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517 results for “turbulence,”
Dataset: Intercomparison of flux, gradient, and variance-based optical turbulence ($C_n^2$) parameterizations
<p>This repository contains the dataset for the manuscript</p> <p>Pierzyna, M, et al. "Intercomparison of flux, gradient, and variance-based optical turbulence (Cn2) parameterizations." <em>Applied Optics</em>, 2024. <a href="https://doi.org/10.1364/AO.519942">https://doi.org/10.1364/AO.519942</a></p> <p>The data is organized in the following structure:</p> <ul> <li>`met_cn2_*_10m.nc`: netCDF files containing Cn2 estimated from meteorological data obtained at the CESAR site<br> using the flux-based and gradient-based methods at the 10 m level.</li> <li>`wrf_cn2_*.nc`: netCDF files containing Cn2 estimated from WRF model output using the variance-based method (80m)<br> and flux, gradient, and variance-based methods (10m).</li> <li>`wrf_meteo_*.nc`: netCDF files containing a cross-section of CESAR site extracted from WRF model output. This data<br> serves as input for `wrf_cn2_*.nc` files.</li> </ul>
Dataset for Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread
<p>30 min turbulence statistics and spectra of 13 datasets from flat to highy complex terrain. Data only cover unstable stratification. </p> <p>Dataset is a companion to the manuscript Charrondiere, C., Stiperski, I., 2024: Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4811<strong><br></strong></p> <p> </p>
Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"
<p>Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)".</p> <p>Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea.</p> <p>Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report.</p> <p>Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.</p> <p> </p> <p>Manuscript abstract:</p> <p>In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.</p> <p> </p> <p> </p>
Precomputed data accompanying the paper "Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics"
<p>Together with the python code in <a href="https://github.com/rik-stra/tau_orthogonal_method_for_2D_turbulence" target="_blank" rel="noopener">https://github.com/rik-stra/tau_orthogonal_method_for_2D_turbulence</a>, this dataset lets the user interact with and reproduce the results in the paper "Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics".</p> <p> All data in this dataset is computed with the provided code.</p>
Run 0 video in "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence"
<p>Run 0 presented in the paper "Ram pressure stripping in elliptical galaxies – I. The impact of the interstellar medium turbulence" (http://adsabs.harvard.edu/abs/2013MNRAS.428..804S or https://doi.org/10.1093/mnras/sts071).</p>
Datasets for ``Dynamo effect in decaying helical turbulence''
<p>In this supplemental material to the paper ``Dynamo effect in decaying helical turbulence,'' Phys. Rev. Fluids, 4, 024608 (2019), DOI: <a href="https://doi.org/10.1103/PhysRevFluids.4.024608">10.1103/PhysRevFluids.4.024608</a>, <a href="http://arXiv.org/abs/1710.01628">(arXiv:1710.01628</a>) we provide the underlying data to most of the figures. The file decdynamo.tar.gz contains a gzipped tar file to the website</p> <p> https://www.nordita.org/~brandenb/projects/decdynamo/</p> <p>For each of the Runs A-H plus 3 addition ones of Figures 13 and 14, the original run directories are given. They contain secondary data such as time series and spectra. The full snapshots are not stored, but all the relevant files needed for rerunning any particular simulation exist. In each directory, the input files (*.in) exist and data are under the directory data (including time series and spectra). In some cases, data directly relevant for particular plots are available, for example the directory "spectra" contains selected spectra used in some plots.</p>
Simulations of shallow water wave turbulence
<p><strong>About</strong></p> <p>This dataset curates all the simulations used to reproduce the paper:</p> <blockquote> <p><em>Shallow water wave turbulence</em><br> DOI: <a href="https://doi.org/10.1017/jfm.2019.375">10.1017/jfm.2019.375</a></p> </blockquote> <p>The source code and scripts necessary to generate the manuscript are archived at:</p> <blockquote> <p><a href="https://github.com/ashwinvis/augieretal_jfm_2019_shallow_water">https://github.com/ashwinvis/augieretal_jfm_2019_shallow_water</a></p> </blockquote> <p>See the README in the repository above to generate the manuscript</p> <p><strong>Abstract</strong></p> <p>The dynamics of irrotational shallow water wave turbulence forced at large scales and dissipated at small scales is investigated. First, we derive the shallow water analogue of the ‘four-fifths law’ of Kolmogorov turbulence for a third-order structure function involving velocity and displacement increments. Using this relation and assuming that the flow is dominated by shocks, we develop a simple model predicting that the shock amplitude scales as <span class="math-tex">\((\epsilon d)^{1/3}\)</span>, where <span class="math-tex">\( \epsilon\)</span> is the mean dissipation rate and <span class="math-tex">\(d\)</span> the mean distance between the shocks, and that the <span class="math-tex">\(p\)</span><sup>th</sup>-order displacement and velocity structure functions scale as <span class="math-tex">\((\epsilon d)^{p/3} r/d\)</span>, where <span