cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/60ys-tt77
title: Reconstruction of the admissible joint-references for the N-trailer kinematics: the source Matlab files.

description: This dataset consists of the source Matlab files (the m-files) which implement the computational Newton-like algorithm solving the reconstruction problem for the joint-references of the non-Standard and Generalized N-Trailers upon a prescribed reference path of the last trailer. A user can examine the effectiveness and properties of the algorithm by selecting one of the seven possible 3-trailer structures with various types of hitching. The default reference path prescribed for the last trailer implemented in the code is a parametrized Lissajous-like closed curve defined on the normalized interval [0,1] of the parametrizing variable. The program computes a set of parameters (the weights) for a Fourier basis with up to 2000 harmonics chosen to approximate an evolution of the reference joint angles. The results being obtained during the computations are plotted on-the-fly, and the approximation accuracy is provided by the values of quality factors.
DOI: 10.3886/e111641v2
title: Codes for "A Single-Judge Solution to Beauty Contests"

description: Matlab codes to replicate the figures in "A Single-Judge Solution to Beauty Contests".
DOI: 10.21227/5jfy-d035
title: Malaria Thick Blood Smears

description: All rights are reserved by National Library of Medicine.We photographed Giemsa-stained thick blood smear slides from 150 P. falciparum infected patients at Chittagong Medical College Hospital, Bangladesh, using a smartphone camera for the different microscopic field of views. Images are captured with 100x magnification in RGB color space with a 3024×4032 pixel resolution. An expert slide reader manually annotated each image at the Mahidol-Oxford Tropical Medicine Research Unit (MORU), Bangkok, Thailand. We de-identified all images andtheir annotations, and archived them at the National Library of Medicine (IRB#12972). Citation of the data:We request that publications resulting from the use of this data attribute the source (National Library of Medicine, National Institutes of Health, Bethesda, MD, USA) and cite the following publications, which have used the data for parasite detection and classification:[1]. Feng Yang, Mahdieh Poostchi, Hang Yu, Zhou Zhou, Kamolrat Silamut, Jian Yu, Richard J Maude, Stefan Jaeger, Sameer Antani. Deep Learning for Smartphone-based Malaria Parasite Detection in Thick Blood Smears. IEEE J Biomed Health Inform. 2019 Sep 23. doi: 10.1109/JBHI.2019.2939121.
DOI: 10.5281/zenodo.3630194
title: Climate change perception interview questions for Kunene, Namibia

description: The files contain interview question used to collect data on climate change perception in Kunene Region Namibia, from a pastoralist Himba tribe.
DOI: 10.5281/zenodo.3630193
title: Climate change perception interview questions for Kunene, Namibia

description: The files contain interview question used to collect data on climate change perception in Kunene Region Namibia, from a pastoralist Himba tribe.
DOI: 10.21227/37j3-vp49
title: Reconstruction of the admissible joint-references for the N-trailer kinematics: the source Matlab files.

description: This dataset consists of the source Matlab files (the m-files) which implement the computational Newton-like algorithm solving the reconstruction problem for the joint-references of the non-Standard and Generalized N-Trailers upon a prescribed reference path of the last trailer. A user can examine the effectiveness and properties of the algorithm by selecting one of the seven possible 3-trailer structures with various types of hitching. The default reference path prescribed for the last trailer implemented in the code is a parametrized Lissajous-like closed curve defined on the normalized interval [0,1] of the parametrizing variable. The program computes a set of parameters (the weights) for a Fourier basis with up to 2000 harmonics chosen to approximate an evolution of the reference joint angles. The results being obtained during the computations are plotted on-the-fly, and the approximation accuracy is provided by the values of quality factors.
DOI: 10.5281/zenodo.3630365
title: Air parcels extracted from LES simulations of a deep convective cloud observed during DC3 field campaign

description: The dataset consists of 500 files describing air parcels retrieved from the simulation of the deep convective cloud event observed on May 29, 2012, during the DC3 field campaign. Values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.
DOI: 10.5281/zenodo.3630366
title: Air parcels extracted from LES simulations of a deep convective cloud observed during DC3 field campaign

description: The dataset consists of 500 files describing air parcels retrieved from the simulation of the deep convective cloud event observed on May 29, 2012, during the DC3 field campaign. Values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.
DOI: 10.5281/zenodo.3630422
title: Coordinated movement of a swarm of nonholonomic wheeled robots modeled as virtual visco-elastic body - raw experiment data

description: Raw experiment data from verification of 2 control algorithms on 5 nonholonomic mobile robots using OptiTrack motion capture system : virtual spring damper mesh control (algorithm A1) with and without obsticles and swarm selforganization using worm creep algorithm (algorithm A4). Conducted experiment allowed for adjustment of the control parameters (describend in Data.m files) to achieve better performance of the swarm movement. Abbreviations: d1, d2 - are desired interrobot distances, NO- no obsticles, Wo - with obsticles, Sor - self-organization
description: "Dataset developed within the TERRINet project, funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 730994"
DOI: 10.5281/zenodo.3630423
title: Coordinated movement of a swarm of nonholonomic wheeled robots modeled as virtual visco-elastic body - raw experiment data

