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33 results for “time-lapse imaging”
Time-lapse electrical resistivity tomography and seismic reflection imaging of a shallow ground-water aquifer (0-50 m): Mississippi River levee seepage across the Duncan Point bar, Baton Rouge, Louisiana, U.S.A.
<p>The electrical resisitivity raw data files are slightly processed to remove bad data points but can be inverted using tomographic inversion code. </p> <p>The seismic data were assembled in Seismic Unix format, a shortened version of the SEG-Y format (Society of Exploration Geophysicists Exchange Format-Y https: //seg. org/Publications/SEG-Technical-Standards), that has the 3200-byte EBCDIC and 400-byte tape header removed. The data uploaded online (<a href="https://zenodo.org/records/14776025">https://zenodo.org/records/14776025</a>) is a CMP brute-stacked seismic section. </p> <p>During data collection, shotpoint location changed proceeding along a 136-degree azimuth (south-easterly direction), and spaced every 1 m.</p> <p>A total of 48, horizontal-component 28-Hz nominal geophones were placed every one meter and shotpoints were located half-way between geophones. Geophones remained fixed at their locations throughout the survey and so the CMP spacing is nominally 0.5-m but fold varies linearly from a value of 1 from either side of the survey to a central maximum of 24. The seismic source consisted of a partially buried 20-lb steel I-beam struck repeatedly on either side three times by an 8-lb sledge hammer. Data of the same striking polarity were added in-phase in the field. Data with opposing polarity at each shotpoint location were subtracted later to enhance SH-wave data and suppress converted SH-to-P waves.</p> <p>Seismic processing is minimal and consists of standard surface-wave muting, elimination of bad seismic traces, normal moveout, bandpass filtering (between 12 Hz and 50 Hz) and preliminary stacking with trace mixing every 3 CMPs. The data were stacked with a single velocity throughout that ranged from 80 m/s (Vs) at 0.2 s, to 100 m/s at 0.35 s and reached 180 m/s at 0.5 s of two-way traveltime.</p> <p> </p>
BioTISR: a time-lapse biological image dataset for super-resolution microscopy
<p>BioTISR is a biological image dataset for super-resolution microscopy, currently including 2D and 3D time-lapse image pairs of low-and-high resolution images of a variety of biology structures, aiming to provide a high-quality dataset of time-lapse biological SR images for the community to spark more developments of computational SR methods.</p> <p>At present, 2D dataset includes five specimens (clathrin-coated pits, lysosomes, outer mitochondrial membrane, microtubules, and F-actin) acquired with the GI/TIRF-SIM mode and nonlinear SIM mode of our Multi-SIM system, and 3D dataset includes three specimens (outer mitochondrial membrane, microtubules, and F-actin) acquired with 3D-SIM mode of the Multi-SIM system. For each type of specimen and each imaging modality, we acquired the raw data from at least 50 distinct regions-of-interest (ROI). For each ROI, we acquired two (3D data) or three (2D data) groups of N-phase × M-orientation × T-timepoint raw images with a constant exposure time but increasing the excitation light intensity, where (N, M, T) are (3, 3, 20) for TIRF-SIM and GI-SIM, (5, 5, 10) for nonlinear SIM, and (3, 5, 10) for 3D-SIM. Specific imaging conditions and scripts for reading MRC file are provided in Supplement Files.</p> <p>The BioTISR dataset is related to the following paper:<a href="https://www.nature.com/articles/s41587-025-02553-8#citeas">Qiao, C., Liu, S., Wang, Y. <em>et al.</em> A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification. <em>Nat Biotechnol</em> (2025). https://doi.org/10.1038/s41587-025-02553-8</a>, which is an extension of our previously published <a href="https://doi.org/10.6084/m9.figshare.13264793.v9">BioSR dataset</a> (https://www.nature.com/articles/s41592-020-01048-5).