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57 results for “light scattering”
Dataset: Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI
<p>This dataset supplements the research article <a href="https://doi.org/10.1101/2022.10.04.509781">"Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI"</a>. It contains images and parameter maps obtained from measurements with Scattered Light Imaging (SLI), small-angle X-ray scattering (SAXS), and diffusion magnetic resonance imaging (dMRI) of a vervet monkey and a human brain sample (containing parts of the corona radiata, the cingulum, and the corpus callosum). Please refer to the research article for more information about the sample preparation, the measurement settings, and the generation of the different parameter maps - as well as for a more detailed analysis of the data.</p> <p>While SLI and SAXS were performed on two sections per sample (vervet monkey brain: sections no. 501 and 511; human brain: anterior section no. 20, posterior section no. 18), dMRI was performed on the entire human brain sample (3.5 x 3.5 x 1 cm³), and evaluated in the corresponding section plane of the anterior and posterior section, respectively. Pixel sizes in SLI are 3 µm, and in SAXS 100 µm (vervet) and 150 µm (human). Voxels in dMRI are 200 µm isotropic.</p> <p>All files are in tif-format and can be opened with standard image processing tools like ImageJ. The files labeled with "dMRI_ODF" contain a set of spherical harmonics for each voxel, describing the orientation distribution of the nerve fibers in the respective section plane obtained from the dMRI measurement, and can be visualized with MRtrix3, using the command 'mrview [filename] -odf.load_sh [filename]'.</p> <p>In addition to the ODFs, the dataset contains the b0-values and the dMRI-based metrics for the whole human brain sample in form of image stacks: fractional anisotropy (FA), axonal water fraction (AWF), axial/mean/radial diffusivity (AD/MD/RD), and axial/mean/radial kurtosis (AK/MK/RK).</p> <p>For the evaluated human brain sections (anterior/posterior), the 3D-orientations of the nerve fibers were derived from the dMRI and SAXS measurements, respectively: The files labeled with "3D-vectors" contain the unit vectors as X-Y-Z stack; the files labeled with "inclination" contain the (absolute) out-of-plane inclination of the fibers with respect to the section plane.</p> <p>All measurements were further evaluated with the software SLIX (https://github.com/3d-pli/SLIX) in order to derive the in-plane fiber directions (up to three fiber directions per pixel). The dataset contains the image stacks used as input (Stack) as well as the resulting parameter maps: average/maximum/minimum of the signal (avg/max/min), distance/prominence/width of peaks in the signal (peakdistance/peakprominence/peakwidth), the computed in-plane fiber directions (direction1,2,3), the fiber orientation map encoding the fiber directions in different colors (fom), as well as the vector maps (vectors) where fiber orientations of several pixels are displayed on top of each other. For the vervet brain section no. 511, the dataset also contains the parameter maps registered onto the SLI parameter maps.</p>
Light scattering photographic dataset of rocks
<p>This dataset comprises digital photographs taken at the Astrophysical Scattering Laboratory of the Department of Physics of the University of Helsinki, Finland. These photographs were taken in a darkened room, with targets illuminated by an Energetiq Laser-Driven Light Source (LDLD) EQ-77-QZ lamp module.</p> <p>We utilized three distinct stones borrowed from the 2024 course materials of the Geological Materials course at the University of Helsinki's Department of Geosciences and Geography. These stones vary in shape and reflectivity: one is regularly shaped with a smooth surface, another has an irregular shape but a smooth surface with high reflectivity, and the third is irregularly shaped with varying reflectivity across its surface. The stones were photographed from a distance of 230 cm at six different camera positions. At each camera position, the setting with the target was rotated. The images were gathered at 10-degree intervals, totaling 36 images for each stone at each camera position and 216 images per stone in total.</p> <p>The dataset consists of 648 unedited 16-bit Canon CR2 raw photos and the same 648 images cropped, color-graded, and processed into TIFF format. The TIFF data package is 625 MB, while the raw data package is 18.8 GB. Further details on data acquisition can be found in the accompanying PDF files.</p> <p>This dataset serves as a resource for testing and benchmarking various image processing and inverse method tasks, such as reconstructing object shapes using convolutional neural networks and inverse methods. It aims to aid researchers in image processing and related endeavors.</p> <p><em><strong>Please note: </strong></em>Depending on your graphics editor, the program might automatically apply an adjustment of saturation, brightness, and contrast to the raw CR2 photos. In this case, the desired outcome of only the target being illuminated in the photo will be lost. Please ensure that your graphics editor does not apply automatic adjustments, or use the provided TIFF files.</p>
