cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5k6c47ff56.1
title: gel figure full form F1-B

description: Figure1-B,D gel figure full form in PLOS ONE submit data
DOI: 10.17632/5k6c47ff56.2
title: gel figure full form F1-B

description: Figure1-B,D gel figure full form in PLOS ONE submit data
DOI: 10.17632/5k6c47ff56.3
title: gel figure full form F1-B

description: Figure1-B,D gel figure full form in PLOS ONE submit data
DOI: 10.17632/593w3vt3g4.1
title: Mechanistic study on the enhanced electrochemical characteristics of a bioelectrode composed of GOx immobilized in carboxyl-functionalized OMC

description: Data include the amount of OMC immobilized enzyme, infrared spectrum, X-ray diffraction spectrum, CV data, and BET data
DOI: 10.21227/bjc3-kb85
title: Taxi trajectory

description: This is the image of data.
DOI: 10.17632/593w3vt3g4.2
title: Mechanistic study on the enhanced electrochemical characteristics of a bioelectrode composed of GOx immobilized in carboxyl-functionalized OMC

description: Data include the amount of OMC immobilized enzyme, infrared spectrum, X-ray diffraction spectrum, CV data, and BET data
DOI: 10.17632/nhtznbp73m.1
title: Image files

description: It is a preparation work for cellulose fiber reinforced polymer lamina.
DOI: 10.17632/p5rjtgnkym.1
title: Role of Government for the Development of Cooperatives dataset in South Wollo Zone, Amhara Regional State, Ethiopia

description: it is a research data on state and cooperative relation for the development of cooperatives. to analyze the major role of government for development of cooperatives, to examine the relationship between role of government and cooperative development and to analyze the effect of role of government on cooperative development on selected cooperative societies of south wollo zone. Samples were drawn from multipurpose, consumer and saving and credit cooperative societies and 161 samples of respondents have taken. Role of government questionnaires and cooperative development questionnaires were used as a tool of data collection. Moreover descriptive statistics, Pearson correlation, and regression analysis were also used for method of data analysis. The result indicates that positive but substantial relationships were found between finance, supervision and management and cooperative development. Supervision, human resource and legal status affect cooperative development in which supervision is the best predictor whereas legal status is the poorest predictor of cooperative development.
DOI: 10.17632/gffbs9xrxs.1
title: Data for: Habitat area and connectivity support cavity-nesting bees in vineyards more than organic management

description: Raw Data for Habitat area and connectivity support cavity nesting bees in vineyards more than organic management. This research was embedded within the European joint project PromESSinG (Promoting Ecosystem Services in Grapes), funded through the 2013-2014 BiodivERsA/FACCE-JPI joint call for research proposals, with the German funder Federal Ministry of Education and Research [grant number 01LC1405A].
DOI: 10.17632/ksy5z9z3fh.2
title: Fear and disgust rankings of snake species covering the full morphological variability 2: Likert Scale

description: Data used in the article "Emotional reaction to fear- and disgust-evoking snakes: Sensitivity and propensity in snake-fearful respondents". This article examines the differences between snake-fearful respondents and controls in evaluation of disgust and fear of disgusting and fear-eliciting snakes.
DOI: 10.17632/p7xskvwktk.1
title: Data for: Luminance information is required for the accurate estimation of contrast in rapidly changing visual contexts
DOI: 10.17199/a8e650db-b223-40a5-a8eb-42fd34313420
title: Final prototype

description: Peter Kadletz, Tobias Richter
DOI: 10.17632/vznyfcx9xk.2
title: Data for: Implementation of Real-Time Online Mouse Tracking Case Study in a Small Online Quiz

description: The data are based from mouse tracking experiments at https://md.hicc.cs.kumamoto-u.ac.jp. 1. Filenames with "P2P" contains network packet captured of one click event submitted from the client to the server. The filenames also have the number of variables included such as name, email, date, current URL, etc, refer to "Summary_of_Data.ods". 2. Filename with "Local" contains resource cost and mouse tracking data of five users attempting a quiz session on the server via the Internet on 25 December 2018. 3. Filename with "Implementation" contains resource cost of a mouse tracking implementation of two quiz session attempted by twenty-two students on each session from School of Engineering and Applied Sciences, National University of Mongolia to the server at Human Interface and Cyber Communication laboratory, Kumamoto University on 03 January 2019. Unfortunately, the mouse tracking data is not open for a limited amount of time.
DOI: 10.5281/zenodo.3630349
title: Data: Algorithms for new types of fair stable matchings

