cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/msmtrgtxfs.1
title: Reading comprehension skills in English as a foreign language for Spanish speaker students

description: L2 reading skills of Spanish native speakers: the present dataset reports aimed at measuring English as a foreign language (EFL) reading comprehension by means of an audiovisual instructional approach. This consisted in examining different reading tasks before and after an intervention programme based on audiovisual media in EFL classes in a Spanish-speaker population of elementary and secondary school students in a rural area of southern Spain (10-14 years).
DOI: 10.17632/svdfjywbtj.1
title: Data for: Machine Learning based Power Management Scheme for Hybrid Microgrid Restoration against Grid Contingencies

description: The data is consisted upon 5 folders: 1. Matlab Files - Optimization files for SVM and GPR files. 2. Optimization Results - Figures that contain optimization results after LSVR, M5/2GPR and RQGPR models are optmized. 3. Plots - Excel file containing analysis plots of the above mentioned models. 4. Simulink Models - Simulink source files. 5. Variables - Matlab variables used for Machine Learning model design.
DOI: 10.17632/gkp484f2kg.1
title: Data for: 3631972

description: 1. Survey on Poland’s population - methodology 2. Data on global COVID-19 vaccine acceptance
DOI: 10.17632/r8sp3z79ff.3
title: Rail deflection measurements on an operational mainline with asphalt-concrete trackbed layer

description: In 2016 a trial section was created on an operational mainline railway track. The trackbed in the section incorporated an asphalt-concrete trackbed layer with 70% of recycled asphalt concrete in the mixture. The purpose of the measurement presented in this dataset was to analyse the behaviour of the section before and after the construction. Two independent sets of data were collected for the analysis. First, a change in track geometry measured by Track Recording Vehicles passing through the section. Second, deflections of the bottom of rails under the load from passing trains at various points along the section. More details about the sets are presented in the description of the respective folders in which the data is uploaded (below).
DOI: 10.17632/dyw2d5kwpc.1
title: Database Kinect

description: Movements evaluation
DOI: 10.17632/y73tv2yv72.1
title: Medical Students’ Commitment during the SARS-CoV-2 Pandemic: Preparedness, Motivation, and Impact on Students’ Skills

description: This repository enables published in our pre-print article with the same title. Aim of the study was to evaluate the preparedness of students involved in the CoronaPreventMainz (CPM) study as assistant staff. Another focus was on the influence of this commitment on the students attitude and skills. The first section of the survey captured the basic demographics (gender and age) of the student assistants, information on any medical apprenticeship before entering medical school, current semester, and details of the students’ commitment in the CPM study (type of work within the study, average hours of work, etc.). The second section asked about their preparedness for the pandemic in medical school. In particular, they were asked for their opinion on dedicated preparation courses for pandemics and whether medical students should be used as additional health care workers during crises like the current SARS-CoV-2 pandemic. The third section comprised questions on their feelings about participating in the fight against the crisis due to their work in the CPM study and their opinion on the appropriateness of students’ participation in this project. Furthermore, the students were asked about the safety precautions taken during their work and possible fear of self-infection. The fourth section asked about the students’ organizational and practical skills and their potential development during the study. Section five focused on their opinion regarding the large-scale testing, their own work within it, and the potential benefit of the study for coping with the SARS-CoV-2 pandemic. The last section of the questionnaire asked about the scientific results of the CPM study and the impact on their own interest in doing research.
DOI: 10.17632/84cg6f23b2.1
title: Spinodal solid and shell CAD files in STL and Abaqus input file format

description: We provide the CAD files of spinodal solids and shells in STL (for 3D printing) and Abaqus input file format (for finite element analysis) according to the solution of Cahn-Hilliard equation: 1. Early stage refers to the relatively initial simulation step and late stage refers to the relatively steady-state simulation step. 2. Spinodal solids are given in either 30% relative density or 50% relative density. 3. Spinodal shells are given as either 50/50 derived (interface between 50%/50% relative density spinodal solid phases) or 30/70 derived (interface between 30%/70% relative density spinodal solid phases). Please refer to the original article: the mechanical response of spinodal topology (https://doi.org/10.1016/j.jmps.2019.01.002) for more detail.
DOI: 10.17632/dz9w684x8p.1
title: Lipid and Metabolic Syndrome Traits in Coronary Artery Disease: A Mendelian Randomization Study

description: Harmonized effect sizes, standard error, and P-value for lipid traits at 343 distinct, genome-wide significant lipid trait SNPs (table_three-trait.txt) and lipid and metabolic syndrome traits at 1492 distinct, genome-wide significant lipid and metabolic syndrome trait SNPs (table_sixtrait.txt).
DOI: 10.17632/6p4v8nssm2.3
title: Data for understanding trust in varied information sources, use of news media, and perception of misinformation regarding COVID-19 in Pakistan
DOI: 10.17632/zv5wbrgdkw.1
title: Data for: Early subjective completion beliefs and the demand for post-secondary education

description: This file includes instructions, replication do-files, and complementary datasets. The main analysis is based on confidential GESOP (v30,v34) data that have to be applied for separately, see https://www.eui.eu/Research/Library/ResearchGuides/Economics/Statistics/DataPortal/GSOEP#Howtoaccessdata
DOI: 10.17632/4dtxs2bwnp.4
title: Dong, L., Polyak, L., Liu Y., Shi X., et al. 2020. Isotopic fingerprints of ice-rafted debris ...Revised.

