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9,302 results for “DETECT”
Data from: Cyclone-anticyclone asymmetry of eddy detection on gridded altimetry product in the Mediterranean Sea
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Detecting phylogenetic signal and adaptation in papionin cranial shape by decomposing variation at different spatial scales
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Detection of ghost introgression requires exploiting topological and branch length information
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Magnetoresistive detection of spin waves
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Data-driven versus consensus diagnosis of MCI: enhanced sensitivity for detection of dementia progression, biomarker status, and neuropathological outcomes
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TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees
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Auxiliary supplementary material for the study on plant diversity relationships with satellite-detected phenological variability in USA wetlands at a national scale
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Confirming an antiphasic bicyclic pattern of forward entrainment in signal detection: a reanalysis of Sun et al. (2021)
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Sea otters in a California estuary: Detecting temporal and spatial dynamics with volunteer monitoring
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Brainport, Automated valet parking, RS camera object detection
<p><strong>Scenario description</strong>:</p> <p>RS Camera object detection and publication of the iot message from type AutoPilot.ObjectDetection to the PMS via IoT platforms</p> <p><strong>Session description</strong>:</p> <p>A car drive on the AVP road segment and stop and the RS camera detect the car as obstacle and send the obstacle information to the PMS for free obstacle route calculation for AD-car</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Brainport, Automated valet parking, parking spot detected by drone
<p><strong>Scenario description</strong>:</p> <p>The drone receives AVP command message (Message type AutoPilot.DroneAVPCommand) from PMS via the IBM IoT Platfom. The command message contains the instruction about the selected parking spots to be checked. The drone takes off and fly to the corresponding parking spots detects the occupancy (FREE or OCCUPIED) of the parking spot and publishes the message from type AutoPilot.ParkingSpotDetection to the PMS via IBM Watson IoT platform und return to the lading position and landed. During the flight the drone sends continuously the message about it current position and some status information as message from type AutoPilot.PositionEstimate to the PMS via IoT Platform.</p> <p><strong>Session description</strong>:</p> <p>Selection of one free parking spot to be check (see command message contain), the drone detects the parking spot and publish the occupancy information to the PMS for parking management purpose</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Livorno, Urban driving, Automated car detects pothole
<p><strong>Scenario description</strong>:</p> <p>Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed.</p> <p><strong>Session description</strong>:</p> <p>Test session with only a connected car with smartphone based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Urban driving, IMU pothole detection
<p><strong>Scenario description</strong>:</p> <p>Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed.</p> <p><strong>Session description</strong>:</p> <p>Test session with only a connected car with IMU based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Urban driving, pothole detection
<p><strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU. The information is sent to the cloud and can be sent back to other connected vehicles for warning. The information is also transmitted via V2V to AD cars that can automatically adapt the speed.</p> <p><strong>Session description</strong>: Test session with only a connected car with 6LoWPAN based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Vigo,Urban driving, GLOSA and VRU detection
<p><strong>Scenario description:</strong></p> <p>GLOSA and VRU detection. The autonomous vehicle makes several turns on the scenario adapting its speed to the state of the traffic light. At an intersection controlled by a camera connected via IoT, a VRU is introduced into one of the laps so that the autonomous vehicle brakes when it receives the information via IoT.</p> <p><strong>Session description:</strong></p> <p>Human factor tests performed at CTAG proving grounds.</p> <p><strong>Datasets descriptions:</strong><br> <br> <strong>AUTOPILOT_Vigo_UrbanDriving_Vehicle: </strong>Data extracted from the CAN of the vehicle. Contains data regarding the state of the vehicle, such as speed, engine speed or fuel consumption.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_VehicleDynamics: </strong>Data extracted from the CAN of the vehicle.Contains data regarding the dynamic state of the vehicle, such as speed limit, acceleration or yaw rate.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_DriverVehicleInteraction: </strong>Data extracted from the CAN of the vehicle. Contains all the information about the actions that the driver can perform, such as the state of the brake, the steering wheel and the throttle.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_EnviromentSensorsAbsolute: </strong>Data extracted from the sensors of the vehicle in absolute coordinates.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving__EnviromentSensorsRelative: </strong>Data extracted from the sensors of the vehicle in relative coordinates.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_Positioning:</strong> Data from the GPS of the vehicle, such as latitude longitude and heading.</p> <p><strong>AUTOPILOT_Vigo_UrbanDriving_IoT: </strong>Data of the communications via IoT, coming from the OBU of the vehicle, the IoT server and the RSU that controls the camera and the traffic light.<br> </p> <p> </p>
Autism Detection Based on Eye Movement Sequences on the Web: A Scanpath Trend Analysis Approach