</p> <p>Limited by quota, the original images uploaded in the current 3D dataset are wide-field images obtained after averaging 15 images, where (N, M, T) are (1, 1, 10). We will update them to raw SIM images after the quota is expanded.</p> <p>2D dataset's url:</p> <p><a href="https://doi.org/10.5281/zenodo.13843670" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13843670</a></p> <p>3D dataset's urls:</p> <p>F-actin:</p> <p>WF input: <a href="https://doi.org/10.5281/zenodo.13843673">https://doi.org/10.5281/zenodo.13843673</a></p> <p>Raw SIM input:<a href="https://doi.org/10.5281/zenodo.13994464" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13994464</a></p> <p>Microtubules:</p> <p>WF input: <a href="https://doi.org/10.5281/zenodo.13932988" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13932988</a></p> <p>Raw SIM input: <a href="https://doi.org/10.5281/zenodo.13989327" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13989327</a></p> <p>Mitochondria:</p> <p>WF input: <a href="https://doi.org/10.5281/zenodo.13843183" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13843183</a></p> <p>Raw SIM input: <a href="https://doi.org/10.5281/zenodo.14000502" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14000502</a></p> <p> </p> <p>Update 2025.5.6</p> <p>Add optical transfer function(OTF) of the microscopy system and the pixel size of each data to the supplementary files.</p>
Phase Contrast Time-Lapse and F-actin Imaging of Mechanically Compressed or Irradiated Pseudostratified Human Bronchial Epithelial Cells
<p><strong>Overview</strong></p> <p>This dataset includes phase contrast time-lapse imaging of <em>in vitro</em> pseudostratified airway epithelial cells to visualize their collective cellular migration after exposure to mechanical compression (mimicking bronchoconstriction) or irradiation. Additionally, the cells were fixed and stained for F-actin to visualize the apical cell boundaries, basal cell boundaries, and basal cell stress fibers.</p> <p><strong>Cell Culture and Treatment</strong></p> <p>Primary human bronchial epithelial cells (from a single donor) were grown on transwells in air-liquid interface (ALI) culture for 14 days to model a well-differentiated, pseudostratified airway epithelium. Cells were then exposed to either mechanical compression (30 cmH2O for 3 hours) mimicking asthmatic bronchoconstriction or irradiation (1Gy of ionizing radiation using a RS 2000 Biological Research Irradiator (RadSource) on ALI days 7, 10, and 14).</p> <p><strong>Phase Contrast Time-Lapse Imaging</strong></p> <p>At 24 or 72 hours after final treatment, cells were imaged to visualize collective cellular migration. For each independent experimental replicate (2 transwells per treatment per timepoint), six fields of view per well were imaged every 6 minutes over 1.5 hours. The imaging chamber was supplied with 37°C, 5% CO2, humidified air on a Zeiss Axio Observer Z1 to collect phase contrast images. <em>The image resolution is 0.586 µm/pixel.</em></p> <p><strong>Immunofluorescence Imaging</strong></p> <p>Cells were fixed (4% PFA for 30 minutes) at 24 or 72 hours after final treatment (and after phase contrast time-lapse imaging). Fixed transwells were stained for F-actin (Alexa fluor 488-Phalloidin, ThermoFisher Scientific, diluted 1:40, 30 minutes). Transwell membranes were cut from the plastic support and mounted on glass slides. Slides were imaged using a Zeiss Axio Observer Z1 with an apotome module controlled using Zen Blue 2.0 software. Five random fields of view were imaged from each transwell membrane in a z-stack from substrate to apical cell surface. To visualize various planes through the pseudostratified epithelial layer (apical cell boundaries, basal cell boundaries, and basal cell stress fibers), maximum intensity projections were generated from regions of interest through the z-stack. <em>The image resolution is 0.293 µm/pixel.