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567 results for “Simulated datasets”
Dataset containing raw simulation data for a paper on decoding bosonic quantum LDPC codes
<p>This is the dataset from evaluations used in code related to a paper on analog information decoding of bosonic quantum LDPC codes, available on Github (https://github.com/cda-tum/mqt-qecc/). For more information we refer to the Github repository and the paper.</p>
Global continuous 0.05 degree atmospheric carbon dioxide dataset (GCXCO2) based OCO-2 satellite, CAMS and CarbonTracker simulation data from 2000 to 2020
<p>This dataset provides global seamless 8-day XCO2 (column-averaged CO2 dry air mole fraction) with a spatial resolution of 0.05 degree from 2000 to 2020. The unit is ppm. The detailed process and product validation accuracy can be found in our paper at https://doi.org/10.1016/j.scitotenv.2024.177051</p>
Simulated Ancient Genomic Kinship Dataset: BAM (5x run7-12) Files for Related (including inbred) Pairs
<p>Simulated Ancient Genomic Kinship Dataset: VCF and BAM (5x (run7-12) Files for Related (including inbred) Pairs</p> <p><strong>Description:</strong></p> <p>This dataset comprises simulated pedigrees (VCF files containing 8,677,101 autosomal biallelic and 298,625 X chromosomal SNP positions) generated using Ped-sim (v1.3) and comprising pairs of diverse familial relationship types up to third-degree. The first-degree relationships are parent-offspring and siblings; the second-degree relationships are half-siblings, grandparent-grandchild, and avuncular pairs; and third-degree relationships are first cousins, great-grandparent-great-grandchild, and grand avuncular pairs. For each of these 8 relationship types, our dataset includes 48 pairs of individuals. It also contains unrelated pairs. Additionally, the dataset includes first- and second-degree relatives, with inbreeding (parent-offspring pairs where the parents of the offspring are the first cousins and grandparent-grandchild pairs where the grandchild is the offspring of first cousins). Our simulations encompass all combinations of kinship types regarding sex. The dataset was further enriched by simulating ancient DNA-like sequencing data (5x and 1x BAM files) of Ped-sim simulated individuals using the gargammel tool, employing procedures akin to standard paleogenomic sequencing libraries. Note that the BAM files contain only randomly chosen 200K autosomal SNP positions. Positions can be found in the "200K_positions" file. Details can be found in Aktürk, Mapelli and Güler et al. 2023.</p> <p><strong>Data Sources and Generation:</strong></p> <p>Founder genotypes for pedigree simulation were created from the Tuscany (TSI) population SNPs within the 1000 Genomes Dataset v3. Notably, the founder genotypes lack background relatedness or runs of homozygosity (ROH).</p> <p><strong>Description of File Naming Conventions:</strong></p> <p>The naming conventions of the BAM files in this dataset are designed to convey key information regarding the specifics of each file.</p> <p><strong>cov1x or cov5x:</strong> This segment denotes the coverage level of the BAM files, indicating whether the sequencing coverage for the individuals in the files is 1x or 5x.</p> <p><strong>run_*:</strong> Signifies the particular batch from which the pedigree and individuals are derived. This name segment also applies to VCF files.</p> <p><strong>parent-offspring_* or similar identifiers:</strong> Reflects the origin of the individual from the corresponding VCF file. For instance, "parent-offspring_1" corresponds to the individuals present in the "run_*_parent-offspring_1.vcf" file.</p> <p><strong>parent-offspring* or similar identifiers: </strong>Indicates the origin of the individual from the sets within the VCF files. For example, "parent-offspring1" signifies the first set of parent-offspring pedigrees within the VCF file. Note that parent-offspring, grandparent-grandchild, and great-grandparent-great-grandchild and the inbreeding VCFs contain only one set, so this identifier is always 1. This convention can be 1 or 2 for the rest of the pedigrees, as the VCF files contain two sets of related pairs.</p> <p><strong>_g*-b*-: </strong>Provides information about the individual's generational level within the VCF. This follows the Ped-sim syntax. For example, for parent-offspring type, "_g1-b1-" indicates the first parent (generation 1) within a specific pedigree, and "_g1-b2-" indicates the second parent (generation 1) while "_g2-b1-" represents the offspring (generation 2).