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195 results for “Coupled models”
Supplementary Material for "TITANIA Model-Free Interpretation of Residual Dipolar Couplings in the Context of Organic Compounds"
<p>Simulation input (RDC data, input geometries, keywords) and output files (simulation / geometry trajectories, alignment data, SECONDA analysis) for isopinocampheol, tubocurarine and strychnine runs with the TITANIA software.</p>
Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
<p>Dynamic changes in the active portion of stream networks represent a phenomenon common to diverse climates and geologic settings. However, the ecological implications of river network expansions/retractions remain poorly understood owing to operational difficulties in mechanistically describing these processes at the relevant spatio-temporal scales. Here we present a novel Bayesian framework for the simulation of event-based channel network dynamics capitalizing on the concept of "hierarchical structuring of temporary streams" - a general principle to identify the activation/deactivation order of network nodes. The framework incorporates a dynamic version of a stochastic occupancy metapopulation model, and is used to analyze the impact of pulsing river networks on species persistence in different scenarios. Climate strongly controls temporal variations of the active length, influencing the preferential configuration of the active channels and the speed of network retraction during drying. We also identify a climate-dependent detrimental effect of network dynamics on species spread and persistence. This effect is enhanced by dry climates, where flashy expansions and retractions of the flowing channels induce metapopulation extinction. Survival probabilities are particularly reduced in settings where the spatial heterogeneity of network connectivity is pronounced. The proposed framework provides novel insight on the multi-faced ecological legacies of channel network dynamics.</p>
Coupled Model Machine Learning Weights
<p>The coupled model weights file contains the necessary weights to the machine learning network to run our machine learning coupled model. The regridded ERA5 file contained a year's worth of initial conditions to be used for starting forecasts or climate simulations. </p>
Data for regional coupled model paper
<p>This data is for the JGR manuscript (A Regional Air-Sea Coupled Model Developed for the East Asia and Western North Pacific Monsoon Region).</p>
Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. Simulation and observational results of meteorological and air quality including four folders:</p> <p> Day_PBLH: Daily PBLH data</p> <p> Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO</p> <p> Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation</p> <p> Hour_radiation: Hourly surface radiation data</p> <p>2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:</p> <p> AOD: Yearly and seasonal AOD data</p> <p> CF: Yearly and seasonal CF data</p> <p> CO: Yearly and seasonal CO data</p> <p> LWP: Yearly and seasonal LWP data</p> <p> NO2: Yearly and seasonal NO2 data</p> <p> O3: Yearly and seasonal O3 data</p> <p> Precipitation: Yearly and seasonal precipitation data</p> <p> Radiation: Yearly and seasonal radiation data</p> <p> SO2: Yearly and seasonal SO2 data</p>
Flow-field correction method to constrain AMOC in coupled model (IPSL-CM6A-LR)
<p>We use the standard version of IPSL-CM6A-LR (Boucher et al., 2020). The ocean component of IPSL-CM6A-LR is the NEMO oceanic model Version 3.6. The dynhpg.F90 file is a modified version of the routine that implements the flow-field correction method, and the namelist_ORCA1_cfg is the modified namelist used to activate the flow field correction, set the parameters, and read the input temperature, salinity, and mask. The mask specifies the region where this method is applied.