description: This data corresponds to the data and experiments described in Section 5 of<br> the following paper: Algorithms for new types of fair stable matchings<br> Authors: Frances Cooper and David Manlove The paper is located at: https://arxiv.org/abs/2001.10875 The software is located at: https://zenodo.org/record/3630383 The data is located at: https://zenodo.org/record/3630349 See the README for more information.
DOI: 10.5281/zenodo.3630350
title: Data: Algorithms for new types of fair stable matchings

description: This data corresponds to the data and experiments described in Section 5 of<br> the following paper: Algorithms for new types of fair stable matchings<br> Authors: Frances Cooper and David Manlove The paper is located at: https://arxiv.org/abs/2001.10875 The software is located at: https://zenodo.org/record/3630383 The data is located at: https://zenodo.org/record/3630349 See the README for more information.
DOI: 10.17632/h3pxvcwzgw.1
title: Inflation Targeting as a Shock Absorber
DOI: 10.17632/kp96k2xxcm.1
title: Dissertação de Mestrado Acadêmico - Apêndice 3

description: Apêndice 3 com as tabelas suplementares de 1 a 7.
DOI: 10.17632/r9mx5y94rc.1
title: Data for: Cell Pyroptosis, a Potential Pathogenic Mechanism of 2019-nCoV Infection

description: Figure.1. A hypothesis of the relationship between 2019-nCoV and cell pyroptosis
DOI: 10.5281/zenodo.3630161
title: Vigo, Automated Valet Parking, Drop off and Pick up

description: <strong>Scenario description:</strong> The vehicle starts parked in the drop off area. A drop off request is made to the parking service through the mobile phone application. Once the parking manoeuvre is finished, a pick up request is made. The test ends when the vehicle has been parked in the pick up area and the app informs the user. <strong>Session description:</strong> Human factor tests performed at Praza do Rei parking in Vigo. It is an enclosed area with no GPS coverage, so a LIDAR indoor positioning system is used. <strong>Datasets descriptions:</strong><br> <br> <strong>AUTOPILOT_Vigo_UrbanDriving_Vehicle: </strong>Data extracted from the CAN of the vehicle. Contains data regarding the state of the vehicle, such as speed, engine speed or fuel consumption. <strong>AUTOPILOT_Vigo_UrbanDriving_VehicleDynamics: </strong>Data extracted from the CAN of the vehicle.Contains data regarding the dynamic state of the vehicle, such as speed limit, acceleration or yaw rate. <strong>AUTOPILOT_Vigo_UrbanDriving_DriverVehicleInteraction: </strong>Data extracted from the CAN of the vehicle. Contains all the information about the actions that the driver can perform, such as the state of the brake, the steering wheel and the throttle. <strong>AUTOPILOT_Vigo_UrbanDriving_EnviromentSensorsAbsolute: </strong>Data extracted from the sensors of the vehicle in absolute coordinates. <strong>AUTOPILOT_Vigo_UrbanDriving__EnviromentSensorsRelative: </strong>Data extracted from the sensors of the vehicle in relative coordinates. <strong>AUTOPILOT_Vigo_UrbanDriving_Positioning:</strong> Data from the GPS of the vehicle, such as latitude longitude and heading. <strong>AUTOPILOT_Vigo_UrbanDriving_IoT: </strong>Data of the communications via IoT, coming from the OBU of the vehicle and the IoT server that contains the Parking Service.
DOI: 10.5281/zenodo.3630162
title: Vigo, Automated Valet Parking, Drop off and Pick up

