cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3603272
title: Livorno, Urban driving, Pedestrian detection

description: <strong>Scenario description</strong>: The pedestrian detection use case aims to demonstrate the possibility for a vehicle to be informed, using V2X communication, of the presence of a pedestrian crossing the road when the traffic light is green for the vehicle. In case of presence of a pedestrian, the RSU with detection capabilities signals the presence of the pedestrian to the vehicles using DENM messages. <strong>Session description</strong>: Pedestrian detection <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Drining 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 <strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: 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. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.17632/7hzj6rfg9v.1
title: Data for: In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks

description: Supplimentary files
DOI: 10.5281/zenodo.3603268
title: Livorno, Urban driving, Automated and connected vehicle and smart traffic light

description: <strong>Scenario description</strong>: Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light. <strong>Session description</strong>: A "smart" traffic light sends SPaT and MAP messages describing the topology, actual status of the traffic light to other connected vehicles and to the oneM2M platform on the cloud.<br> An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, considering also other vehicles moving in front. Goal is to record data for the technical evaluation. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Drining 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 <strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: 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. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3603269
title: Livorno, Urban driving, Automated and connected vehicle and smart traffic light

description: <strong>Scenario description</strong>: Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light. <strong>Session description</strong>: A "smart" traffic light sends SPaT and MAP messages describing the topology, actual status of the traffic light to other connected vehicles and to the oneM2M platform on the cloud.<br> An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, considering also other vehicles moving in front. Goal is to record data for the technical evaluation. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Drining 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 <strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: 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. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3603259
title: Livorno, Urban driving, Automated vehicle and smart traffic light

description: <strong>Scenario description</strong>: <strong>Session description</strong>: <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: 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 <strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: 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. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5519/qd.nhve7wt2
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (6250 records)
DOI: 10.17632/jmdgvz9d62.1
title: Andaman Sea ADM-9

description: 26 thousand years of sea surface temperature (SST) and δ18O of seawater (δ18Osw) from the paired data of Mg/Ca and δ18O on the planktic foraminifer in core ADM-9 collected from the Andaman Sea.
DOI: 10.17632/jmdgvz9d62.2
title: Andaman Sea ADM-9

description: 26 thousand years of sea surface temperature (SST) and δ18O of seawater (δ18Osw) from the paired data of Mg/Ca and δ18O on the planktic foraminifer in core ADM-9 collected from the Andaman Sea.
DOI: 10.5281/zenodo.3603258
title: Livorno, Urban driving, Automated vehicle and smart traffic light

description: <strong>Scenario description</strong>: <strong>Session description</strong>: <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: 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 <strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: 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. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3605617
title: Patient-reported outcomes predict return to work and health-related quality of life 6 months after cardiac rehabilitation: Results from a German multi-centre registry (OutCaRe): dataset

description: This is the resulting data set from the prospective observational study (available cases at the time of follow up monitoring) OutCaRe, that is the basis of all results presented in the article "Patient-reported outcomes predict return to work and health-related quality of life 6 months after cardiac rehabilitation: Results from a German multi-centre registry (OutCaRe)". OutCaRe is a registered study (https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00011418).
DOI: 10.5281/zenodo.3605618
title: Patient-reported outcomes predict return to work and health-related quality of life 6 months after cardiac rehabilitation: Results from a German multi-centre registry (OutCaRe): dataset

description: This is the resulting data set from the prospective observational study (available cases at the time of follow up monitoring) OutCaRe, that is the basis of all results presented in the article "Patient-reported outcomes predict return to work and health-related quality of life 6 months after cardiac rehabilitation: Results from a German multi-centre registry (OutCaRe)". OutCaRe is a registered study (https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00011418).
DOI: 10.17632/rts67csbyx.4
title: Data for: INTRAPERITONEAL HYPERTHERMIC CHEMOTHERAPY AFTER CYTOREDUCTION IN PATIENTS WITH PERITONEAL METASTASES FROM ENDOMETRIAL CANCER. THE NEXT FRONTIER?