</em></p> <p><strong>Dataset</strong></p> <p>Phase contrast time-lapse movies are provided as *.avi files. Immunofluorescence images are provided as *.tif files. For an individual transwell, the imaging dataset includes:</p> <ul> <li>6 phase contrast time-lapse movies</li> <li>5 immunofluorescence images of apical cell boundaries</li> <li>5 immunofluorescence images of basal cell boundaries</li> <li>5 immunofluorescence images of basal cell stress fibers</li> </ul> <p>Phase contrast time-lapse filenames contain</p> <ul> <li>Donor: U13</li> <li>Timepoint: 24 or 72 hours</li> <li>Treatment & Well: control (C), mechanical compression (P), or irradiation (R); well 1 or 2</li> <li>Field of View: (1) – (6)</li> </ul> <p>Immunofluorescence image filenames contain:</p> <ul> <li>Donor: <strong>U13</strong></li> <li>Timepoint: <strong>24</strong> or <strong>72</strong> hours</li> <li>Treatment & Well: control (<strong>C</strong>), mechanical compression (<strong>P</strong>), or irradiation (<strong>R</strong>); well <strong>1</strong> or <strong>2</strong></li> <li>Field of View: <strong>1-5</strong></li> <li>Region of Interest: apical cell boundaries (<strong>ACB</strong>), basal cell boundaries (<strong>BCB</strong>), or basal stress fibers (<strong>SF</strong>)</li> </ul> <p>Phase contrast time-lapse and immunofluorescence from the same transwell will all start with the same “Donor_Timepoint_Treatment/Well...” (i.e. U13_24_C1…). <strong>Note that the images from phase contrast and immunofluorescence are not necessarily from matched locations within the transwell and are at different spatial scales.</strong></p> <p>Immunofluorescence images from the same z-stack field of view will start with the same “Donor_Timepoint_Treatment/Well_FieldofView…” (i.e. U13_24_C1_1…).</p>
Time-lapse (4D) volumetric fluorescence microscopy image sequence of a living zebrafish embryo
<p>The dataset contains a time-lapse (4D) volumetric fluorescence microscopy image sequence of a living zebrafish embryo (cxcr4aMO). The sequence has been captured with a confocal laser-scanning microscope during zebrafish gastrulation and shows endodermal cells that have been fluorescently labelled.</p> <p>The sequence is best viewed with Fiji (https://fiji.sc/) and can be loaded into Matlab with tiffread.m (http://www.cytosim.org/misc/index.html).</p> <p>For the treatment of the specimen see:</p> <p>S. Nair and T. F. Schilling. Chemokine signaling controls endodermal migration during zebrafish gastrulation. Science, 322(5898):89–92, October 2008.</p>
Data from: The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition
<p>With phenotypic heterogeneity in whole cell populations widely recognised, the demand for quantitative and temporal analysis approaches to characterise single cell morphology and dynamics has increased. We present CellPhe, a pattern recognition toolkit for the unbiased characterisation of cellular phenotypes within time-lapse videos. CellPhe imports tracking information from multiple segmentation and tracking algorithms to provide automated cell phenotyping from different imaging modalities, including fluorescence. To maximise data quality for downstream analysis, our toolkit includes automated recognition and removal of erroneous cell boundaries induced by inaccurate tracking and segmentation. We provide an extensive list of features extracted from individual cell time series, with custom feature selection to identify variables that provide the greatest discrimination for the analysis in question. Using ensemble classification for accurate prediction of cellular phenotype and clustering algorithms for the characterisation of heterogeneous subsets, we validate and prove adaptability using different cell types and experimental conditions.</p>
4D time-lapse images of brain and trunk development in the larvacean Oikopleura dioica