description: <strong>Scenario description:</strong> The vehicle starts parked in the drop off area. A drop off request is made to the parking service through the mobile phone application. Once the parking manoeuvre is finished, a pick up request is made. The test ends when the vehicle has been parked in the pick up area and the app informs the user. <strong>Session description:</strong> Human factor tests performed at Praza do Rei parking in Vigo. It is an enclosed area with no GPS coverage, so a LIDAR indoor positioning system is used. <strong>Datasets descriptions:</strong><br> <br> <strong>AUTOPILOT_Vigo_UrbanDriving_Vehicle: </strong>Data extracted from the CAN of the vehicle. Contains data regarding the state of the vehicle, such as speed, engine speed or fuel consumption. <strong>AUTOPILOT_Vigo_UrbanDriving_VehicleDynamics: </strong>Data extracted from the CAN of the vehicle.Contains data regarding the dynamic state of the vehicle, such as speed limit, acceleration or yaw rate. <strong>AUTOPILOT_Vigo_UrbanDriving_DriverVehicleInteraction: </strong>Data extracted from the CAN of the vehicle. Contains all the information about the actions that the driver can perform, such as the state of the brake, the steering wheel and the throttle. <strong>AUTOPILOT_Vigo_UrbanDriving_EnviromentSensorsAbsolute: </strong>Data extracted from the sensors of the vehicle in absolute coordinates. <strong>AUTOPILOT_Vigo_UrbanDriving__EnviromentSensorsRelative: </strong>Data extracted from the sensors of the vehicle in relative coordinates. <strong>AUTOPILOT_Vigo_UrbanDriving_Positioning:</strong> Data from the GPS of the vehicle, such as latitude longitude and heading. <strong>AUTOPILOT_Vigo_UrbanDriving_IoT: </strong>Data of the communications via IoT, coming from the OBU of the vehicle and the IoT server that contains the Parking Service.
DOI: 10.17632/s2jgpv8m9f.1
title: Data_Red-light Running Behavior of Takeaway E-cyclists
DOI: 10.5281/zenodo.3630163
title: Vigo,Urban driving, GLOSA and VRU detection

description: <strong>Scenario description:</strong> GLOSA and VRU detection. The autonomous vehicle makes several turns on the scenario adapting its speed to the state of the traffic light. At an intersection controlled by a camera connected via IoT, a VRU is introduced into one of the laps so that the autonomous vehicle brakes when it receives the information via IoT. <strong>Session description:</strong> Human factor tests performed at CTAG proving grounds. <strong>Datasets descriptions:</strong><br> <br> <strong>AUTOPILOT_Vigo_UrbanDriving_Vehicle: </strong>Data extracted from the CAN of the vehicle. Contains data regarding the state of the vehicle, such as speed, engine speed or fuel consumption. <strong>AUTOPILOT_Vigo_UrbanDriving_VehicleDynamics: </strong>Data extracted from the CAN of the vehicle.Contains data regarding the dynamic state of the vehicle, such as speed limit, acceleration or yaw rate. <strong>AUTOPILOT_Vigo_UrbanDriving_DriverVehicleInteraction: </strong>Data extracted from the CAN of the vehicle. Contains all the information about the actions that the driver can perform, such as the state of the brake, the steering wheel and the throttle. <strong>AUTOPILOT_Vigo_UrbanDriving_EnviromentSensorsAbsolute: </strong>Data extracted from the sensors of the vehicle in absolute coordinates. <strong>AUTOPILOT_Vigo_UrbanDriving__EnviromentSensorsRelative: </strong>Data extracted from the sensors of the vehicle in relative coordinates. <strong>AUTOPILOT_Vigo_UrbanDriving_Positioning:</strong> Data from the GPS of the vehicle, such as latitude longitude and heading. <strong>AUTOPILOT_Vigo_UrbanDriving_IoT: </strong>Data of the communications via IoT, coming from the OBU of the vehicle, the IoT server and the RSU that controls the camera and the traffic light.<br>
DOI: 10.5281/zenodo.3630164
title: Vigo,Urban driving, GLOSA and VRU detection

description: <strong>Scenario description:</strong> GLOSA and VRU detection. The autonomous vehicle makes several turns on the scenario adapting its speed to the state of the traffic light. At an intersection controlled by a camera connected via IoT, a VRU is introduced into one of the laps so that the autonomous vehicle brakes when it receives the information via IoT. <strong>Session description:</strong> Human factor tests performed at CTAG proving grounds. <strong>Datasets descriptions:</strong><br> <br> <strong>AUTOPILOT_Vigo_UrbanDriving_Vehicle: </strong>Data extracted from the CAN of the vehicle. Contains data regarding the state of the vehicle, such as speed, engine speed or fuel consumption. <strong>AUTOPILOT_Vigo_UrbanDriving_VehicleDynamics: </strong>Data extracted from the CAN of the vehicle.Contains data regarding the dynamic state of the vehicle, such as speed limit, acceleration or yaw rate. <strong>AUTOPILOT_Vigo_UrbanDriving_DriverVehicleInteraction: </strong>Data extracted from the CAN of the vehicle. Contains all the information about the actions that the driver can perform, such as the state of the brake, the steering wheel and the throttle. <strong>AUTOPILOT_Vigo_UrbanDriving_EnviromentSensorsAbsolute: </strong>Data extracted from the sensors of the vehicle in absolute coordinates. <strong>AUTOPILOT_Vigo_UrbanDriving__EnviromentSensorsRelative: </strong>Data extracted from the sensors of the vehicle in relative coordinates. <strong>AUTOPILOT_Vigo_UrbanDriving_Positioning:</strong> Data from the GPS of the vehicle, such as latitude longitude and heading. <strong>AUTOPILOT_Vigo_UrbanDriving_IoT: </strong>Data of the communications via IoT, coming from the OBU of the vehicle, the IoT server and the RSU that controls the camera and the traffic light.<br>
DOI: 10.5284/1058992
title: Archaeological Excavation at Nos. 77-81, Sidney Street, Sheffield, South Yorkshire (OASIS ID: archaeol5-308139)

