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1,028 results for “modelling & simulation”
Data of curvature model for the study of nanoparticle size effects on amyloid fibril stability and molecular dynamics simulations data
<p>The data provided refer to our published article:</p> <p>T. John, J. Adler, C. Elsner, J. Petzold, M. Krueger, L.L. Martin, D. Huster, H.J. Risselada, B. Abel, Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation, J. Colloid Interface Sci. 622 (2022), 804–818. <a href="https://doi.org/10.1016/j.jcis.2022.04.134">https://doi.org/10.1016/j.jcis.2022.04.134</a></p> <p>This article is accompanied by a 'Data in Brief' article that explains in more detail the use of the curvature model and our molecular dynamics (MD) simulations:</p> <p>T. John, L.L. Martin, H.J. Risselada, B. Abel, Curvature model for nanoparticle size effects on peptide fibril stability and molecular dynamics simulation data, Data Brief 45 (2022), 108598. <a href="https://doi.org/10.1016/j.dib.2022.108598">https://doi.org/10.1016/j.dib.2022.108598</a></p>
Model simulations of NASA GISS ModelE-BiomeE v.1.0
<p>This dataset contains the data that were used in the manuscript ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (in review by GMDD, https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
Thetis Baltic Sea simulation: model and observation data sets
<p>Model and observation data sets used in article "Adjoint-based optimization of a regional water elevation model".</p>
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character's evolution: R scripts and simulated trees
<p>All R scripts used in this study, and the set of simulated phylogenetic trees used in the study.</p> <p>1. Modern methods of ancestral state estimation (ASE) incorporate branch length information, and it has been demonstrated that ASEs are more accurate when conducted on the branch lengths most correlated with a character's evolution; however, a reliable method for choosing between alternate branch length sets for discrete characters has not yet been proposed.<br><br>2. In this study, we simulate paired chronograms and phylograms, and generate binary characters that evolve in correlation with one of these. We then investigate (1) the effect of alternate branch lengths on ASE error, and (2) whether phylogenetic signal statistics and/or model-fit statistic can be used to select the branch lengths most correlated with a binary character.<br><br>3. In agreement with previous studies, we find that ASEs are more accurate when conducted on the branch lengths most correlated with the character. Phylogenetic signal statistics show limited utility for selecting the correct branch lengths, but model-fit statistics are found to be more accurate, with the correct branch lengths generally returning greater model-fit (lower AICc and BIC values). Using this method to choose between alternate branch length sets is more accurate when tree and character properties are more favorable for model optimization, and when shape differences between alternate phylogenies are greater.<br><br>4. Our results indicate that researchers conducting ASEs on discrete characters should carefully consider which branch lengths are appropriate, and, in the absence of other evidence, we suggest estimating model-fit values over alternate branch length sets and evolutionary models and choosing the branch length/model combination that returns better model fit.</p>
Archive dataset for sugarcane simulations with the JULES model
<p>This dataset contains supporting information to simulate sugarcane growth and development with the JULES model. It provides a collection of csv files with crop responses to CO2, Temperature and Soil moisture conditions (CTW-response), comparison against field and regional observations (performance), and climate change projections (projections). The parameters' values and model configuration are provided in sub-folders "sim_db" and "jules_run". Model runs were carried out with <a href="https://github.com/Murilodsv/wpy-jules">wpy-jules</a>, whereas the scientific documentation is described in Vianna et al. (2022). </p> <p>This work was supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil). We acknowledge the use of the Monsoon HPC system, maintained through a strategic partnership between the Met Office and the Natural Environment Research Council.</p> <p><strong>Summary</strong>:</p> <p>- CTW-response: CSV files initiated with "C", "T" and "W"<br> - performance: CSV files with suffix "perf" as well as "yield_var"<br> - projections: CSV files with suffix "future"<br> - input: Folder "sim_db"<br> - configuration: Folder "jules_run"</p> <p><strong>References</strong></p> <p>Vianna et al. (2022). Improving the representation of sugarcane crop in the JULES model for climate impact assessment. Global Change Biology Bioenergy (<a href="https://doi.org/10.1111/gcbb.12989">https://doi.org/10.1111/gcbb.12989</a>).</p>
Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters
<p>This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters.</p> <p>Contact: Yohei Yamada (yoheiy@jamstec.go.jp)</p>
Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr
<p>This dataset was used in the CenSierraPywr model created for the project "Optimizing Hydropower Operations While Sustaining Ecosystem Functions in a Changing Climate", for the California Energy Commission. Specifically, this data is to support reproducibility of the article describing the basic methods (Rheinheimer et al., in review). The model was built in Pywr, an open-source, linear programming-based Python package for modeling basin-scale water systems in the Central Sierra Nevada, California. Here, we focus on the Stanislaus and Upper San Joaquin River basins as they have high elevation hydropower typically operated to maximize revenue. CenSierraPywr consists of daily water allocations that include both hydroeconomic drivers for hydropower and more advanced environmental flows. Piecewise linear electricity prices from simulated hourly price data are used to drive discretionary hydropower, while environmental flows include the addition of ramping rates. Hydrological inputs include runoff data at the sub-basin level, based on the historical (1950 to 2011) daily gridded (1/16 degree) runoff data generated by the Variable Infiltration Capacity (VIC) hydrologic model developed by Livneh et al. (2013), forced with observed meteorological data and bias-corrected using local gauge data. All data inputs for reproducibility of CenSierraPywr for the Stanislaus and Upper San Joaquin Rivers are included, including original and preprocessed electricity and hydrological data and management-related data specific to certain hydropower projects or facilities.</p>
Sensitivity analysis of a mathematical model simulating the post-hepatectomy hemodynamics response
<p>Recently a lumped-parameter model of the cardiovascular system was proposed to simulate the hemodynamics response to partial hepatectomy and evaluate the risk of portal hypertension (PHT) due to this surgery. Model parameters are tuned based on each patient data. This work focuses on a global sensitivity analysis (SA) study of such model to better understand the main drivers of the clinical outputs of interest. The analysis suggests which parameters should be considered patient-specific and which can be assumed constant without losing in accuracy in the predictions. While performing the SA, model outputs need to be constrained to physiological ranges. An innovative approach exploits the features of the polynomial chaos expansion method to reduce the overall computational cost. The computed results give new insights on how to improve the calibration of some model parameters. Moreover the final parameter distributions enable the creation of a virtual population available for future works. Although this work is focused on partial hepatectomy, the pipeline can be applied to other cardiovascular hemodynamics models to gain insights for patient-specific parameterization and to define a physiologically relevant virtual population.</p>
Simulated tropical stratospheric upwelling and O$_{3}$ interactions with the 1D RCE model konrad
<p>Model output of the control and CO$_{2}$-doubling experiments with the 1D radiative-convective equilibrium (RCE) model `konrad`. The model output consists of temperature, atmospheric composition, radiative flux, heating rate profiles, time series of the convective top, surface temperature, and the net radiative flux at the top of the atmosphere. It also includes metadata such as the parameters used for each run.</p> <p>The experiments varied the relative humidity profile, the representation of O$_{3}$ chemistry, and, more importantly, a parameterization of the cooling due to the tropical stratospheric upwelling caused by the Brewer-Dobson Circulation. With the dataset, one can study the effects the tropical stratospheric upwelling on the tropical equilibrium climate sensitivity.</p>
Output from model simulations of a turbulent ice shelf-ocean boundary current
<p><em>This dataset contains output from 2 LES and 19 MITgcm simulations of an idealised configuration of the ice shelf-ocean boundary current. Core fields are provided such as velocity and density and these are given as ice-plane averaged. The output was generated to make an inter-model comparison of the representation of dynamical processes at the ice shelf-ocean boundary. The two LES configurations differ in their sub-grid-scale parameterisation. The MITgcm simulations investigate the sensitivity to various parameter changes including: resolution, diffusivity coefficients, advection scheme and melt-parameterisation. All configurations are outlined in Patmore et al. (2022).</em></p>
