used for Figure 6b is derived from</p> <p>Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: 4 August 2021,<a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/06"> 10.5067/GPM/IMERG/3B-HH/06</a></p> <p> </p>
A convection-permitting hindcast based on the MOLOCH model and driven by ERA5: hourly precipitation data for years 1994 and 2011 (sample data)
<p>Hourly estimates of rainfall accumulations were produced within the framework of the SPITBRAN Special project, which received computational resources from ECMWF (https://www.ecmwf.int/en/research/special-projects/spitbran-2018).</p> <p>Numerical gridded data at 2.5 km grid spacing were obtained with the MOLOCH model set in a convection-permitting mode and fed by ERA5 data as initial and boundary conditions for the period 1979-2019 and over the Italian domain.</p> <p>Hourly rainfall accumulations of such long-term hindcast are provided for the years 1994 and 2011. File format is Grib2.</p>
Convection in future winter storms over Northern Europe - data
<pre><em>We provide the python code to reproduce the figures of the Environmental Research Letter entitled </em><em>"Convection in future winter storms" by S. Berthou et al.</em></pre> <p>Python library requirements:</p> <p>python 3.8.12</p> <p>pandas 1.4.1</p> <p>seaborn 0.11.2</p> <p>matplotlib 3.5.1</p> <p>numpy 1.22.3</p> <p>scipy 1.8.0</p> <p>statsmodel.api 0.13.2</p>
A Method for Generating a Quasi-Linear Convective System Suitable for Observing System Simulation Experiments: Dataset
<p>This repository provides data files to quickly run the QLCS observing system simulation experiments. The data file includes assimilated observations prepared for the data assimilation research testbed system (./data/obs/). The file also contains a restart files to initialize the nature run simulation (./data/nature_run/) and the initial prior ensemble at the time of the first data assimilation cycle (./data/initial_fcst_ensemble/).</p>
Dataset of 4D conserved tracers for convection simulated by large eddy model and cloud resolving model
<p>There are conserved tracers and active flag for convection used for diagnosis of bulk entrainment rate for four convection cases in this dataset. Total water and moist static energy are selected as tracer for shallow convection (BOMEX and RICO) and deep convection (GATE and KWAJEX), respectively. The two variables simulated by large eddy model for shallow convection and cloud resolving model for deep convection are four-dimension variables with horizontal scales, vertical altitude, and time. </p> <p>The size of domain simulated for BOMEX and RICO is 6.4 km with horizontal grid spacing of 100 m, and that GATE and KWAJEX is 256 km with horizontal grid spacing of 1 km. Besides, vertical layers in the simulation are 75 levels with spacing of 40m for BOMEX and 100 levels with spacing of 40m for RICO. For KWAJEX and GATE, the model was set up with 64 levels vertically, which gradually increases from 75 m at the surface to a spacing of 400 m through the troposphere and a larger spacing of 1 km in the Newtonian damping region. The model is integrated for 6 hours for BOMEX, 24 hours for RICO, 52.25 days for KWAJEX, and 20 days for GATE. Here, the range of time in these variables The four-dimension variables are saved every 3 seconds for shallow convection, and every 6 minutes for deep convection for two consecutive days.</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.