description: Isotope data for potential sources used in this study and Isotope data for core BN05.
DOI: 10.17632/5vxzptg5gz.1
title: Coleman_2020_Tectonics

description: A compilation of supplementary material for Coleman et al., 2020, Lateral termination of a Cycladic-style detachment system (Hymittos, Greece), Tectonics.
DOI: 10.17632/69wsz4p8n9.1
title: (Dataset) Improvement in the furniture manufacturing

description: Data related to improvements in furniture manufacturing
DOI: 10.21227/phr1-2575
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(200+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.Update: I have received a lot of requests, especially from Social Science researchers, to also make the geo-tagged tweets created between March 20, 2020, and April 28, 2020, available in this dataset. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I have started hydrating the IDs provided in the Coronavirus (COVID-19) Tweets Dataset, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task goes on. Bookmark this page for further updates.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.17632/vhxdgjfjnk.1
title: Dataset of Tweets and News Media on COVID-19 in Portuguese

description: Collection of 3,925,366 tweets and 18,413 online news around the online discussion about COVID-19 in Brazil. The data from Twitter were collected through Twitterscraper Python library and we considered a set of keywords in Portuguese regarding to COVID-19. In order to facilitate the identification of tweets that have hashtags, media and retweets for researchers or data enthusiasts, we created three specific datasets for each of these categories. The news on COVID-19 was collected from the UOL portal, the most popular Brazilian website. All the data were gathered from January to May, 2020. These dataset can attract the attention from communities such as data science, social science, natural language processing, tourism, infodemiology, and public health.
DOI: 10.17632/drv4k56wpd.1
title: data for "Detecting efficient-frontier kinks and revealing nonexistence of the tangent portfolio for capital asset pricing models by parametric-quadratic programming"

description: data (return and dividend yield) for "s", "1", and "3"
DOI: 10.25919/5ef03af4a7d8c
title: Parkes observations for project P1050 semester 2020APRS_18

description: This is a request for observing time for the initial follow-up of pulsar discoveries from the re-processing of the low-latitude Galactic plane section of the HTRU survey (P630). We have now completed a first pass re-processing of the 60 % of the survey with GPU based coherent acceleration search and template bank search pipelines, and have discovered potential 54 previously unknown pulsars. Interesting science can usually only be derived from a new pulsar after confirmation and a follow-up timing campaign is carried out. One year of initial timing is the minimum timespan required to fully characterise any newly-discovered pulsars, essential for deriving pulsar parameters such as the characteristic age, magnetic field strength, spin-down rate, as well as to detect any unexpected behaviour of the pulsar which might result from emission instabilities. This follow-up and timing project is necessary for following up on any interesting pulsar systems discovered from the HTRU Galactic plane survey. Since all of the pulsars on the observing list here are being followed-up for the first time, they will produce completely new and exciting results. In addition, this timing project will enable a large-scale examination of the Galactic plane pulsar population, exploring the true boundaries of pulsar parameter space.
DOI: 10.21227/ym23-dt57
title: NPM Data for IEEE TII 0622

description: signal data
DOI: 10.21227/678e-4g68
title: NPM Data for IEEE TII 0622

description: signal data
DOI: 10.21227/y8bp-yh53
title: Losslessly Compressed Ligh Field Datasets

description: This dataset is accompanying the manuscript “Lossless Compression of Plenoptic Camera Sensor Images and of Light Field View Arrays” by Ioan Tabus and Emanuele Palma, submitted to IEEE Access in June 2020. It contains the archives and the programs for reconstructing the light field datasets publicly used in two major challenges for light field compression.
DOI: 10.25919/5ef050bb5fe3b
title: CSIRO Film Collection: videos 103 - 192

description: CSIRO produced its first film in 1946. It was a two-hour, five-part epic in black and white designed for a specialist audience of Commonwealth scientists meeting in London. It showed them - in the most graphic way possible at the time - the nature and extent of the research facilities of an organisation embarking on the problems of post-war reconstruction. Within a few years, a permanent film unit was established and began to specialise in producing films for the specific audiences on particular areas of research. By the 1960's, it became apparent that many of the films were of interest to a much wider audience. Schools, colleges and universities were beginning to introduce the audio-visual medium to the learning process and CSIRO films were in increasing demand. Makers of television programmes looked to CSIRO films for images of science not yet accessible to the public eye.
DOI: 10.5281/zenodo.3903073
title: Fe quantification model samples informations -- VRR targets

