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.
131
datasets available to search
ShareScore release 0.9.0
Dataset results
131 results for “mesoscale”
Dataset for: Anniés et al., "Accessing structural, electronic, transport and mesoscale properties of Li-GICs via a complete DFTB-model with machine-learned repulsion potential"
<p>GPrep training data, GPrep jupyter notebook, .skf files.</p> <p>The GPrep code is available at https://doi.org/10.5281/zenodo.3697913</p>
Source data for "Non-Telecentric two-photon microscopy for 3D random access mesoscale 2 imaging"
<p>Source data used in a manuscript "Non-Telecentric two-photon microscopy for 3D random access mesoscale 2 imaging"</p> <p> </p> <p> </p> <p> </p>
Data from: Microbial metabolic activity in two basins of the Gulf of Mexico influenced by mesoscale structures
<p>Information on microbial metabolic activity is essential for quantifying carbon and energy flows through marine food webs. We quantified community (R<sub>com</sub>) and prokaryotic (R<sub>pro</sub>) respiration rates, bacterial production (BP), bacterial abundance (BA), and bacterial growth efficiencies (BGE) in the Perdido and Coatzacoalcos basins of the Gulf of Mexico (GOM) during summer and winter conditions in 2016. Our results showed seasonal, regional, and mesoscale eddy influences on those metabolic variables. R<sub>pro</sub> accounted for more than 60% of total respiration in both regions, being three times higher in stations influenced by a cyclonic eddy (CE) in September (24.1 μM O<sub>2</sub> d<sup>-1</sup>) than in stations affected by an anticyclonic eddy in March (7.2 μM O<sub>2</sub> d<sup>-1</sup>) within the Coatzacoalcos basin where the eddy-trapping mechanism advected biomass-enriched waters from the Bay of Campeche. The eddy-stirring mechanism produced horizontal and vertical dipole patterns of metabolic variables increasing up to one order of magnitude R<sub>com</sub> and R<sub>pro</sub> while decreasing BGE to 25-fold from the southeastern to the northwestern edges in CEs. This finding indicates that dissolved organic matter is more actively taken up to build bacterial biomass on the eastern edge of CEs in the GOM, while respiration rates increase on the western edges. Satellite integrated primary production was coupled with surface respiration rates at CEs and no eddies. Bacterial production was mainly regulated by CEs and was about 50% higher in the Coatzacoalcos basin (~0.03–0.14 µmol C L<sup>-1</sup> d<sup>-1</sup>). BP increased in zones with high respiration rates, suggesting that R<sub>com</sub> is associated with heterotrophic prokaryote activity in both basins. Bacterial growth efficiency was lower than 25% within the upper 500 m during both cruises, but the highest values were quantified in the euphotic zone and during the September cruise. Metabolic variables integrated over the water column showed that 40–80% of the activity occurred between the base of the euphotic zone and 150 m depth. Our findings contribute to a better understanding of the metabolic activity of the microbial communities in two regions of the GOM influenced by mesoscale eddies.</p>
Mesoscale stereo retrievals from Hunga Tonga-Hunga Ha'apai Eruption of 15 January 2022
<p>Stereo methods using GOES-17 and Himawari-8 applied to the Hunga Tonga-Hunga Ha'apai volcanic plume on 15 January 2022 show overshooting tops reaching 50-55 km altitude, a record in the satellite era. Plume height is important to understand dispersal and transport in the stratosphere and climate impacts. Stereo methods, using geostationary satellite pairs, offer the ability to accurately capture the evolution of plume top morphology quasi-continuously over long periods. Manual photogrammetry estimates plume height during the most dynamic early phase of the eruption and a fully automated algorithm retrieves both plume height and advection every 10 minutes during a more frequently sampled and stable phase beginning three hours after the eruption. Stereo heights are confirmed with Global Navigation Satellite System Radio Occultation (GNSS-RO) bending angles, showing that much of the plume was lofted 30–40 km into the atmosphere. Cold bubbles are observed in the stratosphere with brightness temperature of ~173K.</p>
Data for "Properties of the lateral mesoscale eddy-induced transport in a high-resolution ocean model: Beyond the flux-gradient relation" (Lu et al. JPO)
