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96 results for “Dynamic Characterization”
PIBE project- Experimental characterization of stall noise in static and dynamic regimes using a NACA 63(3)418 airfoil
<p>Dynamic stall noise is one of the potential sources of amplitude modulations associated with wind turbine noise. This phenomenon is related to the periodic separation and reattachment of the boundary layer on the wind turbine blade suction side during its rotation. Within the framework of the PIBE project (Predicting the Impact of Wind Turbine Noise - <a href="https://www.anr-pibe.com/en">https://www.anr-pibe.com/en</a>), experiments were conducted in the anechoic wind tunnel of the École Centrale de Lyon in order to characterize stall noise on a pitching airfoil in both static and dynamic conditions.</p> <p>In version 1.0.0 of the database, <span>data from the second campaign using an instrumented NACA63(3)418 airfoil in static and dynamic conditions are provided. The static data can be found in the file static_data_NACA63418.h5 that contains:</span></p> <ol> <li>static wall pressure data : lift and pressure coefficients;</li> <li>dynamic wall pressure data : Power Spectral Density (PSD) of fluctuating wall pressure;</li> <li>far-field acoustic data : Power Spectral Density (PSD) of acoustic pressure.</li> </ol> <p><span>The structure of the file is described in Tree_structure_static_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_static_Matlab.zip can be used.</span></p> <p><span>The dynamic data can be found in the file dynamic_data_NACA63418.h5 that contains:</span></p> <ol> <li><span>static wall pressure data : phase-averaged lift coefficients;</span></li> <li><span>dynamic wall pressure data : phase-averaged spectrograms of fluctuating wall pressure;</span></li> <li><span>far-field acoustic data : phase-averaged spectrograms of acoustic pressure.</span></li> </ol> <p><span>The structure of the file is described in Tree_structure_dynamic_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_dynamic_Matlab.zip can be used. Only the results for a mean angle of attack of 15° and an amplitude of 15° are provided in this file.</span></p>
Characterizing juvenile dispersal dynamics of invasive barred owls: implications for management
<p>Characterizing natal dispersal can help manage the spread of invasive species expanding their ranges in response to land use and climate change. The Barred Owl (<em>Strix varia</em>) is a prominent example of an apex predator undergoing a rapid range expansion, having spread from eastern to western North America where it is now hyperabundant—threatening the Northern Spotted Owl (<em>S. occidentalis caurina</em>) with extinction and potentially endangering many other native species. We attached satellite tags to 31 Barred Owl juveniles at the southern leading edge of the Barred Owl's expanding range in California to characterize natal dispersal patterns and inform management. Juveniles traveled up to 100km from natal territories and experienced high mortality (annual survival = 0.204). At landscape scales, juveniles preferentially used forests, shrublands, and lower elevations during dispersal and avoided grasslands and burned areas. At finer scales, juveniles preferred shorter (younger) forests, lower elevations, and drainages, and avoided unforested areas. Our results suggest the Barred Owl range expansion is being driven primarily by high reproductive rates and densities despite low juvenile survival rates and dispersal through putatively suboptimal younger forests as a result of exclusion from high-quality habitat by territorial individuals. These findings also point to several strategies for conserving Spotted Owls and other native species in the Barred Owl's expanded range, including: (1) creating and maintaining Barred Owl-free reserves bounded by open or high-elevation areas; (2) creating reserves large enough to reduce immigration by long distance dispersers; and (3) removing Barred Owls from large riparian corridors. </p>
Polymer Electrolyte Membrane Water Electrolyzer Oxygen Bubble Evolution Optical Video Recording For Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli
<p>Dataset used for the training of the segmentation model employed in the work "Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer" by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli. This dataset consists in 35 images and the corresponding manual annotated masks of diverse bubbly scenarios extracted from the optical video recording of a PEMWE with a transparent flow field.</p>
