cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3630490
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: 10.5284/1058997
title: Glebe Farm Magna Park, Lutterworth South. Historic Building Recording (OASIS ID: headland3-368670)

description: Hisrtoric building recording of late 18th earlier 19th century farmhouse, ancillary outbuildings and photographic record of modern nissen hut type outbuildings. The proposed development is located to the south of the A4303 Coventry Road, east of the A5, and to the west of the town of Lutterworth in Leicestershire. Glebe Farm itself is located in the north-east of the development area and is centred around NGR 452241 283885. The farmhouse is of a style typical of the 19th century, with few surviving external features. There is little architectural merit to the farmhouse and it would appear that there are few indications of surviving original features. There are a number of outbuildings which are in a poor state of repair. The farmhouse consists of an 'L-plan' arrangement with the frontage facing east. This frontage forms a three-storey, three-bay, gable ended structure. To the rear of this is a projecting three storey gabled block. The whole is rendered with pale cement and has modern double-glazed windows. An external inspection of the buildings at Glebe Farm was undertaken as part of the desk-based assessment to assess its likely significance in terms of built heritage. Few buildings of the complex as depicted on the 19th century Ordnance Survey mapping have survived. The surviving buildings consist of the main farmhouse, part of an eastern range of a farmyard, and a small part of a western range.
DOI: 10.5281/zenodo.3630491
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.3630492
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: 10.7910/dvn/ylw1az/0mndtd
title: readme.txt

description: Read first
DOI: 10.7910/dvn/ylw1az/0zsf9t
title: referrer-sum-stats-grinberg-45.tab

description: :unav
DOI: 10.7910/dvn/ylw1az/2uzbiq
title: master-for-replication.tab

description: :unav
DOI: 10.7910/dvn/ylw1az/fsibr1
title: diffindiff.tab

description: :unav
DOI: 10.7910/dvn/ylw1az/kl8mri
title: referrer-sum-stats-grinberg-30.tab

description: :unav
DOI: 10.7910/dvn/ylw1az/t8hkpm
title: referrer-sum-stats-grinberg-15.tab

description: :unav
DOI: 10.7910/dvn/ylw1az/tyrfmp
title: gen_plots.R

description: :unav
DOI: 10.7910/dvn/ylw1az/vbsubj
title: hardnews.R

description: :unav
DOI: 10.7910/dvn/ylw1az/davu4a
title: gnr.do

description: :unav
DOI: 10.5281/zenodo.3630485
title: InterFlex WP3 data set_ SGAM diagrams_interface data base_service identification

description: This data set contains the InterFlex demonstration use case descriptions in the form of SGAM diagrams as well as the interface data base which was used for different deliverables and the repective results within work package 3 "Impact and deployment analysis of the innovative solutions". There has also been one publication in this regard ( 10.1109/INDIN.2018.8472053) Furthermore, it includes the service mappings for the InterFlex (under GA 731289) demonstrators as an input for different WP3 3.1 subtasks, deliverbale (D3.2) as well as a scientific publication (ICRERA 2019, ID 239, online ISSN: 2572-6013)
description: The files are in PDF, Micrososft .ppt, .xlsx and .visio formats.
DOI: 10.5281/zenodo.3630486
title: InterFlex WP3 data set_ SGAM diagrams and interface data base

description: This data set contains the InterFlex demonstration use case descriptions in the form of SGAM diagrams as well as the interface data base which was used for different deliverables and the repective results within work package 3 "Impact and deployment analysis of the innovative solutions". There has also been one publication in this regard ( 10.1109/INDIN.2018.8472053)
description: The files are in PDF, Micrososft .ppt and .visio formats.
DOI: 10.5286/isis.e.rb2010707
title: Structural changes in oxidized lipid bilayers

description: The oxidation of the lipids and proteins in biological membranes is implicated in a wide range of biological processes linked to adverse health effects. Oxidation of unsaturated lipids can lead to the formation of lipids with prematurely terminated, and polar, tail groups. Molecular dynamic simulations of lipid bilayers and monolayers have suggested that these oxidized tail groups may undergo major structural changes however experimental evidence for this in lipid bilayers containing oxidized material is still lacking. We have preliminary reflectivity data for bilayers of POnPC:POPC for which we can see the presence of the oxidized chain clearly disrupting the lipid tails and head regions. We would now like to see how the POnPC behaves in bilayers with saturated lipids, and thus propose to study bilayers of DMPC with small levels of POnPC present.
DOI: 10.5286/isis.e.rb2010707-1
title: Structural changes in oxidized lipid bilayers