</p> <p>To implement this method using the IPSL coupled model, consider following these steps:</p> <p>1. Install the standard configuration in a path<br> 2. Copy the provided dynhpg.F90 in modipsl/modeles/NEMOGCM/CONFIG/ORCA1_LIM3_PISCES/MY_SRC<br> 3. Compile<br> 3. Copy and modify the namelist_ORCA1_cfg in modipsl/config/IPSLCM6/testffc/PARAM</p> <p>The input conservative temperature (in degC), absolute salinity (in g/kg), and mask are provided to the model for the flow field correction are available in the following files:</p> <p>data_sal_sa_1.5LFC.nc (salinity) <br> data_sal_sa_weak1.5LFC.nc (salinity)<br> data_tem_bigthetao_1.5LFC.nc (temperature)<br> data_tem_bigthetao_weak1.5LFC.nc (temperature)<br> rhdmsk_data_bothBC2.v2.nc (mask)</p> <p>Note that data_sal_sa_1.5LFC.nc and data_tem_bigthetao_1.5LFC.nc are used to constrain the AMOC to the strong state in Jiang et al., (2023). data_sal_sa_weak1.5LFC.nc and data_tem_bigthetao_weak1.5LFC.nc are used to constrain it to the weak state in Jiang et al., (2023).</p> <p>Finally, some of the main simulated outputs are given. All the outputs are the annual mean ensemble mean (3 members) data lasting for 100 years. The initial states of the 3 members are sampled in the years of 1850, 2000 and 2080 of the CMIP6 piControl simulation available on ESGF, corresponding to neutral, strong, and weak AMOC states respectively.</p> <p>heatc_strong_3runs_1Y.nc (ocean heat content in J/m2)<br> sos_strong_3runs_1Y.nc (sea surface salinity in psu)<br> tos_strong_3runs_1Y.nc (sea surface temperature in degC)<br> precip_strong_3runs_1Y.nc (precipitation in kg/(s*m2))<br> diaptrW_strong_3runs_1Y.nc (meridional streamfunction in Sv)<br> slp_strong_3runs_1Y.nc (sea level pressure in Pa)<br> geop500_strong_3runs_1Y.nc (geopotential height at 500 hPa in m)<br> nettop0_strong_3runs_1Y.nc (clear-sky solar radiation at the top of atmosphere in W/m2)<br> nettop_strong_3runs_1Y.nc (net downward flux at the top of atmosphere in W/m2)<br> t2m_strong_3runs_1Y.nc (air temperature at 2-m in K)<br> vitu850_strong_3runs_1Y.nc (zonal wind at 850 hPa in m/s)<br> cld_strong_3runs_1Y.nc (low-level and high-level cloudiness, unitless)<br> tauuo_strong_3runs_1Y.nc (surface downward stress in the x-direction in N/m2)<br> tauvo_strong_3runs_1Y.nc (surface downward stress in the y-direction in N/m2)</p> <p>The files listed above are the results from simulations where the AMOC is constrained to the strong state (i.e., using the input data_tem_bigthetao_1.5LFC.nc and data_sal_sa_1.5LFC.nc). Similarly, the same fields from simulations where the AMOC is constrained to the weak state (i.e., using the input data_tem_bigthetao_weak1.5LFC.nc and data_sal_sa_weak1.5LFC.nc) are:</p> <p>heatc_weak_3runs_1Y.nc (ocean heat content in J/m2)<br> sos_weak_3runs_1Y.nc (sea surface salinity in psu)<br> tos_weak_3runs_1Y.nc (sea surface temperature in degC)<br> precip_weak_3runs_1Y.nc (precipitation in kg/(s*m2))<br> diaptrW_weak_3runs_1Y.nc (meridional streamfunction in Sv)<br> slp_weak_3runs_1Y.nc (sea level pressure in Pa)<br> geop500_weak_3runs_1Y.nc (geopotential height at 500 hPa in m)<br> nettop0_weak_3runs_1Y.nc (clear-sky solar radiation at the top of atmosphere in W/m2)<br> nettop_weak_3runs_1Y.nc (net downward flux at the top of atmosphere in W/m2)<br> t2m_weak_3runs_1Y.nc (air temperature at 2-m in K)<br> vitu850_weak_3runs_1Y.nc (zonal wind at 850 hPa in m/s)<br> cld_weak_3runs_1Y.nc (low-level and high-level cloudiness, unitless)<br> tauuo_weak_3runs_1Y.nc (surface downward stress in the x-direction in N/m2)<br> tauvo_weak_3runs_1Y.nc (surface downward stress in the y-direction in N/m2)</p> <p> </p>
Analysis of the Scalar and Vector Random Coupling Models For a Four Coupled-Core Fiber
<p>The files with simulation results for ECOC 20223 submission "Analysis of the Scalar and Vector Random Coupling Models For a Four Coupled-Core Fiber".</p><p><strong>"4CCF_eigenvectorsPol"</strong> file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 4-coupled-core fiber (4CCF). These results are uploaded to the python notebook <strong>"4CCF_modelingECOC" </strong>in order to plot them to get Fig. 2 in the paper. <strong>"TransferMatrix"</strong> is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook <strong>"4CCF_modelingECOC"</strong>, where all the calculations for figures in the paper are presented<strong>.</strong></p><p> </p><p><strong>! </strong><i>UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [</i>A. Ankiewicz, A. Snyder, and X.-H. Zheng, "Coupling between parallel optical fiber cores–critical examination", Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986<i>]:</i></p><p>(4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W)))</p><p>The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.</p><p><strong>! </strong><i>UPD 9.12.2023: I have noticed that in the published version of the code I forgot to change the wavelength range for impulse response calculation. So instead of seeing the nice shape as in the paper you will see resolution limited shape. To solve that just change the range of wavelengths, you can add "wl = [1545e-9, 1548e-9]" in the first cell after "Total power impulse response".</i></p><p><strong>P.s. </strong>In case of any questions or suggestions you are welcome to write me an email ekader@chalmers.se</p>