Phanerozoic global climatic fields simulated using the mixed-layer general circulation model FOAM
<p>These files contain the output of Phanerozoic global climate simulations conducted using the “slab” mixed-layer ocean-atmosphere general circulation model FOAM. They are available every 20 Myrs between 540 Ma and 0 Ma, both included. Boundary conditions were adapted to best match each time slice. Continental reconstructions were taken from Scotese and Wright (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/). We defined pCO2 after the proxy data compilation of Foster et al. (doi:10.1038/ncomms14845) when available and Krause et al. (dot:10.1038/s41467-018-06383-y) for older time slices. Solar luminosity followed Gough et al. (doi:10.1007/BF00151270). Continental vegetation was set to Modern-like latitudinal bands between 0 Ma and 100 Ma (included), tropical evergreen, broad-leaved forest between 120 Ma and 360 Ma (included), tundra between 380 Ma and 440 Ma (included) and rocky desert afterwards. The orbital configuration was set to null eccentricity and minimum obliquity. </p> <p>The reader is referred to the associated paper for a full description of the model and boundary conditions.</p> <p>All model output file names use the following pattern: « [age]ebP2_solCgough1981_EccN_pCO2FosterKr_[model_component] _slab.nc", with [age], the age expressed in million years ago, and [model_component] being 'atmos' or 'coupl' (atmospheric component or coupler). For each time slice, the topography-bathymetry data used in FOAM is also provided (« Topobathy_[age]eb_postslarti_cor.nc »).</p>
Archive of NASA-Unified WRF model daily forecasting simulations for DOE TRACER IOP
<pre># Copyright 2022 NASA GSFC All rights reserved. # Creative commons attribution 4.0 international license NASA-Unified WRF model daily simulations for DOE TRACER IOP Document updated: 22 June 2022 Point of contact: Takamichi Iguchi (ESSIC UMD, Code612 NASA GSFC), takamichi.iguchi@nasa.gov Toshi Matsui (ESSIC UMD, Code612 NASA GSFC), toshihisa.matsui-1@nasa.gov Contents: ./READMEtracer.txt # this file ./namelist.wps.tracer_iop_31.template # namelist.wps file to configure WRF Pre-Processing System (WPS) ./namelist.input.real.tracer_iop_31.template # namelist.input file for NU-WRF model real.exe ./namelist.input.wrf.tracer_iop_31.template # namelist.input file for NU-WRF model wrf.exe ./${YYYY}${MM}${DD} # these directories contain files produced from 48-hours NU-WRF forecasting from 00UTC on ${YYYY}${MM}${DD}: pyplot_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for Composite radar reflectivity (dBZ) # PBL height (m) + 10-m horizontal wind (850hPa-level wind in plots before 06/02/2022), # OLR TOA (W m-2), and 5-mins-accumulated IC+CG lighting flash extent density (flash km-2) # Note that this composite dBZ is calculated from NSSL 2-moment microphysics for S-band, # not from POLARRIS radar simulator pyplot.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting accprecip_${YYYY}-${MM}-${DD}_${HH}${MN}${SC}.png # Plot for 1, 3, 6-hours, and total accumulated surface precipitation (mm) accprecip.gif # Gif annimation file combining the png plot files for 1~48 hours in the forecasting polarris_zh_zdr_rh_vr_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ), # differential reflectivity (dB), cross-polar correlation (-), and # radial velocity (m s-1) at 0.5 degree elevation angle polarris_zh_zdr_rh_vr.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting polarris_zh_4sweeps_${YYYY}_${MM}${DD}_${HH}${MN}${SC}.png # Plot from POLARRIS radar simulator in NU-WRF for QCed Reflectivity (dBZ) # at 0.5, 1.8, 4.0 and 8.0 degree elevation angles polarris_zh_4sweeps.gif # Gif annimation file combining the png plot files roughly every hour # for 1~48 hours in the forecasting # following files are produced 3 days late # day1 represent the first 0-24hr forecast, day2 represents the 24-48hr forecast. CFAD_con_day?.png # Convective part of Contoured Frequency of Altitude Diagrams CFAD_str_day?.png # Stratiform part of Contoured Frequency of Altitude Diagrams QVP_con_day?.png # Convective part of QVP-like domain-mean radar profiles QVP_str_day?.png # Stratiform part of QVP-like domain-mean radar profiles RadarFrac_day?.png # Composite Radar Horizontal Fraction (0-1) by different minimum reflectivity thresholds</pre>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
How does STICS crop model simulate crop growth and productivity under shade conditions ?
