cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3606315
title: From a Monolithic Big Data System to a Microservices Event-Driven Architecture

description: [Context] Data-intensive systems, a.k.a. big data systems (BDS), are software systems that handle a large volume of data in the presence of performance quality attributes, such as scalability and availability. Before the advent of big data management systems (e.g. Cassandra) and frameworks (e.g. Spark), organizations had to cope with large data volumes with custom-tailored solutions. In particular, a decade ago, Tecgraf/PUC-Rio developed a system to monitor truck fleet in real-time and proactively detect events from the positioning data received. Over the years, the system evolved into a complex and large obsolescent code base involving a hard maintenance process. [Goal] We report our experience on replacing a legacy BDS with a microservice-based event-driven system. [Method] We applied action research, investigating the reasons that motivate the adoption of a microservice-based event-driven architecture, intervening to define the new architecture, and documenting the challenges and lessons learned. [Results] We perceived that the resulting architecture enabled easier maintenance and fault-isolation. However, the myriad of technologies and the complex data flow were perceived as drawbacks. Based on the challenges faced, we highlight opportunities to improve the design of big data reactive systems. [Conclusions] We believe that our experience provides helpful takeaways for practitioners modernizing systems with data-intensive requirements.
DOI: 10.5281/zenodo.3606316
title: From a Monolithic Big Data System to a Microservices Event-Driven Architecture

description: [Context] Data-intensive systems, a.k.a. big data systems (BDS), are software systems that handle a large volume of data in the presence of performance quality attributes, such as scalability and availability. Before the advent of big data management systems (e.g. Cassandra) and frameworks (e.g. Spark), organizations had to cope with large data volumes with custom-tailored solutions. In particular, a decade ago, Tecgraf/PUC-Rio developed a system to monitor truck fleet in real-time and proactively detect events from the positioning data received. Over the years, the system evolved into a complex and large obsolescent code base involving a hard maintenance process. [Goal] We report our experience on replacing a legacy BDS with a microservice-based event-driven system. [Method] We applied action research, investigating the reasons that motivate the adoption of a microservice-based event-driven architecture, intervening to define the new architecture, and documenting the challenges and lessons learned. [Results] We perceived that the resulting architecture enabled easier maintenance and fault-isolation. However, the myriad of technologies and the complex data flow were perceived as drawbacks. Based on the challenges faced, we highlight opportunities to improve the design of big data reactive systems. [Conclusions] We believe that our experience provides helpful takeaways for practitioners modernizing systems with data-intensive requirements.
DOI: 10.7910/dvn/vgwy3l/mi5lwn
title: 2020-1-12-teacher-evaluations.r

description: R script
DOI: 10.7910/dvn/vgwy3l/pabtlc
title: 2020-1-12-teacher-evaluations.r.Rout

description: Results from R script run with results
DOI: 10.15468/fzsj9x
title: A new species of Rheotanytarsus Thienemann & Bause (Diptera: Chironomidae) from Peruvian Andes, with updated key to South American species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Dantas, Galileu P. S., Huamantinco Araujo, Ana A., Hamada, Neusa (2020): A new species of Rheotanytarsus Thienemann & Bause (Diptera: Chironomidae) from Peruvian Andes, with updated key to South American species. Zootaxa 4722 (2): 195-200, DOI: https://doi.org/10.11646/zootaxa.4722.2.6
DOI: 10.17632/429nhh7hch.1
title: Data for: Member Structure and Sharing Behavior: Social Network Analysis of CALIS Online Cataloging Data in China

description: This is a matrix of 695 CALIS members uploading and download data in 2018, in the table rows and columns respectively corresponding to uploaders and downloaders.
DOI: 10.17632/fg5wrzjv5n.1
title: Records and analysis for all trees measured in the 150 circular plot of 20 m of radius in a perturbed tropical forest of Moloundou council in Cameroon, during surveys that took place from march to June 2018.

description: Despite the perturbation of permanent forest due to previous illegal exploitation and the use of management technics of forest which are unsustainable, there still remain a correlation between non spatialised index of forest and the durability of the management of these forests. We have positioned 25 rectangular plots of 120 x 80m (0.96 ha) following three topographic positions (08 in the ridge lines, 08 in the lowlands and 09 in the basin slope) to facilitate their position in different slope and altitude. Then, six semi-permanent circular plots of 20 m radius were installed in each plot, ie 150 semi-permanent plots using either a multidirectional rangefinder in which all timbers more than 10 cm of diameter have been identified. We have explored the correlation between initial structural parameters (density of stems, basal area, above-ground biomass, vertical stratification, altitude, slope) of these disturbed forests and the sustainability of their management. It appears that the analysis of the correlation between the non-spatialised forest index which are the number of stems.ha-1, the number of stems.ha-1 ≥ 40 cm, the above-ground biomass and the basal area illustrated by the inexistence of correlation between the mean height of the canopy and the number of stems greater than 40 cm in diameter on the one hand and on the other hand between the above-ground biomass and the number of stems greater than 40 cm in diameter. The low initial densities of these forest areas are attributable to the limitation of dispersal in the distribution of trees, but also to illegal logging prior to the application of sustainable management standards. Moreover, in this context, undertaking to enhance promotion species to diversify the exploitation of wood is to be questioned with regard to the low initial potential of stems more than 10 cm available in the FDP in comparison with the densities considered in secondary forest. So, initial densities from 73 permanent forests in Cameroon that are 6 times lower than the initial stem potential of a secondary forest indicate difficulty in the planned renewal of wood resources.
DOI: 10.25919/5e1c46d409e9a
title: Parkes pulsar observations with undefined project IDs for 2019_30