description: Archaeological Research Services Ltd. was commissioned by Elmsdale Estates Ltd to carry out a multi-phase programme of archaeological works on land at Nos. 77-81 Sidney Street, Sheffield, South Yorkshire (NGR SK 35416 86575). The works included a strip, map and sample excavation, as well as evaluation trenching. The aims and objectives of this project were to identify and record any 19-20th century industrial workshops and/ or domestic remains in the proposed development area, to produce dating and phasing for archaeological deposits recorded on the site and to establish the character and extent of archaeological deposits in order to define functional areas of the site (e.g. industrial and domestic). Excavations revealed the remains of workers housing, as well as structural elements of the buildings which pre-date the construction of Sidney Street. Furthermore, the remains of cellars, paths and successive drainage were discovered to be associated with the workers housing. Stone built and brick built structures, relating to the earliest phases of industrial activity on the site were also discovered. The archaeological works at 77-81 Sidney Street revealed a sequence of structures that span the use of the site as agricultural land, its incorporation into an urban setting and through to the present day.
DOI: 10.6084/m9.figshare.11760597.v3
title: Simulated significant wave height (3-hourly, 2015.01-2100.12) from FIO-ESM v2.0 ssp126 experiment r1i1p1f1

description: Simulated significant wave height (3-hourly, 2015.01-2100.12) from FIO-ESM v2.0 ssp126 experiment (one of future scenarios) r1i1p1f1. The grid, named gx1v6, is the displaced pole (the North Pole displaced to the Greenland) grid with 1.1 degree in longitude and 0.27-0.54 degree in latitude.
DOI: 10.3886/e113982v1-22019
title: Replication data for: The Evolution of China's One-Child Policy and Its Effects on Family Outcomes

description: In 1979, China introduced its unprecedented one-child policy, under which households exceeding the birth quota were penalized. However, estimating the effect of this policy on family outcomes turns out to be complicated. China had already enacted an aggressive family planning policy in the early 1970s, and its fertility rates had already dropped sharply before the enactment of the one-child policy. The one-child policy was also enacted at almost the same time as China's market-oriented economic reforms, which triggered several decades of rapid growth, which would also tend to reduce fertility rates. During the same period, a number of other developing countries in East Asia and around the world have also experienced sharp declines in fertility. Overall, finding defensible ways to identify the effect of China's one-child policy on family outcomes is a tremendous challenge. I expound the main empirical approaches to the identification of the effects of the one-child policy, with an emphasis on their underlying assumptions and limitations. I then turn to empirical results in the literature. I discuss the evidence concerning the effects of the one-child policy on fertility and how it might affect human capital investment in children. Finally I offer some new exploratory and preliminary estimates of the effects of the one-child policy on divorce, labor supply, and rural-to-urban migration.
DOI: 10.5281/zenodo.3630396
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: 10.5281/zenodo.3627136
title: Definitive dataset framework of the Data Analysis on Sutri, HISMAGIS protocol.

description: Definitive dataset framework of the Data Analysis on Sutri, HISMAGIS protocol. All the feature class, geodatabase and collector carpets have been translated in English.
DOI: 10.5281/zenodo.3627137
title: Definitive dataset framework of the Data Analysis on Sutri, HISMAGIS protocol.

description: Definitive dataset framework of the Data Analysis on Sutri, HISMAGIS protocol. All the feature class, geodatabase and collector carpets have been translated in English.
DOI: 10.21227/zxpc-mg45
title: Newton-like search algorithm for the reconstruction of admissible joint-references from a prescribed output-reference for the non-Standard and Generalized N-Trailers

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: 10.21227/581g-dq09
title: Newton-like search algorithm for the reconstruction of admissible joint-references from a prescribed output-reference for the non-Standard and Generalized N-Trailers

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: 10.21227/wpry-9098
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.