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  • Research data
  • 2017-2021
  • Open Access
  • Transport Research

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  • Open Access
    Authors: 
    Natural Resources Canada | Ressources naturelles Canada;
    Publisher: Open Data Canada

    Cette collection fait maintenant partie du patrimoine, elle n'est plus maintenue. Elle pourrait ne pas respecter les normes actuelles du gouvernement. Les utilisateurs de l'Atlas du Canada à l'échelle nationale de 1/5 000 000 (publication du mai 2017) devraient envisager l'utilisation du nouveau produit CanVec. La série de Données de l'Atlas du Canada à l'échelle nationale de 1/5 000 000 comprend des jeux de données sur les limites, les côtes, les îles, les noms de lieux, les chemins de fer, les rivières, les routes, les traversiers et les étendues d'eau qui ont été compilés de manière à être utilisés dans des cartes à moyenne échelle (1/5 000 000 à 1/15 000 000) de l'atlas. Ces jeux de données ont été intégrés de sorte que leurs positions relatives soient correctes du point de vue cartographique. Toutes les données à l'extérieur du Canada comprises dans ces jeux de données s'y retrouvent strictement pour avoir le contexte global des données. This collection is a legacy product that is no longer maintained. It may not meet current government standards. Users of Atlas of Canada National Scale Data 1:5,000,000 (release of May 2017) should plan to make the transition towards the new CanVec product. The Atlas of Canada National Scale Data 1:5,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas medium scale (1:5,000,000 to 1:15,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

  • Open Access English
    Authors: 
    Haas, Christian; Casey, Alec; Lensu, Mikko;
    Publisher: PANGAEA
    Project: EC | SAFEWIN (233884)
  • Open Access
    Authors: 
    Cama Pinto, Alejandro;
    Publisher: Mendeley

    1) Resume_Raw_Data.xlsx Is the resume of the primary data that was extracted from the Spanish electricity grid database in Megawatts. 2) Average_electrical_Available_2007-2019_in_Spain_in_Megawatt.xlsx The table contains the Average electrical demand per month during 2007-2019 in Spain in Megawatt 3) Average_electrical_Available_2007-2019_in_Spain_in_Megawatt.xlsx The table contains the Average electrical availability per month during 2007-2019 in Megawatt in Spain 4) EV_amount_recharged.xlsx The table contains the EV amount that can be recharged from 0 to 24 hours in Spain 5) Forecast_Average_electrical_demand_(Megawatt)_2020.xlsx The table contains the Forecast for Average electrical demand (Megawatt) per month during 2020 in Spain 6) Forecast_Average_electrical_demand_(Megawatt)_2021.xlsx The table contains the Forecast for Average electrical demand (Megawatt) per month during 2021 in Spain 7) Forecast_Average_electrical_demand_(Megawatt)_2022.xlsx The table contains the Forecast for Average electrical demand (Megawatt) per month during 2022 in Spain 8) Forecast_Average_electrical_demand_(Megawatt)_2023.xlsx The table contains the Forecast for Average electrical demand (Megawatt) per month during 2023 in Spain 9) Electrical_availability_forecast_(MW)_2020.xlsx The table contains the Electrical availability forecast (MW) from 0 to 24 h in 2020 in Spain 10) Electrical_availability_forecast_(MW)_2021.xlsx The table contains the Electrical availability forecast (MW) from 0 to 24 h in 2021 in Spain 11) Electrical_availability_forecast_(MW)_2022.xlsx The table contains the Electrical availability forecast (MW) from 0 to 24 h in 2022 in Spain 12) Electrical_availability_forecast_(MW)_2023.xlsx The table contains the Electrical availability forecast (MW) from 0 to 24 h in 2023 in Spain 13) EV_amount_forecast_recharged_simultaneously_2020.xlsx The table contains the EV amount forecast that can be recharged simultaneously from 0 to 24 hours in 2020 in Spain 14) EV_amount_forecast_recharged_simultaneously_2021.xlsx The table contains the EV amount forecast that can be recharged simultaneously from 0 to 24 hours in 2021 in Spain 15) EV_amount_forecast_recharged_simultaneously_2022.xlsx The table contains the EV amount forecast that can be recharged simultaneously from 0 to 24 hours in 2022 in Spain 16) EV_amount_forecast_recharged_simultaneously_2023.xlsx The table contains the EV amount forecast that can be recharged simultaneously from 0 to 24 hours in 2023 in Spain When using the data, you must quote the following article: Martínez-Lao, J., Montoya, F.G., Montoya, M.G., Manzano-Agugliaro, F. Electric vehicles in Spain: An overview of charging systems (2017) Renewable and Sustainable Energy Reviews, 77, pp. 970-983. DOI: 10.1016/j.rser.2016.11.239

