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2,188 results for “diffusion”
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Dataset and neural network weights to the paper: "Generative diffusion for regional surrogate models from sea-ice simulations"
<p>All the needed code and data to reproduce the results from the paper: "Generative diffusion for regional surrogate models from sea-ice simulations".<br>While most of the code is a frozen clone of the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, this capsule also includes the dataset and neural network weights to train and apply the surrogate models.</p> <p>The <strong>dataset</strong> for training and evaluation can be found at <em>data/nextsim</em>, which includes three different Zarr folders for training/validation/testing. The dataset is based on neXtSIM simulation data and ERA5 forcing data and extracted from the <a href="https://ige-meom-opendap.univ-grenoble-alpes.fr/thredds/catalog/meomopendap/extract/catalog.html">SASIP shared data OpenDAP server</a>:</p> <ul> <li>The neXtSIM simulations were performed by Gauillaume Boutin and published in the paper "<a href="https://doi.org/10.5194/tc-17-617-2023">Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework</a>" (Boutin et al., 2023) and available as Zenodo <a href="../records/7277523">dataset</a> (Boutin et al., 2022).</li> <li>The forcing data is based on the ERA5 reanalysis dataset published in the paper: "<a href="https://doi.org/10.1002/qj.3803">The ERA5 global reanalysis</a>" (Hersbach et al., 2020) and available as dataset from the Copernicus Climate Change Service (C3S, Copernicus Climate Change Service, 2023). The here used forcing data is based on the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels">hourly reanalysis data on single levels</a> and interpolated with nearest neighbors to the curvilinear grid as used in the output from the neXtSIM simulations. <strong>Disclaimer:</strong> The results contain modified Copernicus Climate Change Service information, 2023. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</li> </ul> <p>The <strong>neural network weights</strong> are included under <em>data/models </em>and split into weights for the deterministic models and the diffusion models.<br>These neural network weights have been used to generate the results presented in the paper.</p> <p>In this capsule, the <em>notebooks</em> folder includes also the figures used within the paper and additional trajectory data used in the qualitative analysis of the paper.</p> <p>Generally, we recommend to just download the <em>data.tar.gz </em>file and use otherwise the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, since the here included code can be outdated. We further refer to the repository for additional information.</p> <p> </p> <p>Contained in this capsule:</p> <ul> <li>configs.tar.gz: The configuration files for the experiments.</li> <li>data.tar.gz: The dataset and neural network weights.</li> <li>diffusion_nextsim.tar.gz: The main code for the neural network etc.</li> <li>environment.yaml: The anaconda environment file, can be used to install the needed packages.</li> <li>notebooks.tar.gz: The notebooks that were used to create the figures in the paper. The figures from the paper and the data from the qualitative analysis are included as well.</li> <li>readme.md: The readme file from the repository.</li> <li>scripts.tar.gz: The scripts used for the experiments.</li> <li>setup.py: the file to install the <em>diffusion_nextsim</em> package in a python environment.</li> </ul> <p>References:</p> <p>Guillaume Boutin, Heather Regan, Einar Ólason, Laurent Brodeau, Claude Talandier, Camille Lique, & Pierre Rampal. (2022). Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7277523</p> <p>Boutin, G., Ólason, E., Rampal, P., Regan, H., Lique, C., Talandier, C., Brodeau, L., and Ricker, R.: Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework, The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, 2023.</p> <p>Copernicus Climate Change Service (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.adbb2d47">10.24381/cds.adbb2d47</a>.</p> <p>Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. <em>Q J R Meteorol Soc</em>. 2020; 146: 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p>
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Data for "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns"
<p>This dataset contains the raw data used for the publication "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns".</p>
Data, plotting scripts, and figures for "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames"
<p>This repository contains the data, plotting scripts, and figures associated with the paper "Assessing diffusion model impacts on enstrophy and flame structure in lean premixed flames" by Aaron J. Fillo, Peter E. Hamlington, and Kyle E. Niemeyer.</p> <p>See the README file for additional details.</p>
CRIRES high-resolution near-infrared spectroscopy of diffuse interstellar band profiles
<p>This archive contains data used for the paper:</p> <p>CRIRES high-resolution near-infrared spectroscopy of diffuse interstellar band profiles. Detection of 12 new DIBs in the YJ band and the introduction of a combined ISM sight line and stellar analysis approach</p> <p>Paper-DOI: 10.1051/0004-6361/202142990</p> <p>It contains reduced oCRIRES spectra. For more details on the reduction see the paper.</p>
