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135 results for “bricks”
Brick Kiln Dataset for Pakistan's IGP Region Using AI
<p>This dataset represents the first geospatial mapping of brick kiln sites in the IGP region of Pakistan, providing an invaluable resource for understanding the spatial distribution of these sites. Each data point captures a brick kiln's precise location, including coordinates, state, and other important information, standardized in Coordinate Reference System (CRS) EPSG:4326 (WGS 84). This dataset, to the best of our knowledge, is the first of its kind to consolidate and geolocate brick kiln operations across this region, where air pollution impacts from kiln emissions are a significant environmental and public health concern.</p> <p>In addition to the primary geolocation data, the dataset also includes an initial, secondary estimation of emissions (PM10, PM2.5, NOx, and SOx) from these sites. This supplementary information supports preliminary risk assessments, emphasizing proximity-based exposure for populations and sensitive areas (e.g., schools, hospitals) within a 1 km radius of each kiln site. </p> <p>The dataset is made available in multiple formats to facilitate wide usage across spatial analysis platforms:</p> <ul> <li><strong>Geojson (.geojson)</strong></li> <li><strong>Shapefile (.shp)</strong></li> <li><strong>Comma-Separated Values (.csv)</strong></li> </ul> <p><strong>Metadata:</strong></p> <ul> <li>Geographic Coverage: IGP - Pakistan</li> <li>CRS: EPSG:4326 (WGS 84)</li> </ul> <p><strong>Data Structure:</strong><br><br>"Main" files have <strong><em>Basic ID & Location </em></strong>information, while the "Emission" files have <strong><em>Basic ID & Location + Production & Emission </em></strong>information. </p> <p><strong>Basic ID & Location</strong></p> <ul> <li><code>id</code>: Unique identifier for the kiln.</li> <li><code>lat</code>: Latitude of the kiln location.</li> <li><code>lon</code>: Longitude of the kiln location.</li> <li><code>state</code>: State where the kiln is located.</li> <li><code>type</code>: Type of kiln (e.g., FCBK).</li> </ul> <p><strong>Production Data</strong></p> <ul> <li><code>avg_bricks</code>: Average number of bricks produced daily. Refer to the GitHub repository for detailed methodology.</li> <li><code>seasonprod(bricks)</code>: Seasonal production of bricks in kilograms (excluding monsoon and smog days, considering 215 operational days).</li> </ul> <p><strong>Daily Emissions</strong></p> <ul> <li><code>pm2.5d(kg)</code>: Daily PM2.5 emissions in kilograms.</li> <li><code>pm10d(kg)</code>: Daily PM10 emissions in kilograms.</li> <li><code>noxd(kg)</code>: Daily NOx emissions in kilograms.</li> <li><code>soxd(kg)</code>: Daily SOx emissions in kilograms.</li> </ul> <p><strong>Seasonal Emissions</strong></p> <ul> <li><code>pm2.5s(kg)</code>: Seasonal PM2.5 emissions in kilograms.</li> <li><code>pm10s(kg)</code>: Seasonal PM10 emissions in kilograms.</li> <li><code>noxs(kg)</code>: Seasonal NOx emissions in kilograms.</li> <li><code>soxs(kg)</code>: Seasonal SOx emissions in kilograms.</li> </ul> <p><strong>Emission Factors</strong></p> <ul> <li><code>emf_coal(pm2.5)</code>: Emission factor for PM2.5 using coal as fuel (kg).</li> <li><code>emf_coal(pm10)</code>: Emission factor for PM10 using coal as fuel (kg).</li> <li><code>emf_coal(nox)</code>: Emission factor for NOx using coal as fuel (kg).</li> <li><code>emf_coal(so2)</code>: Emission factor for SOx using coal as fuel (kg).</li> <li><code>emf_coal(pm2.5)</code>: Emission factor for PM2.5 using biomass as fuel (kg).</li> <li><code>emf_coal(pm10)</code>: Emission factor for PM10 using biomass as fuel (kg).</li> <li><code>emf_coal(nox)</code>: Emission factor for NOx using biomass as fuel (kg).</li> <li><code>emf_coal(so2)</code>: Emission factor for SOx using biomass as fuel (kg).</li> </ul> <p><strong>Seasonal Emissions by Fuel Type</strong></p> <ul> <li><code>pm2.5s_c(kg)</code>: Seasonal PM2.5 emissions in kilograms using coal as a fuel.