Modeling of the micro-focused Brillouin light scattering spectra
<p><strong>This repository contains data and code presented in paper titled: Modeling of the micro-focused Brillouin light scattering spectra</strong></p> <p> </p> <h2><strong>Data</strong></h2> <p>The structure of this archive is divided by the usage of the data in individual figures in paper titled "Modeling of the micro-focused Brillouin light scattering spectra", which can be found in zip file named <em>ModelingOfTheMicro-focusedBrillouinLightScatteringSpectra-1.0.0_FigsData.zip</em></p> <p>the encoding in .dat files is utf-8<br>All the presented data are in .dat files (no need to open <em>.opju </em>to get access to the data)<br>The <em>.opju</em> is source file of OriginLab software and can be open by freely available tools - <a href="https://www.originlab.com/viewer/" target="_blank" rel="noopener">www.originlab.com/viewer/</a></p> <p>Each folder contains another <em>info.txt</em> where the data are described individually</p> <h2>Software</h2> <p>All the codes used to generate figures in the paper can be found on the Github platform in publicly available repository. The code can be used and modified if the authors and paper are credited. <a title="github.com/CEITECmagnonics/ModelingOfTheMicro-focusedBrillouinLightScatteringSpectra" href="https://github.com/CEITECmagnonics/ModelingOfTheMicro-focusedBrillouinLightScatteringSpectra" target="_blank" rel="noopener">https://github.com/CEITECmagnonics/ModelingOfTheMicro-focusedBrillouinLightScatteringSpectra</a></p> <p>Release v1.0.0 is available in <em>ModelingOfTheMicro-focusedBrillouinLightScatteringSpectra-1.0.0_code.zip</em></p> <p> </p> <p>The software also uses another freely available tool for calculating spin wave dispersion: <a title="github.com/CEITECmagnonics/SpinWaveToolkit" href="https://github.com/CEITECmagnonics/SpinWaveToolkit" target="_blank" rel="noopener">https://github.com/CEITECmagnonics/SpinWaveToolkit</a></p>
Datasets for paper "Evaluating the PurpleAir monitor as an aerosol light scattering instrument"
<p>The data sets included will allow the user to reproduce the plots and analyses described in Ouimette et al. (2022). The Collocated*csv file contains data from multiple collocated PurpleAirs that sampled for a few days. The data in this file was used in the precision analysis in section 2.2.9 of the paper. The other files contain nephelometer and PurpleAir data from Mauna Loa (MLO) and Table Mountain (BOS) and DMPS size distribution files from BOS. Their contents are described in the README.TXT file.</p>
The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?
<p>This is a reproduction package for the paper "The Water-ice Feature in Near-infrared Disk-scattered Light around HD 142527: Micron-sized Icy Grains Lifted up to the Disk Surface?" by Tazaki et al. (2021). In this repository, you will find the data files used to make figures in the paper. Source codes and scripts are included as well.</p>
Dataset and Code for Manuscript "Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification"
<p>Dataset of measurements for cell cycle analysis with description:</p> <ul> <li>ReadMe file with explanations on the data set and analysis</li> <li>exemplary Matlab script file for analysis</li> <li>binary data files conatining the pulse shapes in all channels</li> <li>FCS data files containing common flow cytometry parameters in each channel</li> </ul> <p>Data on unsorted HEK cells, HEK cells sorted for cell cycle phases, and unsorted Jurkat cell are included.</p>
Dataset: Fano meets Stokes: Four-order-of-magnitude enhancement of asymmetric Brillouin light scattering spectra
<p>Dataset accompanying publication:</p> <p>Rafał Białek, Thomas Vasileiadis, Mikołaj Pochylski, Bartłomiej Graczykowski, Fano meets Stokes: Four-order-of-magnitude enhancement of asymmetric Brillouin light scattering spectra, Photoacoustics, Volume 30, 2023, 100478, ISSN 2213-5979, https://doi.org/10.1016/j.pacs.2023.100478.</p>
Dataset for 'Intraocular scatter compensation with spatial light amplitude modulation for improved vision in simulated cataractous eyes'
<p>Dataset for the manuscript entitled: Intraocular scatter compensation with spatial light amplitude modulation for improved vision in simulated cataractous eyes</p> <p>includes:</p> <p>1) PSF from numerical simulations</p> <p>2) CSF measured in subjects</p> <p>3) Michelson contrast from numerical simulations</p>
Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities</em>”</strong> in Optics Express (<a href="https://doi.org/10.1364/OE.456139">doi.org/10.1364/OE.456139</a><em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 5-10 minutes.</p> <p>For ideal scanning alignment each dataset includes diffusion, focus (beam waist) calibration, and flow measurements (using both M-scan and B-scan methods) for all used sample lengths. The names “M-scan” and “A-scan” are used interchangeably. The analysis process is as follows: firstly, the diffusion coefficient is determined for every sample size (time series length) to be analyzed using the script ‘Diffusion.py’. Secondly, the beam waist (focus) calibration is performed using the script ‘Beam Waist.py’. Since the beam waist should be constant for each dataset, choose the value obtained from the file with a largest time series length for minimizing the statistical uncertainty and fix it for a given dataset. Beam scanning for our setup is not exactly perpendicular to the optical axis. Therefore, for B-scan Doppler flow measurements the calibration parameter v_d, quantifying the axial scan bias, must be used. This calibration parameter varies with time series length and needs to be obtained for each sample size. This is done with the same script as the beam waist calibration. Thirdly, the Doppler angle is determined using M-scan measurement with the lowest discharge rate using the script ‘Angle.py’. Finally, the flow profiles are obtained both for M-scan and B-scan methods with predetermined calibration parameters using the script ‘Flow.py’. All file names are sufficiently descriptive, showing sample size, scan mode, measurement type and discharge rate. The number on the file name represents the time series length.</p> <p>For arbitrary scanning alignment, the dataset includes one diffusion and one focus (beam waist) measurements for calibration purposes. The diffusion measurement is used only for the beam waist calibration and not for flow measurements. It also contains several B-scan flow measurements (with different scan speeds) for every discharge rate. The analysis process is same as before but without the angle calibration step. Use the script ‘Omnidirectional.py’ for this step.</p> <p>The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Applicability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 12-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.39 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.94 deg and alignment angle of 0.94 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 20-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 1.58 deg and alignment angle of 2.26 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 18-05-2021.zip</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>Dataset for alignment angle of 2.7 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>Both methods</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>DataProcessing.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines diffusion coefficient from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Waist.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines focus beam waist from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Angle.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines M-scan and B-scan flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Omnidirectional.py</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>This script determines flow profiles for arbitrary scan alignment.</p> </td> </tr> </tbody> </table>
Data from Figures in "Selection rules for cavity-enhanced Brillouin light scattering from magnetostatic modes"
<p>Data from figures in our paper "Selection rules for cavity-enhanced Brillouin light scattering from magnetostatic modes" in Physical Review B. The figures are in an Origin file (OriginPro 2016). Matlab code (R2016b) that can be used to generate plots of the magneto-static modes is also included.</p>
Light Scattering by Roman window glass
<p>This archive contains data-sets representing the light scattering properties of four samples of Roman window glass, as described in detail in Grobe, Noback, and Lang. Data-Driven Modelling of Daylight Scattering by Roman Window Glass. ACM Journal on Computing and Cultural Heritage (manuscript accepted with minor revisions). For each sample, a sub-directory contains the measured DSF (BSDF x cos theta) and a transmission and reflection model for use with the light simulation software Radiance.</p> <p>Each directory contains metadata in Dublin Core and Marc21 format, giving detailed information about the individual dataset.</p>
Diffusion Coefficient Analysis by Dynamic Light Scattering Enables Determination of Critical Micelle Concentration
<p>This upload contains dynamic light scattering data files obtained from the work described in the manuscript that is published by Lena Nielinger and co-workers in ChemPlusChem (<a href="https://doi.org/10.1002/cplu.202400645">https://doi.org/10.1002/cplu.202400645</a>) (WILEY). The files in this repository contain dynamic light scattering data obtained from the analysis of different detergents series and can be downloaded and analysed with a Zetasizer software according to the instructions procied in the manuscript. For information on how to obtain the the Zetasizer software, we refer to the customer support and/or website of the company Malvern Panalytical.</p>
Text-fig. 3. Scatter diagrams of a) upper and b) lower teeth from BRS 25 (black profile), French localities (cyan; data from Crochet 1986) and Moncucco Torinese (red ones; unpublished data). in New Light On Parasorex Depereti (Erinaceomorpha: Erinaceidae: Galericini) From The Late Messinian (Mn 13) Of The Monticino Quarry (Brisighella, Faenza, Italy)
Text-fig. 3. Scatter diagrams of a) upper and b) lower teeth from BRS 25 (black profile), French localities (cyan; data from Crochet 1986) and Moncucco Torinese (red ones; unpublished data).