Raw data for the journal article "Cracks as efficient tools to mitigate flooding in gas diffusion electrodes used for the electrochemical reduction of carbon dioxide"
<p>This data set corresponds to the article by Kong et al. entitled "Cracks as efficient tools to mitigate flooding in gas diffusion electrodes used for the electrochemical reduction of carbon dioxide", published in Small Methods</p>
NURBS Enhanced Virtual Element Methods for the Spatial Discretisation of the Multigroup Neutron Diffusion Equation on Curvilinear Polygonal Meshes
<p>This repository holds all of the raw data generated by my C++ code for a paper "NURBS Enhanced Virtual Element Methods for the Spatial Discretisation of the Multigroup Neutron Diffusion Equation on Curvilinear Polygonal Meshes".</p> <p>The C++ code solves the neutron diffusion equation using a novel spatial discretisation called the Virtual Element Method.</p> <p>Alongside the raw data (stored in VTK and HDF5 files) are post-processing python scripts which read the raw data, compute meaningful quantities of interest and generate plots/figures.</p>
Dataset for the article "Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion" by Roman Barth and Haitham Shaban
<p>The data set comprises all raw microscopy images and DFCC analyses as presented in </p> <p><strong>Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion</strong></p> <p>by Roman Barth and Haitham Shaban, published in Nucleus (https://doi.org/10.1080/19491034.2022.2088988)</p> <p>There are two folders for RNAPII and DNA each, one for the raw images and one for the processed DFCC data, supplied as .mat files.</p> <p>Every folder contains three sub-folders containing the data for the conditions: +Serum, -Serum, and +DRB.</p>
Diffuse reflectance spectra of coated plates and corresponding plots transformed Kubelka-Munk function versus the energy of light (eV)
<p>The link contains UV-DRS results of TiO<sub>2</sub>/Fe<sub>2</sub>O<sub>3</sub> layered composites (from commercial nanoparticles) and corresponding bandgap energies</p>
Diffusion weighted MR imaging of post-mortem rat brain to allow reconstruction of the cortical connectome
<h2>Brief description</h2> <p> </p> <p>These data accompany the article by Sinke et al. (Sinke et al., 2018). It contains the dMRI image volumes of 10 rats, a subset of these data was used for the tractography procedures described in the article. In addition high-resolution 3D balanced SSFP data are provided with high contrast between grey and white matter and CBF. The data are also accompanied by T<sub>1</sub> weighted 3D spoiled gradient echo volumes at three different echo times (5,10 and 15 ms) which can be used for T<sub>2</sub>* measurements.</p> <h2>Animals</h2> <p> </p> <p>All animal procedures were approved by the Animal Experiments Committee of the University Medical Center Utrecht and Utrecht University. Experiments were performed in accordance with the guidelines of the European Communities Council Directive. Ten healthy adult (12–13 weeks old) male Wistar rats have been used and are described in the RCR_table.csv file. Animals were sacrificed and their brains were fixed with transcardial perfusion-fixation. Brains were extracted scanned.</p> <p> </p> <h2>MR acquisition</h2> <p> </p> <p>MRI was performed on a 9.4 T horizontal bore MR system (Varian, Palo Alto, CA, USA) equipped with a 6 cm ID gradient insert with gradients up to 1 T/m. A custom made solenoid coil with an internal diameter of 2.6 cm was used for excitation and reception of the MR signal. The perfusion-fixed brains were inserted with the skulls intact in a custom-made holder and immersed in non-magnetic oil (Fomblin, Solvay Solexis). Diffusion MR used a 3D diffusion-weighted spin-echo sequence with an isotropic spatial resolution of 150 mm, where the read- and phase- encode direction were acquired using 8-shot EPI encoding and the second phase direction was linearly phase-encoded (TR/TE 500/32.4 ms, 220*128*108 matrix, FOV 33*19.2*16 mm<sup>3</sup>, D/d 15/4 ms, b 1031,2078,3994,6038,7756 s/mm<sup>2</sup>, 60 diffusion-weighted images in non-collinear directions and 24 images without diffusion weighting (b=0), number of averages 1, total number of images 325). Four 3D BSSFP images were acquired with an isotropic spatial resolution of 100 mm (TR/TE 15.4/7.7 ms, flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm<sup>3</sup>, 6 averages, pulse angle shift 0°, 90°, 180° and 270°). The four images were added as complex images to obtain a single BSSFP image with reduced banding artifacts in the brain. If scanning time allowed, three spoiled gradient-echo acquisitions were also performed with varying echotimes of 15, 10 and 5 ms respectively and TR 20 ms (flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm<sup>3</sup>, 24 averages, pulse angle shift 117°).</p> <h2>Data structure</h2> <p> </p> <p>The repository contains the following data:</p> <p>- READ_ME.txt: this file</p> <p>- RCR_table.csv : Table containing acquisition dates and numbers for the scanned animals.