cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3608014
title: Brainport, Highway pilot, car in manual mode, IMU detection

description: <strong>Scenario description</strong>: The detection car drives around the track in manual mode, with IMU detection ON. <strong>Session description</strong>: 25 laps with VW Tiguan on Automotive Campus Test Track <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle <strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road <strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies <strong>AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle <strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates <strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates <strong>AUTOPILOT_BrainPort_HighwayPilot_Hazard</strong>: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards <strong>AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. <strong>AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem</strong>: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS <strong>AUTOPILOT_BrainPort_HighwayPilot_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles <strong>AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors
DOI: 10.5281/zenodo.3608045
title: Brainport, Highway pilot, control center ADAS instructions publication

description: <strong>Scenario description</strong>: Hazards have been detected and populate the map. Control Center publishes ADAS instructions for some hazards. <strong>Session description</strong>: Control Center published ADAS Instructions for 4 identified hazards of the track. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle <strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road <strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies <strong>AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle <strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates <strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates <strong>AUTOPILOT_BrainPort_HighwayPilot_Hazard</strong>: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards <strong>AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. <strong>AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem</strong>: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS <strong>AUTOPILOT_BrainPort_HighwayPilot_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles <strong>AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors
DOI: 10.5281/zenodo.3608046
title: Brainport, Highway pilot, control center ADAS instructions publication

description: <strong>Scenario description</strong>: Hazards have been detected and populate the map. Control Center publishes ADAS instructions for some hazards. <strong>Session description</strong>: Control Center published ADAS Instructions for 4 identified hazards of the track. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle <strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road <strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies <strong>AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle <strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates <strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates <strong>AUTOPILOT_BrainPort_HighwayPilot_Hazard</strong>: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards <strong>AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. <strong>AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem</strong>: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS <strong>AUTOPILOT_BrainPort_HighwayPilot_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles <strong>AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors
DOI: 10.17632/hcxp9d4zkf.1
title: Genome-wide analysis of DNA uptake by naturally competent Haemophilus influenzae

description: This is the data associated with our paper "Genome-wide analysis of DNA uptake by naturally competent Haemophilus influenzae". The paper used an innovative application of deep sequencing to measure how Haemophilus influenzae takes up genomic fragments. Files attached are the raw sequencing files in fastq formoat of the sequenced Haemophilus influenzae genomic DNA from 86-028NP and PittGG strains sheared into two size distributions and recovered from competent H. influenzae Rd KW20. We used this data to built genomic maps of DNA uptake, which were compared to our predicted uptake calculated using a computational model. This comparison allowed us to increase our understanding of factors explaining differences in uptake of DNA fragments. DNA uptake in Haemophilus influenzae depends on the uptake machinery binding to a short DNA sequence denominated "USS". Our paper was able to show that genomic regions with high and low uptake had USS with distinct DNA-shape, suggesting that shape might have a role in DNA-binding. Preprint of the paper can be found in the link below: https://www.biorxiv.org/content/10.1101/866426v1
DOI: 10.5281/zenodo.3608005
title: Brainport, Highway pilot, car in manual mode, camera detection

description: <strong>Scenario description</strong>: The detection car drives around the track in manual mode, with Camera detection ON. <strong>Session description</strong>: 25 laps with VW Tiguan on Automotive Campus Test Track <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_BrainPort_HighwayPilot_AdasCommand</strong>: Data from the automated driver assistance system Dataset Description This dataset contains the ADAS command in the vehicle <strong>AUTOPILOT_BrainPort_HighwayPilot_Anomaly</strong>: Data sent from detecting vehicle to the service, and from service to vehicles Dataset Description This dataset contains information about all the detected anomalies on the road <strong>AUTOPILOT_BrainPort_HighwayPilot_AnomalyImage</strong>: Data sent from detecting vehicle to service Dataset Description This dataset contains images of the detected anomalies <strong>AUTOPILOT_BrainPort_HighwayPilot_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus,clutchstatus,brakestatus,brakeforce,wipersstatus,steeringwheel for the vehicle <strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates <strong>AUTOPILOT_BrainPort_HighwayPilot_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates <strong>AUTOPILOT_BrainPort_HighwayPilot_Hazard</strong>: Data sent from service to vehicle Dataset Description This dataset contains specific information for a vehicle about anomalies and hazards <strong>AUTOPILOT_BrainPort_HighwayPilot_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. <strong>AUTOPILOT_BrainPort_HighwayPilot_PositioningSystem</strong>: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS <strong>AUTOPILOT_BrainPort_HighwayPilot_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles <strong>AUTOPILOT_BrainPort_HighwayPilot_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors
DOI: 10.7910/dvn/tlsz4s/8ymj8q
title: MetaSILC-2015-DataFile-Goedeme&ZardoTrindade-(2020).tab

