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2,214 results for “Walls”
Microbial narrow-escape is facilitated by wall interactions: Simulation Supplementary material
<p>Simulation codes and simulation results for the paper "Microbial narrow-escape is facilitated by wall interactions".</p>
Liquid Flow and Control Without Solid Walls
<p>This repository contains additional data related to the publication: 10.26434/chemrxiv.7207001</p> <p>Contained in python_magneto_fluidics.zip are all the files needed to calculate magnetic fields of any assembly of cuboid permanent magnets such as used in this paper, along with the equilibrium diameters for each antitube-ferrofluid combination.</p> <p>data figures.zip contains all the experimental data plotted in the figures, consisting of data in figures:</p> <p>Main Text: 2, 3, 4<br> Extended Data: E2, E3, E4, E6, E8</p>
LMD Inconel 718 Thin walls 2019-12-05
<p>Description of dataset 10.5281/zenodo.3981107</p> <p>Deposition of Inconel 718 thin walls with process parameters:<br> - Nominal power = 200 (W)<br> - Nominal velocity = 300 (mm/min)<br> - Powder flux = 0.0825 (g/s)<br> - Nr. layers per wall: 1, 2, 4, 8, 16<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient<br> - Nr. repetitions: 2 (of whole experiment)</p> <p>The dataset is constituted by:<br> - Melt pool images, in file Experiment_2019_12_5__15_58_3.zip (subfolder Deposition_2019_12_05__16_00_17 for rep1, subfolder Deposition_2019_12_05__16_14_23 for rep2), acquired at 200fps with 850 nm narrow band filter, 1ms exposure time. 400x400 px size<br> - trackData_20191205_rep1.csv (or [...]_rep2.csv) containing:<br> - t: timestamp in ms. Synchronized imageData_20191008.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imageData_20191205_rep1.csv (or [...]_rep2.csv) containing:<br> - t: timestamp in ms. Synchronized with trackData_20191205_rep1.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)</p>
Dataset to "Hygrothermal performance of an internally insulated masonry wall: experimentations without vapour barrier in a historic Italian Palazzo"
<p>This record contains pre-processed data of a ten-and-a-half-month monitoring period of the HeLLo project.</p> <p>The datafiles titled MeasProcessed_YYYY-MM-DD.dat correspond to the prepared data into a form for data analysis and processing as presented in “Hygrothermal performance of an internally insulated masonry wall: experimentations without vapour barrier in a historic Italian Palazzo”, accepted for publication in journal energy and buildings (<a href="https://doi.org/10.1016/j.enbuild.2022.111896">https://doi.org/10.1016/j.enbuild.2022.111896</a>).</p> <p>Each file, format MeasProcessed _YYYY-MM-DD.dat, corresponds to the daily registered data monitored every minute.</p> <p>Each file, format MeasProcessed_YYYY-MM-DD.dat, contains temperature (T) and relative humidity (RH) values, monitored through T-RH sensors (Telaire T9602; Amphenol). The general architecture of the acquisition system is based on a Master Slave configuration, as described in “Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls” (doi:10.3390/electronics8060643).</p> <p>Each file, format MeasProcessed_YYYY-MM-DD.dat is a text-based DAT file and can be opened with a standard text editor.</p>
Mechanical characterisation of the developing cell wall layers of tension wood fibres by Atomic Force Microscopy
<p>This dataset corresponds to the Arnould et al. (2022) paper (available at https://www.biorxiv.org/content/10.1101/2021.09.23.461481v1.full) on the mechanical characterization of developing cell wall layers of tension wood fibers by Atomic Force Microscopy. It contains all raw AFM files (Bruker format .spm, readable by the free software Gwyddion for example) corresponding to mechanical measurements of poplar reaction wood cells (clone 717-1B4) along 3 radial lines/rows, starting from the cambium. Each cell is identified by its "macroscopic" distance from the cambium (value in µm in the name of each file corresponding to the displacement of the sample in the AFM) which was corrected after using the AFM optical image captures. Some files, with a -z extension after the distance value, correspond to a zoom into the cell wall. The data also contain measurements made for mechanical calibration on epoxy embedded Kevlar fibers, controlled measurements in the embedding resin between each radial line and measurements in normal wood cells. Two csv files containing final data extracted from AFM measurements that give the value of the indentation modulus and the relative thickness to cell diameter ratio (by AFM and by phase contrast optical microscopy) in each cell wall layer as a function of cambium distance are also provided.</p>