  • Open Access
    Authors: 
    DLR (Deutsches Zentrum für Luft- und Raumfahrt); VICOMTECH; NEVS (National Electric Vehicle Sweden); TNO;
    Publisher: Zenodo
    Project: EC | AUTOPILOT (731993)

    Scenario description: The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination parking spot and the free obstacle route and drives from the drop-off position and parks to the destination parking spot. During the parking process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus) Session description: The dropoff scenario with DLR vehicle. DLR vehicle parks autonomously from the dropoff location to the selected parking spot at the parking area on DLR test area in Brunswick. Datasets descriptions: AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand: Data sent from drone Dataset Description This dataset contains route information for a vehicle to a designated parking spot AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage: Data sent between all devices, vehicles and services 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. AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection: Data sent from drone to parkingService Dataset Description This dataset contains informaton about detected parking spots AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled: Data from GPS on the vehicle Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand: Data sent from ParkingService to vehicle Dataset Description This dataset contains route to parkingspot, and some other environmental information AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus: Data sent from vehicle to ParkingService Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

  • Open Access
    Authors: 
    AVR; CNIT; LINKS; TIM;
    Publisher: Zenodo
    Project: EC | AUTOPILOT (731993)

    Scenario description: Test session for fallen bicycle with AD and connected vehicle Session description: The fallen bicycle use case aims to demonstrate the possibility for a vehicle to detect in advance, using V2X communication, the presence of a fallen bicycle on the road. In case of fall, the bicycle signals its presence to the other vehicles using DENM messages. The AD car safely reduces its speed and stops. Datasets descriptions: AUTOPILOT_Livorno_UrbanDriving_Vehicle_all: Data generated from the vehicle sensors 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 AUTOPILOT_Livorno_UrbanDriving_V2X_all: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. AUTOPILOT_Livorno_UrbanDriving_IoT_all: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.

  • Open Access
    Authors: 
    Gu, Xiaoyan; Lu, K. (Kaihua);
    Publisher: 4TU.Centre for Research Data
    Country: Netherlands

    These data are the statistical tables of the current operating lines and transfer stations of China's high-speed railway and Japan's Shinkansen (up to the end of 2018). Data are collected from China's high-speed rail network and Baidu Japan Shinkansen inquiry. In the first table of the statistical tables of China's high-speed railway and Japan's Shinkansen, the first column is the operation line, followed by the transfer station and terminal station on the line. In the second table, both the first horizontal and the first column are lines. The intermediate parameter is the transfer station between lines.

  • Research data . 2018
    Open Access English
    Authors: 
    Noskov, Alexey; WeGovNowConsortium; OpenStreetMap;
    Publisher: Zenodo
    Project: EC | WeGovNow (693514)

    A collection of data files for the WeGovNow project. It contains the source data provided by stake holders, OpenStreetMap data (under the OSM data license) and data generated by the IGIS.TK applications

  • Open Access
    Authors: 
    AVR; CNIT; LINKS; TIM;
    Publisher: Zenodo
    Project: EC | AUTOPILOT (731993)

    Scenario description: Test session for AD vehicle approaching an intersection with jaywalking at traffic light Session description: A "smart" traffic light with a stereocamera sends SPaT and MAP messages describing the topology, actual status of the traffic light, presence of pedestrian, and jaywalking occurrence to other connected vehicles (via DENM) and to the oneM2M platform on the cloud. An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, or even stop to avoid collision with pedestrian. The influence of other vehicles moving in front is considered too. Datasets descriptions: AUTOPILOT_Livorno_UrbanDriving_Vehicle_all: Data generated from the vehicle sensors 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 AUTOPILOT_Livorno_UrbanDriving_V2X_all: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. AUTOPILOT_Livorno_UrbanDriving_IoT_all: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.

