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1,028 results for “modelling & simulation”
Model input data for the FACETS downscaling simulation with the CAM-MPAS model
<p>The archived file contains input data necessary to reproduce the set of simulations described in Sakaguchi et al., submitted to GWD, "Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM-MPAS variable-resolution model", using the experimental CAM-MPAS code further modified by Sakaguchi and Harrop (2022) for long-term AMIP-type simulations.</p>
Met Office Unified Model outputs for simulations of Proxima Centauri b and TRAPPIST-1e
<p>Five datasets generated by the Met Office Unified Model configured for exoplanets Proxima Centauri b and TRAPPIST-1e. </p> <p>1. Control Prox: A simulation of Proxima Centauri b with a moist N2 atmosphere using observed planetary data (radius, stellar constant) as model parameters</p> <p>2. Warm Prox: A simulation of Proxima Centauri b with a moist N2 atmosphere as if moved to the inner edge of its habitable zone</p> <p>3. Control Trap: A simulation of TRAPPIST-1e with a moist N2 atmosphere using observed planetary data</p> <p>4. Warm Trap: A simulation of TRAPPIST-1e with a moist N2 atmosphere as if moved to the inner edge of its habitable zone</p> <p>5. Dry Trap: A simulation of TRAPPIST-1e with a dry atmosphere</p> <p>The data is used in Cohen et al. (2023). ""Traveling planetary-scale waves cause cloud variability on tidally locked aquaplanets." Submitted to The Planetary Science Journal.</p> <p>Abstract:</p> <p>"Cloud cover at the planetary limb of water-rich Earth-like planets is likely to weaken chemical<br> signatures in transmission spectra, impeding attempts to characterize these atmospheres. However,<br> based on observations of Earth and solar system worlds, exoplanets with atmospheres should have both<br> short-term weather and long-term climate variability, implying that cloud cover may be less during<br> some observing periods. We identify and describe a mechanism driving periodic clear sky events at<br> the terminators in simulations of tidally locked Earth-like planets. A feedback between dayside cloud<br> radiative effects, incoming stellar radiation and heating, and the dynamical state of the atmosphere,<br> especially the zonal wavenumber-1 Rossby wave identified in past work on tidally locked planets, leads<br> to oscillations in Rossby wave phase speeds and in the position of Rossby gyres and results in advection<br> of clouds to or away from the planet’s eastern terminator. We study this oscillation in simulations of<br> Proxima Centauri b, TRAPPIST 1-e, and rapidly rotating versions of these worlds located at the inner<br> edge of their stars’ habitable zones. We simulate time series of the transit depths of the 1.4 μm water<br> feature and 2.7 μm carbon dioxide feature. The impact of atmospheric variability on the transmission<br> spectra is sensitive to the structure of the dayside cloud cover and the location of the Rossby gyres,<br> but none of our simulations have variability significant enough to be detectable with current methods."</p>
Simulated water models with Apoferritin for use in cryo-EM image simulations with amorphous ice
<p>This dataset contains the atomic coordinates of several water models produced using the NAMD molecular dynamics software. The contents of each file is listed below.</p> <ul> <li><strong><em>water_81_coords.pdb</em></strong> - water only in a cubic box with side length 81A</li> <li><strong><em>water_243_coords.pdb</em></strong> - water only in a cubic box with side length 243A</li> <li><strong><em>water_486_coords.pdb</em></strong> - water only in a cubic box with side length 486A</li> <li><strong>water_567_coords.pdb</strong> - water only in a cubic box with side length 567A</li> <li><strong><em>water_645_coords.pdb</em></strong> - water only in a cubic box with side length 645A</li> <li><strong><em>water_735_coords.pdb</em></strong> - water only in a cubic box with side length 735A</li> <li><strong><em>apo_water_723_coords.pdb</em></strong> - water and apoferritin in a cubic box with side length 723A where apoferritin atoms are constrained</li> </ul>
Models simulating abrupt changes in the Chilika lagoon fishery, the Easter Island community, forest dieback and lake water quality