</p> <p><strong>Example Naming Structure:</strong></p> <p>For instance, the file "cov1x_run1_parent-offspring_1_parent-offspring1_g1-b1-i1.all.hs37d5.cons.90perc.trimBAM.bam" signifies a BAM file with 1x coverage, originating from "run1," containing individuals from the "run_*_parent-offspring_1.vcf" file (first set of parent-offspring pairs) where "_g1-b1-" designates the first parent in the first generation. The latter half of the name "hs37d5.cons.90perc.trimBAM.bam" is the same across all files. </p> <p><strong>Note1:</strong> Segments such as <strong>parent-offspring*_g*-b*- </strong>can also be tracked in the naming of the genotype columns in the VCF.</p> <p><strong>Note2: </strong>Sexual information within the VCF files is discernible from the genetic data present at X chromosome positions. Individuals carrying two genotypes on the X chromosome are female, while those with a single genotype are male.</p> <p><strong>Note3: Some of the individuals from distinct pedigrees may</strong>,<strong> in fact</strong>,<strong> be related due to shared ancestry through common founders. To suit specific research objectives, researchers may need to identify and exclude such relatives if the full dataset is used for kinship estimation.</strong></p> <p>For more details about the dataset's generation process, unique characteristics, or any specific inquiries, our team is available for further information. We welcome and encourage inquiries, aiming to provide comprehensive support and additional details that might aid researchers in utilizing this dataset effectively. Please don't hesitate to contact us for any specific information you may need.</p> <p>This repository contains only cov5x BAM files (run7-12). The rest of the files can be found at <strong>10.5281/zenodo.10079685 </strong>and<strong> 10.5281/zenodo.10070958.</strong></p> <p> </p>
2DVD dataset for GMD publication - Simulated prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign
<p>This archive contains the 2DVD measurement of graupel particles used in the GMD paper "Simulated prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign".</p><p>For each identified graupel particle, the following are included:</p><ul><li>Volume-equivalent diameter (mm)</li><li>Density (g cm-3)</li><li>Fall velocity (m s-1)</li></ul>
Simulated dataset for Visual Odomtry - drone flight
<p>This dataset was used in the article entitled <strong>SiaN-VO: Siamese Network for Visual Odometry</strong>.</p><p>The files val_dataset_simulated_32x32.hdf5 and train_dataset_simulated_32x32.hdf5 are used in the training stage. The model generated is tested individually in the other files (which correspond to individual routes).</p>
Multimodal Agricultural Aerial and Ground Robotics Simulation Dataset
<p><strong>Dataset description</strong></p><p>This dataset was generated using an aerial robot and a ground robot in the Webots simulator with the <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation">OpenDR agricultural dataset generator tool</a>.</p><p>It consists of 13980 RGB images and their semantic segmentation counterparts taken at different lighting conditions and robot positions in an agricultural field. It also includes the annotation data comprised of the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box. Furthermore, it includes gps and inertial unit sensor data for UAV and gps, inertial and lidar sensor data for UGV.</p><p><strong>Folder configuration</strong></p><p>The dataset contains 4 folders for different lighting conditions:</p><ul><li>noon cloudy</li><li>noon stormy</li><li>dawn cloudy</li><li>dusk</li></ul><p>Each contains UAV and UGV folders. UAV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>camera: contains generated RGB images.</li><li>gps: contains the three-axis location of global positioning sensor saved in TXT files.</li><li>inertial unit: contains the inertial unit date in TXT files.</li></ul><p>UGV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>front_bottom_camera: contains generated RGB images.</li><li>Hemisphere_v500: contains the three-axis location of the global positioning sensor saved in TXT files.</li><li>imu_robotti: contains the inertial unit date in TXT files.</li><li>velodyne: contains lidar data in PCD files.</li></ul><p><strong>Data format</strong></p><p>The dataset includes</p><ul><li>The inertial measurement TXT files include Euler angles in order of Roll, Pitch, and Yaw.</li><li>The GPS measurement TXT files include the robot position in x, y, and z order.</li><li>Object annotation TXT files include the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box at each line for the corresponding frame.