description: Parkes pulsar observations with undefined project IDs for 2019_30. This collection contains observations from project PX053.
DOI: 10.5281/zenodo.3602443
title: Livorno, Urban driving, Automated car detects pothole

description: <strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed. <strong>Session description</strong>: Test session with only a connected car with smartphone based pothole detector, lap of 1,9 km on the harbour's public road. 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 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.3602444
title: Livorno, Urban driving, Automated car detects pothole

description: <strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed. <strong>Session description</strong>: Test session with only a connected car with smartphone based pothole detector, lap of 1,9 km on the harbour's public road. 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 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.3602477
title: Livorno, Urban driving, autonomous speed adaptation approaching intersection

description: <strong>Scenario description</strong>: Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light with a stereocamera able to detect jaywalking. <strong>Session description</strong>: A "smart" traffic light (with stereocamera) sends SPaT and MAP messages describing the topology and the actual status of the traffic light. If a jaywalking occurrence is detected (pedestrian crossing with the red light) a DENM message is sent to warn vehicles of the presence of the pedestrian to other connected vehicles. The the hazard warning is also sent 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. 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 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.3602478
title: Livorno, Urban driving, autonomous speed adaptation approaching intersection

description: <strong>Scenario description</strong>: Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light with a stereocamera able to detect jaywalking. <strong>Session description</strong>: A "smart" traffic light (with stereocamera) sends SPaT and MAP messages describing the topology and the actual status of the traffic light. If a jaywalking occurrence is detected (pedestrian crossing with the red light) a DENM message is sent to warn vehicles of the presence of the pedestrian to other connected vehicles. The the hazard warning is also sent 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. 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 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: 10.17632/779vdgfmvm.1
title: Randomized controlled trial One anasthomosis gastric bypass

description: This study was conducted from January 2015 to January 2017 in the Department of General and Emergency surgery of "A. Rizzoli" Hospital, Lacco Ameno Naples, Italy. All the surgeries were performed by the same surgeon. Patients were randomized into 3 groups based on the length of the BPL measured from the ligament of Traits: Group A 150 cm, Group B 180 cm, and Group C 200 cm. Early results were obtained at 2 years follow-up. The primary aim was to compare weight loss outcomes of OAGB-200 cm BPL (Group A) and OAGB-150 cm BPL (Group B). The secondary aim was to compare co-morbidity resolution outcomes, nutritional parameters, and morbidity/mortality of the three procedures.
DOI: 10.17632/c25mgp6ptz.1
title: Dataset on RF magnetron sputtering of TiC on commercially pure Titanium

description: The planar morphology and topography of TiC thin films coated on commercially pure Titanium (CpTi) grown by RF magnetron sputtering were investigated using Field emission scanning electron microscope (FESEM) and Atomic force microscope (AFM). The mechanical properties such as Hardness and Young Modulus of the thin film coating was studied using Nanohardness. Furthermore, grazing incidence X-ray diffractometer (GIXRD) and Raman spectroscopy were used to analyse the structural and composition of the TiC thin film coating
DOI: 10.17632/ggwbtwz8gb.1
title: Marine micropaleontological data of SELXH

description: The Marine micropaleontological data of the three boreholes from the southeastern lixiahe area in northern jiangsu, China, in the lower Marine deposits of the site areas, tz-2012, qd-2013 and kz-2013, reflect the changes of the sedimentary environment with time (intertidal zone - supratidal zone), and prove that the area experienced the process of regression to the land during this period.
DOI: 10.17632/ggwbtwz8gb.2
title: Marine micropaleontological data of SELXH

description: The Marine micropaleontological data of the three boreholes from the southeastern lixiahe area in northern jiangsu, China, in the lower Marine deposits of the site areas, tz-2012, qd-2013 and kz-2013, reflect the changes of the sedimentary environment with time (intertidal zone - supratidal zone), and prove that the area experienced the process of regression to the land during this period.
DOI: 10.17632/y9zpdb8wng.1
title: Women and informal labour market in Bogota. Data of an experimental approach

description: The data presented in this study are the outcome of a survey in Bogota-Colombia from July to September 2018. The data was based on an experiment targeting 500 women participants, formal and informal workers, from the city of Bogota. We designed a set of games as means to evaluate social norms, evasion of taxes, risk and temporary discount. The information obtained from these games was then analysed by descriptive statistics including regressions and test of means. The dataset available here can be used to provide insight about how our sample population responded to the remaining parts of the questionnaire and how the experiment could be replicated in context or elsewhere in Bogota.
DOI: 10.5281/zenodo.3601432
title: Livorno, Urban driving, pothole detection

description: <strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU. The information is sent to the cloud and can be sent back to other connected vehicles for warning. The information is also transmitted via V2V to AD cars that can automatically adapt the speed. <strong>Session description</strong>: Test session with only a connected car with 6LoWPAN based pothole detector, lap of 1,9 km on the harbour's public road. 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 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.3601433
title: Livorno, Urban driving, pothole detection

description: <strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU. The information is sent to the cloud and can be sent back to other connected vehicles for warning. The information is also transmitted via V2V to AD cars that can automatically adapt the speed. <strong>Session description</strong>: Test session with only a connected car with 6LoWPAN based pothole detector, lap of 1,9 km on the harbour's public road. 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 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.3601889
title: Livorno, Urban driving, IMU pothole detection

description: <strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed. <strong>Session description</strong>: Test session with only a connected car with IMU based pothole detector, lap of 1,9 km on the harbour's public road. 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 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.3601890
title: Livorno, Urban driving, IMU pothole detection

description: <strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed. <strong>Session description</strong>: Test session with only a connected car with IMU based pothole detector, lap of 1,9 km on the harbour's public road. 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 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.