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2,649 results for “Optical”
Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by Optics and Communication (Supplementary Audio Demo Files)
<p>Audio files that supplement "Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by Optics and Communication". Please refer to the manuscript (preprint) for additional details.</p>
Fast and accurate spectral estimation axial super resolution optical coherence tomography
<p>This depository contains the data and code underlying the results of the publication '<em>Fast and accurate spectral estimation axial super resolution optical coherence tomography</em>' in Optics Express (doi.org/<em>10.1364</em>/<em>OE</em>.<em>439761). </em>The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author.</p> <p><strong>Description of the code and datasets</strong></p> <p>Table 1 describes the Matlab scripts and functions in this depository that were used in the publication. For reproducing the figures of the publication, refer to the scripts <em>SE_OCT_figure(..).m</em>. For understanding the method and applying it on other datasets from the reader, <em>Bscan_reconstruction.m </em>and <em>Cscan_reconstruction.m</em> are the most convenient scripts to start with. For simulating OCT data as presented in the publication, <em>OCT_simulations.m</em> could be applied. Details on the variables and parameters, such as number of iterations, grid interpolation factor and number of data chunks are commented on in the code itself and should be understandable with the publication as reference. </p> <p>Table 2 describes the datasets that have been used for the publication and are free for the readers to be used with their methods. Table 3 then gives a brief explanation of the variables that are contained in the dataset <em>.mat</em> files.</p> <table> <caption>Table 1. The Matlab scripts in this depository with brief description.</caption> <thead> <tr> <th scope="col">script name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>Bscan_reconstruction.m</td> <td>This script loads a B-scan .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>Cscan_resonstruction.m</td> <td>This script loads a 3Ddata .mat file and applies the four processing methods FBW-DFT, PBW-DFT, AR, RFIAA on the data. </td> </tr> <tr> <td>oct_ar.m</td> <td>This function applies the auto-regressive spectral estimation on the input data.</td> </tr> <tr> <td>oct_iaa.m</td> <td>This function applies RFIAA or FIAA on the input data.</td> </tr> <tr> <td>fiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying FIAA (without the recursive scheme) and within rfiaa_oct.m for the first line. This function applies FIAA on a single A-scan.</td> </tr> <tr> <td>rfiaa_oct.m</td> <td>This function is called within oct_iaa.m for applying RFIAA (with the recursive scheme) on a chunk of data. It initializes the first line of the chunk with fiaa_oct.m, and then it applies rec_fiaa_oct.m with the initialization taken from the previous scanline.</td> </tr> <tr> <td>rec_fiaa_oct.m</td> <td>This function applies RFIAA on a single A-scan, taking the initialization from the previous scanline as extra input parameter. </td> </tr> <tr> <td>RayleighThreshold.m</td> <td>This function automatically determines the lower limit of the dynamic range for plotting an OCT image. It fits a Rayleigh distribution on the input data (preferably noise, but also a full image could be used) and returns a threshold in dB. </td> </tr> <tr> <td>morgenstemning.m</td> <td>This function defines the colormap as used in the publication.</td> </tr> <tr> <td>Bscan_reconstruction_function.m</td> <td>This function takes the interference OCT signal, reference spectra and reconstruction parameters as input and returns the reconstructed images according to the four methods in the publication. This function is used in the scripts for reproducing the figures in the publication. It follows the same structure as the script <em>Bscan_reconstruction.m.</em></td> </tr> <tr> <td>SE_OCT_figure3.m</td> <td>This script does the processing for and plots figure 3 in the manuscript. For this script, the .zip file <em>wedge_simulation_data </em>needs to be unpacked and placed as folder in the folder where this script is executed.</td> </tr> <tr> <td>SE_OCT_figure4.m</td> <td>This script reproduces figure 4 in the publication.</td> </tr> <tr> <td>SE_OCT_figure5.m</td> <td>This script reproduces figure 5 in the publication.</td> </tr> <tr> <td>SE_OCT_figure6.m</td> <td> <p>This script reproduces figure 6 in the publication</p> </td> </tr> <tr> <td>OCT_simulations.m</td> <td>This script reproduces the OCT simulations as described in the publication. As the noise is random, any new realization might slightly differ from the data in the publication.</td> </tr> </tbody> </table> <p> </p> <table> <caption>Table 2. The OCT datasets contained in this depository with a brief description. Table 3 describes the variables that are contained in each of these datasets.