Dynamic light scattering datasets used to assess the Raynals software
<p>Updated - 23/04/2023<br> This folder contains the associated data from Burastero et al., 'Raynals, an online tool for the analysis of dynamic light scattering'</p> <p>The experimental datasets can be found at ./experimentalData/<br> The artificially generated datasets at ./SimulatedDataDLS_case1 and ./SimulatedDataDLS_case2</p> <p>To produce the experimental datasets, we performed measurements on Carbonic Anhydrase, Bovine Serum Albumin, Gold Nanoparticles, and three in-house samples: A protein with a beta-Propeller domain, a coiled-coil like protein, and an intrinsically disordered protein.</p> <p>Additionally, you'll find R scripts to generate Fig. 3, Fig. 5, and Fig. S1 to S4.<br> </p>
Data archive for the peer-reviewed journal article "Detailed characterization of the CAPS single scattering albedo monitor (CAPS PMssa) as a field-deployable instrument for measuring aerosol light absorption with the extinction-minus-scattering method"
<p>Data archive accompanying the peer-reviewed journal article "Detailed characterization of the CAPS single scattering albedo monitor (CAPS PMssa) as a field-deployable instrument for measuring aerosol light absorption with the extinction-minus-scattering method". In 2020 this article was accepted for publication in the journal <em>Atmospheric Measurement Techniques</em>. Data are uploaded in the form of ascii text files, Igor Pro experiment files (.pxp), and Jupyter notebook files. In addition, a Jupyter notebook file is included containing an implementation of the error model used in the paper.</p>
Dataset and Code for Manuscript "Cell sorting based on pulse shapes from angle resolved detection of scattered light"
<p>Dataset and code for the cell cycle analysis and cluster selection for sorting:</p> <ul> <li>ReadMe file with explanations of the data set and analysis</li> <li>Python scripts for converting the data and to reproduce the sort cluster selection</li> <li>binary data files containing pulse shapes and wavelet transform coefficients</li> <li>FSC data files containing the respective common flow cytometry parameters</li> <li>text files with event indices that represent the gating</li> </ul>
Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow</em>”</strong> in Biomedical Optics Express (doi.org/10.1364/BOE.505847<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 5-10 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails.</p> <p>For the diffusion measurement under static conditions, there is only one file. However, for experiments involving both flowing and diffusing particles, the dataset comprises diffusion calibration, focus (beam shape) calibration, and flow measurement files. Due to the upload size limitations of the Zenodo repository, only the flow measurements corresponding to one discharge rate have been uploaded. Furthermore, only the non-dilute flow dataset has been uploaded for the same reason. However, for the dilute flow, the analysis logic remains the same, but users will need to utilize the complete g2 formula outlined in Section 2.2 of our article. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. To conduct the analysis, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate.</p> <p>The results are plotted at the end of our analysis routines. The parameters are displayed as a function of depth. Users can readily compute the Signal-to-Noise Ratio (SNR) at each depth by utilizing the fitted autocorrelation amplitudes. Occasionally, the fitted amplitudes may surpass unity. In such instances, users can assume an extremely high (even infinite) SNR.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>Diffusion_03032023.oct</p> </td> <td> <p>Diffusion measurement file.</p> </td> <td> <p>Na=4096, Nb=1100, 5.5 kHz</p> </td> </tr> <tr> <td> <p>Diffusion_07032023.oct</p> </td> <td> <p>Diffusion calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=10, 36 kHz</p> </td> </tr> <tr> <td> <p>Waist_07032023.oct</p> </td> <td> <p>Beam waist calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=40, 36 kHz</p> </td> </tr> <tr> <td> <p>Q=2_07032023.oct</p> </td> <td> <p>Flow measurement file for a discharge rate of 2 ml/min.</p> </td> <td> <p>Na=4096, Nb=1000, 36 kHz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis, simulation and processing routines.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_diffusion.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_flow.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from flowing and diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Diffusion_parallel.py</p> </td> <td> <p>This script is for analyzing static diffusion measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Flow_parallel.py</p> </td> <td> <p>This script is for analyzing flow measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Data associated with the manuscript: 'COLIS: An advanced light scattering apparatus for investigating soft matter onboard the International Space Station'
<p>Experimental data shown in the manuscript.</p>
Nighttime Atmospheric Scattering Phase Function Derived from the Scattered Light of a Laser Beam - supplementary material
<p>The ZIP-package contains:</p> <ul> <li>Canon RAW (CR2) and DCRAW pre-processed (PGM) pictures of the sky with the green laser switched on/off, elevations 10 and 20 degrees</li> <li>Calibration data for Canon EOS 6D Mark II + Fish-Eye lens EF8-16mm at 8mm (wignetting, geometrical distortion)</li> <li>C/C++ software 'green_laser.cpp' for extracting the data from the pictures (incl. Windows-executable). Version 2 has improved median filtering and built-in correction if the laser beam does not go exactly overhead. </li> <li>Detailed description of the software and the method of taking and processing the RAW pictures in PDF format</li> <li>Extracted data in EXCEL workbooks</li> </ul>
Dynamic light scattering differentiate parameters of blood flow
<p>This dataset demonstrates blood perfusion recordings measurements on the 3rd fingers and wrists simultaneously (sitting position) in volunteers of three groups: healthy volunteers younger group (20 years old), healthy volunteers younger group (~55 years old), patients with Diabetes Type 2 (~55 years old).</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.