Application of X‑ray Microcomputed Tomography for the Static and Dynamic Characterization of the Microstructure of Oleofoams
<p>Raw, greyscale image stacks collected during a X-Ray tomography analysis on cocoa butter-based oleofoams. The dataset is divided into three subsets: aeration, storage and heating, which contain samples that have been aerated for different amounts of time, samples that have been stored for 3 and 15 months at 20 °C, and finally samples that have been heated to the melting point of the stabilizing crystals, respectively. The dataset contains instructions and the scripts for ImageJ and MATLAB (as text files) to process and measure the bubble size distribution, and the thickness of the continous phase.</p>
Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica: dataset
<p><strong>Abstract</strong>:<br> (from [1])</p> <blockquote> <p>The addition of fillers can significantly improve the mechanical behavior of polymers. The responsible mechanisms at the molecular level can be well assessed<br> by particle-based simulation techniques, such as molecular dynamics. However, the high computational cost of these simulations prevents the study of macroscopic<br> samples. Continuum-based approaches, particularly micromechanics, offer a more efficient alternative but require precise constitutive models for all<br> constituents, which are usually unavailable at these small length scales. In this contribution, we derive a molecular-dynamics-informed constitutive law by<br> employing a characterization strategy introduced in a previous publication. We choose silicon dioxide (silica) as an exemplary filler material used in polymer<br> composites and perform uniaxial and shear deformation tests with molecular dynamics. The material exhibits elastoplastic behavior with a pronounced anisotropy.<br> Based on the pseudo-experimental data, we calibrate an anisotropic elastic constitutive law and reproduce the material response for small strains accurately. <br> The study validates the characterization strategy that facilitates the calibration of constitutive laws from molecular dynamics simulations. Furthermore, the<br> obtained material model for coarse-grained silica forms the basis for future continuum-based investigations of polymer nanocomposites. In general, the presented<br> transition from a fine-scale particle model to a coarse and computationally efficient continuum description adds to the body of knowledge of molecular science<br> as well as the engineering community.<br> </p> </blockquote> <p><br> <strong>Contact</strong>:<br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><br> <strong>Software</strong>:<br> All simulations were performed with LAMMPS [3], version: 29 Oct 2020 / 20201029<br> Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11<br> Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p><strong>Installed packages:</strong><br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p><br> <strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context</strong>:<br> Data set supplementing journal paper:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.</p> <p><br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content</strong>:<br> The files to reproduce our simulations and their results are structured as follows:</p> <ul> <li>01_potentials<br> tabulated potentials calibrated via iterative Boltzmann inversion in [2] kindly provided by the Müller-Plathe group at Technische Universität Darmstadt <ul> <li>Angle_table<br> angular interactions</li> <li>Bond_table<br> bond interactions</li> <li>Nonbond_table<br> pair interactions</li> </ul> </li> <li>02_sample<br> Lammps data file (molecular style) of the investigated silica sample</li> <li>03_simulations<br> The condensed simulation directories with the naming convention given below are organized in the following subfolders: <ul> <li>01_time-proportional<br> time-proportional simulation data</li> <li>02_time-periodic<br> time-periodic simulation data</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li>lammps input file (*.in) of the specific simulation</li> <li>input.prm: input parameters of the specific simulation (read by the input file)</li> <li>meta.info: meta data of the specific simulation run</li> <li>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li>thermo_out.Dat: raw output</li> <li>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</li> <li>thermo_out_STD.Dat: standard deviation of raw output</li> </ul> </li> </ul> <p><br> <strong>Naming