</caption> <thead> <tr> <th scope="col">dataset name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>wedge_Bscan_data.mat</td> <td>Experimental data from the wedge phantom as visualized in figure 3 of the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>wedge_simulation_data.zip</td> <td>This zipped folder contains 16 simulation datasets with different noise levels, which form the basis of Figure 3 (f) in the publication.</td> </tr> <tr> <td>interfaces_simulation_Bscan_data.mat</td> <td>This file contains the simulation data for 8 interfaces with decreasing intensity and forms the basis of Figure 4 in the publication.</td> </tr> <tr> <td>layered_phantom_Bscan_data.mat</td> <td>This file contains the experimental data from the layered phantom, as used in Figure 4 (c-d) in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>onion_Bscan_data.mat</td> <td>This file contains the experimental data from the onion sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>skin_Bscan_data.mat</td> <td>This file contains the experimental data from the skin sample as used in Figure 5 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>intralipid_Bscan_data.mat</td> <td>This file contains the experimental data from the intralipid sample as used in Figure 6 in the publication. No spectrum averaging is applied.</td> </tr> <tr> <td>speckle_simulation_Bscan_data.mat</td> <td>This file contains simulation data for 3 speckle regions as used in Figure 6 in the publication.</td> </tr> <tr> <td>reference_spectrum.mat</td> <td>This file just contains a spectrum from the used experimental setup which is used as input for the simulations.</td> </tr> <tr> <td>onion_3Ddata.mat</td> <td>This file contains 3D data of the onion sample, which is used for visualization 1. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> <tr> <td>skin_3Ddata.mat</td> <td>This file contains 3D data of the skin sample, which is used for visualization 2. The OCT spectra are obtained from averaging 8 spectra from the experimental setup.</td> </tr> </tbody> </table> <table> <caption>Table 3. This table contains the variables in the .mat files and their description.</caption> <thead> <tr> <th scope="col">variable name</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td><em>iRawdata</em></td> <td>OCT interference spectra interpolated to a linear grid in k-domain, before subtracting the reference spectrum</td> </tr> <tr> <td><em>sk</em></td> <td>the reference spectrum, interpolated to a linear grid in k-domain</td> </tr> <tr> <td><em>phasep</em></td> <td>4 polynomial coeficients, which can be used in 'polyval' to correct for dispersion</td> </tr> <tr> <td><em>sizeX</em></td> <td>the lateral size of the scan in mm</td> </tr> <tr> <td><em>sizeY</em></td> <td>(only for 3D datasets) the lateral size in the direction perpendicular to x in mm</td> </tr> <tr> <td><em>sizeZ</em></td> <td>the axial field of view (one-sided) before range reduction in mm </td> </tr> <tr> <td><em>ROIp</em></td> <td>the best axial region of interest for this dataset to apply RFIAA on a reduced reconstruction range (in pixels of the DFT reconstruction without zero-padding)</td> </tr> </tbody> </table> <p> </p>
Optical polarimetric observations of low-mass X-ray black hole binary MAXI J1820+070 during 2019-2021
<p>The dataset contains raw polarimetric FITS images of the low-mass X-ray black hole binary <a href="https://www.astronomerstelegram.org/?read=11399">MAXI J1820+070</a> (and surrounding field), obtained by the <a href="https://doi.org/10.3847/1538-3881/abc74f">DIPol-UF </a>optical CCD polarimeter in three (BVR) filters while mounted on the 2.56m <a href="https://www.not.iac.es">Nordic Optical Telescope</a>. The data were collected over 5 observing runs throughout 2019--2021. During each observing night, a set of calibration images were also obtained. These typically include 7 dark and 7 bias images per filter per night (sometimes more if weather conditions or instrument settings changed during observations). Bias and dark FITS files have `_bias` or `_dark` labels in their names, as well as FITS key `IMAGETYP` set to either `Bias Frame` or `Dark Frame`, respectively.</p>
Quantitative in situ measurement of optical force along a strand of cleaved silica optical fiber induced by the light guided therewithin
<p>This dataset is associated with "Quantitative in situ measurement of optical force along a strand of cleaved silica optical fiber induced by the light guided therewithin", by Mikko Partanen, Hyeonwoo Lee, and Kyunghwan Oh, Photonics Res. 9, 2016 (2021) [https://doi.org/10.1364/PRJ.433995].</p> <p>It includes data files and Matlab (R2017b) scripts to allow for the replication of the figures. The data files give the oscillator mirror position in the units of nanometers measured at the rate of 200 times per second.</p>