Data used to simulations in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality:</p> <p>YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"</p>
The simulated outputs analyzed in the article: "Understanding the influences of ocean waves on Arctic sea ice simulation: a modeling study with an atmosphere-ocean-wave-sea ice coupled model"
<p>In Ice-mass_[experiment] files, they include daily-averaged sea ice concentration and sea ice mass/area budgets.</p> <p>In Flux_[experiment] files, they include daily-averaged net ice surface flux, net shortwave/longwave radiation at the ice surface, latent/sensible heat flux at the ice surface, conductive heat flux at the top ice layer, and ice-ocean heat flux. </p>
Notebooks and calculation files for: Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Models
<p>The files with simulation results for JLT submission "Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Modelsr".</p> <p><strong>"3CCF_supermodes"</strong> file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 3-coupled-core fiber (4CCF). These results are uploaded to the python notebook <strong>"3CCF_modelingJLTPaper" </strong>in order to plot them to get Fig. 3 in the paper. <strong>"TransferMatrix"</strong> is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook <strong>"3CCF_modelingJLTPaper"</strong>, where all the calculations for figures in the paper are presented<strong>.</strong></p> <p> </p> <p> </p> <p><strong>! </strong><em>UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [</em>A. Ankiewicz, A. Snyder, and X.-H. Zheng, “Coupling between parallel optical fiber cores–critical examination”, Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986<em>]:</em></p> <p>(4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W)))</p> <p>The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.</p> <p> </p> <p><strong>P.s. </strong>In case of any questions or suggestions or if you need more explanations, you are welcome to write me an email ekader@chalmers.se. If it seems like the code does not work or mistakes in simulations are found, I also appreciate letting me know.</p>
Pre-built Sector-coupled Euro-Calliope Model
<p><strong>Sector-coupled Euro-Calliope subnational-scale pre-built models</strong></p> <p>Built using <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a> commit hash: 6fd0bf3dce2a0799ac9821b50e9b1513fa783018</p> <p>This model is pre-packaged and ready to be loaded into Calliope, based on 2010 - 2018 input data. To run the model you will need to do the following:</p> <p>a. Install a specific conda environment to be working with the correct version of Calliope (<code>conda env create -f requirements.yml</code>)</p> <p>b. Include specific scenarios to pick up the relevant sectors. For the study accompanying this release, The following scenarios were included <code>"industry_fuel_shared,transport,heat,config_overrides,res_2h,gas_storage,link_cap_dynamic,freeze-hydro-capacities,add-biofuel"</code>, where:</p> <ul> <li> <p><code>industry_fuel_shared</code>: Includes all non-electrical industry demands and the necessary technologies to generate those fuels synthetically. This includes e.g. annual methanol requirements for the chemical industry. <code>shared</code> refers to the fact that all regions' annual demand is pooled and can be met across all regions. The other option is to set this to <code>industry_fuel_isolated</code>, where a region must meet its own annual demand by generation of fuel within the region.</p> </li> <li> <p><code>transport</code>: This ensures ICE and EV light and heavy vehicle technologies and annual demands are in the model. It also includes reference to constraints required to make smart-charging of EVs work (e.g. weekly demand requirements).