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.
143
datasets available to search
ShareScore release 0.9.0
Dataset results
143 results for “Boundary layer”
Biofilm flow data from: Roughness effects of diatomaceous slime fouling on turbulent boundary layer hydrodynamics
<p>This dataset contains the instantaneous velocity vector fields as well as the time averaged velocity and turbulence fields from PIV data taken over a large acrylic plate fouled with a relatively uniform diatomaceous biofilm. </p> <p>See associated article, Roughness effects of diatomaceous slime fouling on turbulent boundary layer hydrodynamics, for methods description.</p> <p>Time averaged velocity and turbulence fields data are stored in the file velocity_fields.mat, which contains the following variables: </p> <p>X: The streamwise distance of each column in the velocity field matrices [mm]</p> <p>Y: The vertical distance (from the bottom of the frame) of each row in the velocity field matrices [mm]</p> <p>U: Time averaged streamwise velocity [m s^-1]</p> <p>V: Time averaged vertical velocity [m s^-1]</p> <p>tke: Time averaged turbulent kinetic energy [m^2 s^-2]</p> <p>u': Time averaged streamwise Reynolds stress [m^2 s^-2]</p> <p>v': Time averaged vertical Reynolds stress [m^2 s^-2]</p> <p>u'v': Time averaged Reynolds shear stress [m^2 s^-2]</p> <p>The zip file biofilm_vector_fields contains the 4,000 statistically independent velocity vector fields used to compute the time averaged velocity and turbulence fields. The vector field data is in the variable labeled matr. Size calibration: 2302 pixels/ inch (906.3 pixels/ cm). </p> <p>Column 1: X (streamwise distance in [pixels]) </p> <p>Column 2: Y (wall-normal distance from bottom of frame in [pixels])</p> <p>Column 3: U (streamwise velocity vector in [pixels / 250 microseconds]) </p> <p>Column 4: V (vertical velocity vector in [pixels / 250 microseconds]) </p> <p>Column 5: CHC (number of tracked particles. A value < 1 gives the location of the biofilm, which was masked out)</p> <p>Column 6: U2 </p> <p>Column 7: V2</p> <p>Column 8: U3</p> <p>Column 9: V3</p> <p>Column 10: U4</p> <p>Column 11: V4</p>
Is there a scalar atmospheric surface layer within a convective boundary layer? Implications for flux measurements
<p>This dataset contains the data used in the submitted manuscript of Liu, Liu, Zhou, Zhang, Desai, Ghannam, Huang, and Katul 2024. Please refer to the manuscript for the detailed description of the dataset.</p>
Source data for "Submesoscales are a significant turbulence source in global ocean surface boundary layer"
<p>The files here provide the data and codes for reproducing figures from the paper titled "Submesoscales are a significant turbulence source in global ocean surface boundary layer" by Dong et al.</p> <p>It should be clarified that Fig.1 is originally generated by Python and then produced in Illustrator, and Fig.5 is completely produced by Illustrator. All other figures are produced by MATLAB.</p>
Supplementary, raw data, and model for manuscript titled Coupling the TKE-ACM2 Planetary Boundary Layer Scheme with the Building Effect Parameterization Model
<p>This file contains 1. raw data simulated by WRF and PALM, and LiDAR and surface station measurements (<a href="https://zenodo.org/api/records/13959541/draft/files/Raw%20data%20PALM_WRF_Obs.zip/content" target="_blank" rel="noopener noreferrer">Raw data PALM_WRF_Obs.zip</a>); 2. supplementary drawing modeled and simulated U10, T2, and RH2 time series at surface stations (<a href="https://zenodo.org/api/records/13959541/draft/files/Supplementary.docx/content" target="_blank" rel="noopener noreferrer">Supplementary.docx</a>); 3. WRF version containing the TKE-ACM2+BEP (<a href="https://zenodo.org/api/records/13959541/draft/files/WRF433_TKE-ACM2+BEP.tar.gz/content" target="_blank" rel="noopener noreferrer">WRF433_TKE-ACM2+BEP.tar.gz</a>).</p>
Supporting data for Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions
<p>Supporting data for "Optimal closed-loop wake steering, Part 2: Diurnal cycle atmospheric boundary layer conditions" by Michael F. Howland, Aditya S. Ghate, Jesús Bas Quesada, Juan José Pena Martínez, Wei Zhong, Felipe Palou Larrañaga, Sanjiva K. Lele, and John O. Dabiri</p> <p>See README for data description.</p> <p>The code is available at: https://github.com/FPAL-Stanford-University/PadeOps</p>
data for the submission in Marine Geology: Turbulence and Fine Sediment Dynamics in a Coastal Benthic Boundary Layer
<p>Data used for the plots and tables can be found in the submission in Marine Geology:Turbulence and Fine Sediment Dynamics in a Coastal Benthic Boundary Layer. The data are saved as matlab data file.</p>
Atmospheric boundary layer height and energy over the Tibetan Plateau
<p>Processed data of atmospheric boundary layer height and energy over the Tibetan Plateau. </p>
Data for 'Evaluating WRF-GC v2.0 predictions of boundary layer and vertical ozone profiles during the 2021 TRACER-AQ campaign in Houston, Texas'
<p>This repository provides observation datasets, model configuration files, model boundary conditions, model input files, and scripts used in the manuscript titled 'Evaluating WRF-GC v2.0 predictions of boundary layer and vertical ozone profiles during the 2021 TRACER-AQ campaign in Houston, Texas'.</p>
Selected data analyzed in the JGR Atmosphere manuscript "Atmospheric meridional circulation between South Asia and Tibetan Plateau caused by the change of planetary boundary layer depth."