Low overtopping discharges over sea walls and breakwaters
<p>The design of seawalls and breakwaters is often required to achieve very low target overtopping discharges when these structures protect vulnerable infrastructure or activities. The balance between economically viable protection and performance requirements is often difficult to achieve without good knowledge on low overtopping. The paucity of data in this space and the higher uncertainty associated with existing methods, increases the challenge. The occurrence of low number of overtopping waves has the consequence that any test results are substantially more affected by the inherent variation of random waves, therefore more uncertain. Within the multi-institute project RECIPE under the HYDRALAB+ project, experimental studies for RECIPE Task 8.2 have generated new data on the response of seawalls, breakwaters and related coastal structures with the aim of improving future model testing. Tests by HRW have explored wave overtopping with contributions of data from UPORTO and LNEC. These physical model test results explore these issues and provide example data. The tests were successful in obtaining low to very low overtopping discharge test data. For low / very low overtopping discharges, these test data present considerable scatter relative to the latest empirical prediction. A number of repetitions were performed for wave conditions resulting in very low overtopping discharges, which illustrated the inherent uncertainty associated with low overtopping.</p>
3D model of antenna system embedded into building envelope for improved cellular signal transmission through load-bearing walls
<p>The purpose of this dataset is to supplement the data presented in our journal publication "Electromagnetic–Thermal Analyses of Distributed Antennas Embedded Into a Load-Bearing Wall" (see <a href="https://ieeexplore.ieee.org/document/10151683">https://ieeexplore.ieee.org/document/10151683</a>).</p> <p>This dataset contains the 3-D discretized model, without the internal numerical mesh, of the unit cell of the spiral antenna system embedded in a load bearing wall. The 3D model is in .STP format (see ISO 10303-21:2016), which can be imported into most commercial computer-aided design (CAD) software. The wall's dielectric properties are calculated using the model described in ITU-R P.2040-2 (<a href="https://www.itu.int/rec/R-REC-P.2040/en">https://www.itu.int/rec/R-REC-P.2040/en</a>, material parameter and calculation model are on pages 22-23). Materials used in the antenna system and their electrical and thermal parameters are given in the file materials.txt</p>
RIBuild: Hygrothermal performance of hydrophobized masonry walls (KUL Vliet test building)
<p>Measurement data from a field study on a test building at KU Leuven, studying the hygrothermal performance of hydrophobised walls, provided with vapor tight or capillary active internal insulation. As a reference, also non-hydrophobized and non-insulated walls are analysed. The dataset also includes photo documentation of construction and installation of measurement sensors.</p> <p>Further details to be found in RIBuild deliverable D2.3.</p> <p>Overview of data files to be found in 'RIBuild data_WP2 KUL Vliet' as part of this dataset.</p>
Old Mandu (बूढ़ी मांडू), Dhār district, Madhya Pradesh. General view of the fort wall.
<p>Old Mandu (बूढ़ी मांडू), Dhār district, Madhya Pradesh. General view of the north line of the fort wall. Photo 2/2010.</p>
Udayagiri, Madhya Pradesh. View of passage wall.
<p>Udayagiri, Madhya Pradesh. View of passage wall showing steps, shell inscriptions and niches for beams that originally spanned the passage.</p>
Udayagiri, Madhya Pradesh. South wall of the central passage.
<p>Udayagiri, Madhya Pradesh. South wall of the central passage, showing shell inscriptions, adaptations for structural features and water management structures and channels. </p>
EuroPeg_PetroDB: A petrophysical database of European pegmatite ores and wall rocks.