</li></ul><p><strong>File naming convention</strong></p><p>Each data is named "s_i{_segmented, _annotation}.ext", where:</p><ul><li><strong>s</strong> denotes the simulated time in seconds.</li><li><strong>i</strong> denotes the index counting every 10ms of simulated time.</li><li><strong>ext</strong> denotes the extension, "jpg" for images, "pcd" for lidar, and "txt" for the rest.</li><li>Labels <strong>_segmented</strong> and <strong>_annotation </strong>appended to the name for segmentation image and object annotations, respectively.</li></ul><p>Each segmented image uses the following RGB color mapping:</p><ul><li>Tree: 0.1, 0.4, 0.0</li><li>Apple Tree: 0.85, 0.49, 0.57</li><li>Cow: 0.380, 0.220, 0.137</li><li>Sheep: 0.937, 0.921, 0.862</li><li>Fox: 0.992, 0.376, 0.086</li><li>Barn: 0.625, 0.293, 0.226</li><li>Cat: 0.870, 0.580, 0.0</li><li>Deer: 0.415, 0.364, 0.302</li><li>Human: 1.0, 0.855, 0.672</li></ul>
Dataset for "Mo-Si alloys studied by atomistic computer simulations using a novel machine-learning interatomic potential: Thermodynamics and interface phenomena"
<p>This dataset was used to fit a general purpose machine-learning interatomic potential for Mo-Si alloys based on the Atomic Cluster Expansion (ACE) formalism. It supports the paper "Mo-Si alloys studied by atomistic computer simulations using a novel machine-learning interatomic potential: Thermodynamics and interface phenomena".</p>
Dataset for "The effects of increasing coupling frequency and considering skin temperature on the simulation by CAS-ESM2"
<p>This dataset is the simulation for the manuscript "The effects of increasing coupling frequency and considering skin temperature on the simulation by CAS-ESM2" submitted to JGR-Atmospheres.</p>
Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"
<p>Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"</p>
A dataset of brain tumor simulations
<p>We release a dataset of 30k numerical simulations of brain tumors modelled in brain atlas. </p> <p>The tumor model is based on reaction-diffusion partial differential equation. The details about the model can be found in [1], and the brain atlas is taken from [2]. </p> <p>Each folder contains a tumor simulation in <code>Data_0001_thr2.npz</code> file and corresponding tumor model parameters in <code>parameter_tag2.pkl</code> file.</p> <p> </p> <p>References:</p> <p>[1] Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling, Ezhov I. et al., Medical Image Analysis </p> <p>[2] The SRI24 multichannel atlas of normal adult human brain structure, Rohlfing T. et al., Human Brain Mapping </p>
MLPF results on the simulated CLIC dataset
<p>Updates over the previous version:</p> <ul> <li>updated validation outputs for the cluster-based model <ul> <li>fixed a bug with how the PF candidates were stored</li> <li>added single particle gun samples to validation</li> </ul> </li> <li>added new timing runs <ul> <li>for the baseline algo, included memory information</li> <li>run the GNN model up to ~10k inputs</li> </ul> </li> <li>added hypertuning summary tables</li> </ul> <p>Trained models and evaluation results for the upcoming paper "Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors", https://doi.org/10.48550/arXiv.2309.06782.</p> <p>The archive contains the following subfolders:</p> <ul> <li><strong>clusters_best_tuned_gnn_clic_v130</strong> <ul> <li>MLPF GNN model configs and weight files after hypertuning</li> <li>the inputs are reconstructed tracks and Pandora clusters</li> <li>the outputs are reconstructed PF candidates</li> <li>trained on tt and qq v1.3.0 (1M events each)</li> </ul> </li> <li><strong>hits</strong> <ul> <li>MLPF GNN model configs and weight files</li> <li>inputs are reconstructed tracks and calorimeter hits</li> <li>outputs are reconstructed PF candidates</li> <li>trained on tt, qq and gun samples (K0L, gamma, pi+-, pi0, neutron, ele, mu) v1.2.0</li> <li>training was restarted several times from previous checkpoints</li> </ul> </li> <li><strong>hypertuning</strong> <ul> <li>GNN and transformer model before and after hypertuning</li> <li>summary tables of the hypertuning runs</li> </ul> </li> <li><strong>timing</strong> <ul> <li>scaling study of baseline PF with number of gun particles on CPU</li> <li>scaling study of GNN model with number of input elements on GPU</li> </ul> </li> <li><strong>gpu_scaling</strong> <ul> <li>the scaling study of model training on multiple accelerator cards</li> </ul> </li> </ul> <p> </p> <p>The training dataset is available at</p> <ul> <li>Pata, Joosep, Wulff, Eric, Duarte, Javier, Mokhtar, Farouk, Zhang, Mengke, Girone, Maria, & Southwick, David. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8260741</li> </ul>
