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390 results for “LiDAR data”
Data files for A "Boreing" Night of Observations of the Upper Mesosphere and Lower Thermosphere Over the Andes Lidar Observatory
<p>The files in this set are data obtained from the ANI2 airglow imager located at the Andes Lidar Observatory.in Chile (30.23S, 70.73W, 2530 m). The files are named for a JGR paper by J. Hecht et al. entitled A "Boreing" Night of Observations of the Uppe rMesosphere and Lower Thermosphere Over the Andes Lidar Observatory. The files are published here so as to be available for review. This paper should appear in JGR Atmospheres sometime in late 2023 or early 2024. The files that are text files are meant to be read with IDL as discussed in the readme file. </p>
TEAMx-PC22 (TEAMx pre-campaign 2022) – DWD Doppler wind lidar data set (SLXR172)
<p>This dataset contains data measured by DWD with a Doppler Wind Lidar SLXR172 during the TEAMx pre-campaign 2022. More details about TEAMx can be found at <a href="http://www.teamx-programme.org/">http://www.teamx-programme.org</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Measurement location and time period </strong></p> <p>Measurements with the SLXR172 were collected at the site of Brannenburg (47.741547 N / 12.122187 E / 456 m MSL) between 15.June – 19 October 2022.</p> <p><strong>2. Measurement setup</strong></p> <p>During the measurement period, two different scanning modes were applied:</p> <p>15. June - 26. July 2022 and 13. August – 19. October 2022 (VAD_CSM).</p> <ul> <li><strong>VAD (velocity-azimuth display) scans in </strong><strong>continuous scanning mode</strong><strong> : </strong>These scans were conducted at an elevation angle of 35°. Azimuth angle interval of the CSM data sampling was about 1.1°. </li> </ul> <p>27.July – 12. August 2022 (VAD_RHI)</p> <ul> <li><strong>VAD scans in step-stare mode: </strong>Step-stare scans were conducted at an elevation angle of 35° and with azimuth steps of 15°.</li> <li><strong>RHI (</strong><strong>range-height indicator) scans into the Inn Valley</strong>; The RHI scans were performed for 10 azimuth angles from 151° to 160° and covered elevation angles from 3° to 51°.</li> </ul> <p> </p> <p><strong><em>3. Data processing, corrections and filter</em></strong></p> <p>For <strong><em>VAD scans in continuous scanning mode</em></strong> the processed wind fields are provided. The data have not been corrected. The data can be filtered using the parameters R<sup>2 </sup>(coefficient of determination), CN (condition number) and NVRAD (number of radial velocities) as described in Päschke (2015):</p> <p>R<sup>2</sup>> 0.95 and CN<10 and NVRAD>12 </p> <p>Please note that in the postprocessing of the VAD CSM scans, the R<sup>2</sup> filter criterion was set to R<sup>2</sup>>0 in order to include all data and therefore might also include scans where the assumptions of homogeneity are not fulfilled. The parameter qwind is therefore not meaningful due to this configuration and should not be used to filter the data. We recommend the use of the above criterion from Päschke.</p> <p>For scans from the <strong><em>VAD scans in step stare mode</em></strong> as well as the <strong><em>RHI scans</em></strong> the raw data files are provided. They have not been corrected nor filtered.</p> <p><strong>4. Data file structure</strong></p> <p>The data are provided in NetCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. Files are sorted in monthly folders.</p> <p>The data are provided in two zip-files.</p> <ul> <li>VAD_CSM contains the processed wind fields from 15. June - 26. July 2022 and from 13. August – 19. October 2022)</li> <li>VAD+RHI the raw data files for 27.July – 12. August 2022.</li> </ul> <p>Raw data files of the VAD CSM scans can be provided upon request.</p> <p><strong>5. Contact</strong></p> <p>Contact Katrin.sedlmeier(at)dwd.de.at for any questions regarding the data set.</p> <p><strong>6. References</strong></p> <p>Päschke, E., Leinweber, R., and Lehmann, V.: An assessment of the performance of a 1.5 μm Doppler lidar for operational vertical wind profiling based on a 1-year trial, Atmos. Meas. Tech., 8, 2251–2266, https://doi.org/10.5194/amt-8-2251-2015, 2015.</p>