<p>The STICS crop model has been used to predict the response of winter wheat to different shade conditions from an artificial shade treatment. Detailed information on the modeling procedure will be available in the following paper: “ How does STICS crop model simulate crop growth and productivity under shade conditions ” in Field Crops Research Journal.</p> <p>To launch a simulation with STICS, several input data files and parameters are required. The files used in this study are available below: The different inputs files required to launch a simulation:</p> <ul> <li>The complete plant parameters file “<em>Winter_wheat_adjusted_plt.txt</em>”</li> <li>The weather data used “<em>Weather_tot.txt</em>”</li> </ul> <p>The data are compiled at a daily time scale for each treatment: CS constant shade; PS periodic shade; NS no shade. In this data frame, “<em>Temp_min</em>” and “<em>Temp_max</em>” are the minimal and maximal air temperature in degree celcius; “<em>Global_radiation</em>” is the daily cumulated global radiation in MJ/m²; “Rainfall” is the daily cumulated rainfall in mm; “<em>Wind</em>” is the mean wind speed in m/s; and “<em>Relative_humidity</em>” is the mean air relative humidity in %.</p> <ul> <li>The initial soil parameters <em>“INI_2014-2015_ini.txt”</em> and <em>“INI_2015-2016_ini.txt”</em> respectively for the growing season 2014-15 and 2015-16</li> <li>The general soil parameters for both growing season : <em>“Soil_sols.txt”</em></li> <li>The technical itinerary<em> “TEC_2015_tec.txt” and “TEC_2016_tec.txt” </em>respectively for the growing season 2014-15 and 2015-16.</li> </ul>
(c) simulation on Repast: after queen adaptive development-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>On figures (b) and (c), simulations on RePast [11, 16, 18] are<br> provided at successive times. The last figure shows the adaptive mechanism<br> of the queen which grows with time according to the material density around<br> it, like in natural observations.</p>
Dataset and Model Weights for Plasma Sheet Model Graph Network Simulator
<p>This repository contains the simulation data and pre-trained Graph Neural Network (GNN) models produced in [1].</p> <p>Two *.zip files are provided:</p> <ul> <li>data.zip - contains the datasets of train/test simulations produced using the Sheet Model algorithm [1, 2]</li> <li>models.zip - contains the GNN model weights (<em>*.</em>pkl<em>) </em>+ relevant training information and model parameters <em>(*.</em>yml<em> and *</em>.txt)</li> </ul> <p>Dataset subfolders are named according to dataset/{'train' or 'test'}/{number of sheets}/{boundary condition}/. Each subfolder contains multiple simulations and a single info.yml file with relevant information regarding the overall setup. For each i-th simulation the following files are provided:</p> <ul> <li>x_{i}.npy - array with sheet trajectories (#time-steps, #sheets)</li> <li>v_{i}.npy - array with sheet velocities (#time-steps, #sheets)</li> <li>x_eq_{i}.npy - array with sheet equilibrium positions (#time-steps, #sheets)</li> </ul> <p> Model sub-folders are named according to :</p> <ul> <li>models/{time step}/{seed} - default architecture (preferred)</li> <li>models/{time step}/{'collisions', 'nosent' or 'equivariant'}/{seed} - alternative (less performing) architectures mentioned in the paper appendices.</li> </ul> <p>For each model we provide:</p> <ul> <li>params_best.pkl - model weights that performed the best during training on the validation set</li> <li>params_final.pkl - model weights at the end of training</li> <li>model_cfg.yml - GNN architecture metadata</li> <li>train_cfg.yml - training configuration metadata</li> <li>train_data.yml - training dataset metadata</li> <li>loss.txt - training and validation loss per epoch</li> <li>loss_i.txt - training loss per gradient update step</li> </ul> <h3>Source Code</h3> <p>The source code used to produce the data, train, and test the models can be found at: <a href="https://github.com/diogodcarvalho/gns-sheet-model">https://github.com/diogodcarvalho/gns-sheet-model</a></p> <h3>References</h3> <p>[1] D. D. Carvalho, D. R. Ferreira, L. O. Silva, "Learning the dynamics of a one-dimensional plasma model with graph neural networks<em>", Mach. Learn.: Sci. Technol. 5 025048 </em>(2024)</p> <p>[2] J. Dawson, "One‐Dimensional Plasma Model"<em>, The Physics of Fluids</em> 5.4 (1962): 445-459.</p> <p> </p>
