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67 results for “testbeds”
FIG. 3 in Biodiversity of bathyal coral gardens - portrait of a uniserial bryozoan endemic to the South Azorean Seamount Chain: an unexpected evolutionary testbed?
FIG. 3. — Harmelinius uniserialis (Harmelin, 1978), structure of colonies and graphs of zooid proportions: A - a, distal budding of autozooids with long cauda; b, lateral budding of autozooid (AZ) and kenozooid (KZ); c, abutment of kenozooid on ovicelled AZ - 1, KZ (21% white), non-ovicelled AZ (78% grey), ovicelled AZ (1% black); 2, non ovicelled AZ without avicularium (AV) (59% white), with one AV (24% grey), with two AV (17% dark); 3, ovicelled AZ, with one AV (23% grey), with two AV (77% dark); B, ovicellar kenozooid connected to the lateral pore chamber of an adjacent AZ; C, zooidal aggregation with 12 AZ without cauda, 28 AV and three KZ; D, clustering of three AZ without cauda, involving a small vicarious kenozooid abutted on to an avicularium (AV4) in proximal position, co-occurring with another AV type (AV2); E, dense aggregation of KZ. Origin: A, B, Tyro SMT, Stn DW 276; C, D, E, Tyro SMT, Stn DW 278. Scale bars: A, C, E, 400 µm; B, D, 200 µm.
Exposure and fragility of a virtual oil refinery testbed for seismic risk assessment
<p><span>Α</span><span> </span><span>virtual mid-size oil refinery, located in a high-seismicity region of Greece, is offered as a testbed for developing and testing system-level assessment methods. The dataset includes (a) a full geolocated exposure model with all pertinent critical assets, namely tanks, pressure vessels, process towers, chimneys, equipment-supporting buildings, and a flare; (b) the corresponding record-wise asset demands and summarized fragilities derived via nonlinear dynamic analyses on reduced-order numerical models.</span></p>
Beam Alignment Measurements using a Hybrid Massive MIMO Testbed
<p>Measurement data and processing code for Mathworks Matlab underlying the beam alignment results of the publication. The results compare 3 algorithms for beam alignment and are measured at 2.4GHz using the Hybrid Massive MIMO testbed of the CommIT chair of the Technische Universität Berlin.</p> <p>Type of Data: Processed data (Measurement results and processing code)</p> <p>Hardware/software used: Technische Universität Berlin CommIT Hybrid Massive MIMO testbed, Mathworks Matlab</p> <p>Data format: Measurements: Matlab mat data files, Code: m UTF8 text files</p> <p>Source: experiments</p> <p>Number of samples: 2 locations</p> <p>Size per sample: 10 files per location</p> <p>Total size of samples: 170MB</p>
Sensor data set radial forging at AFRC testbed v2
<p><strong>Sensor data set, radial forging at AFRC testbed</strong></p> <p><strong>General information on the data set</strong></p> <p>Radial forging is widely used in industry to manufacture components for a broad range of sectors including automotive, medical, aerospace, rail and industrial. The Advanced Forming Research Centre (AFRC) at the University of Strathclyde, Glasgow, houses a GFM SKK10/R radial forge that has been used as a testbed for this project. Using two pairs of hammers operating at 1200 strokes/min, and providing a maximum forging force per hammer of 150 tons, the radial forge is capable of processing a range of metals, including steel, titanium and inconel. Both hollow and solid material can be formed with the added benefit of creating internal features on hollow parts using a mandrel. Parts can be formed at a range of temperatures from ambient temperature to 1200 °C.</p> <p>For the provided data set, a total of 81 parts were forged over one day of operation. A machine failure occurred during the forging of part number 70, and this part was re-run once the malfunction had been fixed. Each forged part was then measured using a CMM to provide dimensional output relative to a target specification and tolerances. The CMM records 18 dimensional measurements.</p> <p>The aim of the measurement setup is to predict the quality (in terms of dimensional properties) of the forged part from the sensor measurements during the forging process.</p> <p><strong>Structure of the data</strong></p> <ul> <li>The sensor readings for the forging of the parts are provided in 81 csv files in the folder “Scope Traces”, named “Scope0001.csv” to “Scope0081.csv”. Each file contains the readings (columns) against time (rows). The first column displays the clock times (in milliseconds).</li> <li>A commentary on the sensors is provided in the file “ForgedPartDataStructureSummaryv3.xlsx” <strong>(NOTE: Some columns do not have sensor descriptions as this information is not available).</strong></li> <li>The CMM data is provided in the file “CMMData.xlsx”.</li> </ul> <p><strong>Further Information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available here:</p> <p><a href="https://github.com/harislulic/Strathcylde_AFRC_machine_learning_tutorials/releases/tag/v2.0">https://github.com/harislulic/Strathcylde_AFRC_machine_learning_tutorials/releases/tag/v2.0</a></p> <p>These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set.</p>
Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit)
<p><strong>General information on the data set</strong></p> <p>The dataset was generated with two different measurement systems at the ZeMA testbed for electromechanical cylinders.</p> <p> </p> <p><strong>All relevant information can be found within the hdf5 file.</strong></p> <p> </p> <p><strong>Example for reading out the metadata of the hdf5 file in MATLAB:</strong></p> <pre><code># available structures inside file dataset = 'axis11_2kHz_ZeMA_PTB_SI.h5'; h5disp(dataset) % general attributes about file attr = h5info(dataset).Attributes; project = jsondecode(attr(1,1).Value) person = jsondecode(attr(2,1).Value) publication = jsondecode(attr(3,1).Value) experiment = jsondecode(attr(4,1).Value)</code></pre> <p> </p> <p><strong>Example for reading out the metadata of the hdf5 file in Python:</strong></p> <pre><code>import h5py import json # open file h5file = h5py.File("axis11_2kHz_ZeMA_PTB_SI.h5", "r") # general attributes about file for key in h5file.attrs: print(key) val = json.loads(h5file.attrs[key]) for subkey, subval in val.items(): print(" ", subkey, " : ", subval) # available structures inside file h5file.visit(print) # proper exit h5file.close()</code></pre> <p> </p> <p><strong>Metadata output of the hdf5 file:</strong></p> <ul> <li><strong>For the dataset:</strong> <pre><code>HDF5 axis11_2kHz_ZeMA_PTB_SI.h5 Group '/' Attributes: 'Project': '{ "fullTitle":"Metrology for the Factory of the Future", "acronym":"Met4FoF", "websiteLink":"www.met4fof.eu", "fundingSource":"European Commission (EC)", "fundingAdministrator":"EURAMET", "funding programme":"EMPIR", "fundingNumber":"17IND12", "acknowledgementText":"This work has received funding within the project 17IND12 Met4FoF from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program. The authors want to thank Clifford Brown, Daniel Hutzschenreuter, Holger Israel, Giacomo Lanza, Bj\u00f6rn Ludwig, and Julia Neumann fromPhysikalisch-Technische Bundesanstalt (PTB) for their helpful suggestions and support." }' 'Person': '{ "dc:author":[ "Tanja Dorst", "Maximilian Gruber", "Anupam Prasad Vedurmudi" ], "e-mail":[ "t.dorst@zema.de", "maximilian.gruber@ptb.de", "anupam.vedurmudi@ptb.de" ], "affiliation":[ "ZeMA gGmbH", "Physikalisch-Technische Bundesanstalt", "Physikalisch-Technische Bundesanstalt" ] }' 'Publication': '{ "dc:identifier":"10.5281/zenodo.5185953", "dc:license":"Creative Commons Attribution 4.0 International (CC-BY-4.0)", "dc:title":"Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit)", "dc:description":"The data set was generated with two different measurement systems at the ZeMA testbed. The ZeMA DAQ unit consists of 11 sensors and the SmartUp-Unit has 13 differentsignals. A typical working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke of the electromechanical cylinder. The data set does not consist of the entire working cycles. Only one second of the return stroke of every 100rd working cycle is included. The dataset consists of 4776 cycles. One row represents one second of the return stroke of one working cycle.", "dc:subject":[ "dynamic measurement", "measurement uncertainty", "sensor network", "digital sensors", "MEMS", "machine learning", "European Union (EU)", "Horizon 2020", "EMPIR" ], "dc:SizeOrDuration":"24 sensors, 4776 cycles and 2000 datapoints each", "dc:type":"Dataset", "dc:issued":"2021-09-10", "dc:bibliographicCitation":"T. Dorst, M. Gruber and A. P. Vedurmudi : Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit), Zenodo [data set], https://doi.org/10.5281/zenodo.5185953, 2021." }' 'Experiment': '{ "date":"2021-03-29/2021-04-15", "DUT":"Festo ESBF cylinder", "identifier":"axis11", "label":"Electromechanical cylinder no. 11" }'</code></pre> <p> </p> </li> <li><strong>Example for one sensor (BMA 280, acceleration) of the PTB SmartUp