TEAMx-PC22 (TEAMx pre-campaing 2022) – GeoSphere Austria Doppler wind lidar data
<p><strong>ABSTRACT</strong></p> <p><a href="https://www.geosphere.at/">GeoSpere Austria</a> operated a Doppler lidar (<a href="https://metek.de/product/wind-ranger-100-200/">METEK Wind Ranger 200</a>) during the TEAMx pre-campaign 2022 (TEAMx-PC22) from August 17, 2022 to October 3, 2022 next to the <a href="https://oscar.wmo.int/surface/index.html#/search/station/stationReportDetails/0-20000-0-11130">meteorological station at Kufstein</a>, Austria. The wind lidar data from this campaign is provided here. Standard meteorological data is available on the <a href="https://data.hub.geosphere.at/">GeoSphere Austria data hub</a>.</p> <p>The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in <a href="https://www.uibk.ac.at/iup/buch_pdfs/10.1520399106-003-1.pdf">Serafin et al. (2020) </a>and in <a href="https://journals.ametsoc.org/view/journals/bams/103/5/BAMS-D-21-0232.1.xml">Rotach et al. (2022)</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p>The windlidar is operated at Kufstein next to the meteorological station (12.1628°E 47.5753°N 490m asl). 1 VAD scan is measured per second, 100 radial measurements per VAD scan.</p> <p>Provided are daily NetCDF data sets, so-called “averaged files”, i.e. 10min averaged profiles calculated from the instantaneous profiles provided by the operational software of the instrument.</p> <p><strong>Description of variables:</strong></p> <table> <tbody> <tr> <td> <p>lat</p> </td> <td> <p> Latitude</p> </td> </tr> <tr> <td> <p>lon</p> </td> <td> <p> Longitude</p> </td> </tr> <tr> <td> <p>alt</p> </td> <td> <p> Altitude</p> </td> </tr> <tr> <td> <p>height</p> </td> <td> <p> Measuring height</p> </td> </tr> <tr> <td> <p>pitch</p> </td> <td> <p> Tilt towards north arrow</p> </td> </tr> <tr> <td> <p>roll</p> </td> <td> <p> Tilt clockwise looking along north arrow</p> </td> </tr> <tr> <td> <p>heading</p> </td> <td> <p> Azimuth alignment (should be zero)</p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p> Time stamp (seconds since 01.01.1970 00:00 UTC).</p> </td> </tr> <tr> <td> <p>VEL</p> </td> <td> <p> Wind Velocity (vectorial average)</p> </td> </tr> <tr> <td> <p>VEL_SC</p> </td> <td> <p>Wind Velocity (scalar average)</p> </td> </tr> <tr> <td> <p>DIR</p> </td> <td> <p> Direction (vectorial average)</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p> West-East wind component</p> </td> </tr> <tr> <td> <p>V</p> </td> <td> <p> South-North wind component</p> </td> </tr> <tr> <td> <p>W</p> </td> <td> <p> Upward wind component</p> </td> </tr> <tr> <td> <p>SU</p> </td> <td> <p>Standard deviation of U</p> </td> </tr> <tr> <td> <p>SV</p> </td> <td> <p>Standard deviation of V</p> </td> </tr> <tr> <td> <p>SW</p> </td> <td> <p>Standard deviation of W</p> </td> </tr> <tr> <td> <p>SVEL</p> </td> <td> <p>Mean square deviation of radial wind components from fitted values</p> </td> </tr> <tr> <td> <p>DQ</p> </td> <td> <p>Fraction of valid radial components per VAD</p> </td> </tr> <tr> <td> <p>MDT</p> </td> <td> <p>Mean distance to target (Measured distance of focus)</p> </td> </tr> <tr> <td> <p>SNR</p> </td> <td> <p>Signal to noise ratio in dB</p> </td> </tr> <tr> <td> <p>SPW</p> </td> <td> <p>Spectral width (for internal use only)</p> </td> </tr> </tbody> </table> <p> </p> <p>Contact: kathrin.baumann-stanzer@geosphere.at</p>
Geodetic displacement data from Airborne-LiDAR data and Time Series InSAR: Baton Rouge Case Study.
