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Annual measurements of vegetation cover on line-point intercept transects from the three Conmod Pilot study locations at Jornada Basin LTER, 2008-2016
This data package contains annual measurements of vegetation cover, species, and height from transects at the Connectivity Modifier (Conmod) Pilot study on the Jornada Experimental Range from 2008-2016. There were 3 sites for this study: Gravelly Ridges, Aeolian, and Dona Ana. Within each site, there were 8 study plots, 4 of which were treatment plots where connectivity modules (conmods) were installed to decrease gap sizes between perennial vegetation. The plots were 8 x 8 meters and had an 8 x 8 meter buffer zone on both sides of the plot (upwind and downwind). Beginning in 2008, vegetation cover by species, and vegetation heights were collected annually in all plots using the line-point intercept method. These data were collected in 2008-2010, 2012 and 2016. At each plot, four parallel 24-meter transects crossing the upwind buffer, the plot, and the downwind buffer were measured. These parallel transects were spaced at 0.8, 2.8, 4.3, and 7.2 meter intervals across the plot and buffer areas. Vegetation cover and height were read at points arranged at 25 centimeter increments along each transect. This study is complete (finished in 2016) and was the pilot study to the newer Cross Scale Interactions Study.
Bedload data (deposition from overland flow) measured at the three Conmod Pilot study locations at Jornada Basin LTER, 2008-2010
This data package contains measurements of bedload samples (suspended particles) collected during overland flow events in the Connectivity Modifier (Conmod) Pilot study on the Jornada Experimental Range from 2008-2010. There were 3 sites for this study: Gravelly Ridges, Aeolian, and Dona Ana. Within each site, there were 8 study plots, 4 of which were treatment plots where connectivity modules (conmods) were installed to decrease gap sizes between perennial vegetation. The plots were 8 x 8 meters and had an 8 x 8 meter buffer zone on both sides of the plot (upwind and downwind). To collect water from overland flow events, four to six belowground catchment containers were installed in upslope and downslope buffer zones at each study plots. Bedload containers were monitored monthly 2008, 2009, and 2010. Bedload material collected by the containers during overland flow events was weighed and analyzed for organic matter content by the loss on ignition method. This study is complete and was the pilot study to the newer Cross Scale Interactions Study.
Repeat, ground-based photographs of microplots at the three Conmod Pilot study locations at Jornada Basin LTER, 2008-2016
This data package contains ground-based, repeat photographs taken at plots in the Connectivity Modifier (Conmod) Pilot study on the Jornada Experimental Range from 2008-2016. There were 3 sites for this study: Gravelly Ridges, Aeolian, and Dona Ana. Within each site, there were 8 study plots, 4 of which were treatment plots where connectivity modules (conmods) were installed to decrease gap sizes between perennial vegetation. The plots were 8 x 8 meters and had an 8 x 8 meter buffer zone on both sides of the plot (upwind and downwind). Beginning in 2008, photographs were taken once to twice per year at ten microplots located within the 8 study plots per site to document plant litter, plant germination and growth, and soil deposition/removal by wind and water transport. Five photos were taken of each microplot: One overhead (from directly over the microplot) and 4 lateral views at ground level from each cardinal direction. This data package only contains archives with the overhead photographs, but lateral view photos are available on request. Photo filenames are fully descriptive of the site, plot, microplot, photo view, and date taken (see Methods description for details). This study is complete (finished in 2016) and was the pilot study to the newer Cross Scale Interactions Study.
