cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/efowaf
title: Vietnamellidae (Insecta, Ephemeroptera) of Thailand

description: This dataset contains the digitized treatments in Plazi based on the original journal article Auychinda, Chonlakran, Sartori, Michel, Boonsoong, Boonsatien (2020): Vietnamellidae (Insecta, Ephemeroptera) of Thailand. ZooKeys 902: 17-36, DOI: http://dx.doi.org/10.3897/zookeys.902.46844, URL: http://dx.doi.org/10.3897/zookeys.902.46844
DOI: 10.15468/erozzd
title: Taxonomic studies of the ground beetle subgenus Falcinebria Ledoux & Roux, 2005 (Coleoptera, Carabidae, Nebria) from Honshu, Japan

description: This dataset contains the digitized treatments in Plazi based on the original journal article Sasakawa, Koji (2020): Taxonomic studies of the ground beetle subgenus Falcinebria Ledoux & Roux, 2005 (Coleoptera, Carabidae, Nebria) from Honshu, Japan. ZooKeys 902: 37-60, DOI: http://dx.doi.org/10.3897/zookeys.902.46531, URL: http://dx.doi.org/10.3897/zookeys.902.46531
DOI: 10.15468/po02ya
title: Fourteen new species of the spider genus Psiloderces Simon, 1892 from Southeast Asia (Araneae, Psilodercidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Chang, Wan-Jin, Li, Shuqiang (2020): Fourteen new species of the spider genus Psiloderces Simon, 1892 from Southeast Asia (Araneae, Psilodercidae). ZooKeys 902: 61-105, DOI: http://dx.doi.org/10.3897/zookeys.902.38967, URL: http://dx.doi.org/10.3897/zookeys.902.38967
DOI: 10.15468/zlghyn
title: New records of aphid parasitoids (Hymenoptera) from Colombia

description: This dataset contains the digitized treatments in Plazi based on the original journal article Martínez-Chávez, Laura Marcela, Duque-Gamboa, Diana Nataly, Toro-Perea, Nelson (2019): New records of aphid parasitoids (Hymenoptera) from Colombia. Check List 15 (6): 1083-1091, DOI: 10.15560/15.6.1083
DOI: 10.5281/zenodo.3607460
title: amazon_reviews_train_06

description: - New large-scale corpus of short Amazon reviews<br> - Raw data taken from http://jmcauley.ucsd.edu/data/amazon/
DOI: 10.5281/zenodo.3607461
title: amazon_reviews_train_06

description: - New large-scale corpus of short Amazon reviews<br> - Raw data taken from http://jmcauley.ucsd.edu/data/amazon/
DOI: 10.17632/3d4zdm6bh7.2
title: source data

description: source data of TET2-ZSCAN project.
DOI: 10.17632/3d4zdm6bh7.3
title: source data

description: source data of TET2-ZSCAN project.
DOI: 10.17632/55cybn4n32.1
title: The Impact of Rumination Induction on IQ Performance

description: Performance deficits on cognitive tasks consistently occur in depressed and anxious individuals. Processing efficiency theory asserts these deficits might be accounted for by task-irrelevant processes, including the negative impact of rumination. This study seeks to better understand the relationship between cognitive deficits and depression by creating a ruminative state in healthy control subjects to elicit performance deficits similar to those observed in patients with depression. Specifically, we examined the effect of rumination induction on healthy undergraduates completing select subtests of the Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV). Participants were randomized to a rumination or distraction condition and administered WAIS-IV subtests hypothesized to be most affected by rumination and depression. Controlling for trait anxiety, individuals in the rumination condition performed more poorly on one subtest within the processing speed domain and on the Processing Speed Index score. These results support the processing efficiency model of cognitive deficits in depression, suggesting rumination induction interferes with efficient completion of mental tasks. Future research can build on these findings by studying this model in a clinical population and by continuing to improve the effectiveness of mood induction tasks for research on depression and other mood disorders.
DOI: 10.17632/bd84d8n7bm.1
title: Medial Preoptic Area Antagonistically Mediates Stress-induced Anxiety and Parental Behavior

description: Data for "Medial Preoptic Area Antagonistically Mediates Stress-induced Anxiety and Parental Behavior"
DOI: 10.5281/zenodo.3607230
title: pyKNEEr - Demo Images

description: Demo images to get familiar with pyKNEEr: https://sbonaretti.github.io/pyKNEEr/ --------------------------- Version 3: - Added sliders in the notebooks for preprocessing, segmentation, and relaxometry --------------------------- Version 2: - Minor changes: Improved print out of dependences; Changed names of Jupyter notebooks for segmentation (change needed for future releases); Added missing title "Intensity Preprocessing" in preprocessing.ipynb; Variables changed from name1.name2 to name1_name2 in .csv files --------------------------- Version 1: Contains: - Input: Folder with the demo images to get familiar with <em>pyKNEEr</em> <em>- </em>Output: Folder with the outputs of the demo, to compare your findings with ours
DOI: 10.5281/zenodo.3607428
title: PROTON deliverable 5.1 dataset