  • Open Access
    Authors: 
    López Vilar, Jordi; Gutiérrez Garcia-Moreno, Anna;
    Publisher: Repositori de Dades de Recerca

    Documentació arqueològica de la prospecció efectuada a la Roca Plana (Tarragona) entre els dies 7 i 16 d’abril de 2017. Inclou fotografies, planimetries I informes. Aquesta infraestructura romana és un moll fet a partir del retoc d'una plataforma rocosa natural que va fer les funcions de moll per a la càrrega de la pedra extreta al Mèdol. Els resultats obtinguts demostren que es tracta el primer moll pròpiament industrial d’època romana detectat a Catalunya i un dels pocs de l’Occident mediterrani, de gran importància per l’arqueologia. Documentation of the archaeological prospection carried out in La Roca Plana (Tarragona) between 7th and 16th April 2017. Includes photos, planimetries and reports. This Roman infrastructure is a pier made from the retouching of a natural rock platform that served as a dock to load the stone extracted stone into the Mèdol. The results obtained show that it is the first industrial pier from the Roman period detected in Catalonia and one of the few in the Western Mediterranean.

  • Open Access English
    Authors: 
    Lalas, Dimitri; Gakis, Nikolaos; Mirasgedis, Sebastian; Georgopoulou, Elena; Sarafidis, Yannis; Doukas, Haris;
    Publisher: Zenodo
    Project: EC | PARIS REINFORCE (820846)

    This dataset contains the underlying data for the following publication: Lalas, D., Gakis, N., Mirasgedis, S., Georgopoulou, E., Sarafidis, Y., & Doukas, H. (2021). Energy and GHG Emissions Aspects of the COVID Impact in Greece. Energies, 14(7), 1955. https://doi.org/10.3390/en14071955.

Advanced search in
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arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
349 Research products, page 1 of 35
  • Open Access
    Authors: 
    Natural Resources Canada | Ressources naturelles Canada;
    Publisher: Open Data Canada

    Cette collection fait maintenant partie du patrimoine, elle n'est plus maintenue. Elle pourrait ne pas respecter les normes actuelles du gouvernement. Les utilisateurs de l'Atlas du Canada à l'échelle nationale de 1/5 000 000 (publication du mai 2017) devraient envisager l'utilisation du nouveau produit CanVec. La série de Données de l'Atlas du Canada à l'échelle nationale de 1/5 000 000 comprend des jeux de données sur les limites, les côtes, les îles, les noms de lieux, les chemins de fer, les rivières, les routes, les traversiers et les étendues d'eau qui ont été compilés de manière à être utilisés dans des cartes à moyenne échelle (1/5 000 000 à 1/15 000 000) de l'atlas. Ces jeux de données ont été intégrés de sorte que leurs positions relatives soient correctes du point de vue cartographique. Toutes les données à l'extérieur du Canada comprises dans ces jeux de données s'y retrouvent strictement pour avoir le contexte global des données. This collection is a legacy product that is no longer maintained. It may not meet current government standards. Users of Atlas of Canada National Scale Data 1:5,000,000 (release of May 2017) should plan to make the transition towards the new CanVec product. The Atlas of Canada National Scale Data 1:5,000,000 Series consists of boundary, coast, island, place name, railway, river, road, road ferry and waterbody data sets that were compiled to be used for atlas medium scale (1:5,000,000 to 1:15,000,000) mapping. These data sets have been integrated so that their relative positions are cartographically correct. Any data outside of Canada included in the data sets is strictly to complete the context of the data.