</li> <li><code>pm10s_c(kg)</code>: Seasonal PM10 emissions in kilograms using coal as a fuel.</li> <li><code>noxs_c(kg)</code>: Seasonal NOx emissions in kilograms using coal as a fuel.</li> <li><code>so2s_c(kg)</code>: Seasonal SO2 emissions in kilograms using coal as a fuel.</li> <li><code>pm2.5s_b(kg)</code>: Seasonal PM2.5 emissions in kilograms using biomass as a fuel.</li> <li><code>pm10s_b(kg)</code>: Seasonal PM10 emissions in kilograms using biomass as a fuel.</li> <li><code>noxs_b(kg)</code>: Seasonal NOx emissions in kilograms using biomass as a fuel.</li> <li><code>so2s_b(kg)</code>: Seasonal SO2 emissions in kilograms using biomass as a fuel.</li> </ul> <p><strong>Funding Sources</strong>: This research and data collection were funded by Amazon Web Services (AWS) and Smith School of Enterprise and The Environment (University of Oxford). </p>
GRTSmh_brick: the raster data source GRTSmaster_habitats converted to 10 hierarchical cell address levels at the original resolution
<p>The data source file is a 10-layered GeoTIFF file, derived from the raster data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Both GeoTIFFs (<code>GRTSmaster_habitats</code>, <code>GRTSmh_brick</code>) use the <code>INT4S</code> datatype. The <code>GRTSmh_brick</code> data source (resolution 32 m) holds the decimal integer ranking numbers of 10 hierarchical levels of the GRTS cell addresses, including the one from <code>GRTSmaster_habitats</code> (with GRTS cell addresses at the resolution level).</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_20_GRTSmh_brick">'n2khab-preprocessing' at commit ecadaf5</a> for its creation from the <code>GRTSmaster_habitats</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>The higher-level ranking numbers of the RasterBrick allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes. The provided hierarchical levels correspond to the resolutions vector <code>32 * 2^(0:9)</code> (minimum: 32 meters, maximum: 16384 meters).</p> <p>Beware that not all GRTS ranking numbers are present in the data source, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>
Brick Kiln Dataset for Pakistan's IGP Region Using AI
<p>This dataset represents the first geospatial mapping of brick kiln sites in the IGP region of Pakistan, providing an invaluable resource for understanding the spatial distribution of these sites. Each data point captures a brick kiln's precise location, including coordinates, state, and other important information, standardized in Coordinate Reference System (CRS) EPSG:4326 (WGS 84). This dataset, to the best of our knowledge, is the first of its kind to consolidate and geolocate brick kiln operations across this region, where air pollution impacts from kiln emissions are a significant environmental and public health concern.</p> <p>In addition to the primary geolocation data, the dataset also includes an initial, secondary estimation of emissions (PM10, PM2.5, NOx, and SOx) from these sites. This supplementary information supports preliminary risk assessments, emphasizing proximity-based exposure for populations and sensitive areas (e.g., schools, hospitals) within a 1 km radius of each kiln site. </p> <p>The dataset is made available in multiple formats to facilitate wide usage across spatial analysis platforms:</p> <ul> <li><strong>Geojson</strong></li> <li><strong>Shapefile (SHP)</strong></li> <li><strong>Comma-Separated Values (CSV)</strong></li> </ul> <p><strong>Metadata:</strong></p> <ul> <li>Geographic Coverage: IGP - Pakistan</li> <li>CRS: EPSG:4326 (WGS 84)</li> <li>File Formats: GeoJSON, Shapefile (SHP), CSV<br>- Columns: <p>1. id: Unique identifier for each brick kiln site.</p> <p>2. lat: Latitude coordinate of the brick kiln location, in decimal degrees (CRS: EPSG:4326).</p> <p>3. lon: Longitude coordinate of the brick kiln location, in decimal degrees (CRS: EPSG:4326).</p> <p>4. type: Type or classification of the brick kiln (Either FCBK or ZigZag)</p> <p>5. state: Administrative state or region where the brick kiln is located.