<p>This repository the results produced by Hurtado-Pulido, Amer, Ebinger, and Holcomb “Variations in subsidence patterns in the Gulf of Mexico passive margin from Airborne-LiDAR data and Time Series InSAR: Baton Rouge Case Study”.</p> <p>This repository presents data sets for figures 4, 5, 6, 7 and 8. Processing methods are described in the paper. The READme file contains details about each file. Please address any questions about this dataset to Hurtado-Pulido.</p> <ul> <li>LiDAR data from 1999 is stored and distributed by the Atlas: The Louisiana Statewide GIS (<a href="https://maps.ga.lsu.edu/lidar2000/">https://maps.ga.lsu.edu/lidar2000/</a>). LiDAR data from 2018 is stored and distributed by the USGS Server through The National Map Download Manager (<a href="https://apps.nationalmap.gov/downloader/">https://apps.nationalmap.gov/downloader/</a>).</li> <li>EnviSAT SAR images were retrieved from the Earth Observation Catalogue (<a href="https://eocat.esa.int/sec/#data-services-area">https://eocat.esa.int/sec/#data-services-area</a>). Sentinel-1 SAR images from the Copernicus Open Access Hub (<a href="https://scihub.copernicus.eu/dhus/#/home">https://scihub.copernicus.eu/dhus/#/home</a>). Both property of the European Space Agency.</li> <li>GNSS information was processed by the Nevada Geodetic Laboratory (Blewitt et al., 2018; <a href="http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html">http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html</a>).</li> <li>Data from water, injection, and extraction wells is stored in the Strategic Online Natural Resources Information System property of the Louisiana Department of Natural Resources (<a href="http://sonris-www.dnr.state.la.us/gis/agsweb/IE/JSViewer/index.html?TemplateID=181">http://sonris-www.dnr.state.la.us/gis/agsweb/IE/JSViewer/index.html?TemplateID=181</a>).</li> </ul>
LiDAR reveals a preference for intermediate visibility by a forest-dwelling ungulate species: Deer locational data
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Data from: Flying high: Sampling savanna vegetation with UAV-lidar
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LiDAR reveals a preference for intermediate visibility by a forest-dwelling ungulate species: Code and LiDAR data
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High resolution LiDAR Data for Hog Island, VA, 2013
High Resolution LiDAR elevation and nearshore bathymetry data for Hog Island, Northampton County, VA, collected on May 26, 2013 on behalf of the USACE Engineer Research and Development Center using the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. CZMIL integrates a lidar sensor with topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. Hyperspectral imagery is provided as a separate VCRLTER dataset. Four data entities are included here: (1) the LiDAR point cloud (approximate point density of 5-30 points per square meter [denser over structures and dense vegetation]) in standard LiDAR LAS file format; (2) a rasterized digital elevation model (DEM) derived from the point cloud depicting elevation of the water-free first return surface with a cell resolution of 1 meter; (3) a DEM of the same first return surface with a cell resolution of 5 meters; and (4) a DEM depicting the bare earth surface with vegetation and structures removed, at a 1 meter resolution. DEMs are in georegistered TIFF format. ArcGIS and FGDC metadata files in XML format are also included. To obtain vegetation and building heights, subtract the bare earth model from the surface model. Areas of open water with sparse or no bottom returns (either due to water depth or clarity issues) are masked out in the DEM data.
High resolution LiDAR Data for Hog Island, VA, 2011
High Resolution LiDAR elevation and nearshore bathymetry data for Hog Island, Northampton County, VA, collected on October 11, 2011 on behalf of the USACE Engineer Research and Development Center using the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. CZMIL integrates a lidar sensor with topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. RGB air photo imagery is provided as a separate VCRLTER dataset. Two data entities are included here: (1) the LiDAR point cloud (approximate point density of 100 points per square meter [denser over structures and dense vegetation], average point spacing of 0.48 m.) contained in a mosaic of 211 LAS files (standard LiDAR LAS file format); and (2) a polygon INDEX shapefile showing the footprint of each LAS file and containing a summary description of each LAS file in the attribute table (LAS file name, point count, point spacing, and minimum and maximum elevation). Note that the two co-collected 2011 USACE datasets (LiDAR and RGB ) are in different coordinate systems: (A) the horizontal and vertical units of the LiDAR data are in US feet, not meters. (B) the horizontal units of the associated RGB mosaic images are in [standard] meters. Also Note that THESE ARE VERY LARGE DATASETS and download should not be attempted unless you have a fast network connection and plenty of disk space. The LAS file data collection is 7.3 GB compressed (11.7 GB uncompressed). The RGB imagery mosaic data collection is 12.9 GB compressed (30.8 GB uncompressed).