<p>Preprocessed data to reproduce results and figures in "Properties of the lateral mesoscale eddy-induced transport in a high-resolution ocean model: Beyond the flux-gradient relation" (Lu et al., In Review of <em>Journal of Physical Oceanography</em>). </p> <p>Feel free to contact Yueyang Lu via <strong>yxl1496@miami.edu</strong> if you have any questions.</p>
Material for manuscript submitted to Earth and Space Science "Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin"
<p>Configuration files for AROME Indian Ocean, NEMO and OASIS which are necessary to reproduce the results in the publication :</p> <p>Corale, L; Malardel S. , Bielli S. and M-N Bouin (2022) Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. <em>Earth and Space Science.</em></p>
Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"
<p>Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"</p>
Data and Code for "Mesoscale modeling of deformations and defects in thin crystalline sheets"
<p>Research data and code supporting the paper ""Mesoscale modeling of deformations and defects in thin crystalline sheets"".</p> <p> </p> <p><strong>Code (apfc-python-surf.zip)</strong></p> <p>The implementation of the APFC model is performed in python by exploiting the pseudo-spectral Fourier method. Library pyfftw is adopted. However, standard fft libraries can be used as well by changing the corresponding module/functions. The code supports equations of the APFC model both coupling with the evolution of the surface considered in this work and on a simple flat domain. Updates can be found in the GitLab repository linked below.</p> <p> </p> <p><strong>GitLab repository for the code</strong></p> <p><a href="https://gitlab.com/3ms-group/apfc_python/">https://gitlab.com/3ms-group/apfc_python/</a></p> <p> </p> <p><strong>Data</strong></p> <p>The data.zip files contain the simulation results and auxiliary scripts used to produce the results illustrated in the paper's figures. The folder numbering refers to the one used for the figures in the final version.</p> <p> </p> <p>For further information please contact the authors.</p>
Post-processed SAM (System for Atmospheric Modeling) simulation output for "Tipping to an Aggregated State by Mesoscale Convective Systems"
<p>Statistics output files for all variables, for a select number of SAM (System for Atmospheric Modeling v. 6.11) simulation runs used in the study "Tipping to an Aggregated State by Mesoscale Convective Systems". The following simulations are included: DIU, OCEAN, DIU2OCEAN branch A1, DIU2OCEAN branch A2.</p>
Data for Origins of UTLS Turbulence: Insights from the RRJ-ClimCORE Mesoscale Reanalysis - The ACCLIP Flight Over the Super Typhoon Hinnamnor (2022)
<p>Data used in our paper entitled "Origins of UTLS Turbulence: Insights from the RRJ-ClimCORE Mesoscale Reanalysis - The ACCLIP Flight Over the Super Typhoon Hinnamnor (2022)".</p> <p>The user may use these data only for the purpose of scientific evaluation of this paper and secondary distribution is prohibited. See README file for more details.</p>
On the cell broadening of mesoscale cellular convection in a well-mixed boundary layer
Open the record for dataset details and reuse information.
[Dataset] Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimisation
<p>[Dataset] Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimisation</p> <p>Roman Kulagin*, Patrick Reiser, Kyryl Truskovskyi, Arnd Koeppe, Yan Beygelzimer, Yuri Estrin, Pascal Friederich, Peter Gumbsch</p> <p>[*] Dr. R. Kulagin, Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. E-Mail: roman.kulagin@kit.edu</p> <p>Dr. Patrick Reiser, Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany; Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131 Karlsruhe, Germany.</p> <p>Kyryl Truskovskyi, Georgian, Toronto, Canada</p> <p>Dr. Arnd Koeppe, Institute for Applied Materials (IAM-MMS), Karlsruhe Institute of Technology, Straße am Forum 7, 76131 Karlsruhe, Germany.</p> <p>Prof. Pascal Friederich, Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany; Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131 Karlsruhe, Germany.</p> <p>Prof. Y. Beygelzimer, Donetsk Institute for Physics and Engineering named after A.A. Galkin, National Academy of Sciences of Ukraine, Nauki ave., 46, 03028 Kyiv, Ukraine.</p> <p>Prof. Y. Estrin, Department of Materials Science and Engineering, Monash University, 22 Alliance Lane, Clayton 3800, Australia; Department of Mechanical Engineering, The University of Western Australia, Crawley 6009, Australia.</p> <p>Prof. P. Gumbsch, Institute for Applied Materials, Karlsruhe Institute of Technology, Straße am Forum 7, 76131, Karlsruhe, Germany; Fraunhofer Institute for Mechanics of Materials, Freiburg, Wöhlerstraße 11, 79108 Freiburg, Germany.</p> <p>Part of the work was supported by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) through the POLiS Cluster of Excellence (grant no. UP 33/1) under project ID 390874152 and by the Helmholtz association under the KNMFi program (grant no. 43.31.01).</p>