Mouse_rest_psilocybin_pilot
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Brainport, Highway pilot, control center ADAS instructions publication
<p><strong>Scenario description</strong>:</p> <p>Hazards have been detected and populate the map. Control Center publishes ADAS instructions for some hazards.</p> <p><strong>Session description</strong>:</p> <p>Control Center published ADAS Instructions for 4 identified hazards of the track.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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, Highway pilot, car in manual mode, camera detection
<p><strong>Scenario description</strong>:</p> <p>The detection car drives around the track in manual mode, with Camera detection ON.</p> <p><strong>Session description</strong>:</p> <p>25 laps with VW Tiguan on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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, Highway pilot, car in manual mode, but receiving adaption instructions
<p><strong>Scenario description</strong>:</p> <p>The driving adaptation car is driven around the track in manual mode, but driving instructions are communicated to the driver.</p> <p><strong>Session description</strong>:</p> <p>12 laps with Jaguar F-Pace on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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, Highway pilot, car in manual mode, IMU detection
<p><strong>Scenario description</strong>:</p> <p>The detection car drives around the track in manual mode, with IMU detection ON.</p> <p><strong>Session description</strong>:</p> <p>25 laps with VW Tiguan on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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, Highway pilot, simulated autonomous mode
<p><strong>Scenario description</strong>:</p> <p>The driving adaptation car drives around the track in simulated autonomous mode (ACC).</p> <p><strong>Session description</strong>:</p> <p>18 laps with Jaguar F-Pace on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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, Highway pilot, detection car reference laps
<p><strong>Scenario description</strong>:</p> <p>The detection car drives around the track in manual mode, at an average speed close to laps for detections, but with all detections systems disabled.</p> <p><strong>Session description</strong>:</p> <p>25 laps with VW Tiguan on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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, Highway pilot, driving adaptation at hazards locations
<p><strong>Scenario description</strong>:</p> <p>The driving adaptation car drives around the track in simulated autonomous mode (ACC) and applies ADASINs at Hazards locations.</p> <p><strong>Session description</strong>:</p> <p>25 laps with Jaguar F-Pace on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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, Highway pilot, detection car, manual mode, camera and IMU detection on
<p><strong>Scenario description</strong>:</p> <p>The detection car drives around the track in manual mode, with Camera and IMU detection ON.</p> <p><strong>Session description</strong>:</p> <p>25 laps with VW Tiguan on Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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, Highway pilot, road side camera detection
<p><strong>Scenario description</strong>:</p> <p>The road side camera detects objects (obstacle) on the test track and publishes the ANO IoT message.</p> <p><strong>Session description</strong>:</p> <p>10 minute observation of a section of the Automotive Campus Test Track</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system</p> <p>Dataset Description This dataset contains the ADAS command in the vehicle</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles</p> <p>Dataset Description This dataset contains information about all the detected anomalies on the road</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service</p> <p>Dataset Description This dataset contains images of the detected anomalies</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_Hazard</strong>: Data sent from service to vehicle</p> <p>Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards</p> <p><strong>AUTOPILOT_BrainPort_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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_HighwayPilot_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, Highway pilot, only connected cars
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events:</p> <p>1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with only connected cars, lap of 12,3 km on the highway. The goal is to check all the systems and data management before the next test session with AD cars.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting 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_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, data management connected car
<p><strong>Scenario description</strong>: Dynamic speed adaptation due to puddle on the road</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway" at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events:</p> <p>1 The puddle monitoring system of the highway trigger a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of dangerous area, as notified by the «smart road», the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>pre-test session with only connected cars, lap of 12,3 km on the highway. Goal is to check all the system and data management.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting 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_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated car, two connected cars, smart highway
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events: </p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and two connected cars, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting 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_HighwayPilot_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 Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated and connected car and one connected car
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events:</p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and one connected car, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting 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_HighwayPilot_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 Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
VR360 Consumption DataSet - ImAc Open Pilot
<p>This dataset has been created under the framework of the EU H2020 ImAc project<br> (<a href="https://www.imac-project.eu/">https://www.imac-project.eu/</a>). It contains traces from Quality of Service (QoS) and Quality of Experience (QoE) related</p> <p>metrics when watching 360º videos augmented with access services (mainly subtitles, and sign language),<br> by using different branches of the ImAc player (<a href="http://imac.i2cat.net/player/">http://imac.i2cat.net/player/</a>) in an open environment.</p> <p>The dataset is comprised of two folders related to two pilots launched in the ImAc project, led by a German and a Spanish broadcaster. In total, it contains traces from more than 800 video watching sessions.</p> <p>[More details in the README file.]</p> <p>CONTACT INFO: <a href="mailto:media.team@i2cat.net">media.team@i2cat.net</a></p> <p> </p>
CPTu Lisbon pilot site campaign 2
<p>Raw CPTu data from the field investigation works at the Lisbon pilot site of the LIQUEFACT project.</p> <p>Location map of the site investigation points and further details are provided in Viana da Fonseca et al. (2019).</p> <p>Viana da Fonseca, A., Ferreira, C., Coelho, C., Coelho, J., Quintero, J., Rios, S., Millen, M., Cordeiro, D. (2019). Chapter 3 of Deliverable D2.7 - Methodology for assessment of earthquake‐induced risk of soil liquefaction at the four European testing sites (microzonation). LIQUEFACT project, Horizon 2020 European Union funding for Research & Innovation, GA nº. 700748 (<a href="http://www.liquefact.eu">www.liquefact.eu</a>).</p>
CPTu Lisbon pilot site campaign 1
<p>Raw CPTu data from the field investigation works at the Lisbon pilot site of the LIQUEFACT project.</p> <p>Location map of the site investigation points and further details are provided in Viana da Fonseca et al. (2019).</p> <p>Viana da Fonseca, A., Ferreira, C., Coelho, C., Coelho, J., Quintero, J., Rios, S., Millen, M., Cordeiro, D. (2019). Chapter 3 of Deliverable D2.7 - Methodology for assessment of earthquake‐induced risk of soil liquefaction at the four European testing sites (microzonation). LIQUEFACT project, Horizon 2020 European Union funding for Research & Innovation, GA nº. 700748 (<a href="http://www.liquefact.eu">www.liquefact.eu</a>).</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.