Unit (SUU) and one sensor of ZeMA DAQ (pressure):</strong> <pre><code>HDF5 axis11_2kHz_ZeMA_PTB_SI.h5 Group '/PTB_SUU' Group '/PTB_SUU/BMA_280' Group '/PTB_SUU/BMA_280/Acceleration' Attributes: 'qudt:hasQuantityKind': '[ "qudt:Acceleration", "qudt:Acceleration", "qudt:Acceleration" ]' 'misc': '{ "interpolation_scheme":"cubic" }' 'si:unit': '"\\metre\\second\\tothe{-2}"' 'sosa:madeBySensor': '"BMA 280"' 'rdf:type': '"qudt:Quantity"' Dataset 'qudt:standardUncertainty' Size: 4766x1000x3 MaxSize: 4766x1000x3 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '[ "X acceleration uncertainty", "Y acceleration uncertainty", "Z acceleration uncertainty" ]' Dataset 'qudt:value' Size: 4766x1000x3 MaxSize: 4766x1000x3 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '[ "X acceleration", "Y acceleration", "Z acceleration" ]' Group '/ZeMA_DAQ' Group '/ZeMA_DAQ/Pressure' Attributes: 'qudt:hasQuantityKind': '"qudt:Pressure"' 'sosa:madeBySensor': '"Festo VPPM"' 'si:unit': '"\\pascal"' 'rdf:type': '"qudt:Quantity"' Dataset 'qudt:standardUncertainty' Size: 4766x2000 MaxSize: 4766x2000 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '"Pneumatic pressure uncertainty"' Dataset 'qudt:value' Size: 4766x2000 MaxSize: 4766x2000 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '"Pneumatic pressure"' 'misc': '{ "raw_data":false, "comment":"Converted from ADC values based on appropriate conversion." }'</code></pre> </li> </ul>
SPEA TESTBED TEST DATA FOR ML DEVELOPMENT
<p>The dataset files were generated at 3 different temperature with the improved ATE system developed in WP3 of the MET4FOF Project. </p> <p>The data are available for ML purposes and metrological investigation.</p>
Instances and detailed results for the whole testbed of "The Storage Location Assignment and Picker Routing Problem: A Generic Branch-Cut-and-Price Algorithm"
<p>This repository contains the instances and detailed results used for the computational experiments in the article "The Storage Location Assignment and Picker Routing Problem: A Generic Branch-Cut-and-Price Algorithm". Two sets of instances are used:</p> <p><br> The first set of instances comes from the paper "Integrating storage location and order picking problems in warehouse planning" authored by Allyson Silva, Leandro C. Coelho, Maryzam Darvish and Jacques Renaud.<br> https://doi.org/10.1016/j.tre.2020.102003<br> Their instances are available on the following website: https://www.leandro-coelho.com/slot-assignment-and-order-picking/</p> <p><br> The second set of instances comes from the paper "Storage assignment for newly arrived items in forward picking areas with limited open locations" authored by Xiaolong Guo, Ran Chen, Shaofu Du and Yugang Yu.<br> https://doi.org/10.1016/j.tre.2021.102359<br> The set of small instances is made available on this repository, with the kind permission of the authors.</p>
CARL-W: a Testbed for Empirical Analyses of 5G and Starlink Performance
<p>The deployment of 5G networks, including 5G Non-Public Networks (5G-NPNs) for private use in several verticals, is rapidly taking place worldwide. However, deploying these networks in under-served areas, where there may be limited Internet access or backhauling capabilities, presents challenges. To address these challenges, there is a growing interest in using Low Earth Orbit (LEO) satellites, such as SpaceX's Starlink, which can provide high-throughput and low-latency Internet access via dense satellite constellations.</p> <p>In this paper, we present CARL-W, the Wireless module of the Communications Advanced Research Laboratory (CARL) at Karlstad University, which combines a 5G-NPN and a Starlink deployment. CARL-W serves as a platform for empirical analyses on both systems, thus contributing towards the study of their possible integration. In particular, we outline the CARL-W experimentation framework and provide access to the CARL-W visualization and data exporting platform. We also open-source a 1-month Starlink dataset, facilitating further analyses of this relatively new technology.</p>
SDS_Benchmark: a testbed for shoreline mapping algorithms using satellite imagery
<p>This is an archived copy of the following Github repository: https://github.com/SatelliteShorelines/SDS_Benchmark</p>
Wireshark captures of a residential WLAN testbed