<p>1. The PBL depth dataset including the PBL type (the convective boundary layer, the neutral boundary layer, and the stable boundary layer) and the calculated the PBL depths at the 19 stations for the 2013-2015 summers.</p> <p>2. The control experiment (WRF-CTL) and the MEP experiment (WRF-MEP) simulations results including PBL depth and geopotential height at 500 hPa at 00:00 UTC, 06:00 UTC, 12:00 UTC, and 18:00 and hourly sensible and latent heat. The simulation period was 1 June to 31 August 2015 with 30 hours from 12:00 UTC (20:00 Beijing time (BJT)) each day.</p>
Selected data analyzed in the JGR Atmosphere manuscript "Atmospheric meridional circulation between South Asia and Tibetan Plateau caused by the change of planetary boundary layer depth."
<p>1. The PBL depth dataset including the PBL type (the convective boundary layer, the neutral boundary layer, and the stable boundary layer) and the calculated the PBL depths at the 19 stations for the 2013-2015 summers.</p> <p>2. The control experiment (WRF-CTL) and the MEP experiment (WRF-MEP) simulations results including PBL depth and geopotential height at 500 hPa at 00:00 UTC, 06:00 UTC, 12:00 UTC, and 18:00 and hourly sensible, latent heat and thermal radiation (net longwave radiation). The simulation period was 1 June to 31 August 2015 with 30 hours from 12:00 UTC (20:00 Beijing time (BJT)) each day.</p>
Dataset and code for the manuscript 'Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks'
<p>This repository contains the code and data used in the manuscript 'Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks'. <br> Manuscript authors: Dr. Aakash Sane, Dr. Brandon G. Reichl, Dr. Alistair Adcroft, Dr. Laure Zanna<br> Manuscript preprint link: https://doi.org/10.48550/arXiv.2306.09045</p>
Data from: Influence of leaf trichomes on boundary layer conductance and gas-exchange characteristics in Metrosideros polymorpha (Myrtaceae)
Open the record for dataset details and reuse information.
Atmospheric boundary layer intensive sounding data at Cangzhou site
Open the record for dataset details and reuse information.
Toward low-cloud-permitting cloud superparameterization with explicit boundary layer turbulence -- simulation data
Open the record for dataset details and reuse information.
Codes and datasets associated with the paper "Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers"
<p>From here, you will find some of the codes, simulation data in the article: </p> <p>Dai, Y., Basu, S., Maronga, B. and de Roode, S.R., 2020. Addressing the Grid-size Sensitivity Issue in Large-eddy Simulations of Stable Boundary Layers. <em>arXiv preprint arXiv:2003.09463</em>.</p> <p><strong>Simulation results from DALES: </strong></p> <p>within the folder of DALES, D52 denotes default scheme of Deardorff, H52 denotes the revised Deardorff scheme. Numbers of each fold indicates the grid number in each direction. </p> <p><strong>Simulation results from MATLES:</strong></p> <p>file name: MATLES/variable.out</p> <p>Example: MATLES/aT.out, the potential temperature data (2D, time and height) from MATLES</p> <p><strong>Python code for plotting: </strong></p> <p>dalesfunc.py is the function file used to process data</p> <p>DALES_plotting.py is the code file used for plotting the simulation results from DALES and MATLES</p> <p><strong>DALES input:</strong></p> <p>namoptions in each folder is the file for DALES options input</p>
Wind profile in the wave boundary layer and its application in a coupled atmosphere-wave model
<p>The simulation data for the study</p>
The importance of alkyl nitrates and sea ice emissions to atmospheric NOx sources and cycling in the summertime Southern Ocean marine boundary layer.
<p>This data set includes finalized atmospheric nitrate concentrations, nitrogen and oxygen isotopic compositions of atmospheric nitrate, wind speeds, atmospheric temperature, relative humidity and sea surface nitrite concentrations.</p>
Vertical coupling of gusts in the lower boundary layer during super typhoons and squall lines
<p>10 Hz Wind data of four cases.</p>
The unsteady shock boundary layer interaction in a compressor cascade - Part 3: Mechanisms of shock oscillation - Promotional video 1
<p>Promotional video for the ASME Turbo Expo 2024 open access publication with identifier GT2024-128197 and title "The unsteady shock boundary layer interaction in a compressor cascade - Part 3: Mechanisms of shock oscillation."</p>
Effect of planetary boundary layer evolution on new particle formation events over Cyprus
<p>The data set is related to the article:</p> <h3>Effect of planetary boundary layer evolution on new particle formation events over Cyprus.</h3> <div> <p>Neha Deot<sup>1</sup>, Vijay P. Kanawade<sup>1,2</sup><em>, Alkistis Papetta<sup>1</sup>, Rima Baalbaki<sup>3,1</sup>, Michael Pikridas<sup>1</sup>, Franco Marenco<sup>1</sup>, Markku Kulmala<sup>3</sup>, Jean Sciare<sup>1</sup>, K. Lehtipalo<sup>3,4</sup>, Tuija Jokinen</em><sup><em>1</em></sup></p> <p> </p> </div>
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.