class="math-tex">\(r\)</span> is the separation. Then we carry out a series of forced simulations with resolutions up to 7680<sup>2</sup>, varying the Froude number,<span class="math-tex">\(F_{f} = (\epsilon L_f)^{1/3}/ c \)</span>, where <span class="math-tex">\(L_f\)</span> is the forcing length scale and <span class="math-tex">\(c\)</span> is the wave speed. In all simulations a stationary state is reached in which there is a constant spectral energy flux and equipartition between kinetic and potential energy in the constant flux range. The third-order structure function relation is satisfied with a high degree of accuracy. Mean energy is found to scale approximately as <span class="math-tex">\(E \sim \sqrt{\epsilon L_f c}\)</span>, and is also dependent on resolution, indicating that shallow water wave turbulence does not fit into the paradigm of a Richardson–Kolmogorov cascade. In all simulations shocks develop, displayed as long thin bands of negative divergence in flow visualizations. The mean distance between the shocks is found to scale as <span class="math-tex">\( d \sim F_f^{1/2} L_f\)</span>. Structure functions of second and higher order are found to scale in good agreement with the model. We conclude that in the weak limit, <span class="math-tex">\(F_f \rightarrow 0 \)</span>, shocks will become denser and weaker and finally disappear for a finite Reynolds number. On the other hand, for a given <span class="math-tex">\(F_f\)</span>, no matter how small, shocks will prevail if the Reynolds number is sufficiently large.</p>
Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)
<p>This repository contains the WRF configuration files necessary to reproduce the simulations <br> as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100)</p> <p>The file windturbines_GMD.txt contains the locations of <br> all windturbines implemented in the simulations. The corresponding attributes of each <br> wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019).</p> <p>The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that <br> there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files.</p> <p>The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. <br> Note that the dates and pathes have to be adjusted in the python files. <br> After downloading the surface and model level data some postprocessing <br> is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this<br> purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.</p>
Processed data from SnoHATS and METCRAX II: anisotropic turbulence and geometry of the Reynolds stress tensor in a streamline coordinate system
<p>Datasets used for the paper 'Interpreting turbulence anisotropy in a streamline coordinate system'. Data from SnoHATS and METCRAX II field campaigns. Datasets include turbulent quantities calculated on 30- and 1-min averaging windows for unstable and stable conditions, with prior linear detrending. Planar fit was used in METCRAX II and double rotation in SnoHATS to rotate the flow into the mean wind direction. Datasets include quantities to characterize the anisotropy of the Reynolds stress tensor, such as eigenvalues, eigenvectors, and the angles between the eigenvectors and the streamline coordinate system, defined in the direction of the mean wind vector.</p> <p>1c: one-component Reynolds stress tensor</p> <p>2c: two-component axisymmetric Reynolds stress tensor</p> <p>3c: isotropic Reynolds stress tensor</p>
IAO Data: Turbulent Enhancement Ratio and Flux Ratio
<p>This dataset comprises measurements from the Innsbruck Atmospheric Observatory (IAO) spanning from mid-2018 to early 2022. It includes flux data for CO2 and NOx, QA/QC flags, Turbulent Enhancement Ratios (NOx/CO2), VMR for CO2 and NOx, as well as various meteorological parameters utilized in different publications.</p> <p>The dataset is processed in Matlab R2023a and is a structured mat-file containing the following fields:</p> <table> <tbody> <tr> <td> <p>> EC > CO2<br>> EC > NOx<br>> EC > flag<br>> MET > dd<br>> MET > ff<br>> MET > Tair<br>> MET > p<br>> TER > NOx_CO2<br>> TER > flag<br>> time</p> </td> <td> <p>CO2 flux [umol/m2s]<br>NOx flux [nmol/m2s]<br>QA/QC flag<br>wind direction [°]<br>wind speed [m/s]<br>air temperature [K]<br>pressure [mbar]<br>Turbulent Enhancement Ratio NOx over CO2 [nmol/umol]<br>QA/QC flag<br>time in matlab format (datenum) [UTC]</p> </td> </tr> </tbody> </table> <p>Additionally, the dataset includes fields for contact information, headers, and metadata. Each data point is calculated at a 30-minute interval.</p>
Datasets for "Turbulent magnetic decay controlled by two conserved quantities"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Turbulent magnetic decay controlled by two conserved quantities" by A. Brandenburg, & A. Banerjee. If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Intermediate data products for: Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar (Zippel et al. 2021, Journal of Atmospheric and Oceanic Technology)