description: :unav
DOI: 10.17632/8m34tr5xs9.1
title: An Automatic Clustering Technique for Query Plan Recommendation

description: Source Code of NSGA-II Optimized Density-based Clustering (NODC)
DOI: 10.17632/dz272vg4gm.1
title: Prediction report

description: A prediction report of machine-learning-based algorithms
DOI: 10.5281/zenodo.1886062
title: Fine-Grained Just-In-Time Defect Prediction - Appendix

description: Defect prediction models focus on identifying defect-prone code elements, for example to allow practitioners to allocate testing resources on specific subsystems and to provide assistance during code reviews. While the research community has been highly active in proposing metrics and methods to predict defects on long-term periods (i.e., at release time), a recent trend is represented by the so-called short-term defect prediction (i.e., at commit-level). Indeed, this strategy represents an effective alternative in terms of effort required to inspect files likely affected by defects. Nevertheless, the granularity considered by such models might be still too coarse. Indeed, existing commit-level models highlight an entire commit as defective even in cases where only specific files actually contain defects. In this paper, we first investigate to what extent commits are partially defective; then, we propose a novel fine-grained just-in-time defect prediction model to predict the specific files, contained in a commit, that are defective. Finally, we evaluate our model in terms of (i) performance and (ii) the extent to which it decreases the effort required to diagnose a defect. Our study highlights that: (1) defective commits are frequently composed of a mixture of defective and non- defective files, (2) our fine-grained model can accurately predict defective files with an AUC-ROC up to 82% and (3) our model would allow practitioners to save inspection efforts with respect to standard just-in-time techniques.
DOI: 10.5281/zenodo.1886063
title: Fine-Grained Just-In-Time Defect Prediction - Appendix

description: Defect prediction models focus on identifying defect-prone code elements, for example to allow practitioners to allocate testing resources on specific subsystems and to provide assistance during code reviews. While the research community has been highly active in proposing metrics and methods to predict defects on long-term periods (i.e., at release time), a recent trend is represented by the so-called short-term defect prediction (i.e., at commit-level). Indeed, this strategy represents an effective alternative in terms of effort required to inspect files likely affected by defects. Nevertheless, the granularity considered by such models might be still too coarse. Indeed, existing commit-level models highlight an entire commit as defective even in cases where only specific files actually contain defects. In this paper, we first investigate to what extent commits are partially defective; then, we propose a novel fine-grained just-in-time defect prediction model to predict the specific files, contained in a commit, that are defective. Finally, we evaluate our model in terms of (i) performance and (ii) the extent to which it decreases the effort required to diagnose a defect. Our study highlights that: (1) defective commits are frequently composed of a mixture of defective and non- defective files, (2) our fine-grained model can accurately predict defective files with an AUC-ROC up to 82% and (3) our model would allow practitioners to save inspection efforts with respect to standard just-in-time techniques.
DOI: 10.5281/zenodo.3609022
title: precipitación diaria de los años 2000-2010 para 86 estaciones meteorológicas distribuidas por Europa

description: Precipitación diaria (2000-2010) en 86 estaciones europeas
DOI: 10.5281/zenodo.3609023
title: precipitación diaria de los años 2000-2010 para 86 estaciones meteorológicas distribuidas por Europa

description: Precipitación diaria (2000-2010) en 86 estaciones europeas
DOI: 10.5281/zenodo.3609032
title: Localización de las estaciones meteorológicas europeas.

description: Archivo .stn con los datos de localización de las estaciones meteológicas europeas.
DOI: 10.5281/zenodo.3609033
title: Localización de las estaciones meteorológicas europeas.

description: Archivo .stn con los datos de localización de las estaciones meteológicas europeas.
DOI: 10.5281/zenodo.3603291
title: Data for The Generally Curious: Thematically Distinct Datasets of 4chan's /pol/ Discussion Forum's 'General Threads'

description: Over the second half of the 2010s, the /pol/ (‘politically incorrect’) forum on the 4chan image board has emerged as a space within which various extreme political ideologies are discussed and cultivated, occasionally informing off-site acts of political extremism. While previous research has often studied this space as a unified whole, it is relevant to more specifically demarcate different publics within 4chan’s /pol/ board, apart from studying it as an ‘amorphous blob’. This paper focuses specifically on ‘generals’ - recurring threads with a specific thematic focus identified by a particular vernacular phrase or tag. By identifying them it is possible to subset the board’s archive into multiple distinct datasets comprising discussions about a particular topic, such as Donald Trump, the Syria war, or British politics. We provide a dataset containing 58,841 opening posts and 13,697,738 replies to those, divided over 329 thematically distinct ‘general thread’ collections. In this paper we outline our data collection and query protocol, the structure of the data and its rationale, as well as a number of suggested research uses for this new data.
description: Please refer to '_index.csv' for file metadata.
DOI: 10.5281/zenodo.3603292
title: Data for The Generally Curious: Thematically Distinct Datasets of 4chan's /pol/ Discussion Forum's 'General Threads'