description: This is the set of simulations used for the deliverable 5.1 in the PROTON project, for the organized crime. Reference videos: https://www.youtube.com/watch?v=zJJY0-qj6qs https://www.youtube.com/watch?v=rA3aKLaVlR4
DOI: 10.5281/zenodo.3606456
title: amazon_reviews_train_04

description: - New large-scale corpus of short Amazon reviews<br> - Raw data taken from http://jmcauley.ucsd.edu/data/amazon/
DOI: 10.5281/zenodo.3606457
title: amazon_reviews_train_04

description: - New large-scale corpus of short Amazon reviews<br> - Raw data taken from http://jmcauley.ucsd.edu/data/amazon/
DOI: 10.5281/zenodo.3607427
title: PROTON deliverable 5.1 dataset

description: This is the set of simulations used for the deliverable 5.1 in the PROTON project, for the organized crime. The simulation is available on github. In detail: data used for section 2.7.1 data used for sections 2.7.2 and 2.7.4 data used for section 2.7.3 Reference videos: https://www.youtube.com/watch?v=zJJY0-qj6qs https://www.youtube.com/watch?v=rA3aKLaVlR4
DOI: 10.17632/t3dppv8bk8.1
title: Defining rural image collector

description: Images of the corresponding paper
DOI: 10.17632/j8gzjzrh86.1
title: graphene's permittivity

description: graphene's permittivity at 3 micron
DOI: 10.5281/zenodo.3607320
title: Livorno, Highway pilot, one automated car, two connected cars, smart highway

description: <strong>Scenario description</strong>: Precondition: 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. Actions or events: 1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform. 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. 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 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 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). <strong>Session description</strong>: Test session with one AD+connected car and two connected cars, lap of 11,6 km on the highway. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors 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 <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607321
title: Livorno, Highway pilot, one automated car, two connected cars, smart highway

description: <strong>Scenario description</strong>: Precondition: 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. Actions or events: 1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform. 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. 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 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 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). <strong>Session description</strong>: Test session with one AD+connected car and two connected cars, lap of 11,6 km on the highway. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors 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 <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607314
title: Livorno, Highway pilot, one automated and two connected cars

description: <strong>Scenario description</strong>: Precondition: 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. Actions or events: 1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone. 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. 3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed. 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). <strong>Session description</strong>: Test session with one AD+connected car and two connected cars, lap of 12,3 km on the highway. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Pilot session 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_HighwayPilot_V2X_all</strong>: V2V messages during Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Pilot session in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607315
title: Livorno, Highway pilot, one automated and two connected cars

description: <strong>Scenario description</strong>: Precondition: 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. Actions or events: 1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone. 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. 3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed. 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). <strong>Session description</strong>: Test session with one AD+connected car and two connected cars, lap of 12,3 km on the highway. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Pilot session 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_HighwayPilot_V2X_all</strong>: V2V messages during Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Pilot session in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.17632/6s5hn73jcv.2
title: Proteomics explains client specificity of the human Sec62/Sec63 complex in ER protein import

description: In mammalian cells, one-third of all polypeptides are transported into/across the ER-membrane via the Sec61-channel. While the Sec61-complex facilitates translocation of all polypeptides with amino-terminal signal peptides (SP) or transmembrane helices, the Sec61-associated Sec62/Sec63-complex supports translocation of only a subset, i.e. in a substrate-specific manner. To characterize Sec62- and Sec63-dependent precursors, we combined siRNA-mediated Sec62- or Sec63-depletion in human cells, label-free quantitative proteomics, and differential expression analysis. Alternatively, their CRISPR/Cas9-mediated knock-out in HEK 293 cells was carried out. The results were validated by western blotting under similar steady-state conditions and after short-term over-expression and simultaneous proteasome inhibition, respectively. SP analysis of Sec62- and Sec63-clients revealed the distinguishing feature. Thus, Sec62/Sec63-complex acts as an sp-receptor on the ER-membrane’s cytosolic face for certain precursor polypeptides and triggers substrate-specific (and regulated) opening of the Sec61-channel by its interactions with Sec61α.
DOI: 10.5281/zenodo.3607310
title: Livorno, Highway pilot, one automated and connected car and one connected car

description: <strong>Scenario description</strong>: Precondition: 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. Actions or events: 1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform. 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. 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 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 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). <strong>Session description</strong>: Test session with one AD+connected car and one connected car, lap of 11,6 km on the highway. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors 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 <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607311
title: Livorno, Highway pilot, one automated and connected car and one connected car

description: <strong>Scenario description</strong>: Precondition: 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. Actions or events: 1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform. 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. 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 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 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). <strong>Session description</strong>: Test session with one AD+connected car and one connected car, lap of 11,6 km on the highway. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors 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 <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607303
title: Livorno, Highway pilot, only connected cars

description: <strong>Scenario description</strong>: Precondition: 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. Actions or events: 1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone. 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. 3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed. 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). <strong>Session description</strong>: 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. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors 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 <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.