description: The oxidation of the lipids and proteins in biological membranes is implicated in a wide range of biological processes linked to adverse health effects. Oxidation of unsaturated lipids can lead to the formation of lipids with prematurely terminated, and polar, tail groups. Molecular dynamic simulations of lipid bilayers and monolayers have suggested that these oxidized tail groups may undergo major structural changes however experimental evidence for this in lipid bilayers containing oxidized material is still lacking. We have preliminary reflectivity data for bilayers of POnPC:POPC for which we can see the presence of the oxidized chain clearly disrupting the lipid tails and head regions. We would now like to see how the POnPC behaves in bilayers with saturated lipids, and thus propose to study bilayers of DMPC with small levels of POnPC present.
DOI: 10.7910/dvn/ueari0/a3dyet
title: markets_SEV_repdata.tab

description: main dataset with all experimental results
DOI: 10.7910/dvn/ueari0/fv4udn
title: datafig1.tab

description: :unav
DOI: 10.7910/dvn/ueari0/h3nzns
title: Open Ended Responses.tab

description: :unav
DOI: 10.7910/dvn/ueari0/kvx00h
title: Figure1.R

description: :unav
DOI: 10.7910/dvn/ueari0/rauph4
title: replication_FINAL.do

description: :unav
DOI: 10.7910/dvn/ueari0/y3hyti
title: GSS figure.R

description: :unav
DOI: 10.7910/dvn/ueari0/zrfvw2
title: Data for GSS graph.tab

description: :unav
DOI: 10.18430/m36vel
title: X-ray diffraction data for the crystal Structure of Human E-cadherin bound by mouse monoclonal antibody 66E8Fab (6VEL)
DOI: 10.18430/m36vh5
title: X-ray diffraction data for the crystal structure of prephenate dehydratase from brucella melitensis biovar abortus 2308 in complex with phenylalanine (6VH5)
DOI: 10.7910/dvn/ueari0/flekml
title: Codebook.pdf

description: Codebook
DOI: 10.7910/dvn/ueari0/u41qap
title: Read_Me.txt

description: Read_Me
DOI: 10.5281/zenodo.3630467
title: Landslides and gullies in the mountaintop removal mines of eastern Kentucky and southwestern West Virginia

description: The extent datasets are high-res, hand-drawn outlines of mountaintop removal extents from large lidar scenes (county-scale). Could be useful in the creation of automated delineation schemes. The fill slides are point datasets of landslides or deformations consistent with incipient landsliding within valley fills created by mountaintop removal. The perimeter slides dataset contains outlines of landslides occurring along the edge of reclaimed mines. The gullies datasets are point locations of gullies associated with reclaimed mountaintop removal mines. Each point is classified by location of the gully. Outside of several gullies in Holden (where I did field work), these datasets have not been assessed for accuracy as most mines are private property. High-res lidar is often considered to be a source of "ground truth" data, though. I would encourage attempts at creating landslide or gully detection schemes using these datasets for training/calibration. Kentucky lidar is available from http://kyfromabove.ky.gov/ as point clouds or rasters. West Virginia lidar is available from http://www.wvgis.wvu.edu/lidar/.
DOI: 10.5281/zenodo.3630438
title: Convergence in voice fundamental frequency in a joint speech production task - Dataset

description: This dataset contains fundamental frequency values for 30 pairs of participants performing an alternate reading task. Fundamental frequency in each speaker's speech was artificially modified in real-time during the task. We provide both the untransformed and transformed fundamental frequency values. Full description of the experimental setup is found in &lt; insert paper DOI here &gt; The data is organised as follows: data for each pair is stored in a separate folder with the pair ID as the folder name each folder contains two repetitions of the task, as produced in a zero-phase and pi-phase condition, respectively file names ending with '<strong>f0</strong>' contain fundamental frequency data, sampled every 10 ms file names ending with '<strong>turns</strong>' contain time onsets of speaking turns The format of '<strong>f0</strong>' files is as follows: '<strong>t</strong>': time in seconds '<strong>ch</strong>': channel of the recording, indicating the participant (<em>A</em> or <em>B</em>) '<strong>f0_unstransf</strong>': fundamental frequency values as produced by the participant (untransformed) in Hertz '<strong>f0_transf</strong>': fundamental frequency values as heard by the other participant (transformed) in Hertz The format of '<strong>turns</strong>' files is as follows: '<strong>ch</strong>': channel of the recording, indicating the participant (<em>A</em> or <em>B</em>), or both participants at once (<em>joint</em>) '<strong>turn</strong>': index of the reading turn '<strong>type</strong>': turn type. Either <em>speech</em> or <em>silence</em> for each participant, or <em>turn</em> for the joint description. '<strong>t</strong>': turn onset in seconds
description: We acknowledge financial support from the French National Research Agency and the Excellence Initiative of Aix-Marseille University (A*MIDEX). Grant Agreements no. ANR-08-BLAN-0276-01 (SPIM), ANR-16-CONV-0002 (ILCB) and ANR-11-LABX-0036 (BLRI).
DOI: 10.5281/zenodo.3630439
title: Convergence in voice fundamental frequency in a joint speech production task - Dataset