Targeted Balloon Observations of Stratosphere-to-Troposphere Transport From a Mesoscale Convective System
<p>Data used for analysis in the JGR-Atmospheres Paper Targeted Balloon Observations of Stratosphere-to-Troposphere Transport From a Mesoscale Convective System. Files include ozonesonde data, gridded radar data, and backward trajectory calculations. </p>
Main text figure data and scripts for "Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach"
<p>(as README.txt):</p> <p>Main text figure data and scripts for “Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach”, by Tarun Gera, Lipeng Chen, Alex Eisfeld, Jeffrey R. Reimers, Elliot J. Taffet and Doran I. G. B. Raccah.</p> <p>Each directory is dedicated to a particular figure published in the paper. In each directory there are sub-directories which contains the data plotted in each panel. Each data file is a 2-D list in the format of (x,y) for each plot. There are python scripts (Fig_X.py) in each directory to plot the data.</p> <p>Table of contents:</p> <p>Figure_2:</p> <p> - 4_site_edge_contri.npy: Calculated edge sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_inner_contri.npy: Calculated inner sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_total_spectra.npy: Calculated total absorption spectrum for a 4-site chain system v/s energy. </p> <p><br> Figure_3:</p> <p>Panel A:<br> <br> - Mean_Error_Edge.npy: Mean error for the edge case v/s number of trajectories.<br> - Mean_Error_Inner.npy: Mean error for the inner case v/s number of trajectories.<br> - Mean_Error_SS.npy: Mean error for a single site initial condition v/s number of trajectories.<br> - Mean_Error_GD.npy: Mean error for a 4-site chain system with Gaussian distributed site energies v/s number of trajectories.</p> <p>Panel B: </p> <p> - Scaled_error_SS.npy: Mean error for a single site initial condition normalized by the square-root of one v/s number of trajectories.<br> - Scaled_error_PS.npy: Mean error for a pair site initial condition normalized by the square-root of two v/s number of trajectories.<br> - Scaled_error_AS.npy: Mean error for an all site initial condition normalized by the square-root of four v/s number of trajectories.</p> <p>Figure_4: </p> <p>Panel_A:</p> <p> - List_Error.npy: Calculated mean error for a 4-site chain for a set of auxiliary error bounds.</p> <p>Panel_B:</p> <p> - Cw_4S_HOPS.npy: Absorption spectrum for a 4-site chain calculated using dyadic HOPS v/s energy.<br> - Cw_4S_DadHOPS.npy: Absorption spectrum for a 4-site chain calculated using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_12S_DadHOPS.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS without including state adaptivity v/s energy.<br> - Cw_12S_DadHOPS_SA.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS with state adaptivity v/s energy.</p> <p>Panel_D:</p> <p> - Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-pigment system.<br> - Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-pigment system.<br> - N_states_DadHOPS.npy: Number of site states required to run a DadHOPS calculation for each N-pigment system.<br> - N_states_HOPS.npy: Number of site states required to run a dyadic HOPS calculation for each N-pigment system.<br> </p> <p>Figure_5:<br> <br> Panel_C: </p> <p> - PSI_Cw_HEOM.npy: PSI absorption spectrum calculated using HEOM v/s energy.<br> - PSI_Cw_HOPS.npy: PSI absorption spectrum calculated using dyadic HOPS v/s energy.</p> <p>Panel_D:</p> <p> - PSI_Error_Random.npy: Calculated mean error, where clusters of 4 were assigned randomly v/s number of trajectories.<br> - PSI_Error_Coupling.npy: Calculated mean error, where clusters of 4 were assigned based on electronic coupling values v/s number of trajectories.</p> <p><br> Figure_6:</p> <p>Panel_A:</p> <p> - PBI_Exp_data_dil.npy: Experimental data for a dilute solution of PBI v/s energy.<br> - PBI_Cw_DadHOPS_300.npy: Calculated spectrum for a PBI monomer with the spread in static disorder of value 300 cm^{-1} v/s energy.