<p>This deposit is in support of Willcock et al "Earlier collapse of Anthropocene ecosystems driven by multiple faster and noisier drivers". It covers the following items: (i) A list of the files contained within this data deposit; (ii) How to access and download the specialist software required to view and simulate the system dynamics models (STELLA ‘isee Player’); (iii) How to run isee Player to simulate the models; (iv) How to access and download the standard statistical software ‘R’ to run the R scripts; (v) How to load ‘R’ and modify the standard R script to analyse a subset of the model runs. This file will also details the ‘required content’ (e.g. software versions), as specified in the ‘nr-software-policy.pdf’ document.</p> <p>The full descriptions of each of the four system dynamics models used in this manuscript can be read in the following papers:</p> <ol> <li>Lake Chilika – Cooper, G. S. & Dearing, J. A. Modelling future safe and just operating spaces in regional social-ecological systems. <em>Sci. Total Environ.</em> <strong>651</strong>, 2105–2117 (2019), <a href="https://doi.org/10.1016/j.scitotenv.2018.10.118">https://doi.org/10.1016/j.scitotenv.2018.10.118</a></li> <li>Easter Island – Brandt, G. & Merico, A. The slow demise of Easter Island: Insights from a modeling investigation. <em>Front. Ecol. Evol.</em> <strong>3</strong>, 13 (2015), <a href="https://www.frontiersin.org/article/10.3389/fevo.2015.00013">https://www.frontiersin.org/article/10.3389/fevo.2015.00013</a></li> <li>Lake phosphorus – Wang, R. <em>et al.</em> Flickering gives early warning signals of a critical transition to a eutrophic lake state. <em>Nature</em> <strong>492</strong>, 419–22 (2012), <a href="http://dx.doi.org/10.1038/nature11655">http://dx.doi.org/10.1038/nature11655</a></li> <li>TRIFFID - Ritchie, P. D. L., Clarke, J. J., Cox, P. M. & Huntingford, C. Overshooting tipping point thresholds in a changing climate. <em>Nat. 2021 5927855</em> <strong>592</strong>, 517–523 (2021), <a href="http://dx.doi.org/10.1038/nature11655">http://dx.doi.org/10.1038/nature11655</a></li> </ol>
SMILE GaN microLED Comsol simulation models
<p>Comsol models with results and Matlab postprocessing scripts for ray tracing simulation of GaN-based micro-LED arrays. These models have been developed in H2020 Project SMILE, grant no. 952135.</p> <p>The zip archive contains the following files:</p> <ul> <li><strong>refined_model_phi.mph</strong>: parameterized micro-LED array model, including results, without micro-lenses, with or without sapphire substrate</li> <li><strong>in_GaN_ulenses.mph</strong>: parameterized micro-LED array model, including results, with GaN micro-lenses, using a single dipole emitter</li> <li><strong>in_GaN_ulenses_more_sources.mph</strong>: parameterized micro-LED array model, including results, with GaN micro-lenses, using an array of dipole emitters</li> <li><strong>postprocessing/angular_dist</strong> (directory): Matlab script to obtain angular distribution of far field</li> <li><strong>postprocessing/gaussian_fit</strong> (directory): Matlab script to create a gaussian fit of the spatial intensity distribution at the substrate/air interface </li> </ul> <p> </p>
A Linked Application of Discrete Differential Evolution Algorithm Coupled with Simulation- Optimization Model and Comparative Analysis by Genetic Algorithm for Discrete Groundwater Management Problems
<p>Complete dataset of publication name as "The complete publication dataset is "A Discrete Differential Evolution- Linear Programming Algorithm for Groundwater Management Problems." You can find all the written codes in the zip file.</p>
BISICLES ice-sheet model for the Amundsen Sea Embayment, Antarctica : ensemble simulations to 2050
<p>BISICLES ice-sheet model simulations for the Amundsen Sea Embayment. Full details of the model set-up and ensemble design are described in the attached manuscript which has been accepted for publication in Journal of Glaciology.<br> In brief, a 213-member ensemble of simulations was created by varying four different model parameters. The parameters are the u0 value in a regularised Coulomb friction law, the rate of imposed thinning of floating ice (∂h/∂t(Ωf)), and scaling factors for sliding and viscosity coefficients (<em>C</em> and ϕ) between 0.9 and 1.1. We attach a summary text file of results, as well as NetCDF files of simulated variables land ice thickness and u and v components of velocity.<br> <strong>ASE2050_bisicles.csv </strong>contains annual (2007 to 2050, columns 5 to 48) sea level equivalent (mm) mass losses of ice from the Pine Island and Thwaites Glacier catchment basins. The parameters, given in columns 1 to 4, respectively, are the u0 (m/a), the rate of imposed thinning of floating ice (m/a), and the scaling factors for sliding and viscosity coefficients.<br> The NetCDF files in <strong>ASE_BISICLES.tar.gz</strong> contain annual (2007 to 2050) simulated output variables for the Amundsen Sea region at a spatial resolution of 1 km, with one file per ensemble member. The variables follow the ISMIP6 naming protocol:<br> (https://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica#A2.3_Model_output_variables_and_README_file).<br> We include state variables lithk, uvelmean, and vvelmean. Each file is named according to the variable, the simulation parameters, and the resultant 2050 SLE value of ice loss (mm). For example, <strong>lithk_ASE_BISICLES.uj_20.dhfdt_5.C_0.90.phi_0.90.slr_43.06.nc </strong>is the land ice thickness data for simulation u0=20 m/a, ∂h/∂t(Ωf) = 5 m/a, C scaled by 0.9, ϕ scaled by 0.9, and a final SLE of 43.06 mm.</p> <p> </p>