</p> </li> <li> <p><code>heat</code>: This ensures that all heat provision technologies and hourly demands are in the model. Carriers added are <code>heat</code> (space heating and hot water) and <code>cooking</code>. Technologies added can be found in <code>heat-techs.yaml</code>.</p> </li> <li> <p><code>config_overrides</code>: This includes high-level simplifications, such as removal of technologies that are considered redundant (e.g. less interesting combined heat and power technologies).</p> </li> <li> <p><code>res_2h</code>: Sets the model with a 2h resolution. Can be omitted or can be one of <code>res_2h</code>, <code>res_3h</code>, <code>res_6h</code>, <code>res_12h</code>. The full hourly resolution model takes ~2 days to complete.</p> </li> <li> <p><code>gas_storage</code>: Includes underground methane storage facilities, based on latest data on a national level. Can be omitted to remove the option of this technology.</p> </li> <li> <p><code>link_cap_dynamic</code>: Sets a limit on transmission line capacities. The limits are chosen subjectively based on current capacity, such that lines with smaller current capacities can proportionally increase much more (e.g. 100x) than larger lines (e.g. 2x). See <code>national/links.yaml</code> for other override options to apply here.</p> </li> <li> <p><code>freeze-hydro-capacities</code>: Sets hydro capacities to equal "today's" capacities. This seems more reasonable than setting current capacities as upper limits, as this causes the model to install no hydro.</p> </li> <li> <p><code>add-biofuel</code>: Enables a biofuel supply stream with a distinct <code>biofuel</code> carrier, with annual limits on biofuel that can be provided (based on JRC residuals). This differs from Euro-Calliope v1.0 which is a black box technology converting biofuel to electricity directly.</p> </li> </ul> <p>c. decide on a SPORES run to undertake, e.g. the scenario <code>spores_supply</code> will run SPORES for primary energy supply technologies. SPORES scenarios can be found in the file <code>spores.yaml</code>.</p> <p>d. pick your run year, by pointing to the relevant model config file (e.g. <code>model-2018.yaml</code> for the 2018 weather year).</p> <p>e. run the model via the dedicated scripts found in this directory. These scripts include the addition of custom constraints and have been copied directly from the workflow, where they would normally be initiated as part of the internal process. However, you can load and run them in an interactive session / with your own python script to call them:</p> <p><code class="language-python">[1] import create_input </code></p> <p><code class="language-python">[2] create_input.build_model(path_to_model_yaml, scenarios_string, path_to_netcdf_of_model_inputs) </code></p> <p><code class="language-python">[3] import run </code></p> <p><code class="language-python">[4] run.run_model(path_to_netcdf_of_model_inputs, path_to_netcdf_of_results)</code></p> <p> </p> <p><code class="language-python">Note: The only difference between this version and v0.1 is that spurious hidden files specific to MacOS have been removed from the dataset.</code></p>
Lake-Watershed coupling model in Qinghai Lake Basin, with SHUD model
<p>Files structure</p> <p> </p> <p> </p> <ul> <li> <p>Calibration: The souce code and the part of results of the calibration.</p> <ul> <li> <p>CalibFiles: The configuration of the CMAES methods.</p> </li> <li> <p>cames_out: Part of the output of CMAES method, as the entire file is huge.</p> </li> <li> <p>input: the SHUD model input for the calibration method.</p> </li> <li> <p>qhh.nse.dai.R: the kernel R code to run the CMAES method.</p> </li> <li> <p>sub.sh: the script to submit job to LSF managment on High Performance Computer.</p> </li> </ul> </li> <li> <p>Code_Analysis: The R code for analysis the result and plot figures.</p> </li> <li> <p>Code_AutoSHUD: The R code to build the QHH model. To run the script, please call <strong>Step1_RawDataProcessng.R</strong>, Step2_DataSubset.R, <strong>Step3_BuidModel.R</strong> sequentially.</p> <ul> <li> <p>autoSHUD_qhh.txt: The configration of the autoshud project for the QHH project.</p> </li> <li> <p>GerReady.R: The initial files to build the environment, which is called by other script automatically.</p> </li> <li> <p><strong>Step1_RawDataProcessng.R</strong>: Raw data processing.</p> </li> <li> <p><strong>Step2_DataSubset.R</strong>: Data processing for soil, landuse and forcing data .</p> </li> <li> <p><strong>Step3_BuidModel.R</strong> : build the SHUD model.</p> </li> </ul> </li> <li> <p>Code_SHUD: the source code of SHUD used in this project.</p> </li> <li> <p>Data: The rawdata download from data centers.</p> </li> <li> <p>Modeling: The output folder for AutoSHUD code. These is the foundation of the simulation.</p> <p> </p> </li> </ul> <p> </p>