<p>The larvacean, <em>Oikopleura</em> <em>dioica</em> is a planktonic chordate, which is an emerging model organism with a short life cycle of 5 days and belongs to tunicates (urochordates). Organ formation in the trunk proceeds in seven hours from hatching of tailbud larvae at three hours after fertilization (hpf) to completion of organ formation in fully functional juveniles that start feeding at 10 hpf and are just miniature of adult form. Development of <em>O. dioica</em> has been described (Nishida, H., 2008 Development of the appendicularian <em>Oikopleura</em> <em>dioica</em>: culture, genome, and cell lineages. Dev. Growth Differ. 50, S239–S256.). The dataset contains 4D (3D+time) time-lapse images that were acquired during larval development using differential interference contrast optics, and wide-field fluorescent microscope, which visualize cell membrane and nuclei of the entire trunk region. In some cases, animal or vegetal hemisphere blastomeres are labelled to trace the descendants. The dataset would be generally utilized as basic morphological data during the larval development and for tracing cell lineages at a single cell level. This data set is related to the manuscript "Formation of the brain by stem cell divisions of large neuroblasts in <em>Oikopleura</em> <em>dioica</em>, a simple chordate".</p>
Engineered cardiac microbundle time-lapse microscopy image dataset
<p>The "Microbundle Time-lapse Dataset" contains 24 experimental time-lapse images of cardiac microbundles using three distinct types of experimental testbed of beating lab grown hiPSC-based cardiac microbundles. Of the 24 experimental time-lapse images, 23 examples are brightfield videos, and a single example is a phase contrast video. We categorize the different experimental testbeds into 3 types, where "Type 1" includes movies obtained from standard experimental microbundle platforms termed microbundle strain gauges [1,2,3]. We refer to data collected from non-standard platforms termed FibroTUGs [4] as "Type 2" data, and "Type 3" data represents a highly versatile and diverse nanofabricated experimental platform [5,6].</p> <p><strong>References:</strong></p> <p>[1] Boudou T, Legant WR, Mu A, Borochin MA, Thavandiran N, Radisic M, Zandstra PW, Epstein JA, Margulies KB, Chen CS. A microfabricated platform to measure and manipulate the mechanics of engineered cardiac microtissues. Tissue Engineering Part A. 2012 May 1;18(9-10):910-9.</p> <p>[2] Xu F, Zhao R, Liu AS, Metz T, Shi Y, Bose P, Reich DH. A microfabricated magnetic actuation device for mechanical conditioning of arrays of 3D microtissues. Lab on a Chip. 2015;15(11):2496-503.</p> <p>[3] Bielawski KS, Leonard A, Bhandari S, Murry CE, Sniadecki NJ. Real-time force and frequency analysis of engineered human heart tissue derived from induced pluripotent stem cells using magnetic sensing. Tissue Engineering Part C: Methods. 2016 Oct 1;22(10):932-40.</p> <p>[4] DePalma SJ, Davidson CD, Stis AE, Helms AS, Baker BM. Microenvironmental determinants of organized iPSC-cardiomyocyte tissues on synthetic fibrous matrices. Biomaterials science. 2021;9(1):93-107.</p> <p>[5] Jayne RK, Karakan MÇ, Zhang K, Pierce N, Michas C, Bishop DJ, Chen CS, Ekinci KL, White AE. Direct laser writing for cardiac tissue engineering: a microfluidic heart on a chip with integrated transducers. Lab on a Chip. 2021;21(9):1724-37.</p> <p>[6] Karakan MÇ. A Direct-Laser-Written Heart-on-a-Chip Platform for Generation and Stimulation of Engineered Heart Tissues (Doctoral dissertation, Boston University, 2023).</p>
Data from: The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition
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4D time-lapse images of brain and trunk development in the larvacean Oikopleura dioica
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Engineered cardiac microbundle time-lapse microscopy image dataset
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Time-lapse videos and snapshot images of mycobacterial cells alone or during infection under drug treatment
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Fluorescence time-lapse images of MDA-MB-231 cells expressing FUCCI(CA)2 (Part 1/2)
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Super-resolved Reflectance Confocal Microscopy time-lapse imaging of a living MEF cell lamellipod