</p> <p>6. schools1km: Number of schools located within a 1 km radius of the brick kiln, indicating nearby educational facilities potentially exposed to emissions.</p> <p>7. hosp1km: Number of hospitals within a 1 km radius of the brick kiln, indicating health facilities potentially affected by air pollution exposure.</p> <p>8. pop1km: Estimated population within a 1 km radius of the brick kiln, indicating the number of individuals potentially at risk from emissions.</p> <p>9. avg_bricks: Average number of kilns in operation per day.</p> <p>10. dailyprod(kg): Estimated daily production weight of bricks in kilograms. The production is calculated based on the brick weight multiplied by a factor of 3.</p> <p>11. pm2.5d(kg): Daily emissions estimate of particulate matter (PM2.5) in kilograms, representing fine particles with a diameter of 2.5 micrometers or smaller, which are a health hazard.</p> <p>12. pm10d(kg): Daily emissions estimate of particulate matter (PM10) in kilograms, representing inhalable particles with a diameter of 10 micrometers or smaller.</p> <p>13. noxd(kg): Daily emissions estimate of nitrogen oxides (NOx) in kilograms, a pollutant contributing to respiratory issues and environmental harm.</p> <p>14. soxd(kg): Daily emissions estimate of sulfur oxides (SOx) in kilograms, a pollutant associated with respiratory problems and acid rain.</p> <p>15. seasonprod(kg): Seasonal production estimate in kilograms, representing total brick production over a defined seasonal period (Outside of Monsoon and Smog Season). </p> <p>16. pm2.5s(kg): Seasonal emissions estimate of particulate matter (PM2.5) in kilograms over the specified seasonal period.</p> <p>17. pm10s(kg): Seasonal emissions estimate of particulate matter (PM10) in kilograms over the specified seasonal period.</p> <p>18. noxs(kg): Seasonal emissions estimate of nitrogen oxides (NOx) in kilograms over the specified seasonal period.</p> <p>19. soxs(kg): Seasonal emissions estimate of sulfur oxides (SOx) in kilograms over the specified seasonal period.</p> <p> </p> </li> </ul> <p><strong>Funding Sources</strong>: This research and data collection were funded by Amazon Web Services (AWS) and Smith School of Enterprise and The Environment (University of Oxford). </p>
Supplementary material to article "Hygrothermal performance of a brick wall with interior insulation in cold climate: vapour open vs vapour tight approach"
<p>Supplementary material to article "Hygrothermal performance of a brick wall with interior insulation in cold climate: vapour open vs vapour tight approach"</p>
Tan-Tári Bricks graphical abstract in iGEM Design League 2022
<p>Graphical abstract of Tan-Tári Bricks Team from the University of Chile participating in iGEM Design League 2022.</p>
Dataset for Crack Detection in Images of Bricks and Masonry Using CNNs
<p><strong>Dataset for training CNN built from aerial drone images of buildings in Hamburg</strong></p> <p>This dataset contains images extracted from aerial surveillance photos of the <a href="https://www.bing.com/ck/a?!&&p=b45d6a7b67c7b3c6JmltdHM9MTY1ODMzOTc0MCZpZ3VpZD04NTU2MjdiYS1kYjljLTQyOTMtOTFlOC0xYmM0NmE1ZWViOGMmaW5zaWQ9NTIyMQ&ptn=3&hsh=3&fclid=2a810abc-0855-11ed-8af8-d88619cb2403&u=a1aHR0cHM6Ly9kZS53aWtpcGVkaWEub3JnL3dpa2kvU3BlaWNoZXJzdGFkdA&ntb=1">Speicherstadt</a> and <a href="https://www.bing.com/ck/a?!&&p=750d82f6afc564d9JmltdHM9MTY1ODMzOTg0OSZpZ3VpZD1mOWMwYzE5OC01NjU3LTQ1NzMtOGE0YS1mNzYxN2VlOTlmMmEmaW5zaWQ9NTIxNA&ptn=3&hsh=3&fclid=6b854f27-0855-11ed-9794-60e2f5efc96c&u=a1aHR0cHM6Ly93d3cuaGFmZW5jaXR5LmNvbS9pbmZvY2VudGVyL2tlc3NlbGhhdXM&ntb=1">Kesselhaus</a> buildings in Hamburg, provided by the City of Hamburg. Original 834 high resolution images (5472 x 3648 pixels) have been separated into smaller images (227 x 227 pixels) of the size that could be processed using SqueezeNet, a deep Convolutional Neural Network (CNN). This resulted in more than 350 thousand images that had to be subsequently processed automatically to retain images containing solely bricks and mortar and concrete. The final stage contained tedious manual/visual verification of images and their separation into positive (containing cracks) and negative (clear bricks and mortars) sets of images. The final set contains nearly 40 thousand images.