Data for ECHAM-HAM in the Publication "Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions"
<p>This repository contains 3-hourly data of the ECHAM-HAM experiment in the paper:</p> <p>"Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020."</p>
PROTECT-SLR BISICLES ice-sheet model simulations for Amundsen Sea Embayment to 2050
<p>BISICLES ice-sheet model results. The NetCDF files in ASE_BISICLES.tar.gz contain simulated output variables for the Amundsen Sea Embayment sector of the West Antarctic Ice Sheet at a spatial resolution of 1 km. The model start date is 2007 and the outputs are yearly to 2052. Each of the 30 simulations is a result of a different combination of model parameters. The parameters are the u<sub>0</sub> value in a regularized Coulomb friction law, the rate of imposed thinning of floating ice, and scaling factors for sliding and viscosity coefficients between 0.9 and 1.1. The final part of each dataset name gives the sea-level equivalent (SLE) of loss of ice above floatation within Pine Island and Thwaites Glacier catchment basins. Each output was randomly selected from a 2 cm 2050 SLE band of a histogram of a large ensemble of simulations.</p> <p>See the pdf report included for further details.</p>
Bern3D model output data from idealized co2 increase-decrease simulations to investigate reversibility in the Earth system
<p>The data described below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations to investigate reversibilty and hysteresis for different maximum co2 forcings.</p> <p><br> The data are provided as .csv and .nc files<br> The first row in the .csv files contains the header, which describes the variable. The naming convention is as follows:</p> <p>c#k#_VARIABLE</p> <p>c# indicates the maximum co2 as times pre-industrial (c2 to c5)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> and VARIABLE indicates the value of the respective variable, which are:<br> co2: change in atmospheric co2 concentration in [ppm]<br> amoc: change in maximum of the Atlantic meridional overturning circulation in [Sv]<br> ohc: change in ocean heat content in [10^24 J]<br> seaice: sea-ice area remaining as fraction of the pre-industrial cover<br> Om_arag: fraction of water with Omega_arag > 3 in the upper 175 m<br> o2_thermo: change in thermocline (200-600 m) oxygen concentration in [mmol m^-3]<br> for each variable a separate file exists where the variable and co2 are provided.</p> <p><br> Spatial data to create the maps of hysteresis on a grid-cell basis are provided for the two scenarios as .nc files. The naming is as follows:</p> <p>c#k#_hyst_o2thermo.nc</p> <p>where c# corresponds again to maximum co2 as times pre-industrial and k# to the equilibrium climate sensitivity. The .nc files contain the coordinate (latitude, longitude) centers (lat_t, lon_t) and edges (lat_u, lon_u) as well as the hysteresis area (hystA_o2thermo) in [mmol m^-3].</p> <p><br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename')</p> <p><br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch</p>
Outputs of the Jupyter Notebook - Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)
<p>The dataset contains the outputs of the notebook "Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)" published in The Environmental Data Science Book.</p>
Initial Conditions for ATOM-COBALT dynamic N:P model simulations in GBC paper
<p>Adjustment of standard initial conditions file for COBALT simulations to add dynamic phytoplankton P fields. </p>
SNOWISO model snow- and firn core simulations for the EastGRIP drilling site in Greenland
<p>This dataset (.csv) includes four SNOWISO v2 snowpack simulations of the stable water isotopes (δ<sup>18</sup>O, δD, d-excess) and is the result of snowpack simulations in:<br><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters</em>, <a href="https://doi.org/10.1029/2023GL104249">http</a><a href="https://doi.org/10.1029/2023GL104249">s://doi.org/10.1029/2023GL104249</a></p> <p>The SNOWISO model is a 1-D isotope-enabled snowpack and surface exchange model. The model accumulates snowfall (input) and applies water vapor exchange (input) at the snow surface with subsequent isotopic fractionation of the surface snow. In addition, diffusion of water isotopes in the accumulated snowpack is applied. This dataset is simulated in a 1 cm vertical layer resolution.