<p>This movies presents a time-lapse of a label-free living Mouse embryonic fibroblast cell observed with super-resolved rescanned reflectance confocal microscopy.. </p>
Time-lapse images of yeast cells with different levels of TDH3 median expression and expression noise
<p>Time-lapse images of strains YPW3064, YPW3047, YPW2868 and YPW2879. Images were acquired with a Zyla sCMOS camera (Andor) on an Olympus IX83 inverted microscope equipped with a CoolLED pE-300 illumination system in Lab513 at University Paris Diderot. These images were used to produce Figure 5 - figure supplement 1 as well as the corresponding Source Data tables in the paper: Fitness effects of altering gene expression noise in Saccharomyces cerevisiae. Fabien Duveau, Andrea Hodgins-Davis, Brian P.H. Metzger, Bing Yang, Stephen Tryban, Elizabeth A. Walker, Patricia Lybrook, Patricia J Wittkopp (doi: https://doi.org/10.1101/294603).</p>
STrack: A tool to Simply Track bacterial cells in microscopy time-lapse images
<p>The datasets consist of time-lapse images underlying the research article "STrack: A tool to Simply Track bacterial cells in microscopy time-lapse images". Please visit the STrack github page for instructions on how to install and use STrack to track cells in images containing segmented cell masks: https://github.com/Helena-todd/STrack</p> <p>The data was generated at the Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland, by Tania Miguel Trabajo. The datasets are organised in four folders, one per bacterial species (<em>Pseudomonas putida, Pseudomonas veronii, Rahnella and Lysobacter</em>), that each contain 5 time-lapse datasets. Each of the 20 folders is organised in two subfolders, containing:</p> <p>- the raw, phase contrast, timelapse images (taken with a Nikon ECLIPSE Ti Series inverted microscope coupled with a Hamamatsu C11440 22CU camera and a Nikon CFI Plan Apo Lambda 100X Oil objective)</p> <p>- the manually segmented masks (manually generated using the QuPath open-source software for bioimage analysis )</p> <p>Dowload and unzip to view the contents.</p>
"CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data" datasets
<p>Datasets from the article:</p> <p><strong>CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data </strong></p> <p>by Franck Simon, Maria Colomba Comes, Tiziana Tocci, Louise Dupuis, Vincent Cabeli, Nikita Lagrange, Arianna Mencattini, Maria Carla Parrini, Eugenio Martinelli, Hervé Isambert.</p> <p> </p> <p>The <strong>original videos</strong> are uploaded as: "20161230.zip", "20170105.rar", "Video_2017_0517.zip".</p> <p><strong>Details </strong>for each <strong>experiment </strong>can be found in: "Experiments' details.zip".</p> <p>The <strong>ROIs </strong>(ROI: Region of Interest) are uploaded as .tif files in: "EXTRACTED ROIs.zip".</p> <p>The <strong>MATLAB data</strong> is uploaded in "MATLAB_DATA.rar" and includes: the cancer cells' trajectories (subfolder: "TUMOR TRAJECTORIES"), the immune cells' trajectories (subfolder: "IMMUNE TRAJECTORIES"), the ROIs videos as .mat files for the detection of cells (subfolder: "ROI MAT"), the ROIs further cropped for the extraction of shape descriptors (folder: "ROI_TU MAT"). The ROIs videos .mat files included in the last two subfolders are stopped after their apoptosis has been detected.</p>
Ultra-fast time-lapse synchrotron radiation CT imaging of compressive failure in unidirectional glass fibre-epoxy composite
<p>This series of ultra-fast X-ray computed tomography datasets were acquired on the TOMCAT beamline at the Swiss Light Source by the composite group at Henry Moseley X-ray Imaging Facility (within the Henry Royce Institute @Manchester).</p> <p>The experiment was designed to help understand the catastrophic failure of unidirectional fibre reinforced composites under compression, as part of Ying Wang's PhD project (<em>Damage Mechanisms Associated with Kink-Band Formation in Unidirectional Fibre Composites</em>) supervised by Prof. Philip J. Withers.