description: Data on which the study is based
DOI: 10.6073/pasta/5b595f9d24c4ae17e4be0385316f2865
title: Mohonk Preserve Amphibian and Water Quality Monitoring Dataset at 11 Vernal Pools from 1931-Present

description: "The Mohonk Preserve's Daniel Smiley Research Center has been monitoring species occupancy, reproductive success, and water quality of 11 vernal pools (Ski Loop, Bonticou, Terrace, Long Woodland Pool, Long Woodland Swamp, Oakwood, Sleepy Hollow, Hermits, North Mud Pond, Canaan, and Talus) on the Preserve each spring from April 1931 to May 2019 (present). This project aims to document changes in the reproductive behavior and phenology of amphibians and allow research access to historical, longitudinal records. The dataset is a paired record of amphibian occurence with environmental indicators spanning nearly 90 years of data collection. The dataset includes environmental conditions for the 730 sampling events associated with the species occurences with complete coverage air temperature and precipitation records and partial coverage for a variety of other weather and water quality measures. Species occurence data collection has included species identification and counts of live and dead adults, mated pairs, spermatophores, egg masses, juveniles, and tadpoles counts as well as a record of the level of frog calling. Weather conditions including precipitation, sky and wind codes; and water quality measurements including water temperature, pH, and depth. Collection of data was sporadic from 1931 - 1991 but has been collected consistently from 1991 to present. We also began monitoring dissolved oxygen, nitrate concentrations, and conductivity of the vernal pools using a YSI Sonde Professional Plus Instrument and turbidity using a turbidity tube in February 2018. The data collection is ongoing, as are digitization efforts, and the data package will receive periodic updates."
DOI: 10.17632/c37p323wvs.1
title: Comparison results of PR, SR and CS between MMSSA and state-of-the-art multimodal methods on 20 functions at different accuracy levels

description: We propose a multimodal version of squirrel search algorithm named as MMSSA. When solving multimodal optimization problems, MMSSA combines squirrel search with clustering, crowding, and sampling techniques to enhance its multimodal optimization ability. We tested MMSSA on twenty functions recommended by CEC'2013 which was specifically designed as benchmark problems for multimodal optimization.
DOI: 10.5281/zenodo.3602665
title: Livorno, Urban driving, Automated vehicle approaching intersection

description: <strong>Scenario description</strong>: Test session for AD vehicle approaching an intersection with jaywalking at traffic light <strong>Session description</strong>: 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. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: 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 <strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: 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. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3602666
title: Livorno, Urban driving, Automated vehicle approaching intersection

description: <strong>Scenario description</strong>: Test session for AD vehicle approaching an intersection with jaywalking at traffic light <strong>Session description</strong>: 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. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: 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 <strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: 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. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3606270
title: Supplementary materials for the paper "Benefits of Stabilization versus Rollback in Self-Stabilizing Graph-Based Applications on Eventually Consistent Key-Value Stores"

description: Supplementary materials for the paper "Benefits of Stabilization versus Rollback in Self-Stabilizing Graph-Based Applications on Eventually Consistent Key-Value Stores" The materials include experimental results and source code.
DOI: 10.5281/zenodo.3606271
title: Supplementary materials for the paper "Benefits of Stabilization versus Rollback in Self-Stabilizing Graph-Based Applications on Eventually Consistent Key-Value Stores"

description: Supplementary materials for the paper "Benefits of Stabilization versus Rollback in Self-Stabilizing Graph-Based Applications on Eventually Consistent Key-Value Stores" The materials include experimental results and source code.
DOI: 10.6073/pasta/6dcebcefed8b536dc387fe232306134e
title: Mohonk Preserve Amphibian and Water Quality Monitoring Dataset at 11 Vernal Pools from 1931-Present