Sound modelling techniques for an interactive audio-rendering simulation of an electric vehicle
<p>Dataset for conference paper "Sound modelling techniques for an interactive audio-rendering simulation of an electric vehicle"</p>
Global soil moisture simulated by SoilClim and mHM models at 0.5° resolution for the 1980–2022 period
<p>This deposit contains two .zip archives (SoilClim_AWR_2m_1980_2022.zip and mHM_SM_2m_1980_2022.zip), each containing 1570 GeoTIFF files. </p> <p>The file SoilClim_AWR_2m_1980_2022.zip contains 10-day simulations of relative available water (AWR), where 100% represents the full field capacity and 0% represents the wilting point, produced the SoilClim water balance model for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.</p> <p>The file mHM_SM_2m_1980_2022.zip contains 10-day simulations of soil moisture (SM), produced the mesoscale Hydrologic Model (mHM) for the 2.0 m soil depth, covering a global nonglaciated land with a 0.5° resolution, excluding latitudes above 72° N and all of Antarctica, for the 1980–2022 period.</p>
Dataset of 4D conserved tracers for convection simulated by large eddy model and cloud resolving model
<p>There are conserved tracers and active flag for convection used for diagnosis of bulk entrainment rate for four convection cases in this dataset. Total water and moist static energy are selected as tracer for shallow convection (BOMEX and RICO) and deep convection (GATE and KWAJEX), respectively. The two variables simulated by large eddy model for shallow convection and cloud resolving model for deep convection are four-dimension variables with horizontal scales, vertical altitude, and time. </p> <p>The size of domain simulated for BOMEX and RICO is 6.4 km with horizontal grid spacing of 100 m, and that GATE and KWAJEX is 256 km with horizontal grid spacing of 1 km. Besides, vertical layers in the simulation are 75 levels with spacing of 40m for BOMEX and 100 levels with spacing of 40m for RICO. For KWAJEX and GATE, the model was set up with 64 levels vertically, which gradually increases from 75 m at the surface to a spacing of 400 m through the troposphere and a larger spacing of 1 km in the Newtonian damping region. The model is integrated for 6 hours for BOMEX, 24 hours for RICO, 52.25 days for KWAJEX, and 20 days for GATE. Here, the range of time in these variables The four-dimension variables are saved every 3 seconds for shallow convection, and every 6 minutes for deep convection for two consecutive days.</p>
Figure 1 in Integrating landscape simulation models with economic and decision tools for invasive species control
Figure 1. Example state and transition simulation model for an invasive species. Landscape change is captured by defining the processes (transitions) that can move a cell from one state to another. These include both natural transitions (e.g., species dispersal, establishment, growth, fire, disturbance) and management transitions (e.g., inventory, treatment, and other activities related to invasion control). In this example, modified from Jarnevich et al. (2015), each box represents the state of a simulation cell with respect to invasive species cover (uninvaded, <5% cover, 5–50% cover, or> 50% cover; left to right) and detection (undetected or detected; top to bottom). The different color-coded arrows represent different types of transitions including growth (invasion, establishment, spread), detection (failure and success), and management (treatment and maintenance failure and success). Solid lines represent success; dotted lines represent failure.
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.