<p>A petrophysical database of European pegmatite ores and wall rocks. Samples of the first three versions cover LCT- and NYF-type pegmatites, their wall rocks, and country rocks from pegmatite locations in Austria, Ireland, Norway, Portugal, and Spain. The database contains petrophysical properties derived from rock samples and from geophysical borehole logging. The petrophysical sample analysis was carried out at the laboratory of the Geological Survey of Norway (NGU). Borehole logging was carried out by terratec Geophysical Services. A detailed description of the database, its content, the measuring instruments, and uncertainties is found in the <a href="https://aps.ngu.no/pls/oradb/rf.Visdok?c_dokid=0000067784">NGU Report 2022.017</a> and in <a href="https://www.mdpi.com/2075-163X/12/12/1498">Haase & Pohl (2022)</a>. Please consult the README provided below.</p> <p>The database was compiled as part of the GREENPEG project: New Exploration Tools for European Pegmatite Green-Tech Resources. The project was funded by European Commission’s Horizon 2020 innovation programme under grant agreement No 869274 and ended in October 2024. The database is planned to be updated in the future if relevant data is provided.</p> <p>For more information on the project, please visit the project website: <a href="https://www.greenpeg.eu/">https://www.greenpeg.eu/</a></p>
3DO Dataset | On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios
<p><strong>On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios</strong></p> <p>This repository contains the <strong>3DO dataset</strong> proposed in <a href="https://doi.org/10.1007/978-3-031-78354-8_13">[1]</a>.</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the 3DO dataset is provided at: <a href="https://github.com/StrohmayerJ/3DO/tree/main">https://github.com/StrohmayerJ/3DO</a></p> <p><strong>Dataset Description</strong></p> <p>The 3DO dataset comprises 42 five-minute recordings (~1.25M WiFi packets) of three human activities performed by a single person, captured in a WiFi through-wall sensing scenario over three consecutive days. Each WiFi packet is annotated with a 3D trajectory label and a class label for the activities: no person/background (0), walking (1), sitting (2), and lying (3). (<strong>Note:</strong> The labels returned in our dataloader example are walking (0), sitting (1), and lying (2), because background sequences are not used.)</p> <p>The directories <code>3DO/d1/</code>, <code>3DO/d2/</code>, and <code>3DO/d3/</code> contain the sequences from days 1, 2, and 3, respectively. Furthermore, each sequence directory (e.g., <code>3DO/d1/w1/</code>) contains a <code>csiposreg.csv</code> file storing the raw WiFi packet time series and a <code>csiposreg_complex.npy</code> cache file, which stores the complex Channel State Information (CSI) of the WiFi packet time series. (If missing, <code>csiposreg_complex.npy</code> is automatically generated by the provided dataloader.)</p> <p>Dataset Structure:</p> <p>/3DO</p> <p>├── d1 <em><-- day 1 subdirectory</em></p> <p> └── w1 <em><-- sequence subdirectory</em></p> <p> └── csiposreg.csv <em><-- raw WiFi packet time series</em></p> <p> └── csiposreg_complex.npy <em><-- CSI time series cache</em></p> <p>├── d2 <-- day 2 subdirectory</p> <p>├── d3 <-- day 3 subdirectory</p> <p> </p> <p>In [1], we use the following training, validation, and test split:</p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Day</strong></td> <td><strong>Sequences </strong></td> </tr> <tr> <td>Train</td> <td>1</td> <td>w1, w2, w3, s1, s2, s3, l1, l2, l3</td> </tr> <tr> <td>Val</td> <td>1</td> <td>w4, s4, l4</td> </tr> <tr> <td>Test</td> <td>1</td> <td>w5 , s5, l5</td> </tr> <tr> <td>Test</td> <td>2</td> <td>w1, w2, w3, w4, w5, s1, s2, s3, s4, s5, l1, l2, l3, l4, l5</td> </tr> <tr> <td>Test</td> <td>3</td> <td>w1, w2, w4, w5, s1, s2, s3, s4, s5, l1, l2, l4</td> </tr> </tbody> </table> <p><em>w = walking, s = sitting and l= lying</em></p> <p><strong>Note: </strong>On each day, we additionally recorded three ten-minute background sequences (b1, b2, b3), which are provided as well.</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a>.</p> <p><a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a> Strohmayer, J., Kampel, M. (2025). On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios. In: Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15315. Springer, Cham. <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-78354-8_13</a></p> <p>BibTeX citation:</p> <pre>@inproceedings{strohmayerOn2025, author="Strohmayer, Julian and Kampel, Martin",<br> title="On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios",<br> booktitle="Pattern Recognition",<br> year="2025",<br> publisher="Springer Nature Switzerland",<br> address="Cham",<br> pages="194--211",<br> isbn="978-3-031-78354-8" }</pre>
Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project
<p>This file contains the ADV data of <em>Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project</em>.</p>
Data from: Reproducibility of the Quantification of Reversible Wall Interactions in VOC Sampling Lines
<p>Dataset from the following publication (<a href="https://doi.org/10.3390/atmos12020280">https://doi.org/10.3390/atmos12020280</a>). In the paper, a method to quantify the amount of substance segregated by reversible interactions on sampling lines is proposed. The areic amount of a VOC (Acetone) interacting with the pipe is measured for a commercial test pipe (Sulfinert®) as the amount of substance per unit area of the internal surface of the test pipe segregated from the flowing gas mixture. The areic amount is function of numerical integrals estimated under different conditions and reproducibility is evaluated. The data used to estimate the integrals described in this work is organised in folders. Each folder correspond to a sample. Sample information is available on Table 3 of the paper.</p>
Dataset for configurational entropy of a finite number of dumbbells close to a wall
<p><strong>Introduction:</strong> Dataset from numerical simulations to quantify the reduction in configurational entropy of dumbbells due to the presence of a wall, as developed and described in detail in the paper</p> <ul> <li>Markus Hütter: Configurational entropy of a finite number of dumbbells close to a wall. Eur. Phys. J. E, 45(1): 6 (19 pages), 2022. DOI: 10.1140/epje/s10189-022-00160-y WWW: https://doi.org/10.1140/epje/s10189-022-00160-y</li> </ul> <p>which should be cited whenever this dataset is used. The data compiled here is the basis for figures 5, 6, and 9 in that paper.</p> <p><strong>Format</strong>: The files are provided in plain-text format (ascii).</p> <p><strong>Filenames</strong>: The nomenclature for the filenames follows the following scheme:</p> <ul> <li>data-normal-Lone{L1}-Ltwo{L2}-N{N}-{method}-{timestamp}.txt</li> </ul> <p>where (see the original paper for details) {L1} and {L2} specify the confining slab, {N} denotes the number of dumbbells, and {method} is either "SPLIT" (for the data presented in figures 5 and 6) or "WangLandau-MERGED" (for the data presented in figure 9).</p> <p><strong>File content</strong>: Each datafile contains 8 headerlines, in which the values of L1, L2, and N are repeated, and furthermore the following quantities are specified: nsteps is the total number of steps for the random sampling; clow = (4*L1^2)/N; cupp = 4*L2^2.</p> <p>After these headerlines, the data is presented in tab-delimited columns, as<br> follows for the "SPLIT"-files:</p> <ul> <li>column 1: conformation value (mid-bin position)</li> <li>column 2: number of successful placings in that bin (i.e., compatible with wall confinement)</li> <li>column 3: number of attempted placings in that bin</li> <li>column 4: (not used)</li> </ul> <p>(where the ratio of column 2 to column 3 gives the partition coefficient), whereas for the "WangLandau-MERGED"-files the columns represent the following:</p> <ul> <li>column 1: conformation value (mid-bin position)</li> <li>column 2: natural logarithm of the partition coefficient</li> </ul> <p>Details are explained in the paper mentioned above.</p>
Temnothorax rugatulus ants do not change their nest walls in response to environmental humidity