<p>We propose a novel approach to detect autism based on the eye-movement paths of users on the Web. This approach based on Scanpath Trend Analysis (STA) proposed by Eraslan et al. (2016, 2017). This dataset is created to provide supplementary data for our paper entitled "Autism Detection Based on Eye Movement Sequences on the Web: A Scanpath Trend Analysis Approach" presented at <a href="http://www.w4a.info/2020/">the 17th International Web for All Conference (W4A'20)</a>. This dataset includes all the individual paths used for the evaluation of our proposed approach. The dataset also includes the Python code to re-run the evaluation.</p> <p><strong>References:</strong></p> <ul> <li>Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2016. Scanpath Trend Analysis on Web Pages: Clustering Eye Tracking Scanpaths. ACM Transactions on the Web, (SCI-E), 10, 4, Article 20.</li> <li>Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2017. Engineering web-based interactive systems: trend analysis in eye tracking scanpaths with a tolerance. In Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS '17). ACM, New York, NY, USA, 3-8.</li> </ul>
Hypernym-LIBre: A free Web-based Corpus for Hypernym Detection [PoS and Dep-parsed Version]
<p>Hypernym-LIBre ( DOI: 10.5281/zenodo.3662204 ) is a free Web-based corpus for Hypernym detection.</p> <p>Here we provide its part-of-speech tagged and dependency-parsed version. Both have been annotated in the text as follows:</p> <p>For every word in the corpus, there is a PoS tag and dependency annotation separated by a '-'.</p> <p>Example: corrected-VERB-ROOT where corrected is the raw word followed by its PoS and dependency annotation.</p> <p> </p> <p>The file is provided as a zip file:</p> <p>Compressed File Size: ~15GB</p> <p>Total Size: 80.6GB uncompressed</p> <p>Number of Files: 442 files, ~180MB each</p> <p> </p> <pre>10.5281/zenodo.3695237</pre> <p>hyponym-hypernym pairs extracted from Hypernym-LIBre using Hearst patterns</p>
Topological Botnet Detection
<p>Large-scale topological botnet detection datasets for graph machine learning and network security, containing 4 different types of synthetic botnet topologies and 2 different real botnet topologies overlaid onto large real-world background traffic communication networks.</p> <p>Each dataset contains a specific botnet topology, with 960 graphs in total, randomly split to train/val/test sets. There are labels on both nodes and edges indicating whether they were in the botnet (evil) community. Learning tasks could target at predicting on nodes to detect whether they are botnet nodes, or recovering the whole botnet community by also predicting on edges as whether they belong to the original botnet.</p> <p>There are no unique features on nodes or edges, as the detection is purely based on topological properties within the graph (topological discovery).</p> <p> </p>
Sub-pixel accuracy in electron detection using a convolutional neural network
<p><strong>Abstract</strong></p> <p>Modern direct electron detectors (DEDs) provided a giant leap in the use of cryogenic electron microscopy (cryo-EM) to study the structures of macromolecules and complexes thereof. However, the currently available commercial DEDs, all based on the monolithic active pixel sensor, still require relative long exposure times and their best results have been obtained at 300 keV. There is a need for pixelated electron counting detectors that can be operated at a broader range of energies, at higher throughput and higher dynamic range. Hybrid Pixel Detectors (HPDs) of the Medipix family were reported to be unsuitable for cryo-EM at energies above 80 keV as those electrons would affect too many pixels. Here we show that the Timepix3, part of the Medipix family, can be used for cryo-EM applications at higher energies. We tested Timepix3 detectors on a 200 keV FEI Tecnai Arctica microscope and a 300 keV FEI Tecnai G2 Polara microscope. A correction method was developed to correct for per-pixel differences in output. Timepix3 data were simulated for individual electron events using the package Geant4Medipix. Global statistical characteristics of the simulated detector response were in good agreement with experimental results. A convolutional neural network (CNN) was trained using the simulated data to predict the incident position of the electron within a pixel cluster. After training, the CNN predicted, on average, .39 pixel and 0.42 pixel from the incident electron position for 200 keV and 300 keV electrons respectively. The CNN improved the MTF of experimental data at half Nyquist from 0.39 to 0.70 at 200 keV, and from 0.06 to 0.65 at 300 keV respectively. We illustrate that the useful dose-lifetime of a protein can be measured within a 1 second exposure using Timepix3.</p> <p><strong>Data description</strong></p> <p>Data has been split up in experimental data, simulations, neural net models and ToT correction results. In general: each directory contains individual READMEs with steps how to reproduce the data.</p> <p><strong>Experimental data</strong></p> <p>For each type of data at 200 kV or 300 kV only the input raw data has been added and the resulting image file. Intermediate files have been left out.</p> <p><strong>Models</strong></p> <p>The models are the CNN models generated at 200 and 300 kV.</p> <p><strong>Simulated data</strong></p> <p>The simulated data consists of the dataset used for training the neural network and the indepedently simulated validation set. </p> <p><strong>ToT correction</strong></p> <p>The ToT correction file only contain the resulting correction matrix. The experimental flat field data has been left out, due to its volume. It's about 300 GiB of data for both 200 and 300 kV.</p> <p><strong>Software </strong></p> <p>The software used can be found as related identifiers to this deposit.</p>
Exploring Blink-Rate Behaviors for Cybersickness Detection in VR
<p>This Dataset was constructed for the paper "Exploring Blink-Rate Behaviors for Cybersickness Detection in VR" by Lopes, Tian and Boulic; published at the IEEE VR conference in 2020.</p> <p>All data was recorded using the HTC Vive Pro Eye at a sampling rate of 120hz. Furthermore, this dataset also includes the SSQ responses obtained from the participants.</p> <p>Further details are included in the .zip file on how to interpret and process this data.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.