description: Raw experiment data from verification of 2 control algorithms on 5 nonholonomic mobile robots using OptiTrack motion capture system : virtual spring damper mesh control (algorithm A1) with and without obsticles and swarm selforganization using worm creep algorithm (algorithm A4). Conducted experiment allowed for adjustment of the control parameters (describend in Data.m files) to achieve better performance of the swarm movement. Abbreviations: d1, d2 - are desired interrobot distances, NO- no obsticles, Wo - with obsticles, Sor - self-organization
description: "Dataset developed within the TERRINet project, funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 730994"
DOI: 10.21227/egax-2r31
title: Taxi trajectory

description: This is the image of data.
DOI: 10.25345/c58m3b
title: MassIVE MSV000084852 - Maximizing Confidence and Sensitivity in quantitative Phosphoproteomics
title: Proteome-wide analysis of phosphorylation events is a challenging, yet essential, task for the comprehensive and unbiased investigation of kinase action. Here we developed a phosphoproteomic approach in which quantitation consistency among reversed isotopically labeled samples is used as a central filtering rule for achieving reliability with minimal loss of data content. Exclusion of non-reverting data-points from the dataset not only reduces quantitation error and variation, but also reduces false positive identifications. This dataset corresponds to SILAC labeling swapping control experiments.
DOI: 10.5281/zenodo.3630454
title: Livorno, Highway pilot, data management connected car

description: <strong>Scenario description</strong>: Dynamic speed adaptation due to puddle on the road 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 trigger 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 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>: 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. <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.
DOI: 10.5523/bris.1f90wa0f4eyee25w5dwte8h41c
title: Crystal and Electronic Structure of Bismuth Thiophosphate, BiPS4: An Earth-Abundant Solar Absorber

description: The PVTEAM network aims at developing materials for thin film photovoltaic technologies based on earth abundant materials and low-cost solution processing. This project is funded by EPSRC.
DOI: 10.25387/g3.11292143.v2
title: Supplemental Material for Zhang et al., 2020

description: Revised supplemental text, supplemental figures S1 to S10, and supplemental tables S1 to S9 for manuscript G3/2019/400618
DOI: 10.5281/zenodo.3630456
title: Livorno, Highway pilot, connected car system and data management

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 shares 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>: pre-test session with only connected cars, lap of 11,6 km on the highway. Goal is to check all the system and data management. <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.
DOI: 10.5519/qd.dz47t4xt
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1000 records)
DOI: 10.5519/qd.agzlmooq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.21227/b3xk-nn11
title: Taxi trajectory

description: This is the image of data.
DOI: 10.3886/e110681v1
title: Imperial Russian Factory Database, 1894-1908

description: This database digitizes the 1894, 1900, and 1908 Imperial Russian manufacturing censuses. For each factory, the database includes industry, province, enterprise form, total workers, total revenue, and identifiers that allow factories to be matched over time. The 1894 and 1908 years also include information on the factory's total machine power. The dataset was constructed to study why some Russian firms chose to become corporations despite the Empire's expensive and time-consuming concession system. Note that the final analysis files exclude factories located outside of European Russia and, in the main data files, factories subject to the excise tax.
description: Manufacturing establishments in the European part of the Russian Empire. Smallest Geographic Unit: Imperial Russian Province
description: The dataset collects all industrial factories listed in the 1894, 1900, and 1908 Imperial Russian manufacturing censuses. Some factories are excluded because of industry (non-industrial categories, categories subject to the excise tax), and some are excluded by geographic (non-European Russia).
DOI: 10.5281/zenodo.3630461
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.
DOI: 10.5281/zenodo.3630462
title: Livorno, Highway pilot, one automated and connected car

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, 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 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.
DOI: 10.5281/zenodo.3630463
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: 10.6073/pasta/8b9be348a38add0526f94b9d9ec44bb2
title: Fisher Meteorological Station at Harvard Forest since 2001

description: The Fisher Meteorological Station became operational on 11 Feb 2001. The Station is located in an open field 200 m north of the Shaler Station, in a site chosen to minimize the angle of surrounding trees above the horizon (currently 15-25 degrees from the station at breast height). The Fisher Station records air temperature, relative humidity, dew point, precipitation (water equivalent of snow), global solar radiation, PAR radiation, net radiation, barometric pressure, scalar wind speed, vector wind speed, peak gust speed (1-second), vector wind direction, standard deviation of wind direction (wind measurements at 10 m height), and soil temperature (10 cm depth). Instruments are scanned once per second, and 15-minute (hourly before 2005) and daily values are calculated and stored by a datalogger. Data for the current month are available online, updated every 15 minutes, with out-of-range values replaced by NA but values not otherwise checked. Earlier data are checked and archived with missing, questionable, and estimated values flagged, following methods of the LTER ClimbDB project. A log of events affecting station measurements (e.g., instrument repair and recalibration, ice storms, lightning) and selected monthly and annual values are also posted. For current data, please see: http://harvardforest.fas.harvard.edu/meteorological-hydrological-stations. For a complete set of archived data, please see: http://harvardforest.fas.harvard.edu:8080/exist/xquery/data.xq?id=hf001.
DOI: 10.21227/pp10-gw14
title: Taxi trajectory

description: This is the image of data.