  • Open Access English
    Authors: 
    Haas, Christian; Casey, Alec; Lensu, Mikko;
    Publisher: PANGAEA
    Project: EC | SAFEWIN (233884)
  • Open Access
    Authors: 
    Cama Pinto, Alejandro;
    Publisher: Mendeley

    1) Resume_Raw_Data.xlsx Is the resume of the primary data that was extracted from the Spanish electricity grid database in Megawatts. 2) Average_electrical_Available_2007-2019_in_Spain_in_Megawatt.xlsx The table contains the Average electrical demand per month during 2007-2019 in Spain in Megawatt 3) Average_electrical_Available_2007-2019_in_Spain_in_Megawatt.xlsx The table contains the Average electrical availability per month during 2007-2019 in Megawatt in Spain 4) EV_amount_recharged.xlsx The table contains the EV amount that can be recharged from 0 to 24 hours in Spain 5) Forecast_Average_electrical_demand_(Megawatt)_2020.xlsx The table contains the Forecast for Average electrical demand (Megawatt) per month during 2020 in Spain 6) Forecast_Average_electrical_demand_(Megawatt)_2021.xlsx The table contains the Forecast for Average electrical demand (Megawatt) per month during 2021 in Spain 7) Forecast_Average_electrical_demand_(Megawatt)_2022.xlsx The table contains the Forecast for Average electrical demand (Megawatt) per month during 2022 in Spain 8) Forecast_Average_electrical_demand_(Megawatt)_2023.xlsx The table contains the Forecast for Average electrical demand (Megawatt) per month during 2023 in Spain 9) Electrical_availability_forecast_(MW)_2020.xlsx The table contains the Electrical availability forecast (MW) from 0 to 24 h in 2020 in Spain 10) Electrical_availability_forecast_(MW)_2021.xlsx The table contains the Electrical availability forecast (MW) from 0 to 24 h in 2021 in Spain 11) Electrical_availability_forecast_(MW)_2022.xlsx The table contains the Electrical availability forecast (MW) from 0 to 24 h in 2022 in Spain 12) Electrical_availability_forecast_(MW)_2023.xlsx The table contains the Electrical availability forecast (MW) from 0 to 24 h in 2023 in Spain 13) EV_amount_forecast_recharged_simultaneously_2020.xlsx The table contains the EV amount forecast that can be recharged simultaneously from 0 to 24 hours in 2020 in Spain 14) EV_amount_forecast_recharged_simultaneously_2021.xlsx The table contains the EV amount forecast that can be recharged simultaneously from 0 to 24 hours in 2021 in Spain 15) EV_amount_forecast_recharged_simultaneously_2022.xlsx The table contains the EV amount forecast that can be recharged simultaneously from 0 to 24 hours in 2022 in Spain 16) EV_amount_forecast_recharged_simultaneously_2023.xlsx The table contains the EV amount forecast that can be recharged simultaneously from 0 to 24 hours in 2023 in Spain When using the data, you must quote the following article: Martínez-Lao, J., Montoya, F.G., Montoya, M.G., Manzano-Agugliaro, F. Electric vehicles in Spain: An overview of charging systems (2017) Renewable and Sustainable Energy Reviews, 77, pp. 970-983. DOI: 10.1016/j.rser.2016.11.239

  • Open Access
    Authors: 
    DLR (Deutsches Zentrum für Luft- und Raumfahrt); VICOMTECH; NEVS (National Electric Vehicle Sweden); TNO;
    Publisher: Zenodo
    Project: EC | AUTOPILOT (731993)

    Scenario description: The AD-vehicle receives parking command message (AutoPilot.VehicleCommand) containing the destination parking spot and the free obstacle route and drives from the drop-off position and parks to the destination parking spot. During the parking process the vehicle send two type of messages (AutoPilot.PositionEstimate and AutoPilot.VehicleAVPStatus) Session description: The dropoff scenario with DLR vehicle. DLR vehicle parks autonomously from the dropoff location to the selected parking spot at the parking area on DLR test area in Brunswick. Datasets descriptions: AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand: Data sent from drone Dataset Description This dataset contains route information for a vehicle to a designated parking spot AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage: Data sent between all devices, vehicles and services 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. AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection: Data sent from drone to parkingService Dataset Description This dataset contains informaton about detected parking spots AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled: Data from GPS on the vehicle Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand: Data sent from ParkingService to vehicle Dataset Description This dataset contains route to parkingspot, and some other environmental information AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus: Data sent from vehicle to ParkingService Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

  • Open Access
    Authors: 
    AVR; CNIT; LINKS; TIM;
    Publisher: Zenodo
    Project: EC | AUTOPILOT (731993)

    Scenario description: Test session for fallen bicycle with AD and connected vehicle Session description: The fallen bicycle use case aims to demonstrate the possibility for a vehicle to detect in advance, using V2X communication, the presence of a fallen bicycle on the road. In case of fall, the bicycle signals its presence to the other vehicles using DENM messages. The AD car safely reduces its speed and stops. Datasets descriptions: AUTOPILOT_Livorno_UrbanDriving_Vehicle_all: Data generated from the vehicle sensors 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 AUTOPILOT_Livorno_UrbanDriving_V2X_all: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. AUTOPILOT_Livorno_UrbanDriving_IoT_all: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.