<p>This dataset contains the Wireshark captures obtained from the WLAN testbed used in the magazine paper entitled "Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks". More details regarding the experimental setup can be found at <a href="https://github.com/fwilhelmi/usage_of_simulators_in_future_networks">https://github.com/fwilhelmi/usage_of_simulators_in_future_networks</a>. </p>
Data and Codes for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
<p>This is part of the code and data related to the paper entitled Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime, available at https://arxiv.org/abs/2309.09024.</p><p>The original sources of the codes are the v1.0.0 version of open source software EnsembleKalmanProcesses.jl for EKI analysis, accessible at zenodo.org/records/7806813, and the \emph{qbo1d} code for the 1D-QBO model simulations, accessible at github.com/DataWaveProject/qbo1d.git.</p>
RSSI Traces from the WSN-Testbed at the I4, Friedrich-Alexander University Erlangen-Nuremberg
<p>This is a one week RSSI-trace from an office-based WSN-Testbed at the I4, Friedrich-Alexander University Erlangen-Nuremberg. 9 Tmote Sky node were used to sample the RSSI at approximately 8192 Hz.</p> <p>The data was collected using base_rssi2.c, which is part of the code provided with https://doi.org/10.5281/zenodo.582277. It can be decoded using the code in /tools/scala in the the same repo.</p> <p>The trace was taken in the week of 2015-05-06.</p>
Global MIMICS-CN from biogeochemical testbed
These data document ecosystem biogeochemical responses from carbon-nitrogen and carbon-only versions of the Microbial-MIneral Carbon Stabilization Model (MIMICS) and Carnegie–Ames–Stanford Approach (CASA) soil biogeochemical models, Results are for steady-steate pools and fluxes simulated at initialization and over the historical period (1901-2014). Boundary conditions for the soil biogeochemical testbed were generated by the Community Land Model, version 5 (CLM5) simulations using satelite phenology mode (SP) forced with data with the Global Soil Wetness Program, version 3 (GSWP3). Simulations are run at a nominal two degree resolution. Input data are daily resolution for the entire historical record (1901-2014). Model output includes annual mean states and fluxes. Additional, daily mean results are reported for the first five and last five years of the simulation for for four different model experiments (MIMICS-CN, MIMICS-Conly, CASA-CN, and CASA-Conly). All file are in netcdf classic format.
DEDICAT6G-5G testbed measurements-CGB
<p>- MEASUREMENTS: Downlink Throughput (+ packet loss), RTT, Jitter, Latitude and Longitude<br>- NR5G_SA: RSRP, RSRQ, RSSI, SINR, Band, Bandwidth</p>
Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed (Release 2)
<p>Environmental and AIS data collected during the second phase of EUMR TNA experiments using the CMRE LOON testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy)</p> <p>Temperature measured across the water column in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p><br> Sound velocity measured close to the surface (SVP1) and close to the sea bottom (SVP2) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for:<br> i) June 9-11, 17-18, 25-26 - 2021<br> ii) July 5-7, 21-27, 30-31 - 2021<br> iii) August 3-5, 10-14, 19-21, 23-24, 28-30 - 2021</p> <p>SVP1 data missing for June 17-18 (2021) and July 5-7 (2021).</p> <p><br> Automatic Identification System (AIS) data for the ships transiting close to the LOON area (Gulf of La Spezia, Italy). The dataset includes AIS data for:<br> i) June 9-11 - 2021</p> <p>AIS recorded data not available after June 11, 2021</p> <p>For reference, see: "Environmental data collected on the CMRE LOON tested during the EUMR project: dataset description", Petroccia, Roberto; Zappa, Giovanni; Cimino, Giampaolo; Grati, Alberto; Alves, João. CMRE-DA-2021-001. July 2021, available at https://www.cmre.nato.int/research/publications/latest-techreports/1638-cmre-da-2021-001</p>
The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales
<p>This is the companion data for the manuscript titled 'The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales', submitted to the Journal of Advances in Modeling Earth Systems in September 2022.</p> <p>derecho_simulation_result: this folder contains part of the SCREAM RRM outputs I ran on NERSC Cori in 2021-2022 corresponding to the simulation in Table 1 of the manuscript.</p> <p>wrf_simulation_result: this folder contains part of the WRF outputs run by Jianfeng Li from PNNL (jianfeng.li@pnnl.gov) in 2022 corresponding to the simulations in Table 4 of the manuscript.</p> <p>plot_script: this folder includes python scripts to plot figures shown in the manuscript.</p> <p>ASOS_station: this txt file contains the processed ASOS station wind speed used in the manuscript.</p> <p><br> Unfortunately, all model outputs are large (~ 3.8 TB for SCREAM RRM and 3.1 TB for WRF). Therefore, I only provide the variables (i.e., OLR, precipitation, composite radar reflectivity, and 10-m wind speed) used directly to generate figures in this repository. All model outputs are archived on tape at NERSC.</p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu).</p>