The diurnal data of the aerosol extinction coefficient of the Mount Qomolangma lidar, as well as precipitation, low cloud cover, relative humidity of three adjacent stations (Tingri, Lazi, Nyalam) of the Mount Qomolangma
<p><strong>The diurnal data of the the vertical average aerosol extinction coefficient of 0.15-2.5 km of the Mount Qomolangma lidar, as well as precipitation, low cloud cover, relative humidity of three adjacent stations (Tingri, Lazi, Nyalam) of the Mount Qomolangma in July 2018 and July 2019.</strong></p>
Data and code for the article "Advancing Fine Branch Biomass Estimation with LiDAR and Structural Models"
<p>This is the repository for the data and code to reproduce the article "Advancing Fine Branch Biomass Estimation with LiDAR and Structural Models".</p> <p>Summary:</p> <p><span><span>·</span></span><span><span><span> </span><em>Background and Aims</em></span></span></p> <p><span><span>Lidar is a promising tool for fast and accurate measurements of trees. There are several approaches to estimate aboveground woody biomass using lidar point clouds. One of the most widely used methods involves fitting geometric primitives (<em>e.g.</em> cylinders) to the point cloud, thereby reconstructing both the geometry and topology of the tree. However, current algorithms are not suited for accurate estimation of the volume of finer branches, because of the unreliable point dispersions from <em>e.g. </em>beam footprint compared to the structure diameter.</span></span></p> <p><span><span>·</span></span><span><span><span> </span><em>Methods</em></span></span></p> <p><span><span>We propose a new method that couples point cloud-based skeletonization and multi-linear statistical modelling based on structural data to make a model (structural model) that accurately estimates the aboveground woody biomass of trees from high-quality lidar point clouds, including finer branches. The structural model was tested at segment, axis, and branch level, and compared to a cylinder fitting algorithm and to the pipe model theory.</span></span></p> <p><span><span>·</span></span><span><span><span> </span><em>Key Results</em></span></span></p> <p><span><span>The model accurately predicted the biomass with 1.6% nRMSE at the segment scale from a k-fold cross-validation. It also gave satisfactory results when up-scaled to the branch level with a significantly lower error (13% nRMSE) and bias (-5%) compared to conventional cylinder fitting to the point cloud (nRMSE: 92%, bias: 82%), or using the pipe model theory (nRMSE: 31%, bias: -27%).</span></span></p> <p><span><span>The model was then applied to the whole-tree scale and showed that the sampled trees had more than 1.7km of structures on average and that 96% of that length was coming from the twigs (<em>i.e.</em> <5 cm diameter). Our results showed that neglecting twigs can lead to a significant underestimation of tree aboveground woody biomass (-21%).</span></span></p> <p><span><span>·</span></span><span><span><span> </span><em>Conclusions</em></span></span></p> <p><span><span>The structural model approach is an effective method that allows a more accurate estimation of the volumes of smaller branches from lidar point clouds. This method is versatile but requires manual measurements on branches for calibration. Nevertheless, once the model is calibrated, it can provide unbiased and large-scale estimations of tree structure volumes, making it an excellent choice for accurate 3D reconstruction of trees and estimating standing biomass.</span></span></p>
Data from: Handheld lidar sensors can accurately measure herbaceous biomass
<p>Data and code used in <em>Handheld lidar sensors can accurately measure herbaceous biomass</em>. Lidar data is provided for MLS and iPad sensors in <em>las_files.zip</em>. Las files are named by site, plot and subplot (e.g., FP-1-5). Data for response and predictors are avilable in <em>data.zip</em>. R code is provided for predictor creation, modeling, and figure creation in <em>Rcode.zip</em>. </p>
Data for "Measurement report: Comparison of airborne in-situ measured, lidar-based, and modeled aerosol optical properties in the Central European background – identifying sources of deviations"
<p>A unique set of data is presented, derived from measurements conducted at the rural central European observatory at Melpitz, Germany. Data derived from remote sensing (lidar), airborne platforms (helicopter, balloon), and ground-based in-situ methods is included. Measured and Mie-modeled optical aerosol parameters are presented in the dry- and ambient state. Modeled optical parameters are based on Mie-theory. For ambient state hygroscopic growth simulations are utilized.</p>
Polarization lidar data for detecting dust orientation