</p> <p>A notched unidirectional glass fibre-epoxy composite specimen was loaded in-situ under compression in a tension/compression rig developed at INSA-Lyon. An initial scan of the composite gauge section was acquired before loading (GFRP_Initial.zip). During the in-situ loading process, the composite specimen was imaged statically at 200 N (GFRP_Static_200N.zip) and 600 N (GFRP_Static_600N.zip), after which the acquisition mode was changed to continuous streaming in order to capture the final stages immediately leading up to failure, at 876 N (GFRP_Continuous_876N.zip), 893 N (GFRP_Continuous_893N.zip), 895 N (GFRP_Continuous_895N.zip), and right after collapse (at 79 N, GFRP_Failed.zip).</p> <p>The CT acquisition speed attained 1 tomogram per second. The voxel size of the reconstructed CT data-sets is (1.1 μm)<sup>3</sup>.</p> <p> </p> <p><strong>Update regarding the fibre trajectories on 20 August 2024:</strong></p> <p>The extracted fibre trajectories dataset for Fig.4 in the published paper "<em>Wang, Y., Emerson, M. J., Conradsen, K., Dahl, A. B., Dahl, V. A., Maire, E. and Withers, P. J. (2021). Evolution of Fibre Deflection Leading to Kink-band Formation in Unidirectional Glass Fibre/Epoxy Composite Under Axial Compression. Composites Science and Technology, 213, 108929. " </em>has been added to this new version.</p> <p>Please note that the centre positions of 5229 fibres (the row number of x and y coordinates corresponds to the fibre number) on the 1264 xy CT slices (the column number of x and y coordinates corresponds to the slice number) were stored in this file. The x, y, and z (slice number) coordinates all need to be multiplied by the voxel size of 1.1 μm to get the physical positions of the fibre trajectories.</p> <p> </p> <p><strong>For use of the data, please cite the DOI of the repository </strong><strong>and the relevant papers -</strong></p> <p><em><strong>http://doi.org/10.5281/zenodo.13348028</strong></em></p> <p><em>Wang, Y., Emerson, M. J., Conradsen, K., Dahl, A. B., Dahl, V. A., Maire, E. and Withers, P. J. (2021). Evolution of Fibre Deflection Leading to Kink-band Formation in Unidirectional Glass Fibre/Epoxy Composite Under Axial Compression. Composites Science and Technology, 213, 108929. </em></p> <p><em>Emerson, M. J., Wang, Y., Withers, P. J., Conradsen, K., Dahl, A. B., and Dahl, V. A. (2018). Quantifying fibre reorientation during axial compression of a composite through time-lapse X-ray imaging and individual fibre tracking. Composites Science and Technology, 168, 47-54. </em></p> <p><em>Wang, Y., Garcea, S. C., Lowe, T., Maire, E., Soutis, C. and Withers, P. J. (2016). Ultra-fast time-lapse synchrotron radiographic imaging of compressive failure in CFRP. In ECCM16-16th European Conference on Composite Materials, Munich, Germany.</em></p> <p><em>Garcea, S. C. , Wang, Y. and Withers, P. J. (2018). X-ray computed tomography of polymer composites, Composites Science and Technology (156), 305-319.</em></p> <p><em>Wang, Y., Garcea, S. C. and Withers, P. J. (2018). Computed Tomography of Composites in Comprehensive Composite<br>Materials II (7), 101-118. Eds. Beaumont PWR, Zweben CH. Elsevier.</em></p> <p> </p> <p><strong>Contact details of the authors: </strong></p> <p>Ying Wang - ywang1@buaa.edu.cn</p> <p>Philip J. Withers - p.j.withers@manchester.ac.uk</p>
Time-lapse images of Arabidopsis thaliana photoreceptor mutants under darkness and blue-light conditions
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Fluorescence time-lapse images of MDA-MB-231 cells expressing FUCCI(CA)2 (Part 2/2)
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Long-term time-lapse live imaging reveals extensive cell migration during annelid regeneration
<p>Supporting Materials for the article:<br /> Long-term time-lapse live imaging reveals extensive cell migration during annelid regeneration</p> <p>Eduardo E. Zattara, Kate W. Turlington and Alexandra E. Bely</p> <p>BMC Developmental Biology, 2016</p> <p>Includes 11 movies and one compressed file with R code and data tables.</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.