convention</strong>:<br> Silica-[deformation]-[direction]_[deformation function]-[deformation magnitude]_[deformation rate]<br> ● [deformation]: uniaxial tension (UT), simple shear (SS)<br> ● [direction]: deformation carried out in X/Y/Z (UT) or XY/XZ/YZ (SS)<br> ● [deformation function]: time-proportional (strain), time-periodic (strain_ampl)<br> ● [deformation magnitude]: maximum strain (time-proportional), strain amplitude (time-periodic); unitless<br> ● [deformation rate]: rate-[strain rate] (only time-proportional): 0.001/ns-0.1/ns</p> <p><br> <strong>Output quantities</strong> (columns of *.Dat files):<br> ● Step: time step<br> ● Time: time in fs<br> ● TotEng: total energy in kcal/mol<br> ● PotEng: potential energy in kcal/mol<br> ● KinEng: kinetic energy in kcal/mol<br> ● E_pair: pair energy in kcal/mol<br> ● E_bond: bond energy in kcal/mol<br> ● E_angle: angle energy in kcal/mol<br> ● E_dihed: dihedral energy in kcal/mol<br> ● Temp: temperature in K<br> ● Press: hydrostatic pressure in atm<br> ● Pxx: xx component of pressure tensor in atm<br> ● Pyy: yy component of pressure tensor in atm<br> ● Pzz: zz component of pressure tensor in atm<br> ● Pxy: xy component of pressure tensor in atm<br> ● Pxz: xz component of pressure tensor in atm<br> ● Pyz: yz component of pressure tensor in atm<br> ● Volume: volume of simulation box in (Angstroms)^3<br> ● Lx: box length in x direction in Angstroms<br> ● Ly: box length in y direction in Angstroms<br> ● Lz: box length in z direction in Angstroms<br> ● Density: density in g/(cm^3)<br> ● c_RG: radius of gyration in Angstroms<br> ● c_RG[1]: squared radius of gyration tensor (xx component) in (Angstroms)^2<br> ● c_RG[2]: squared radius of gyration tensor (yy component) in (Angstroms)^2<br> ● c_RG[3]: squared radius of gyration tensor (zz component) in (Angstroms)^2<br> ● c_RG[4]: squared radius of gyration tensor (xy component) in (Angstroms)^2<br> ● c_RG[5]: squared radius of gyration tensor (xz component) in (Angstroms)^2<br> ● c_RG[6]: squared radius of gyration tensor (yz component) in (Angstroms)^2<br> ● c_bondave[1]: bond energy averaged over all atoms in kcal/mol<br> ● c_bondave[2]: bond distance averaged over all atoms in Angstroms<br> ● c_bondave[3]: squared bond distance averaged over all atoms in (Angstroms)^2<br> ● c_angleave[1]: angle energy averaged over all atoms in kcal/mol<br> ● c_angleave[2]: angle averaged over all atoms degree<br> ● c_angleave[3]: cosine of angle (unitless)<br> ● c_angleave[4]: squared cosine of angle (unitless)<br> ● c_MSD[1]: mean squared displacement x-direction in (Angstroms)^2<br> ● c_MSD[2]: mean squared displacement y-direction in (Angstroms)^2<br> ● c_MSD[3]: mean squared displacement z-direction in (Angstroms)^2<br> ● c_MSD[4]: total mean squared displacement in (Angstroms)^2<br> ● c_COM[1]: x coordinate of center of mass in Angstroms<br> ● c_COM[2]: y coordinate of center of mass in Angstroms<br> ● c_COM[3]: z coordinate of center of mass in Angstroms<br> ● v_strain_xx: xx component of engineering strain tensor (unitless) <br> ● v_strain_yy: yy component of engineering strain tensor (unitless) <br> ● v_strain_zz: zz component of engineering strain tensor (unitless) <br> ● v_vMisesequivstress: von Mises equivalent stress in MPa<br> ● v_Cauchy_xx: xx component of stress tensor in MPa <br> ● v_Cauchy_yy: yy component of stress tensor in MPa<br> ● v_Cauchy_zz: zz component of stress tensor in MPa<br> ● v_Cauchy_xy: xy component of stress tensor in MPa<br> ● v_Cauchy_xz: xz component of stress tensor in MPa<br> ● v_Cauchy_yz: yz component of stress tensor in MPa<br> ● v_strain_xy: xy component of engineering strain tensor (unitless) <br> ● v_strain_xz: xz component of engineering strain tensor (unitless) <br> ● v_strain_yz: yz component of engineering strain tensor (unitless) </p> <p><strong>References</strong>:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.<br> [2] Ghanbari, A.; Ndoro, T. V. M.; Leroy, F.; Rahimi, M.; Böhm, M. C. & Müller-Plathe, F., “Interphase Structure in Silica-Polystyrene<br> Nanocomposites: A Coarse-Grained Molecular Dynamics Study”, Macromolecules, 2012, 45, 572-584.<br> [3] Plimpton, S., “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p> </p>
Fig.1 in Population Dynamics And Characterization Of Clostridium Macerans On Host Plant Of Flax
Fig.1. The AUDPC of Clostridium macerans as pathogen on the genotypes of flax during ontogenesis. abcd - AUDPC followed by the same letters in each column are not statistically significant by LSD0.05 (1.69).