Chemical, optical, and oxidizing properties of three kinds of water-soluble organic matter in PM2.5 from biomass and coal combustion in rural areas in Northwest China
<p>this data set is about the molecular carbon content, light absorption, infrared spectra, and oxidation activity in PM2.5</p>
Research data and code for Numerical investigation of the influence of the source and detector position for optical measurement of lung volume and oxygen content in preterm infants
<p># Research data repository</p> <p>## Introduction</p> <p>This repository contains the research data, scripts and codes to process the simulation and generate the figures in research article: <br> <em>"Numerical investigation of the influence of the source and detector position for optical measurement of lung volume and oxygen content in preterm infants"</em></p> <p>This work uses the discrete 3D mesh of the thorax of a newborn that is available at: http://doi.org/10.5281/zenodo.4916863</p> <p>This article has been submitted and publied in Journal of Biophotonics:<br> - DOI: 10.1002/jbio.202200041<br> - Link: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.202200041">https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.202200041</a></p> <p>## Article status</p> <p> [X] Submitted <br> [X] Under review <br> [X] Corrections <br> [X] Published<br> <br> ## Content</p> <p>- Folder "data": This folder needs to be unzipped and contains the raw data from the simulation, as well as some processed data needed to generate the figures. This folder contains all the data necessary to generate the figures. However some intermediate data files (the Photon Hitting Density values interpolated on the elements of the mesh) are not given here because the files are too big. These can be created using the convertPHD2element script.<br> - Folder "function": This folder needs to be unzipped and contains some functions that are used by the scripts to process data or generate the figures<br> - Matlab .m files: The .m files are scripts that are used to generate the figures (generateFigX.m) or to process the data computeYYY.m. The description of each script and function is given in the comment section at the beginning of each file.</p> <p>## Licence<br> This data is published under the creative common CC-BY licence. You are free to use this data as long as you cite this dataset and the article (when DOI available)</p> <p>## Digital Object Identifier<br> DOI: 10.5281/zenodo.5996855</p> <p>## Authors<br> Simulation: Andrea Pacheco<br> Article writing: Andrea Pacheco<br> Data processing and figure generation: Andrea Pacheco and Baptiste Jayet<br> Conceptualisation, investigation, review and editing: Emilie Krite Svanberg, Hamid Dehghani and Eugene Dempsey<br> Project supervision: Stefan Andersson-Engels</p> <p>## Funding<br> The research leading to these results was funded by Science Fundation Ireland project no. SFI/15/RP/2828</p>
Optical data of PSR J1555-2908
<p>The optical data used in the analysis of PSR J1555-2908 presented in Kennedy et al. 2021.</p> <p>The photometry was taken with the ULTRACAM instrument on ESO's New Technology Telescope. The spectroscopy was taken using X-SHOOTER mounted on one of ESO's VLT units.</p>
Demonstration of kilohertz operation of hydrodynamic optical-field-ionized plasma channels
<p>The compressed file contains the raw data used in the publication "Demonstration of kilohertz operation of Hydrodynamic Optical-Field-Ionized Plasma Channels," <em>Physical Review Accelerators and Beams </em><strong>25</strong>, 011301 (2022) DOI: 10.1103/PhysRevAccelBeams.25.011301.</p> <p>Further information on the organization of the data is provided in the README file included in the compressed file.</p> <p> </p>
Datasets for Background and Shading Correction of Optical Microscopy Images by BaSiC
<p>This repository holds all the example data for publication: "<strong>A BaSiC tool for background and shading correction of optical microscopy images, Nature Communications (2017)</strong>" DOI: <a href="https://doi.org/10.1038/ncomms14836">https://doi.org/10.1038/ncomms14836</a>. A downsampled version is available at Zenodo repository with DOI: <a href="https://zenodo.org/record/6974039#.YvD8G3ZBxD8">10.5281/zenodo.6974039</a>.</p>
DataSet: Structural and optical properties of gold nanosponges revealed via 3D nano-reconstruction and phase-field models