A global hybrid tropical cyclone risk model based upon statistical and coupled climate models - Supporting figures and data
<p><strong>Introduction</strong></p><p>This contribution consists of 1) supporting figures and 2) supporting data for the submitted manuscript "A Global Hybrid Tropical Cyclone Risk Model based upon Statistical and Coupled Climate Models." Supporting figures are presented in two interactive HTML documents. The supporting datasets contain tropical cyclone event sets, catalogs, and an example analysis that plots summaries of the simulated catalogs and compares them to historical observations. All files are provided for the 400 ensemble members (event sets) that represent climate model years 1981-2020 (40 years) from the first 10 CESM-LE members.</p><p><strong>Contents</strong></p><p>./Catalog/sim_650</p><p>Tropical cyclone annual catalog for each basin based on the CESM-LE distribution of ENSO phases. 650 simulations are provided for each of 400 event sets. Each file contains the catalog for a single basin and is written as catalog_tc_(BASIN)_sim650_my400_nbinom_condmeanensojma.csv., where BASIN can be NA, EP, WP, NI, SI, SP.</p><p>./Documentation</p><p>TCMODEL_UQAM_EXAMPLE.html: Analysis script showing example of use of the tropical cyclone catalogs and comparison to historical observations.</p><p>UQAM_TC_Model_Data_Supplement_Dictionary.xlsx: Data dictionary of all data supplement file contents.</p><p>./IBTRACS</p><p>Summary of IBTrACS data required in the analysis script.</p><p>./TrajectoryBanks</p><p>Contains subdirectories for each basin (EP, NAT, NI, SI, SP, WP)</p><p>Each subdirectory contains several files summarizing the event sets, or banks, of tropical cyclone trajectories.</p><p><strong>Versions</strong></p><p>Version 1.0.1: Updated TCMODEL_UQAM_SUPPORTING_FIGURES.zip for revisions to submitted manuscript.</p><p>Version 1.0.0: Original version.</p><p> </p>
TOGETHER: A Couple's Model to Enhance Relationships and Economic Stability
ClinicalTrials.gov study NCT04227405. IPD Sharing: NO. Countries: 1. Publications: 1.
Coupled PPE model output - land parameter impacts on the mean climate state
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Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
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Data from: Excitation-contraction coupling, cardiomyocyte electrophysiology, and transcriptome profiles in two HFpEF murine models: Etiology and sex-dependent differences
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Data for: Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression
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A hierarchy of global ocean models coupled to CESM1
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Model-derived backbarrier marsh width from GEOMBEST+ simulations of barrier island-marsh coupled evolution for a range of environmental conditions.
Model-derived backbarrier marsh width from GEOMBEST+ simulations of barrier island-marsh coupled evolution for a range of environmental conditions. The input parameters were varied for a range of 10 different values for each of the flux of overwash (.2-2 m^3/yr), the rate of sea level rise (1-10mm/yr), and the flux of bay sediment (2-20 m^3/yr) Each simulation was for a one meter change in sea level, starting from one of three initial conditions, an empty backbarrier basin, starting with a narrow (400m wide) backbarrier marsh, and starting with a marsh-filled (2000m wide) backbarrier basin. With 10 variants of each parameter input and 3 initial conditions, this results in 3,000 total simulations run. Each row in the spreadsheet contains the input and output values for an individual simulation. The outputs reported are the final backbarrier marsh width, defined as the distance from the backside of the barrier island to the landward most marsh cell, the change in marsh width, and the rate of change in marsh width.
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.