description: These tables provide informations about the sample set used in the Fe quantification model. This Fe quantification are aimed to refine the iron prediction for Chemcam data. Informations about martian targets on the Vera Rubin ridge are also provided.
DOI: 10.5281/zenodo.3903082
title: Depth of Martian Magnetization From Localized Power Spectrum Analysis

description: Supplementary data to the article Gong, S. and Wieczorek, M. (2020) Depth of Martian Magnetization From<br> Localized Power Spectrum Analysis, Geophys. Res. Lett. This archive contains the final inversion results for the employed magnetized<br> caps model using a magnetic field that expanded to degree and order 134. For<br> each localized analysis, the bandwidth and angular radius of the window were<br> 23 and 20 degrees, respectively, which yields six well-localized windows that<br> concentrated more than 99% of the power with the region of interest. The<br> analyses were performed at 150 km altitude and on an equally spaced grid with a<br> spacing corresponding to 10 degrees of latitude. FILE DESCRIPTIONS 20_23_6_134_150.dat This file contains the best fitting values and the corresponding reduced<br> chi^2 value of the inversion. The first two columns correspond to the<br> latitude and longitude, and the third to sixth columns correspond to the<br> best fitting values of the angular radii (in km) of the magnetized caps,<br> the magnetization depth (in km), the square root of the metric N&lt;M^2&gt;V^2<br> (in A m^2), and the reduced chi^2 value of the best fitting model at each<br> location, respectively. 20_23_6_134_150_lower.dat<br> 20_23_6_134_150_upper.dat These two files contain the 1-sigma lower and upper limits of the<br> parameters. For each file, the first two columns correspond to the latitude<br> and longitude, and the third to fifth columns correspond to the 1-sigma<br> lower or upper limits of the angular radii (in km) of the magnetized caps,<br> the magnetization depth (in km), the square root of the metric N&lt;M^2&gt;V^2<br> (in A m^2), respectively. The last column corresponds to the 1-sigma<br> confidence level of the reduced chi^2 that were obtained from Monte Carlo<br> simulations. The 1-sigma lower and upper limits were set to 1e+100 when the<br> minimum reduced chi^2 value of the best fitting model was above this 1-sigma<br> confidence level
DOI: 10.5281/zenodo.3903086
title: Pinnacle Test Data missing image

description: Test for case where image is missing for Pinnacle Export Tool in pymedphys.
DOI: 10.5281/zenodo.3903085
title: Pinnacle Test Data missing image

description: Test for case where image is missing for Pinnacle Export Tool in pymedphys.
DOI: 10.5281/zenodo.3903076
title: Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence

description: This is the replication package for the paper: "Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence". It contains the dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package in the following. <strong>1. dataset folder</strong> dataset_user_reviews.xlsx contains 1278 labelled non-requirement user reviews. readme.txt describes the meaning of the data in dataset_user_reviews.xlsx in detail.
DOI: 10.5281/zenodo.3903075
title: Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence

description: This is the replication package for the paper: "Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence". It contains the dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package in the following. <strong>1. dataset folder</strong> dataset_user_reviews.xlsx contains 1278 labelled non-requirement user reviews. readme.txt describes the meaning of the data in dataset_user_reviews.xlsx in detail.
DOI: 10.21227/s6an-n015
title: Losslessly Compressed Ligh Field Datasets

description: This dataset is accompanying the manuscript “Lossless Compression of Plenoptic Camera Sensor Images and of Light Field View Arrays” by Ioan Tabus and Emanuele Palma, submitted to IEEE Access in June 2020. It contains the archives and the programs for reconstructing the light field datasets publicly used in two major challenges for light field compression.
DOI: 10.21227/qeh7-b943
title: Losslessly Compressed Ligh Field Datasets

description: This dataset is accompanying the manuscript “Lossless Compression of Plenoptic Camera Sensor Images and of Light Field View Arrays” by Ioan Tabus and Emanuele Palma, submitted to IEEE Access in June 2020. It contains the archives and the programs for reconstructing the light field datasets publicly used in two major challenges for light field compression.
DOI: 10.21227/g44v-8f98
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for these keywords - “corona”, "coronavirus", "covid", "pandemic", "lockdown", "quarantine", "hand sanitizer", "ppe", "n95", different possible variants of "sarscov2", "nCov", "covid-19", "ncov2019", "2019ncov", "flatten(ing) the curve", "social distancing", "work(ing) from home" and the respective hashtag of all these keywords. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).-------------------------------------------------------------------------Coronavirus (COVID-19) Tweets Dataset(200+ Million English Language Tweets; ongoing collection)-------------------------------------------------------------------------Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs shared in the Coronavirus (COVID-19) Tweets Dataset.Update: I have received a lot of requests, especially from Social Science researchers, to also make the geo-tagged tweets created between March 20, 2020, and April 28, 2020, available in this dataset. Hydrating the millions of tweet IDs may come as a tedious task for people with less technical expertise. Therefore, I have started hydrating the IDs provided in the Coronavirus (COVID-19) Tweets Dataset, and I will be sharing the geo-tagged tweets posted in between these dates as the hydration task goes on. Bookmark this page for further updates.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.21227/rf85-v397
title: Adaptive Distributed Video Block Compressive Sensing: Leveraging Adaptive Sensing Schemes - Supplementary Material