<p><strong>Overview</strong></p> <p>Data used for manuscript: <em>Temnothorax rugatulus</em> ants do not change their nest walls in response to environmental humidity</p> <p> </p> <p><strong>Structure of the data</strong></p> <p>SupplementalHygrometerDatabase.csv</p> <p>Raw hygrometer data that is used to calculate the average environmental humidity and temperature for each Trial:Salt combination</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>TrialNumber: Sequential trial number (1-4) that is NOT unique for each colony - see "Trial"</li> <li>Salt: Saturated salt solution used</li> <li>"Date Time, GMT-07:00": Date and time of each observation</li> <li>Temp: Temperature in celcius</li> <li>RH: Relative humidity (%)</li> <li>Trial: The trial number for each individual colony, each colony underwent two trials</li> </ul> <p>HumidityExperimentalDatabase.csv</p> <p>Raw experimental data with nest features and colony size</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Trial: The trial number for each individual colony, each colony underwent two trials</li> <li>TrialNumber: Sequential trial number (1-4) that is NOT unique for each colony - see "Trial"</li> <li>Day: The day in the experimental timeline (always 10, but days 1 and 5 were captured and not considered)</li> <li>Area: Area of the built nest wall (mm<sup>2</sup>)</li> <li>Length: Length of the built nest wall (mm)</li> <li>Nest.Area: Area of the internal nest space (mm<sup>2</sup>)</li> <li>HumLevel: Whether the colony started with a higher or lower relative humidity (High/Low)</li> <li>Number.Ant: The number of workers in the colony</li> <li>Number.Brood: The number of brood in the colony</li> <li>Number.Queens: The number of brood in the colony</li> <li>Salt: Saturated salt solution used</li> <li>SubstrateISide: Substrate I placement in the container from the perspective of looking out from the nest entrance</li> <li>StartWtI: The initial weight (g) of the available substrate I building nest wall material</li> <li>UsedWtI: The weight (g) of the available substrate I building nest wall material following the experimental building phase</li> <li>StartWtII: The initial weight (g) of the available substrate II building nest wall material</li> <li>UsedWtII: The weight (g) of the available substrate II building nest wall material following the experimental building phase</li> <li>CollWallWt: The weight (g) of the experimental nest wall that each colony built</li> </ul> <p>HumidMortalityRaw.csv</p> <p>Raw experimental data with proportion of workers and brood dead after each colony underwent Trial 1</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>WorkerDeath: Proportion of workers that died</li> <li>BroodDeath: Proportion of brood that died</li> <li>TrialNumber: Sequential trial number (1-4) that is NOT unique for each colony</li> </ul> <p>PorosityComparisonRaw.csv</p> <p>Data used for comparing the porosities of each experimental substrate, experimentally built walls, and collected <em>Temnothorax rugatulus</em> walls</p> <p>Porosity is the percentage of void space in compact substrate - PoreVolume/TotalVolume</p> <ul> <li>SubstrateID: A unique identifier for each substrate replicate</li> <li>Trial: The trial number for each individual colony, each colony underwent two trials (only applicable for experimentally built walls)</li> <li>SubCategory: The type of substrate (Sub I, Sub II, Built, Natural)</li> <li>TotalVolume: The combined pore (void space in compact substrate grains) and soil volume (ml) of a substrate</li> <li>PoreVolume: The void space in between compact substrate grains (ml)</li> </ul>
Wall (w1463)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Wall<br><u>musiXplora-ID</u>: w1463<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/w1463">https://musixplora.de/mxp/w1463</a><br><u>Gender</u>: m<br><u>Confessions</u>: römisch-katholisch<br><u>First Mentioned</u>: 1828<br><u>Sectors</u>: Instrumentenbau, Kirche<br><u>Professions (Musical)</u>: Orgelbauer<br><u>Main Place of Activity</u>: Bernried/Starnberger See<br><u>Other Places of Activity</u>: Seeshaupt<br><br><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Bernhard 2003a</td><td>Orgeldatenbank Bayern</td><td><a href="https://musixplora.de/mxp/5001131">5001131</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Herbarium specimen image of Campanula aristata Wall., part of the collection of Botanic Garden and Botanical Museum Berlin
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Herbarium specimen image of Fagraea volubilis Wall., part of the collection of Royal Botanic Garden Edinburgh
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
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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.