</p> <p>Since images extracted from Hamburg buildings contained only specific type of bricks and our intention was to extend the CNN to be able to deal with wider range of brick types as well as concrete surfaces, we added to our training set also images from the following Open Access databases (note that such images required resizing to 227 x 227 pixel size before use):</p> <ul> <li><a href="https://data.mendeley.com/datasets/5y9wdsg2zt/1">Concrete Crack Images for Classification (Mendeley Data)</a></li> <li><a href="https://zenodo.org/record/5108846#.YthGSLbP0bB">Dataset for Crack Detection in Images of Masonry Using CNNs</a></li> </ul> <p>Such a combined data set resulted in over 80 thousand of images.</p> <p><strong>Matlab WebApp Server application based on trained SqueezeNet CNN </strong></p> <p>The integrated database of images has been used to train the SqueezeNet CNN using a method proposed by <a href="https://www.linkedin.com/in/kenta-itakura-b88129202/">Kenta Itakura</a> in his article published on Matlab Central: <a href="https://www.mathworks.com/matlabcentral/fileexchange/75418-classify-crack-image-using-deep-learning-and-explain-why?s_tid=srchtitle">Classify crack image using deep learning and explain "WHY"</a>, which in turn is based on the work of <a href="https://ieeexplore.ieee.org/author/37280177000">Lei Zhang</a> reported in his IEEE article: <a href="https://ieeexplore.ieee.org/abstract/document/7533052">Road crack detection using deep convolutional neural network</a> published at <a href="https://ieeexplore.ieee.org/xpl/conhome/7527113/proceeding">2016 IEEE International Conference on Image Processing (ICIP)</a>.</p> <p>The "Matlab" subfolder contains the complete software to allow building the application to run under Matlab WebApps Server. The provided version of the "<em>netTransfer.mat</em>" file has been compiled for Matlab revision 2020b, but it should also work when compiled for other revisions from 2019a onwards. BTW, the original location of the files was "D:\Cracks (2-class)\". For instructions how to use the provided Matlab files, refer to Matlab instructions at <a href="https://www.mathworks.com/products/matlab-web-app-server.html">MATLAB Web App Server</a> and <a href="https://www.mathworks.com/help/webappserver/getting-started-with-matlab-web-app-server.html?s_tid=CRUX_lftnav">Get Started with MATLAB Web App Server</a>.</p> <p>After producing and uploading the application to the Matlab WebApps Server, the application can be found at http://localhost:9988/webapps/home/ if deployed locally. It can be also deployed on a WEB server, subject to installation of the compliant Matlab Runtime package on the custom server, whcih can be found at <a href="https://www.mathworks.com/products/compiler/matlab-runtime.html">MATLAB Runtimes (mathworks.com)</a>.</p> <p>The important function included in the package is "unscramble.m", which <strong>corrects the error that exists in all known revisions of Matlab</strong> in uploading images selected by open file function in the Matlab App Designer. The effect is that image is "scrambled beyond recognition" after uploading to the Matlab WebApps Server. Our function de-scrambles such images, converting them into their original form.</p>
Dataset for "Correlation between sonic pulse velocity and flat-jack tests for the estimation of the elastic properties of unreinforced brick masonry"