description: This dataset contains fundamental frequency values for 30 pairs of participants performing an alternate reading task. Fundamental frequency in each speaker's speech was artificially modified in real-time during the task. We provide both the untransformed and transformed fundamental frequency values. Full description of the experimental setup is found in &lt; insert paper DOI here &gt; The data is organised as follows: data for each pair is stored in a separate folder with the pair ID as the folder name each folder contains two repetitions of the task, as produced in a zero-phase and pi-phase condition, respectively file names ending with '<strong>f0</strong>' contain fundamental frequency data, sampled every 10 ms file names ending with '<strong>turns</strong>' contain time onsets of speaking turns The format of '<strong>f0</strong>' files is as follows: '<strong>t</strong>': time in seconds '<strong>ch</strong>': channel of the recording, indicating the participant (<em>A</em> or <em>B</em>) '<strong>f0_unstransf</strong>': fundamental frequency values as produced by the participant (untransformed) in Hertz '<strong>f0_transf</strong>': fundamental frequency values as heard by the other participant (transformed) in Hertz The format of '<strong>turns</strong>' files is as follows: '<strong>ch</strong>': channel of the recording, indicating the participant (<em>A</em> or <em>B</em>), or both participants at once (<em>joint</em>) '<strong>turn</strong>': index of the reading turn '<strong>type</strong>': turn type. Either <em>speech</em> or <em>silence</em> for each participant, or <em>turn</em> for the joint description. '<strong>t</strong>': turn onset in seconds
description: We acknowledge financial support from the French National Research Agency and the Excellence Initiative of Aix-Marseille University (A*MIDEX). Grant Agreements no. ANR-08-BLAN-0276-01 (SPIM), ANR-16-CONV-0002 (ILCB) and ANR-11-LABX-0036 (BLRI).
DOI: 10.5519/qd.swqp62fk
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1343675 records)
DOI: 10.5519/qd.brtznnf6
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (150990 records)
DOI: 10.21227/666z-ff51
title: Malaria Thick Blood Smears

description: 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). Reference: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 publication, which has used the data for parasite detection and classification: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: 10.21227/kmdt-7n69
title: Malaria Thick Blood Smears

description: 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). Reference: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 publication, which has used the data for parasite detection and classification: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: 10.21227/bhq2-4198
title: Malaria Thick Blood Smears

description: 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). Reference: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 publication, which has used the data for parasite detection and classification: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: 10.21227/3r2c-5f39
title: Malaria Thick Blood Smears

description: 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). Reference: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 publication, which has used the data for parasite detection and classification: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.3630556
title: Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations: supporting dataset

description: Relevant data for reproducing figures and tables from Herbert et al., 2020, Atmospheric Chemistry and Physics: "Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations". The tar file contains the relevant netcdf files that contain simulation output from the MET Office large eddy model (LEM). Each netcdf file corresponds to a single model setup and experiment - details of which can be found within the published manuscript. Information concerning the LEM, and details on how to obtain access to the code, can be found at http://appconv.metoffice.com/LEM/index.html. The file 'relevant_information.pdf' contains two tables. The first provides the names of each netcdf file that was used to produce each figure in the manuscript. The second provides the metadata for the netcdf variables that were used to produce the figures and tables. The netcdf filename provides information on the experiment setup as follows: run-identifier-number _ length-of-run _ AOD-of-layer _ GAP-between-cloud-and-layer _ THICKNESS-of-layer _ 'bigarray' _ special-setups-for-sensitivity-experiments For example, r1360_8day_transient_aod02_0dz_250th_bigarray_nodrizz is run number r1360, simulating 8 days, with the aerosol layer properties AOD=0.2, cloud-to-aerosol gap of 0m, layer thickness of 250m, in the sensitivity setup where precipitation has been switched off.