</p> <p>The scientific theory of the SNOWISO model is described in:<br><em>Wahl, S., Steen‐Larsen, H.C., Hughes, A.G., Dietrich, L.J., Zuhr, A., Behrens, M., Faber, A.K. and Hörhold, M., 2022. Atmosphere‐Snow Exchange Explains Surface Snow Isotope Variability. Geophysical Research Letters, 49(20), p.e2022GL099529.</em></p> <p>The documentation of the SNOWISO model v2 operational set-up is given in:<br><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, </em><a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a></p> <p>This model dataset consists of simulations for two model configurations each, with (control) and without (no_frac) fractionation during vapor exchange: </p> <ol> <li>daily average isotopes in the <strong>surface snow</strong> (top 2 cm) for the periods 11/05/2018-5/8/2018 and 17/5/2019-31/7/2019 <ul> <li>surface_snow_simulation_2018-2019_control.csv</li> <li>surface_snow_simulation_2018-2019_no_frac.csv</li> </ul> </li> <li>three 1-m long <strong>snow cores </strong>ending in 2017, 2018, and 2019, respectively <ul> <li>snowpack_core_simulation_2017_control.csv</li> <li>snowpack_core_simulation_2018_control.csv</li> <li>snowpack_core_simulation_2019_control.csv</li> <li>snowpack_core_simulation_2017_no_frac.csv</li> <li>snowpack_core_simulation_2018_no_frac.csv</li> <li>snowpack_core_simulation_2019_no_frac.csv</li> </ul> </li> <li>one <strong>firn core </strong>simulation in the period 1990-2011 (~6 m) <ul> <li>snowiso_model_1990-2012_control.csv</li> <li>snowiso_model_1990-2012_no_frac.csv</li> </ul> </li> <li>one <strong>firn core </strong>simulation in the period 1990-2020 (~8.5 m) <ul> <li>snowiso_model_1990-2020_control.csv</li> <li>snowiso_model_1990-2020_no_frac.csv</li> </ul> </li> </ol> <p>Model input:</p> <ul> <li>6-hourly precipitation rate, vapor, and precipitation water stable isotopes from ECHAM6-wiso simulation nudged to the ERA-5 reanalysis (https://zenodo.org/record/8341390)</li> <li>hourly latent heat flux, near-surface meteorological variables, and snowpack variables from MARv3.12 simulation driven by the ERA-5 reanalysis (https://zenodo.org/record/8335402)</li> </ul> <p>Please be encouraged to contact me (Laura.Dietrich@uib.no) if you have any questions or ideas regarding these SNOWISO model simulations.<br><br><strong>Data usage notice:</strong></p> <p>When using the <strong>SNOWISO model</strong>, you should refer to:<br><em>Wahl, S., Steen‐Larsen, H.C., Hughes, A.G., Dietrich, L.J., Zuhr, A., Behrens, M., Faber, A.K. and Hörhold, M., 2022. Atmosphere‐Snow Exchange Explains Surface Snow Isotope Variability. Geophysical Research Letters, 49(20), p.e2022GL099529.</em></p> <p>If you use <strong>any of these simulations</strong>, you should refer to:<br><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, <a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a></em></p> <p> </p>
Disturbance legacies and resilience simulation using an individual-based forest landscape model on the Andrews Experimental Forest
Disturbances are key drivers of forest ecosystem dynamics, and forests are well adapted to their natural disturbance regimes. However, as a result of climate change, disturbance frequency is expected to increase in the future in many regions. It is not yet clear how such changes might affect forest ecosystems, and which mechanisms contribute to (current and future) disturbance resilience. We here studied the 6364-ha HJ Andrews Experimental Forest landscape to investigate how patches of remnant old-growth trees (as one important class of biological legacies) affect the resilience of forest ecosystems to disturbance. Using the spatially explicit, individual-based forest landscape model iLand we analyzed the effect of three different levels of remnant patches (0%, 12%, and 24% of the landscape) on 500-year recovery trajectories after a large, high severity wildfire. In addition, we evaluated how three different levels of fire frequency (no fire, a historic fire return interval of 262 years, and a reduced fire return interval of 131 years) modulate the effects of initial legacies. The study investigated effects of legacies on the resilience of forest ecosystem structure (represented by canopy complexity as described by the rumple index), composition (proportion of late-seral species), and functioning (total ecosystem carbon storage). For each scenario of initial legacy and fire return interval 25 replicates were simulated. More information on the simulation methodology as well as the code and executable used for this study can be obtained at http://iLand.boku.ac.at. The dataset is completed and no further analyses are planned at this point. The results are published in Ecological Applications http://dx.doi.org/10.1890/14-0255.1.
Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set
<p>For a better understanding of magnetic field in the solar corona and dynamic activities such as flares and coronal mass ejections, it is crucial to measure the time-evolving coronal field and accurately estimate the magnetic energy. Recently, a new modeling technique called the data-driven coronal field model, in which the time evolution of magnetic field is driven by a sequence of photospheric magnetic and velocity field maps, has been developed and revealed the dynamics of flare-productive active regions. Here we report on the first qualitative and quantitative assessment of different data-driven models using a magnetic flux emergence simulation as a ground-truth (GT) data set. We compare the GT field with those reconstructed from the GT photospheric field by four data-driven algorithms. It is found that, at least, the flux rope structure is reproduced in all coronal field models. Quantitatively, however, the results show a certain degree of model dependence. In most cases, the magnetic energies and relative magnetic helicity are comparable to or at most twice of the GT values. The reproduced flux ropes have a sigmoidal shape (consistent with GT) of various sizes, a vertically-standing magnetic torus, or a packed structure with curled field lines. The observed discrepancies can be attributed to the highly non-force-free input photospheric field, from which the coronal field is reconstructed, and to the modeling constraints such as the treatment of background atmosphere, the bottom boundary setting, and the spatial resolution.</p>
Simulations from the SEIB-DGVM dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison
<p>Outputs from the SEIB-DGVM dynamic global vegetation model. One forced by CRU-NCEP v5 climate data for the period 1901-2014 and one forced by bias-corrected IPSL-CM5A-LR RCP 8.5 climate data for the period 1901-2099. Only potential natural vegetation was simulated, i.e. no human land-use. Both simulations are at 0.5 x 0.5 degree spatial resolution. Carbon turnover fluxes for live vegetation for each individual turnover-causing process in the model were outputted individually. For a full description of the simulations, please refer to the accompanying paper.</p>
Simulation data and software scripts used in calculus of ∆36 signature from EMAC clumped O2 isotope-inclusive model
<p>This publication contains simulation data and software scripts for calculating quantities related to clumped oxygen isotope signature (∆<sub>36</sub>) derivation, as described in the "static" framework of Yeung‍ et‍ al. (2016), hereinafter "Y16") and subsequently used in Yeung‍ et‍ al.‍ (2019) analysis. We provide the output of the 1950–2011 transient simulation with EMAC model with explicit "dynamic" simulation of ∆<sub>36</sub> (i.e. <sup>18</sup>O<sup>18</sup>O isotopologues undergoing transport, mixing and O(<sup>3</sup>P)-mediated isotope equilibration) to demonstrate the importance of several assumptions/simplifications involved in the static calculus.</p> <p> </p> <p>Please refer to .README.pdf for details.</p>
3D models for the ligaments of the Interosseous Membrane of 5 forearms with their biomechanical simulation scenes
<p>This dataset contains a group of 15 ligaments corresponding to the five specimens (3 per forearm) modeled as 3D tetrahedral meshes. In addition, 15 simulation scenes written in SOFA framework (INRIA) are supplied to implement stretch experiments. Details about the study are provided in the technical report.</p>
The ECHAM/MESSy idealized (EMIL) model set-up: data of reference simulations
<p>This data set contains the data from simulations performed with the ECHAM/MESSy IdeaLized (EMIL) dry dynamical core model, as presented in the GMD(D) publication by Garny et al., available under doi https://doi.org/10.5194/gmd-2019-330. For details, please refer to the enclosed data description file.</p>
The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil
<p>This video shows de simulation of scenarios presented in the article "The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil"</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.