<p>This repository contains data from the sonic test experimental campaign carried out in eight structures at various location in Croatia.</p> <p>The data set is structured in 3 levels of folders:</p> <p>- At first level, the 8 folders correspond to the 8 tested buildings.</p> <p>- At second level, for each building, each folder corresponds to a different test location within the building, e.g. "Data FJ1".</p> <p>- At third level, for each location, each folder corresponds to a different setup (i.e. distance and location of hammer and accelerometer), , e.g. "FJ1 1-2".</p> <p>Sonic data are presented in .txt files in three columns corresponding to the time, the hammer (emitter) and the accelerometer (receptor), respectively.</p> <p>Please cite the following related publication:</p> <p>Ortega J, Stepinac M, Lulic L, Nuñez Garcia M, Saloustros S, Aranha C, Greco F, Correlation between sonic pulse velocity and flat-jack tests for the estimation of the elastic properties of unreinforced brick masonry, under review (2022)</p>
A comparative study of red brick powder and lime as soft soil stabilizer (Dataset)
<p>This data is the result of laboratory CBR testing in soaked and unsoaked conditions with or without additional stabilization</p>
Tan-Tári Bricks Presentation Video in iGEM Design League 2022
<p>Tan Tári Bricks is this year's iDL Chilean Team. We propose a platform based on synthetic and computational biology to evaluate the risk of Antarctic beta-lactamases for developing inhibitory proteins.<br> By focusing on three key areas: a new scientific approach, education for the Chilean people, and taking political action to tackle this problem, we aim to develop an integrated solution in the Chilean territory for the Antimicrobial Resistance problem.<br> </p>
Experimental data: Single- and double-wythe brick masonry walls subjected to four-point bending tests under different support conditions: Simply supported, rigid, non-rigid
<p>This dataset contains the results of laboratory quasi-static monotonic four-point bending tests conducted at RISE Research Institutes of Sweden on eleven natural-scale unreinforced brick masonry walls. The walls were spanning vertically between two reinforced concrete slabs and were tested under three different support conditions defined according to the American manual UFC 3-340-02: simply supported, rigid, non-rigid. The influence of these support conditions on the out-of-plane behavior of the walls was studied on elements with varying thickness – single and double wythe – and subjected to different levels of axial compression (or overload). The walls were tested inside of a bi-axial test setup that allowed not only the lateral, out-of-plane force but also the axial, arching action to be measured throughout the tests. Optical full-field displacement measurements were also acquired by two systems of cameras making use of the 2D and 3D Digital Image Correlation (DIC) technique.</p> <p>The data generated from these tests are made here available to support further investigations on masonry structures subjected to extreme lateral, out-of-plane actions. The dataset includes 3 compressed folders, ordered from 01 to 03, along with an auxiliary document describing the content and organization of the dataset. </p> <p>The data presented here are described in the following research article:</p> <blockquote> <p><a href="https://www.sciencedirect.com/science/article/pii/S0950061823022602?via%3Dihub">Godio M, Flansbjer M, Williams Portal N (2023). Single- and double-wythe brick masonry walls subjected to four-point bending tests under different support conditions: simply supported, rigid, non-rigid, Construction and Building Materials</a></p> </blockquote> <p>To cite this dataset, please refer to the article.</p> <p>The Authors</p>
GEO-Bench: m-brick-kiln
<p><strong>GEO-Bench: m-brick-kiln dataset</strong></p> <p> This dataset has been modified to be included in the <a href="https://zenodo.org/communities/geo-bench/">GEO-Bench dataset</a>. All changes with respect to the original version are documented at <a href="https://github.com/ServiceNow/geo-bench">https://github.com/ServiceNow/geo-bench</a>. </p> <p> The original version of this dataset is due to Lee et al. (2021) and is available at: <a href="https://sustainlab-group.github.io/sustainbench/docs/datasets/sdg13/brick_kiln.html">https://sustainlab-group.github.io/sustainbench/docs/datasets/sdg13/brick_kiln.html</a>. </p> <p> See the LICENSE file provided alongside this dataset for applicable licensing information. </p>