Fig.2 in Population Dynamics And Characterization Of Clostridium Macerans On Host Plant Of Flax
Fig.2. Correlation coefficient between disease severity index of Clostridium macerans and sum of precipitation (mm). * − correlation significant at p≤0.05, ** − correlation significant at p≤0.01
Supplementary data for manuscript: "Characterizing dynamic heterogeneities during nanogel degradation"
<p>Contains data files and code (python Jupyter notebook) to reconstruct plots for manuscript: "Characterizing dynamic heterogeneities during nanogel degradation"<br><br>Contact: zmira@g.clemson.edu<br><br><br>This work is supported by the National Science Foundation under NSF Award No. 2110309.</p>
AlphaFold2-Based Characterization of Apo and Holo Protein Structures and Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics and Ligand-Induced Conformational Changes
<p>Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2 that combines<span> randomized</span> <span><span> </span>alanine<span> </span>sequence masking<span> </span>with shallow multiple sequence alignment<span> </span>subsampling to expand the conformational diversity of the predicted structural<span> </span>ensembles and<span> </span>capture conformational changes between apo and holo protein forms. Using several well-established datasets of<span> </span>structurally diverse apo-holo protein pairs, the proposed approach </span><span>enables<span> </span>robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with<span> </span>the view </span><span> </span>that the intrinsic dynamics of allosteric proteins is defined by the structural topology of the fold and favors conserved conformational motions driven by soft modes among orthologs. We also found<span> </span>a significant <span>correlation </span>between conformational flexibility and <span> </span>AlphaFold2 metric of statistical significance pLDDT for the apo-holo pairs in which ligand binding induced local moderate conformational changes. For apo-holo pairs exhibiting larger structural changes, this relationship<span> </span>becomes nonlinear, reflecting inability of AlphaFold2 confidence metrics to identify high energy functional conformations. Our findings support the notion that AlphaFold2 approaches can yield reasonable accuracy in predicting minor conformational adjustments between apo and holo states, especially for proteins with <span> </span>moderate localized changes upon ligand binding. However, for large, hinge-like domain movements, AF2 tends to predict the most stable domain orientation which is typically the apo form rather than the full range of functional conformations characteristic of the holo ensemble. These results indicate that modeling of multiple functional states of proteins may require more accurate detection of flexible region conformations and cannot solely rely on the pLDDT metric as the major determinant of the prediction accuracy in reproducing functional conformational ensembles.<span> </span></p>
Silica in Silico: a Molecular Dynamics Characterization of the Early Stages of Protein Embedding for Atom Probe Tomography
<p>The .zip archive contains the trajectories of all the simulations performed and analysed within the manuscript. The water molecules were removed for control systems.</p>
Data from: Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.
<p>This data accompanies the paper entitled <strong><em>Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.</em></strong></p> <p>The zip archive contains the results of molecular dynamics simulations of the 2 systems investigated in the paper: <em>S. rum</em> and <em>T. mar</em> LDHs. The systems have been simulated at 315 K for <em>S. rum </em>and 340 K for <em>T. mar</em>. Final configurations of the proteins after productions are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the proteins every 100 ps are provided for all the systems in XTC gromacs format.</p>
Extending the application of connectivity metrics within the framework of the characterization of the dynamic behaviour of a WDS subjected to users' activity
<p>Water distribution networks (WDNs) are complex combinations of nodes and links, and the current tendency is to modify their topological structure through the closure of isolation valves for monitoring and water quality reasons. For their analysis, several approaches based on graph theory have recently been proposed, mainly considering steady-state flow conditions. However, in their real functioning, WDNs are continuously subjected to pressure transients generated by manoeuvres on regulation devices or by users’ activity. This study investigates the application of some metrics from graph theory, already used in the context of steady-state analysis, for assessing the effects of changes in the topological structure of a network ‒ due for example to sectorization or branching operations ‒ on its transient response when subjected to manoeuvres on devices such as hydrants, pumps, etc. or users’ activity. The analysis shows that some connectivity metrics can effectively reflect the dynamic pressure behaviour of the network and, thus, provide useful indications for design and management operations taking into account unsteady flow features.</p>
Characterizing juvenile dispersal dynamics of invasive barred owls: implications for management