RS3L: A jet tagging dataset for self-supervised learning based on re-simulation
<p>Jet tagging dataset used for "Re-Simulation-based Self-Supervised Learning" (RS3L, <a href="https://arxiv.org/abs/2403.07066">arXiv:2403.07066</a>). Simulated partons are re-showered with various parton shower configurations and reconstructed with the Delphes3 detector software. <br><br>The <code>singletons</code> array contains information about the jet and has dimensions <code>N_examples x N_augmentations x N_singletons</code>. The augmenations are ordered by: <br><br>1. nominal scenario: jet showered with Pythia8<br>2. changing the numerical seed in Pythia8<br>3. changing the scale controlling the probability for final state radiation by 1/sqrt(2)<br>4. changing the scale controlling the probability for final state radiation by sqrt(2)<br>5. using Herwig7 as parton shower</p> <p>The variables stored in the <code>singletons</code> array are:</p> <p><code>singletons = ['jettype','parton1_pt','parton1_eta','parton1_phi','parton1_e','parton2_pt','parton2_eta','parton2_phi','parton2_e', 'jet_pt','jet_eta','jet_phi','jet_e','jet_msd','jet_n2']</code><br><br></p> <p>For gluon-initiated jets, <code>parton1</code> and <code>parton2</code> are the two gluon daughters (see below). For any other jet type, parton1 is the main particle (W, H, Z, or single quark), and parton2 is filled with 0s.</p> <p>The <code>jet_pflow_cands</code> contains the particles clustered into the jet. The array has a shape <code>N_examples x N_augmentations x N_features x N_particles</code>.<br><br>The features per particle are:</p> <p><code>features = ['pt','relpt','eta','phi','dr','e','rele','charge','pdgid','d0','dz']</code><br><br>The <code>jettype</code> gives the parton at the origin of the jet: <br><br><code>jettype: <br>1: q<br>2: c<br>3: b<br>4: H->bb<br>5: g->qq<br>6: g->cc<br>7: g->bb<br>8: g->gg<br>9: W->two quarks<br>10: Z->qq<br>11: Z->bb</code></p> <p> </p>
Observational dataset for "Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations"
<p>This observational dataset include the DOE ARM ground based observations and satellite for the paper titled “Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations” on GRL.</p> <p>For the original data source, all ARM observational data sets used in this study are publicly available from the ARM data archive site (https://adc.arm.gov/discovery/#/results/iopShortName::amf2019comble/datastream::anxarmbeatmM1.c1/datastream::anxarmbecldradM1.c1/datastream::anxarsclkazr1kolliasM1.c0). MODIS MOD06 L2 cloud product are publicly available from (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD06 L2, DOI:10.5067/MODIS/MOD06 L2.061).</p> <p>CloudSat products can be ordered from the CloudSat Data Processing center (https://www.cloudsat.cira.colostate.edu/order/). To download CloudSat data, a new user must first create an account by filling out the signup form (https://www.cloudsat.cira.colostate.edu/accounts/signup/).</p>
Datasets from hydrus simulations for three soils
<p>The data were obtained from hydrus simulation results and consisted of three parts for different texture soils.</p>
Design and assembly of core/shell nanostructures as investigated by microfluidics and molecular dynamics simulation_dataset_DLS_TEM_MD
<p><span>He we like to publish data related to modified and non-modified MSN cores analysed using microfluidics platform against acetalated dextran (AcDEX)/spermine modified acetalated dextran (SpAcDEX) polymers. </span></p> <p><span>The data contains Dynamic light scattering (DLS) and TEM images which help us to to the demarcation of combinations which formed successful core/shell particles along with, <em>in-silico</em> modelling and molecular dynamics (MD) simulations data showing molecular interactions between the core particles and <a>the encapsulant </a></span><span><span></span></span><span>polymer. </span></p>
Dataset of velocity and density fields from numerical simulations
<p>This dataset contains the outcomes of numerical simulations conducted for a research paper titled "Transformation of internal solitary waves at the edge of ice cover" with the use a non-hydrostatic model (Maderich et al., 2012) and code of the non-hydrostatic model. <br>Folder FIG3 contains a *.zip archive with data corresponding to the following parameters: x-coordinate length (m), z-coordinate depth (m) and module of horizontal velocity field (m/s). Folders FIG4 and FIG6 within the archive contain information on the following parameters: x-coordinate length (m), z-coordinate depth (m) and density field (kg/m^3).<br>Folder MODEL contains fortran source code files, input files, and a short model description.</p>