description: Reconsructed images (video sequences) and results for the paper: "Adaptive Distributed Video Block Compressive Sensing: Leveraging Adaptive Sensing Schemes"
DOI: 10.21227/2gr6-ah08
title: Adaptive Distributed Video Block Compressive Sensing: Leveraging Adaptive Sensing Schemes - Supplementary Material

description: Reconsructed images (video sequences) and results for the paper: "Adaptive Distributed Video Block Compressive Sensing: Leveraging Adaptive Sensing Schemes"
DOI: 10.5281/zenodo.3844157
title: LuBiO3 nanoparticles for attenuation of ionising radiation

description: Supplementary data for bachelor thesis: ENEDIKOVÁ Alexandra, 2020. <em>LuBiO3 nanoparticles for attenuation of ionizing radiation</em>. Liberec<em>.</em> Bachelor thesis. Technical University of Liberec. Faculty of Mechatronics, Informatics and Interdisciplinary Studies. Supervisor: Jan Grégr. After thorough research three methods of preparing LuBiO3 nano- and microparticles were chosen for preparataion. Afterwards, effectivity of these methods was compared, and products were characterised. Polymer solution was prepared from PVB andnanoparticles, which was then processed by electrospinning. LuBiO3 nanofibers were characterised and tested on X-Ray attenuation effect.
DOI: 10.5281/zenodo.3844156
title: LuBiO3 nanoparticles for attenuation of ionising radiation

description: Supplementary data for bachelor thesis: ENEDIKOVÁ Alexandra, 2020. <em>LuBiO3 nanoparticles for attenuation of ionizing radiation</em>. Liberec<em>.</em> Bachelor thesis. Technical University of Liberec. Faculty of Mechatronics, Informatics and Interdisciplinary Studies. Supervisor: Jan Grégr. After thorough research three methods of preparing LuBiO3 nano- and microparticles were chosen for preparataion. Afterwards, effectivity of these methods was compared, and products were characterised. Polymer solution was prepared from PVB andnanoparticles, which was then processed by electrospinning. LuBiO3 nanofibers were characterised and tested on X-Ray attenuation effect.
DOI: 10.14457/tu.the.2010.806
title: ศึกษาระดับความเครียดของญาติผู้ป่วยจิตเวชในโรงพยาบาลจิตเวชนครพนมราชนครินทร์
DOI: 10.14457/tu.the.2009.941
title: การรับรู้ด้านการสื่อสารการตลาดเชิงบูรณาการ และปัจจัยที่มีอิทธิพลต่อการตัดสินใจเลือกเรียนในศูนย์อบรมคอมพิวเตอร์โรงเรียนอินเทอร์เน็ตและการออกแบบ (เน็ตดีไซน์)
DOI: 10.14457/tu.the.2009.942
title: การประเมินประสิทธิภาพสื่อประชาสัมพันธ์ภายในองค์การ :
DOI: 10.14457/tu.the.2009.943
title: การเปิดรับ ความพึงพอใจ และการใช้ประโยชน์ของผู้ชมรายการ คริส ดีลิเวอรี่ ทางสถานีโทรทัศน์กองทัพบกช่อง 5 ในเขตกรุงเทพมหานคร
DOI: 10.14457/tu.the.2009.944
title: ความพึงพอใจของชุมชนรอบโรงไฟฟ้าต่องานชุมชนสัมพันธ์ของการไฟฟ้าฝ่ายผลิตแห่งประเทศไทย : ศึกษาเฉพาะกรณี โรงไฟฟ้าพระนครเหนือ
DOI: 10.14457/tu.the.2009.945
title: ความสัมพันธ์ระหว่างการรับรู้บรรยากาศองค์การในเชิงบวก ความผูกพันต่อองค์การ และพฤติกรรมการเป็นสมาชิกที่ดีขององค์การ ในวิสาหกิจขนาดย่อม ประเภทอุตสาหกรรมการผลิตถุงพลาสติก
DOI: 10.14457/tu.the.2009.946
title: กลยุทธ์การบริหารงานข่าวของสถานีโทรทัศน์ผ่านดาวเทียม :