Available Wireless Sensor Network and Internet of Things testbed facilities: dataset
<p>In this data set, we present data collected for the purpose of carrying out a systematic review of the available Wireless Sensor Network and Internet of Things testbed facilities. The data was collected through multiple stages and in each stage the pre-defined criteria were applied. We provide a dataset describing the hardware and software aspects of Wireless Sensor Network and Internet of Things testbed facilities available in the market and scientific community. The data were gathered through an extensive systematic review process of scientific articles published between the years 2011 and 2021. The review aims to obtain good quality data for people who are actively researching the Internet of Things facilities or anyone who is interested in that field.</p>
NANCY SNS-JU PROJECT "ITALTEL ITALIAN IN-LAB TESTBED - LATENCY METRICS"
<p>In the context of the NANCY project (https://nancy-project.eu/), this Dataset provides input data for the development of the B-RAN and attacks models for the NANCY framework, to model training and model inference functions. The data collected plays the role of ML algorithm-specific data preparation. The dataset contains time-series, collected transmitting a video content through the Italtel "VTU - video streaming and transcoding application", that can convert audio and video streams from one format to another, at multiple encodings schemes, changing resolution, bitrate, and video parameters. The data collected are related to the observation of some of the resources involved in the Usage Scenario: “Fronthaul network of fixed topology – Direct Connectivity/CoMP Connectivity”. In the Italtel Italian in-lab testbed, a MEC assisted 5G network scenario with a video streaming application for generating traffic is provided. Two different scenarios were set-up, related to downstream and upstream video flows. Each file captured is associated to a 10min video streaming of the “Big Buck Bunny” video. This video was transmitted with different resolutions, 480p, 720p, 1080p, 2160p; both in uplink (UL) and in downlink (DL); the type of metrics monitored is RTT (Round Trip Time).</p>
Numerical model-informed testbed for surface PM2.5 concentration over China and its estimates during 2013-2021
<p>This is an updated version of https://doi.org/10.5281/zenodo.11122294, the data provided in NetCDF format.</p> <p>In addition to the long-term PM2.5 dataset created from Li et al (2024), which can be used for health assessments and studying air pollution influences, here we also provide testbed data crucial for evaluating machine learning-based retrieval methods, especially in scenarios where no ground-truth data is available.<br>The testbed dataset includes all inputs and outputs following the physical model simulation, which naturally correlates with physical laws such as emissions, diffusion, advection, and deposition, representing typical conditions that any prediction method should meet. This data can be used to evaluate and compare methods using the same dataset, allowing for continuous improvement. Besides traditional cross-validation, the proposed testbed validation is highly recommended to examine a method’s predictive ability. We will continue updating the testbed data for other pollutants and with different resolutions and regions in future studies.</p>
Results from network experiments conducted on ULiège testbed with the Network Performance Framework (NPF) tool
<p>This dataset results from network experiments on a router configuration with replayed traffic. The network trace comes from a border router of our campus network. The router is used in both directions, i.e. both for reception and transmission of packets (to and from our campus network). Each experiment was done 3 times (for a total of 11466 experiments).</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.