<br> - PBI_Cw_DadHOPS_400.npy:: Calculated spectrum for a PBI monomer with the spread in static disorder of value 400 cm^{-1} v/s energy.</p> <p>Panel_B:</p> <p> - PBI_Exp_data_conc.npy: Experimental data for a concentrated solution of PBI v/s energy.<br> - PBI_trimer_Cw_DadHOPS.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_PBI_monomer.npy: Calculated spectrum for a PBI monomer using DadHOPS v/s energy.<br> - Cw_PBI_dimer.npy: Calculated spectrum for a PBI dimer using DadHOPS v/s energy.<br> - Cw_PBI_trimer.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.<br> - Cw_PBI_heptamer.npy: Calculated spectrum for a PBI heptamer using DadHOPS v/s energy.<br> - Cw_PBI_1000mer.npy: Calculated spectrum for a PBI 1000mer using DadHOPS v/s energy.</p> <p>Panel_D:</p> <p> - peak_00_position.npy: relative position of the 00 peak for different number of pigments.<br> - peak_00_position_1000.npy: relative position of the 0,0 peak for a system with 1000 pigments. (Single value file)<br> - peak_I_ratio.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for different number of pigments.<br> - peak_I_ratio_1000.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for a system with 1000 pigments. (Single value file)</p> <p><br> Figure_7:</p> <p> - PBI_N_states_DadHOPS.npy: Number of states required to run a DadHOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-PBI molecules system. </p> <p>The packaged scripts may be run with Python 3.10 and the associated versions of the os, numpy, and matplotlib packages. <br> </p>
Dataset: Periodic variation of mesoscale ultraviolet contrast at the cloud top of Venus
<p>Filename:<br> mean_A_283.txt : wavelength of 283 nm<br> mean_A_365.txt : wavelengths of 365 nm</p> <p>Description:<br> Empirical equigonal UV albedo of Venus as a function of the phase angle determined from Akatsuki UVI data.</p> <p>Format (text file): <br> 1st column: Phase angle (deg)<br> 2nd column: A:Mean albedo<br> 3rd column: SD:Standard deviation</p> <p> </p> <p>Filename:<br> period1_283nm.txt : Period 1, 283 nm<br> period1_365nm.txt : Period 1, 365 nm<br> period2_283nm.txt : Period 2, 283 nm<br> period2_365nm.txt : Period 2, 365 nm</p> <p>Description:<br> Time series of the standard deviation of mesoscale UV contrast and the mean brightness of Venus.</p> <p>Format (text file):<br> 1st column: Image file name<br> 2nd column: Elapsed time (h)<br> 3rd column: Phase angle (deg)<br> 4th column: Standard deviation (W/m2/str/m)<br> 5th column: Standard deviation in AM<br> 6th column: Standard deviation in PM<br> 7th column: Mean brightness (W/m2/str/m)<br> 8th column: Mean brightness in AM<br> 9th column: Mean brightness in PM</p> <p> </p> <p>Filename:<br> uvi_20160425_131340_283_l3b_v10.nc<br> uvi_20160425_151341_283_l3b_v10.nc<br> uvi_20160425_171339_283_l3b_v10.nc<br> uvi_20160506_101341_283_l3b_v10.nc<br> uvi_20160506_121340_283_l3b_v10.nc<br> uvi_20160506_141343_283_l3b_v10.nc<br> uvi_20160517_141339_283_l3b_v10.nc<br> uvi_20160517_161339_283_l3b_v10.nc</p> <p>Description:<br> 2.02-micrometer mapped data taken by the IR2 camera onboard Akatsuki in Level-3 netCDF format. The data have been corrected for the point-spread function, the flat-field pattern, and the detector temperature dependence by Sato et al. (2020, Icarus, 345, 113682, https://doi.org/10.1016/j.icarus.2020.113682).</p> <p>Format (binary file):<br> netCDF<br> The file names indicate the year, date and time of each observation.</p> <p> </p>
Classification Of Large-Scale Environments That Drive The Formation Of Mesoscale Convective Systems Over Southern West Africa
<p>This is a set of datasets used for the publication of the article titled: Classification Of Large-Scale Environments That Drive The Formation Of Mesoscale Convective Systems Over Southern West Africa</p>
Mesoscale stereo retrievals from Hunga Tonga-Hunga Ha’apai Eruption of 15 January 2022
Open the record for dataset details and reuse information.
Data from: Dynamics of mesoscale brain network during decision-making learning revealed by chronic, large-scale single-unit recording
Open the record for dataset details and reuse information.
Data from: Microbial metabolic activity in two basins of the Gulf of Mexico influenced by mesoscale structures
Open the record for dataset details and reuse information.
Ex vivo Mesoscale human temporal lobe dataset
Open the record for dataset details and reuse information.
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.