<p>Polarization lidar data used in publication:</p> <p>Tsekeri, A., Amiridis, V., Louridas, A., Georgoussis, G., Freudenthaler, V., Metallinos, S., Doxastakis, G., Gasteiger, J., Siomos, N., Paschou, P., Georgiou, T., Tsaknakis, G., Evangelatos, C., and Binietoglou, I.: Polarization lidar for detecting dust orientation: System design and calibration, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2021-30, in review, 2021.</p>
MUESLI Hyperspectral & LiDar Data Set
<p>The data set contain the hyperspectral images and the corresponding LiDar data from the MUESLI project.</p> <p>The meta data is included in the tif files. For the spectral bands, a copy of the original hdr file is below:</p> <p>fwhm = 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wavelength = 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<p> </p> <p> </p> <p> </p>
3D Point Cloud Data for LiDAR-based Mobile Robot
<p>LiDAR point cloud data serves as an machine vision alternative other than image. Its advantages when compared to image and video includes depth estimation and distance measurement. Low-density LiDAR point cloud data can be used to achieve navigation, obstacle detection and obstacle avoidance for mobile robots. autonomous vehicle and drones. In this metadata, we scanned over 1400 objects and classified it into 6 groups of object namely, human, cars, motorcyclist, signboard, road divider and others.</p>
Data from: Evaluating the use of lidar to discern snag characteristics important for wildlife
<p>Standing dead trees (known as snags) are historically difficult to map and model using airborne laser scanning (ALS), or lidar. Specific snag characteristics are important for wildlife; for instance, a larger snag with a broken top can serve as a nesting platform for raptors. The objective of this study was to evaluate whether characteristics such as top intactness could be inferred from discrete-return ALS data. We collected structural information for 198 snags in closed-canopy conifer forest plots in Idaho. We selected 13 lidar metrics within 5 m diameter point clouds to serve as predictor variables in random forest (RF) models to classify snags into four groups by size (small [<40 cm diameter] or large [≥40 cm diameter]) and intactness (intact or broken top) across multiple iterations. We conducted these models first with all snags combined, and then ran the same models with only small or large snags. Overall accuracies were highest in RF models with large snags only (77%), but kappa statistics for all models were low (0.29–0.49). ALS data alone were not sufficient to identify top intactness for large snags; future studies combining ALS data with other remotely sensed data to improve classification of snag characteristics important for wildlife is encouraged.</p>
Atmospheric visibility inferred from continuous-wave Doppler wind lidar, data set
<p>Visibility data from Pershore, UK, between 2018 and 2020</p>
Data from: Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes
<p>Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering -- that is, excluding returns from LiDAR point clouds based on simple height thresholds -- is a common practice meant to improve the 'signal' content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types.<br> <br> In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman's $\rho$) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha^-1^ statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful 'signal' than extraneous 'noise' in these cases.</p>
Fine-scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data
<p>In recent years, LiDAR technology has gained widespread attention for its ability to provide precise 3D vertical structural data for various objects, particularly forests. In our dataset, we utilized LiDAR data to reconstruct intricately detailed three-dimensional representations of specific larch forest landscapes. These detailed forest structural models enable us to drive three-dimensional radiative transfer models, analyze the radiation budget of the forest canopy, and gain valuable insights into fine-scale forest management strategies.</p> <p>This is the research work we conducted by combining the aforementioned 3D forest scenes with the 3D RTM LESS. If you use our data, please cite our article. You can access our publication via DOI: 10.34133/plantphenomics.0166.</p> <p>We welcome researchers interested in a wide range of fields, such as vegetation ecological applications, to communicate with us by combining 3D vegetation modeling.</p> <p><br><br></p>
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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
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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.