<p>This repository contains some of the intermediate data products needed to reproduce the results in the <em>Journal of Atmospheric and Oceanic Technology</em> article "Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar" by S.F. Zippel, J. T. Farrar, C. J. Zappa, U. Miller, L. St. Laurent, T. Ijichi, R. A. Weller, L. McRaven, S. Nylund, and D. Le Bel. Specifically, this material should allow reproduction of Figures 3, 5-7, 12 and 13. Reproduction of Figures 8-11 also requires data from associated glider deployments nearr the SPURS-1 mooring, which may be requested from co-author L. St. Laurent.</p> <p>Code to do the analysis and make the plots is here: https://github.com/zippelsf/MooredTurbulenceMeasurements</p> <p>Matlab data files:</p> <p>(1) 677404_burst1865.mat</p> <p>Single-burst data used for the example spectral fit in Figure 7. The burst was collected during the SPURS-1 project at 21.5m depth. The data collection and processing methods are described in detail in Section 2. </p> <p>(2) 811604_burst0510.mat (Single-burst data used in the unwrapping example, Figure 5)</p> <p>(3) 8116_dissipation_timeseries.mat (Used for associated ancillary data in Figure 6)</p> <p>(4) 913411_burst2879.mat (Single-burst data, used for ancillary data to make Figure 3).</p> <p>(5) BuoyancyFlux_b.mat</p> <p>Ocean buoyancy flux estimates for SPURS-2 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(6) BuoyancyFlux_c.mat</p> <p>Ocean buoyancy flux estimates for SPURS-1 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(7) SPURS1_dissipation_grid_v1d.mat</p> <p>Gridded TKE dissipation rates for SPURS-1 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 8-13. Dissipation rates also available on NASA's PODAAC.</p> <p>(8) spurs1_met_1hr.mat (Processed met data from SPURS-1 mooring. Also available on WHOI's UOP website.)</p> <p>(9) SPURS2_dissipation_grid_v1c.mat</p> <p>Gridded TKE dissipation rates for SPURS-2 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 12. Dissipation rates also available on NASA's PODAAC.</p>
Data and scripts for reproducing "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Optimisation and Analysis of Streamwise-Varying Wall-Normal Blowing in a Turbulent Boundary Layer", submitted to Flow, Turbulence and Combustion.</p>
Dataset of "Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer"
<p>Dataset of the article "Genetically-inspired convective heat transfer enhancement in a turbulent boundary layer" (<a href="https://doi.org/10.1016/j.applthermaleng.2023.120621">https://doi.org/10.1016/j.applthermaleng.2023.120621</a>). The dataset contains:</p> <p>- the velocity fields, measured with Particle Image Velocimetry, for the case of the boundary layer without actuation, with actuation with a steady jet, and for the best individual obtained after the optimization of the pulsed jet parameters.</p> <p>- the parameters of the individuals generated in the optimization process.</p>
Data and scripts for reproducing "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow", currently under review.</p>
Methane ebullition and diffusion rates, turbulence, water temperature, and water depth data from Falling Creek Reservoir (Virginia, USA) in the ice-free period during 2016-2019
This dataset includes weekly and subweekly methane ebullition and diffusion rates collected from March through November in 2016, 2017, 2018, and 2019 in Falling Creek Reservoir (FCR), a drinking water reservoir owned and managed by the Western Virginia Water Authority and located in Vinton, Virginia, USA. In 2016, ebullition rates were measured at four near-shore sites along a longitudinal gradient in FCR that included four replicate locations within each site, for a total of 16 sites in the reservoir. In 2017, methane emission rates and five different potential environmental driver variables (water temperature, wind speed, pressure, turbulence, primary production) were measured at five longitudinal transects that included four replicate sites per transect, for a total of 20 sites in the reservoir. In 2018, ebullition was measured at the same five longitudinal transects but with two replicate sites per transect, for a total of 10 sites. In 2019, ebullition was only measured at the furthest upstream transect monitored in 2017-2018, with four replicate sites within that transect. The dataset consists of four tables: 1) weekly methane ebullition and diffusion rates and the water depths at each of the 24 unique monitoring sites; 2) 10-minute resolution sediment-water interface and surface water temperatures from 16 sites in 2017; 3) weekly sediment-water interface and surface turbulence at the four transects in 2017; and 4) geographical coordinates of each of the 24 sites in the reservoir.
Turbulence and flux data from eddy flux platform on Toolik Lake, Alaska 2009-2015.
Yearly file describing the turbulence conditions on Toolik Lake including the CH4, CO2 and H2O fluxes over the lake adjacent to the Toolik Field Research Station (68 38'N, 149 36'W). This location is a floating platform where eddy flux measurements have been made, and should not be confused with either the Toolik Field Station Climate site, which is a land-based station, or the Toolik Lake Climate Station that is lake-based but at a different location (approximately 300 m from the eddy platform). These data were used in the Eugster et al. (2020) article that appeared in Environmental Science: Processes and Impacts. Note that the quality flagging system in this file is only for reference, it was not used in the named article. Instead, the 9-level system by Foken et al. was calculated (see Eugster et al. 2020), but no data were eliminated to avoid bias from wrong assumptions made in this approach if measurements are not performed over a terrestrial vegetated land surface.