description: "The Mohonk Preserve's Daniel Smiley Research Center has been monitoring species occupancy, reproductive success, and water quality of 11 vernal pools (Ski Loop, Bonticou, Terrace, Long Woodland Pool, Long Woodland Swamp, Oakwood, Sleepy Hollow, Hermits, North Mud Pond, Canaan, and Talus) on the Preserve each spring from April 1931 to May 2019 (present). This project aims to document changes in the reproductive behavior and phenology of amphibians and allow research access to historical, longitudinal records. The dataset is a paired record of amphibian occurence with environmental indicators spanning nearly 90 years of data collection. The dataset includes environmental conditions for the 730 sampling events associated with the species occurences with complete coverage air temperature and precipitation records and partial coverage for a variety of other weather and water quality measures. Species occurence data collection has included species identification and counts of live and dead adults, mated pairs, spermatophores, egg masses, juveniles, and tadpoles counts as well as a record of the level of frog calling. Weather conditions including precipitation, sky and wind codes; and water quality measurements including water temperature, pH, and depth. Collection of data was sporadic from 1931 - 1991 but has been collected consistently from 1991 to present. We also began monitoring dissolved oxygen, nitrate concentrations, and conductivity of the vernal pools using a YSI Sonde Professional Plus Instrument and turbidity using a turbidity tube in February 2018. The data collection is ongoing, as are digitization efforts, and the data package will receive periodic updates."
DOI: 10.17632/mznbhf2zbk.1
title: Treatment of hidradenitis suppurativa using long-pulsed hair removal Nd:Yag laser. A multicentric prospective randomized intra-individual comparative trial Supplementary information

description: Supplementary information
DOI: 10.18112/openneuro.ds002278.v1.0.1
title: Dense Investigation of Variability of Affect (DIVA)
DOI: 10.5519/qd.t7vrt7zb
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (22 records)
DOI: 10.15468/dok0ie
title: Redescription of the cicada genus Pycna Amyot & Audinet-Serville, 1843 (Hemiptera: Cicadidae: Cicadinae: Platypleurini) with the formation of two new genera, one new species, one revised species status and twenty-four new combinations

description: This dataset contains the digitized treatments in Plazi based on the original journal article Sanborn, Allen F. (2020): Redescription of the cicada genus Pycna Amyot & Audinet-Serville, 1843 (Hemiptera: Cicadidae: Cicadinae: Platypleurini) with the formation of two new genera, one new species, one revised species status and twenty-four new combinations. Zootaxa 4722 (2): 157-174, DOI: https://doi.org/10.11646/zootaxa.4722.2.3
DOI: 10.15468/tfmnuj
title: Qorimayus, a new genus of relictual, high-altitude harvestmen from western Argentina (Arachnida, Opiliones, Gonyleptidae) reveals trans-Andean phylogenetic links

description: This dataset contains the digitized treatments in Plazi based on the original journal article Acosta, Luis E. (2020): Qorimayus, a new genus of relictual, high-altitude harvestmen from western Argentina (Arachnida, Opiliones, Gonyleptidae) reveals trans-Andean phylogenetic links. Zootaxa 4722 (2): 129-156, DOI: https://doi.org/10.11646/zootaxa.4722.2.2
DOI: 10.15468/zuievw
title: A new species of Diphascon (Tardigrada: Hypsibiidae) from Northern China supported by integrated taxonomy

description: This dataset contains the digitized treatments in Plazi based on the original journal article Sun, Xue-Ling, Zhang, Jing-Yu, Wang, Ning, Zhao, Min, Luo, Xue-Gang (2020): A new species of Diphascon (Tardigrada: Hypsibiidae) from Northern China supported by integrated taxonomy. Zootaxa 4722 (2): 185-194, DOI: https://doi.org/10.11646/zootaxa.4722.2.5
DOI: 10.5281/zenodo.3602545
title: Livorno, Urban driving, Connected vehicle detects fallen bicyle

description: <strong>Scenario description</strong>: Test session for fallen bicycle with connected but not automated vehicle <strong>Session description</strong>: 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. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: 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 <strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: 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. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3602546
title: Livorno, Urban driving, Connected vehicle detects fallen bicyle

description: <strong>Scenario description</strong>: Test session for fallen bicycle with connected but not automated vehicle <strong>Session description</strong>: 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. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: 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 <strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: 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. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.