</p> <p>- rawdata.zip : Zipped data directory ‘rawdata’ containing acquired images in NIfTI data format per animal. Data can be unzipped using the ‘unzip’ command. Directory rawdata contains subdirectories RCR01 to RCR10 (individual rat directories). Each rat directory contains the following NIfTI files:</p> <p>o bal.nii.gz and balsumcom.nii.gz : The separate acquisitions of the BSSFP experiment and the complex summation of the data respectively.</p> <p>o dtitot.nii.gz : The diffusion weighted volumes in the order that they were acquired.</p> <p>o bvals and bvecs : Text files containing the b-values and b-vectors in the order that they were acquired, so this corresponds with the dtitot.nii.gz file.</p> <p>o zerob: Text file containing the image numbers where images with no diffusion weighting were acquired.</p> <p>o ubal1.nii.gz, ubal2.nii.gz and ubal3.nii.gz : The three 3D spoiled gradient acquisitions with TE 15,10, and 5 ms respectively.</p> <p>- derivatives.zip : Zipped data directory ‘derivatives’ containing calculated images of the diffusion parameters after application of FMRIB’s diffusion toolbox DTIfit. In addition it contains a file dti3D_b0.nii.gz which is a summation of all the b0-images and a file mask.nii.gz containing the ‘brain’ mask used for application of DTIfit.</p> <p>- Sinke_BrainStructureFunction2018.pdf : The article based on (part) of these data.</p>
Task 3 Dataset for Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models
Open the record for dataset details and reuse information.
Two-time correlation function based on speckle patterns from x-ray photon correlation spectroscopy associated with "Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass" (scientific article published in Nature Communications, 2024)
<p>This dataset consists of contrast data, i.e., the two-time correlation function, based on speckle patterns measured at the at the 8ID-E beamline of the Advanced Photon Source at Argonne National Laboratory.</p> <p>Experimental details are stated in the paper specified under "related work" and in the accompanying supplementary information.</p> <p>You are welcome to use this dataset in compliance with the CC BY 4.0 licence assigned to this dataset.</p> <p>Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.</p> <p>____________________________________________________________________</p> <p>The data consists of 32 text files in total, which correspond to the main and lower panel Figure 2 of the main publication. </p> <p>30 of these text files are contrast data, which are named "contrast_DT250s_nn.text" wiith "nn" as the identifier of consecutive data sets going from 1 to 30. Each data set consists of p rows and q columns, DT250s denotes the time resolution of data points, which is 250 s along both row and column values.</p> <p>The data set called "Time_Contrast_1to30s.txt" states the start time in seconds of the first data point of each of the thirty contrast data set.</p> <p>The data set called "ScatteredIntensity.txt" states the scattered intensity at full time resolution, i.e. 2.5 s.</p> <p>The files are plain text files with the data points separated by "space" along rows and "new line" along columns.</p>
Diffusion coefficients on amorphous polystyrene and modelling of migration levels from plastic packaging
<p>This dataset is actually supplementary data of the scientific article:</p> <p>Martinez-Lopez, Brais; Gontard, Natalie and Peyron, Stephane "Worst case prediction of additives migration from polystyrene for food safety purposes: a model update" in Food Additives and Contaminants Part A, doi:10.1080/19440049.2017.1402129.</p> <p>If you use it, please cite it using the reference file we have provided.</p> <p>This description is the same as in the file "readme.txt", included in the upload.</p> <p>List of files:</p> <ul> <li>The file database_D contains the experimental diffusivity data for amorphous polystyrene used for the figure 1b. It is a spreadsheet file with two tabs. In the first tab, the diffusion coefficients can be found by choosing molecule family (and the publication were they were found) and temperature in celsius degrees. The second tab contains the same diffusivity data, but they are ranged by increasing molecular weight and temperature. This file is available in open document (.ods) and microsoft excel (.xlsx) formats.</li> <li>The file migration modelling is also a spreadsheet file, and contains several tabs. The first tab (diffusion coefficient) is an implementation of equation 1, the predictive model for overestimated diffusion coefficients. The given Ap and tau parameter sets are the ones specified in Table 2 for amorphous polystyrene. The second tab (migration levels) is an implementation of equation 3, the solution to Fick's second law that is used to predict migration levels in food, for pre-selected values of alpha (equation 5). The tabs labeled alpha =... contain the sums used in the equation, whereas the tab "roots" contains the first 200 roots of trascendental equation 4, needed to calculate the sum or terms. This file is also available in open document (.ods) and microsoft excel (.xlsx) formats.</li> <li>The file "table.pdf" sums the main characteristics of the molecule families, together with the references where they were found (in the second page).</li> <li>The file reference.bib contains the reference that should be cited if you use this dataset for your own work.</li> <li>Finally, the file readme.txt contains this very same description.</li> </ul> <p>These files have undergone thorough check, so there should not be any mistakes. In the rare event that you find one, please report it to the author so it can get fixed.</p> <p>bramar@food.dtu.dk</p> <p>Brais Martínez López, PhD<br> Assistant professor<br> DTU Fødevareinstituttet<br> Danmarks Tekniske Universitet<br> Søltofts Plads<br> Bygning 227<br> 2800 Kgs. Lyngby</p> <p> </p> <p> </p> <p> </p>