  • Open Access
    Authors: 
    Gu, Xiaoyan; Lu, K. (Kaihua);
    Publisher: 4TU.Centre for Research Data
    Country: Netherlands

    These data are the statistical tables of the current operating lines and transfer stations of China's high-speed railway and Japan's Shinkansen (up to the end of 2018). Data are collected from China's high-speed rail network and Baidu Japan Shinkansen inquiry. In the first table of the statistical tables of China's high-speed railway and Japan's Shinkansen, the first column is the operation line, followed by the transfer station and terminal station on the line. In the second table, both the first horizontal and the first column are lines. The intermediate parameter is the transfer station between lines.

  • Research data . 2018
    Open Access English
    Authors: 
    Noskov, Alexey; WeGovNowConsortium; OpenStreetMap;
    Publisher: Zenodo
    Project: EC | WeGovNow (693514)

    A collection of data files for the WeGovNow project. It contains the source data provided by stake holders, OpenStreetMap data (under the OSM data license) and data generated by the IGIS.TK applications

  • Open Access
    Authors: 
    AVR; CNIT; LINKS; TIM;
    Publisher: Zenodo
    Project: EC | AUTOPILOT (731993)

    Scenario description: Test session for AD vehicle approaching an intersection with jaywalking at traffic light Session description: A "smart" traffic light with a stereocamera sends SPaT and MAP messages describing the topology, actual status of the traffic light, presence of pedestrian, and jaywalking occurrence to other connected vehicles (via DENM) and to the oneM2M platform on the cloud. An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, or even stop to avoid collision with pedestrian. The influence of other vehicles moving in front is considered too. Datasets descriptions: AUTOPILOT_Livorno_UrbanDriving_Vehicle_all: Data generated from the vehicle sensors 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 AUTOPILOT_Livorno_UrbanDriving_V2X_all: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. AUTOPILOT_Livorno_UrbanDriving_IoT_all: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.

  • Open Access
    Authors: 
    López Vilar, Jordi; Gutiérrez Garcia-Moreno, Anna;
    Publisher: Repositori de Dades de Recerca

    Documentació arqueològica de la prospecció efectuada a la Roca Plana (Tarragona) entre els dies 7 i 16 d’abril de 2017. Inclou fotografies, planimetries I informes. Aquesta infraestructura romana és un moll fet a partir del retoc d'una plataforma rocosa natural que va fer les funcions de moll per a la càrrega de la pedra extreta al Mèdol. Els resultats obtinguts demostren que es tracta el primer moll pròpiament industrial d’època romana detectat a Catalunya i un dels pocs de l’Occident mediterrani, de gran importància per l’arqueologia. Documentation of the archaeological prospection carried out in La Roca Plana (Tarragona) between 7th and 16th April 2017. Includes photos, planimetries and reports. This Roman infrastructure is a pier made from the retouching of a natural rock platform that served as a dock to load the stone extracted stone into the Mèdol. The results obtained show that it is the first industrial pier from the Roman period detected in Catalonia and one of the few in the Western Mediterranean.

  • Open Access English
    Authors: 
    Lalas, Dimitri; Gakis, Nikolaos; Mirasgedis, Sebastian; Georgopoulou, Elena; Sarafidis, Yannis; Doukas, Haris;
    Publisher: Zenodo
    Project: EC | PARIS REINFORCE (820846)

    This dataset contains the underlying data for the following publication: Lalas, D., Gakis, N., Mirasgedis, S., Georgopoulou, E., Sarafidis, Y., & Doukas, H. (2021). Energy and GHG Emissions Aspects of the COVID Impact in Greece. Energies, 14(7), 1955. https://doi.org/10.3390/en14071955.

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