MCR LTER: Coral Reef: Coral size, temperature and turbulence data in support of Edmunds and Burgess MarBio 2017
In this study, we tested the effects of colony size and seawater turbulence on the response of the common Indo-Pacific branching coral, Pocillopora verrucosa, to different seawater temperatures. Using whole-colony calcification as a response variable, 12 tanks (each 150 l) were used in two trials lasting 14 days to contrast the effects of seawater turbulence (two levels) and temperature (25.5°C vs 29.5°C) on colonies varying in size from ~4 to 13-cm diameter. Turbulence in the tanks was measured as the root mean square (rms) turbulent flow speed (q rms ), which quantified the variation in speed of complex and non-linear flow, regardless of direction. Treatments contrasted low (q rms = 0.63 cm s -1 ) and high (q rms = 2.07 cm s -1 ) turbulence that were ecologically relevant for shallow coral reefs. This worksheet contains data for the table and figures from the manuscript Edmunds and Burgess entitled 'Colony size modulates the calcification response of the coral Pocillopora verrucosa to turbulent flow speed and temperature', Marine Bio, 2017.
ADCP data of ice-covered and open-channel (macro-turbulent) flow, Pulmanki River, 2016-2020
<p>README of ADCP_data_Lotsari_et_al_Water_opened.zip</p> <p><br> The ADCP data was the basis of the following paper:<br> Macro-turbulent flow and its impacts on sediment transport potential of a subarctic river during ice-covered and open-channel conditions <br> Eliisa Lotsari (1, 2), Michael Dietze (3), Maria Kämäri (4), Petteri Alho (2,5), Elina Kasvi (6,2)</p> <p>1 Department of Geographical and Historical Studies, University of Eastern Finland, Yliopistokatu 2, P.O. Box 111, FI-80101, Joensuu, Finland. eliisa.lotsari@uef.fi<br> 2 Department of Geography and Geology, University of Turku, FI-20014 Turun yliopisto, Turku, Finland.<br> 3 Section 4.6 Geomorphology, German Research Centre for Geosciences GFZ Potsdam, D-14473 Potsdam, Germany. mdietze@gfz-potsdam.de<br> 4 Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland. maria.kamari@ymparisto.fi<br> 5 Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430, Masala, Finland. mipeal@utu.fi<br> 6 Turku University of Applied Sciences, Joukahaisenkatu 3, FI-20520, Turku, Finland. elina.kasvi@turkuamk.fi</p> <p>(Note: During the time of data gathering, Maria Kämäri worked at the University of Eastern Finland, and Elina Kasvi at the University of Turku)</p> <p>The data set has been measured with Sontek M9 or S5 sensors, depending on the time step (Table 1).</p> <p>Table 1. The measurement times, their acronyms (applied in the above mentioned publication)<br> and applied sensors. In the acronyms of the measurement times W=winter low flow period, S=spring<br> (snow-melt flood period), A=autumn low flow period. These information are presented also<br> in the Table 1 of the above-mentioned publication.<br> Date Acronym Sensor<br> 17.2.2016 W2016 M9<br> 25.5.2016 S2016 S5 <br> 10.9.2016 A2016 M9<br> 16.2.2017 W2017 M9 <br> 31.5.2017 S2017 M9 <br> 9.9.2017 A2017 M9 <br> 9.2.2018 W2018 M9 <br> 23.5.2018 S2018 S5 <br> 8.9.2018 A2018 S5 <br> 8.2.2019 W2019 M9 <br> 21.5.2019 S2019 M9 <br> 6.2.2020 W2020 M9 </p> <p>RiverSurveyor Live software, and its most recent version, was used each time.<br> The measurements have been done at Pulmanki River (69°55'59.09" N; 28° 2'34.32" E), Northern Finland, during 2016 - 2020. <br> The data is in directories of corresponding measurement times. The measurement<br> locations cs1, cs2, cs3, cs4, csA, csB and csC can be found in the paper (Figs. 1 and 2). <br> The data is in raw Matlab file format, as exported from the RiverSurveyor Live software.<br> The coordinate system is ENU.</p> <p>When used, the referencing to the paper and DOI ( 10.5281/zenodo.3855035 ) are required.</p>
Some Similarities and Differences between the Observed Alfvénic Fluctuations in the Fast Solar Wind and Navier-Stokes Turbulence
<p>Three text data sets are uploaded: wind tunnel data (modane1.txt), solar-wind magnetic-field data (Flat1maginterp.txt) and solar-wind velocity data (Flat15interp3DP.txt)</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.