description: Over the second half of the 2010s, the /pol/ (‘politically incorrect’) forum on the 4chan image board has emerged as a space within which various extreme political ideologies are discussed and cultivated, occasionally informing off-site acts of political extremism. While previous research has often studied this space as a unified whole, it is relevant to more specifically demarcate different publics within 4chan’s /pol/ board, apart from studying it as an ‘amorphous blob’. This paper focuses specifically on ‘generals’ - recurring threads with a specific thematic focus identified by a particular vernacular phrase or tag. By identifying them it is possible to subset the board’s archive into multiple distinct datasets comprising discussions about a particular topic, such as Donald Trump, the Syria war, or British politics. We provide a dataset containing 58,841 opening posts and 13,697,738 replies to those, divided over 329 thematically distinct ‘general thread’ collections. In this paper we outline our data collection and query protocol, the structure of the data and its rationale, as well as a number of suggested research uses for this new data.
description: Please refer to '_index.csv' for file metadata.
DOI: 10.5281/zenodo.3608655
title: Correlation of Prevotella abundancy with a particular carbohydrate-active CAZy enzyme profile in healthy gut microbiota

description: Microbiota data
DOI: 10.5281/zenodo.3608656
title: Correlation of Prevotella abundancy with a particular carbohydrate-active CAZy enzyme profile in healthy gut microbiota

description: Microbiota data
DOI: 10.5281/zenodo.3609035
title: breast cancer

description: Dataset sobre variables del cancer de pecho.
DOI: 10.5281/zenodo.3609036
title: breast cancer

description: Dataset sobre variables del cancer de pecho.
DOI: 10.6073/pasta/84d21da05c60a17db50fce3b212cdc82
title: Minocqua Dam Monthly Meteorological Data at North Temperate Lakes LTER 1905 - current

description: Minoqua Dam, Wisconsin. Meteorological measurements are being gathered at a site at the Minocqua Dam for these purposes: 1) to supplement the data from the raft on Sparkling Lake and 2) to provide standard meteorological measurements for the North Temperate Lakes site. The following parameters are measured and stored as monthly values: 1) mean daily air temperature, 2) mean maximum air temperature, 3) mean minimum air temperature, 4) total precipitation, and 5) total snowfall. Sampling Frequency: data averaged to monthly values Number of sites: 1
DOI: 10.17632/9pgfdgjfc7.1
title: Clauder et al. "IgG Fc N-glycosylation translates MHCII haplotype into autoimmune skin disease"

description: The major histocompatibility complex (MHC)-haplotype represents the most prevalent genetic risk factor for the development of autoimmune diseases. However, the mechanisms by which MHC-associated genetic susceptibility translates into autoimmune disease are not fully understood. Epidermolysis bullosa acquisita (EBA) is an autoimmune skin blistering disease driven by autoantibodies to type VII collagen (COL7). Here, we investigated autoantigen-specific plasma cells, CD4+ T cells and IgG Fc-glycosylation in murine EBA in congenic mouse strains with the disease-permitting H2s or -non-permitting H2b MHCII haplotypes. Mice with an H2s haplotype showed increased numbers of autoreactive CD4+ T cells and elevated IL-21- and IFN-γ-production, associated with a higher frequency of IgG autoantibodies with an agalactosylated, proinflammatory N-glycan moiety. Mechanistically, we show that the altered antibody glycosylation leads to increased ROS release from neutrophils, the main driver of autoimmune inflammation in this model. These results indicate that MHCII-associated susceptibility to autoimmune diseases acuminates in a proinflammatory IgG Fc N-glycosylation pattern and provide a mechanistic link to increased ROS release by neutrophils.
DOI: 10.17632/t6bmb24c9w.1
title: Dataset for Improve Accessibility and Visibility
DOI: 10.17632/ztpr528m2c.1
title: Income Distribution and Financial Reforms: An Empirical Evaluation

description: Dataset for 'Income Distribution and Financial Reforms: An Empirical Evaluation '
DOI: 10.18430/m36nmo
title: X-ray diffraction data for the crystal structure of 2C-methyl-D-erythritol 2,4-cyclodiphosphate synthase (IspF) Burkholderia pseudomallei in compomplex with ligand SR-4. (6NMO)
DOI: 10.18430/m36uzi
title: X-ray diffraction data for the crystal structure of Dihydrolipoyl dehydrogenase from Elizabethkingia anophelis NUHP1 (6UZI)
DOI: 10.18430/m36v91
title: X-ray diffraction data for the crystal structure of Stringent starvation protein A (BTH_I2974) from Burkholderia thailandensis (6V91)