Dataset: Measurements and simulations of rate coefficients for the deuterated forms of the H2+ + H2 and H3+ + H2 reactive systems at low temperature
<p>Raw measurement data and processign scripts used to produce the reaction rate results in the paper: "Measurements and simulations of rate coefficients for the<br>deuterated forms of the H2+ + H2 and H3+ + H2 reactive systems at low temperature" by Miguel Jiménez-Redondo, Olli Sipilä, Pavol Jusko, and Paola Caselli;</p> <p>DOI: <a href="https://doi.org/10.1051/0004-6361/202451757">10.1051/0004-6361/202451757</a></p>
Numerical dataset for quantum trajectory simulations of a dissipative qubit under continuous measurement and feedback
<p>The data consist of numerical results obtained from quantum trajectory simulations of a dissipative qubit under continuous measurement and feedback.</p> <p>These data are used in the preprint titled <em>"Heat current and fluctuations between a dissipative qubit and a monitor under continuous measurement and feedback."</em></p> <p>We performed numerical simulations of the stochastic master equation using the supercomputer at ISSP.</p> <p>The contents are as follows:</p> <ul> <li>Numerical results for the correlation functions (<code>F*.dat</code>).</li> <li>Numerical results for the Fano factor and the power spectrum (<code>fano_factor*.dat</code>).</li> <li>Python code to calculate the Fano factor and the power spectrum from the correlation functions (<code>fano_factor_v6.py</code>).</li> <li>Figures illustrating the Fano factor and the power spectrum (<code>fig*.eps</code>).</li> </ul>
Processed dataset used for developing tsunami inundation emulators(Part-III Simulation and Emulation Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed datasets covering tsunami inundation depths using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effectwith project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <br> <div>The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz):</div> <br> <div>Processed inundation depth files from simulation and emulation typically stored in model/CT/ptha or model/SR/ptha</div> <div> -<strong>pthaSR.tar.gz </strong>- max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))</div> <div> -<strong>pthaCT.tar.gz</strong> - max inundation depth1d(eve x m) for true(simulation and pred(emulation of different models))</div> <br> <div>The filename follows the nomenclature with:</div> -true - simulation results</div> <div>-pred - emulator with pretraining</div> <div>-pred_direct - emulator without pretraining</div> <div>-pred_nodeform - emulator without pertaining and deformation input</div> <div><br> <div> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div> </div> </div> <p> </p>
Processed files used for OQ based tsunami loss modelling using HPC based inundation and emulators(Part-IV OQ Simulation and Emulation Hazard Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed OpenQuake files covering tsunami inundation depth hazard using simulation results and emulation results discussed in the article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <div> </div> <div>The risk calculation and procedure is available in the main repo: <a href="https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk">https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk </a></div> <div> </div> <br> <div>The processed files for the test locations of Catania(CT) are provided in compressed gzip files(.gz):</div> <br> <div>Processed hazard information used to prepare and run OQ event based risk analysis are as below,</div> <div> -<strong>hazard.tar.gz </strong>- preliminary numpy files with sitcol, eventid and hazard magnitude info</div> <div> -<strong>loss.tar.gz</strong> -final hdf5 files with both event hazard and site info</div> <br> <div>The filename follows the nomenclature with:</div> </div> <div>ML4SicilyTsunami/risk/loss/tsunami_prob_892.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_1658.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_3454.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_7071.hdf5<br>ML4SicilyTsunami/risk/loss/tsunami_prob_true.hdf5</div> <div><br> <div> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/main-dev/risk</strong></div> <div><strong>https://github.com/naveenragur/OQ-Tsunami</strong></div> </div> </div>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.