<p>These are the main raw and processed data for the publication "Structural and optical properties of gold nanosponges revealed<br> via 3D nano-reconstruction and phasefield models".</p> <p>Abstract:<br> Nanoporous gold nanoparticles are subject of intensive research due to their unique morphology, which leads to electric field localizations generating a strongly nonlinear optical response, allowing a wide range of applications. However, accurate predictions of physical properties require detailed knowledge of the sponges’ chaotic nanometer-sized geometrical structures, posing a metrological challenge. Therefore, a main goal is to obtain computer models with equivalent structural and optical properties. To understand the sponges’ morphology, a procedure for their accurate three-dimensional reconstruction using focused ion beam tomography is presented. Next, a small number of morphological key parameters is derived that sufficiently characterize the complex topology. Additionally, a new simulation method for the computer-aided creation of finite-sized sponges with adjustable geometric properties is presented. It is shown that if certain morphological parameters are similar for computer-generated and experimental sponges, their optical response, including number and locations of field localizations, are also similar. Finally, the anisotropy of the experimental sponges is analyzed and an easy-to-use procedure to replicate arbitrary anisotropies in computer-generated sponges is presented.</p>
Data - Coherent combining of low-power optical signals based on optically amplified error feedback
<p>This dataset contains measurement data and processing code for the results published in "Coherent combining of low-power optical signals based on optically amplified error feedback". Code for the Micro-controllers used in the work is also attached.</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
Data of publication A Frequency-Multiplexed Coherent Electro-optic Memory in Rare Earth Doped Nanoparticles
<p>Data corresponding to main text figures of publication : A. Fossati, S. Liu, J. Karlsson, A. Ikesue, A. Tallaire, A. Ferrier, D. Serrano, and P. Goldner, <em>A Frequency-Multiplexed Coherent Electro-Optic Memory in Rare Earth Doped Nanoparticles</em>, Nano Lett. <strong>20</strong>, 7087 (2020).</p>
Laboratory validation of a smartphone-based sensor for diffuse optical volume properties
<p>This data set contains raw image data for laboratory validation of a diffuse-optical, smartphone-based sensor. The measurements were taken using scattering phantoms with known scattering and absorption coefficient. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the three scattering phantoms (One, Two, Three) or spatial calibration data using a 9mm x 9mm calibration pattern (calib). The raw images are located in the folder ./calib. Matlab code is contained in the filder ./matlab. It can be run on Matlab R2021b.</p> <p>For analyzing the raw data set, use "CameraBatchCalib.m". It wraps around the function "CameraAnalysisCalib.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfG.m". The resulting data is plotted for comparison with the nominal attenuation length of the scattering phantoms.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset and code. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022).</p> <p>The underlying fit function is based on the calculations from:</p> <p>Markus Allgaier and Smith, Diffuse optics for glaciology, Opt. Express 29, 18845–18864 (2021)</p> <p> </p>
Estimation of groundwater flow rate by an actively heated fiber optics based thermal response test in a grouted borehole
<p>The dataset contains the numerical data and the <em>in-situ</em> measurements in the manuscript titled "Estimation of groundwater flow rate by an actively heated fiber-optics-based thermal response test in a grouted borehole". The data is stored in MAT files, which are Binary MATLAB files. There are a series of codes used in this manuscript to estimate groundwater flow rates. The codes were written in MATLAB Live Script, version 2021b.</p> <ul> <li>The numerical data contains temperatures of the heating stage in different thermal response tests in a numerical model, which considers the borehole effects. The model is set up by COMSOL Multiphysics, and a series of flow rates is set to the model respectively for different thermal response tests.</li> <li>The <em>in-situ</em> measurements include temperatures of the heating stage in an actively heated fiber-optics-based thermal response test, which was performed in July 2021 in the grouted borehole, which is located in the lower section of the Sima bend of the Yangtze River. The temperature for the flow rate estimation was thinned to 120 s records from 10s records for the limited computing resources.</li> <li>The <em>data_process.mlx</em> provides pre-processing for the observational data recorded by Silixa Ultima-M MK2 DTS. The <em>estimation_process.mlx </em>gives a groundwater flow estimation case in a grouted borehole.</li> </ul>
Data of the paper Optical homogeneous and inhomogeneous linewidths in 171Yb3+:Y2SiO5
<p>Data of the paper: </p> <p>E. Lafitte-Houssat, A. Ferrier, M. Afzelius, P. Berger, L. Morvan, S. Welinski, and P. Goldner, <em>Optical homogeneous and inhomogeneous linewidths in 171Yb3+:Y2SiO5</em>, Оптика и спектроскопия <strong>130</strong>, 23 (2022). (in Russian).</p>
Data to reproduce the results presented in Sehgal et al. 2022. Water Resources Research, https://doi.org/10.1029/2021WR030624 ("Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer")