Brick : Tiled material
4k textures.these can be used on a flat geometry.not just the high poly model uploaded here. Original scan made out of 120 photos, Agisoft. Zbrush for the detail projection-> subd plane Substance Painter for the editing and tileing. Source: Objaverse 1.0 / Sketchfab
Brick Church Wall - High Poly Photoscan
**Brick Church Wall ** - Scaled to size - Color Accurate 4K Albedo Thank you for checking out my High Poly photoscan of a brick wall, captured in my small home village. Liking and following is just a click away and significantly boosts my self confidence, so why not do it? And let me know if you've used this wall, would love to see your work! High Poly & 4K texture model available upon request. Contact: hello@notoir.xyz More 3D goodness: https://notoir.xyz/featured-links/ Source: Objaverse 1.0 / Sketchfab
Castlecary brick
Brick found in ballast at Reeking Gill on the Rosedale Mineral Railway (east section). From Castlecary, so a long way from home. https://www.scottishbrickhistory.co.uk/castlecary-fireclay-company-limited/ Castlecary operational late 19th century to 1930s, well after the railway was built in 1863-5, so presumably used as ballast repair during operation. Perhaps linked to Castlecary's Middlesbro' offices. Source: Objaverse 1.0 / Sketchfab
Mock Brick Stamp Test
Impression of a brick stamp found during the 2016 Cosa Excavations. (C16.15) The stamp is CIL XV.368. This mock-up was created with modelling clay and a carved bar of soap. I did not overlay the texture created with the model. Source: Objaverse 1.0 / Sketchfab
Brick Bread Oven
A potentially late 17th, early 18th century brick built bread oven constructed within a blocked window aperture on an industrial site in Monmouthshire. Source: Objaverse 1.0 / Sketchfab
Old Brick Granary (Alter Speicher) Rostock
The original purpose of this old granary has long ceased to exist. There has been speculation for some time that this historical building will be converted into a hotel or turned into an office building. At the moment two businesses share the building. An energy company and a local surf shop. The structure was taken with a total of 595 images from a Mavic 2 Pro. Processed with RealityCapture and a bit of Blender for some cleaning and smoothing of the model. I will replace the model with a better version soon, because I want to have a proper model taken during a cloudy day, with smooth ligthning conditions. And I will try to get more pictures from the ground next time. The surrounding area is not for low altitude flying though, so I need to take my non-flying camera with me . ;-) And I have to figure out why the floor texture is so blurry/ghostly. https://www.google.com/maps/@54.091979,12.1414259,69a,35y,46h,61.57t/data=!3m1!1e3 Source: Objaverse 1.0 / Sketchfab
Harshita's Rotunda brick
Thomas Jefferson era (ca.1820s) brick with evidence of lime mortar found in a pile of debris behind the wall of the eastern ground floor oval room of the University of Virginia Rotunda in the Academical Village during the 2015 restoration. 3D scanned and processed by Harshita Batra for ARH 5600 : 3D Cultural Heritage Informatics Spring 2021 semester at UVA. The brick was scanned using a Creaform GoScan 50 structured light scanner and processed using 3D Systems Geomagic DX v.2019.0.3.48 Source: Objaverse 1.0 / Sketchfab
Aged Brick Wall with Normal Map Photogrammetry
Corner of Coglin and Second Street in Brompton McLeays Carpet Bulk store Source: Objaverse 1.0 / Sketchfab
Brick and Mortar (Photogrammetry low bake)
Baked low poly of a Macro test-scan I did yesterday while walking by one of my favorite looking buildings on the CU Boulder campus. Source: Objaverse 1.0 / Sketchfab
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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.