RTI Experiment Simulation assuming Convective and Diffusive Interstitial Transport in the Brain: Concentration over time (and space)
<p>Simulation of interstitial transport in the brain assuming convective and diffusive transport with perivascular efflux routes. The movie shows the transient concentration of TMA ions in a real-time iontophoresis (RTI) experiment, where a small molecular probe is applied to brain tissue at a known rate and its concentration measured over time a a point 100-200um away, here 150 um. RTI experiments are used to characterize the properties of interstitial tissue to determine its void volume and tortuosity, 0.18 and 1.85 for the condition shown here. In this simulation, a model of combined diffusion and convection (superficial velocity=50 um/min) is applied to fit experimental data and range. (Convection assumes Darcy's Law with a hydraulic conductivity of 2x10<sup>-6</sup> cm<sup>2</sup> mmHg s<sup>-1</sup> and pressure difference of 2.15 mmHg). The model domain is a cube 750 um on a side with 8 penetrating arterioles and 8 penetrating venules. The first and third columns from the left are venules and the second and fourth are arterioles, with convective flow from arteriole to venule. As transport of molecules in the perivascular space is known to be faster than in the interstitium, the concentration is assumed to be c=0 at the vascular walls. The solute (TMA) must pass through a perivascular wall with lower diffusivity than the interstitium to leave the domain through a vascular wall (D<sub>wall</sub>=5%D<sub>interstitium</sub>). Although it is difficult to see in the movie, both the presence of convection and the perivascular efflux routes cause range(variability) in the measured concentration curves for different source and detection point combinations that is consistent with experimental data--see additional posted data. Computations performed using FEniCS, movie made using Paraview. </p>
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields (data sets)
<p>Supplementary dataset for Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Output functionals of interest from finite element simulations together with postprocessing source code. </p>
TwitCID: a Collection of Data Sets for Studies on Information Diffusion on Social Networks
<p>The TwitCID collection consists of five Twitter datasets which were extracted from the 1 percent of tweets from Twitter API. </p> <p>The Firstweek and Secondweek data set were collected during the first week and second week of January 2017 while the Iphone, Gucci and Galaxy data sets were collected from 21 September 2015 to 31 May 2017 using the keywords “iphone”, “gucci” and “galaxys” respectively. </p> <p>We publish these datasets on behalf of our academic institution – IRIT, France and for the sole purpose of non-commercial research under the license CC BY-NC-SA (Attribution-NonCommercial-ShareAlike). In accordance with Twitter's Terms of Service, we only provide identifiers of tweets. In order to collect the actual tweets in JSON, you could use the script Collect_JSONtweets.py attached.</p> <p>If you would like to use this collection, please cite our paper: </p> <p>Hoang, T. B. N., Mothe, J., & Baillon, M. (2019, September). TwitCID: a collection of data sets for studies on information diffusion on social networks. In <em>International Conference of the Cross-Language Evaluation Forum for European Languages</em> (pp. 88-100). Springer, Cham.</p>
Science ready spectra and their best-fitting models described in the research paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al.
<p>Science ready spectra of nine ultra-diffuse galaxies in the Coma cluster collected with the Binospec multi-object spectrograph and their best-fitting PEGASE.HR templates obtained using the NBursts full spectrum fitting code. These spectra were presented in the paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al. accepted for publication in the Astrophysical Journal on Sep/3/2019 (arXiv:1901.05489).</p> <p>Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For six galaxies there are two files provided: (i) one-dimensional optimally extracted integrated spectrum and (ii) two dimensional spectrum for spatially resolved radial velocity information. For the remaining three galaxies, only spatially resolved spectra are provided.</p>
Femtosecond electron diffuse scattering data of black phosphorus
<p>Femtosecond electron diffuse scattering data of black phosphorus measured at the Fritz Haber Institute in Berlin. The dataset contains an experiment at 100 K (measurement _1.h5) .</p>
ScienceDex guides
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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.