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Data from: A pioneering experimental investigation of a novel in-situ dynamic characterization of the tensile/compression stress-strain mechanism on human plantar soft tissue
<p><span>We have conducted the first in-situ and in-vivo dynamic mechanical test on human plantar soft tissue. A dynamic mechanical analysis (DMA)-like device has been invented to perform the in-situ and in-vivo stress-strain tests on living plantar in order to characterize the material mechanism of biological soft tissue, whereas it is nearly impossible to prepare a sample from a living body for classical tests. A series of pioneering tests of tensile/compression on the heel of ten volunteers are reported, with the reference of tests on mimic foot model made by silicon rubber, standard silicon rubber brick sample, and finite elementary analysis. In addition to demonstrating the effectiveness of the device and approach, interesting correlations between the results and clinic data were found, suggesting considerable potential for the invention in future research.</span></p>
Data supplement for "Wetting dynamics under periodic switching on different scales: Characterization and mechanisms"
<p>Data set and python code to recreate the figures of "Wetting dynamics under periodic switching on different scales: Characterization and mechanisms". Additionally, it includes the oomph-lib Code to reproduce the data for the simulations in the mesoscopic thin-film model.</p>
Data: Characterizing the sediment dynamics through in-situ measurements in the abyssal Manila Trench, northeast South China Sea
<p>Along the Manila Trench, a total of four moorings were deployed in <a name="OLE_LINK5"></a>September 2019 and recovered in August 2020. The field measurements in velocity and turbidity were resampled to create hourly dataset.</p>
Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization (DataSet)
<p>Data from the article: <br>"Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization", L. Reyes, K. Campos, G. D. Avendaño, L. González-Paz, A. Vivas, Y. J. Alvarado, and S. Flores.</p> <p>Data to be used with some implementation of the forecasting method of reference:<br>Sugihara G. and May R. M., Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series, <em>Nature</em> <strong>344</strong>, 734–741 (1990).</p>
Data deposition for "Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins"
<p>The deposited data for the publication "Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins".</p> <p>Data contains folder and sub-folders for the raw data, Main excel sheet named as "MasterTable_FRET-Challenge-Protein-Dynamics_Nat_Meth_Agam et al" has most of the data used in the publication. Another excel sheets "Data List for FIgures for Agam et al_revised" and "Data List for Supplmentary FIgures for Agam et al_revised" have the information regarding the Figure-wise data description and where the respective data locates.</p>
Model for: Characterizing long‐term population conditions of the elusive red tree vole with dynamic individual‐based modeling
<div class="abstract"> <p>Old growth forests are declining globally, threatening dependent wildlife. Many arboreal old‐growth obligates, such as the threatened red tree vole, are difficult to monitor for changes in habitat occupancy, and abundance. Yet, conservation planning relies on this information to prevent population declines. We integrated a range of species, habitat, and landscape change information to develop a dynamic habitat‐population model. The spatial individual‐based model simulated dynamic patterns of occupancy that responded to annual habitat maps, describing 36 years of observed change. We simulated population dynamics and local movement to characterize changes in occupancy and abundance, and the capacity of remaining habitat to support red tree voles. Red tree vole redistribution patterns strongly corresponded to wildfire footprints and timber extraction locations. Population strongholds are likely to exist in clumped pockets of old‐growth forest that were unaffected by wildfire and in protected old forest reserves. However, the exact number and locations of local clusters remain uncertain. Simulated population losses occurred at different paces in different places, underscoring the need for recurring evaluation of population changes with field occupancy surveys and modeled evaluations that can anticipate potential connectivity and extirpation thresholds. This modeling approach was effective at leveraging existing information for a data‐light species to assess how historical changes to the quantity, quality, and configuration of habitat likely influenced the potential landscape capacity, species abundance, and distribution. Dynamic individual‐based modeling can benefit conservation planning for red tree vole and other reclusive forest species by providing biologically nuanced assessments of abundance and distribution. Such models can also project the long‐term benefits and impacts of spatially explicit land management plans.</p> </div> <div class="abstract"></div>
Dynamic snow surface aerodynamic roughness lengths (z0) characterized by snow depths using LIDAR
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Allen Brain Atlas
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International Brain Laboratory public data
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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.