<p>This repository consists data to reproduce results as presented in: "Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer", Water Resorces Research. Kindly refer to the readme.text file to navigate through the dataset.</p> <p> </p> <p> </p>
Designing of Fiber Bragg Gratings for Long-distance Optical Fiber Sensing Networks
<p>Research data of <em>Modelling and Simulation in Engineering </em>journal article “Designing of Fiber Bragg Gratings for Long-distance Optical Fiber Sensing Networks”.</p> <p>Most optical sensors on the market are optical fiber Bragg grating (FBG) sensors with low reflectivity (typically 7-40%) and low side-lobe suppression (SLS) ratio (typically SLS <15dB), which prevents these sensors from being effectively used for long-distance remote monitoring and sensor network solutions. This research is based on designing the optimal grating structure of FBG sensors and estimating their optimal apodization parameters necessary for sensor networks and long-distance monitoring solutions. Gaussian, sine and raised sine apodizations are studied to achieve the main requirements, which are - maximally high reflectivity (at least 90%) and side-lobe suppression (at least 20 dB), as well as maximally narrow bandwidth (FWHM<0.2 nm), FBGs with uniform (without apodization). Results gathered in this research propose high-efficiency FBG grating apodizations, which can be further physically realized for optical sensor networks and long-distance (at least 40 km) monitoring solutions.</p> <p> </p>
Data and original code for: A generalized approach to characterise optical properties of natural objects
<p>To understand the diversity of ways in which natural materials interact with light, it is important to consider how their reflectance changes with the angle of illumination or viewing and to consider wavelengths beyond the visible. We chose a set of existing measurements and parameters that are generalisable to any wavelength range and spectral shape and we highlight which subsets of measures are relevant to different biological questions. As a case study, we applied these measures to 30 species of Christmas beetles. Here we provide the raw spectral data of angle integrated and angle-dependent reflection by the beetle elytra. We also provide the original code used for our analysis and figures.</p>
Optical Properties of MoO3 and MoO2
<p>Files conatining the dielectric function and refractive index of crystalline MoO3 and MoO2 obtained by spectroscopic ellipsometry.</p> <p>The files are structured as:</p> <p>eV nm Real(epsilon) Imag(epsilon) n k</p>
Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity
<p># Data and plotting code for "Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity" by Battulga Munkhbat, Piotr Wróbel, Tomasz J. Antosiewicz, and Timur O. Shegai, ACS Photonics (2022); https://doi.org/10.1021/acsphotonics.2c00433</p> <p><br> ## Contents</p> <p>* <TMD-material>: directories with raw and derived data for all 10 TMDs<br> * f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt: obtained permittivities<br> * plot_*_v1.m: Matlab scripts for plotting data</p> <p>## Description of the data</p> <p>The raw and derived data stored in directories <TMD> contain the following files:</p> <p>* <TMD>/<date>-<TMD>.SEsnap: binary data file with collected data, CompleteEASE format<br> * <TMD>/<date>-<TMD>-E*.mat: ascii text file with permittivity data separated into individual components as exported from CompleteEASE software<br> * <TMD>/<date>-<TMD>-full.mat: ascii text file with fitted model parameters as exported from CompleteEASE software<br> * <TMD>/<TMD>-data/*.txt: selected raw data and fits for all considered samples (Mueller Matrix or Delta/Psi/depolarization).</p> <p>The structure of the data file names is as follows:<br> <order-number-in-CompleteEASE>-s<sample-name>-<data-type>.txt for general ellipsometry (delta, psi, depolarization) or<br> <order-number-in-CompleteEASE>-s<sample-name>-o<in-plane-sample-rotation-number>-mm.txt for Mueller Matrix measurements.</p> <p>The following two scripts can be used to plot the raw measured data (solig lines) along with corresponding fits (black dotted lines):</p> <p>* plot_mm_v1.m: Matlab script for plotting Mueller Matrix data for WTe2 and ReS2<br> * plot_psi_delta_depol_v1.m: Matlab script for plotting psi, delta, and depolarization data for other TMDs</p> <p>The diagonal permittivity tensor data are saved in the f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt files which can be plotted using the plot_permittivity_v1.m Matlab script. The format of this file is as follows:</p> <p>wavelength in nanometers; real part of epsilon_xx; imaginary part of epsilon_xx; real part of epsilon_yy; imaginary part of epsilon_yy; real part of epsilon_zz; imaginary part of epsilon_zz;</p> <p> </p> <p> </p>
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.