cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3756597
title: LG-covid19-HOTP: Literature Graph of Scholarly Articles Relevant to COVID-19 Study

description: Parallel to the dataset CORD-19 of scholarly articles, we provide the literature graph LG-covid19-HOTP composed of not only articles (graph nodes) that are relevant to the study of coronavirus, but also in and out citation links (directed graph edges) to base navigation and search among the articles. The article records are related and connected, not isolated. The graph has been updated weekly since March 26, 2020. The current graph includes <strong>3,870</strong> hot-off-the-press (HOTP) articles since January 2020. It contains <strong>109,386</strong> articles and <strong>958,043</strong> links. The link-to-node ratio is remarkably higher than some other existing literature graphs. In addition to the dataset we provide more functionalities at lg-covid-19-hotp.cs.duke.edu such as new articles, weekly meta-data analysis in terms of publication growth over time, ranking by citation, and statistical near-neighbor embedding maps by similarity in co-citation, and similarity in co-reference. Since April 11, we have enabled a novel functionality - self-navigated surf-search over the maps. At the site we also take courtesy input of COVID-19 articles that are missing from the current collection.
description: {"references": ["Semantic Scholar Open Research Corpus. 2019. Version 2019-11-01. Retrieved from http://s2-public-api-prod.us-west-2.elasticbeanstalk.com/corpus/download/. Accessed 2019-12-06.", "Elsevier Scopus Citation Overview API. Accessed 2020-03-25.", "COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-03-20. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2020-03-26. 10.5281/zenodo.3727291", "Crossref REST API. Available at www.crossref.org. Accessed 2020-03-25."]}
DOI: 10.21227/nwre-tj78
title: YawDD: Yawning Detection Dataset

description: A dataset of videos, recorded by an in-car camera, of drivers in an actual car with various facial characteristics (male and female, with and without glasses/sunglasses, different ethnicities) talking, singing, being silent, and yawning. It can be used primarily to develop and test algorithms and models for yawning detection, but also recognition and tracking of face and mouth. The videos are taken in natural and varying illumination conditions. The videos come in two sets, as described next: In the first set, a camera is installed under the front mirror of the car. This set provides 322 videos in 3 different situations: 1- normal driving (no talking), 2- talking or singing while driving, and 3- yawning while driving. Each subject has 3 or 4 videos.In the second set, the camera is installed on the driver’s dash. This set provides 29 videos, one for each subject, and each video containing scenes with driving silently, driving while talking, and driving while yawning.
DOI: 10.21227/c0hd-ez67
title: YawDD: Yawning Detection Dataset

description: A dataset of videos, recorded by an in-car camera, of drivers in an actual car with various facial characteristics (male and female, with and without glasses/sunglasses, different ethnicities) talking, singing, being silent, and yawning. It can be used primarily to develop and test algorithms and models for yawning detection, but also recognition and tracking of face and mouth. The videos are taken in natural and varying illumination conditions. The videos come in two sets, as described next: In the first set, the camera is installed under the front mirror of the car. This set provides 322 videos, each for a different situation: 1- normal driving (no talking), 2- talking or singing while driving, and 3- yawning while driving. Each subject has 3 or 4 videos.In the second set, the camera is installed on the driver’s dash. This set provides 29 videos, one for each subject, and each video containing all of driving silently, driving while talking, and driving while yawning.
DOI: 10.21227/9a1p-cd24
title: YawDD: Yawning Detection Dataset

description: A dataset of videos, recorded by an in-car camera, of drivers in an actual car with various facial characteristics (male and female, with and without glasses/sunglasses, different ethnicities) talking, singing, being silent, and yawning. It can be used primarily to develop and test algorithms and models for yawning detection, but also recognition and tracking of face and mouth. The videos are taken in natural and varying illumination conditions. The videos come in two sets, as described next: In the first set, the camera is installed under the front mirror of the car. This set provides 322 videos, each for a different situation: 1- normal driving (no talking), 2- talking or singing while driving, and 3- yawning while driving. Each subject has 3 or 4 videos.In the second set, the camera is installed on the driver’s dash. This set provides 29 videos, one for each subject, and each video containing all of driving silently, driving while talking, and driving while yawning.
DOI: 10.15161/oar.it/23579
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.21227/xmwm-4d89
title: YawDD: Yawning Detection Dataset

description: A dataset of videos, recorded by an in-car camera, of drivers in an actual car with various facial characteristics (male and female, with and without glasses/sunglasses, different ethnicities) talking, singing, being silent, and yawning. It can be used primarily to develop and test algorithms and models for yawning detection, but also recognition and tracking of face and mouth. The videos are taken in natural and varying illumination conditions. The videos come in two sets, as described next: In the first set, the camera is installed under the front mirror of the car. This set provides 322 videos, each for a different situation: 1- normal driving (no talking), 2- talking or singing while driving, and 3- yawning while driving. Each subject has 3 or 4 videos.In the second set, the camera is installed on the driver’s dash. This set provides 29 videos, one for each subject, and each video containing all of driving silently, driving while talking, and driving while yawning.
DOI: 10.5281/zenodo.3756642
title: PowNet-Laos: Unit Commitment / Economic Dispatch model for the Laotian power system (with sample data)

description: This repository contains PowNet-Laos, an implementation of PowNet model on the Laotian power system. The model is developed based on the power system facilities (power plants, substations, import/export nodes, and high-voltage transmission lines) operated in Laos during the year 2016. The majority of data are extracted from technical reports, publicly available, published by Electricite Du Laos (EDL) and the Department of Energy Policy and Planning (DEPP), Ministry of Energy and Mine, Lao PDR. The input data concerning the production of the hydropower reservoirs are generated via simulation with VIC-Res. We note that these data could be generated with other hydrological-hydraulic models. Please refer to Chowdhury et al. (2020a) for additional details on (1) the Laotian power system, and (2) PowNet-Laos setup. The basic functionalities and PowNet input-output structure are described in Chowdhury et al. (2020b). Step-by-step instructions on how to run and customize PowNet for any power system are provided in this GitHub repository. References: Chowdhury, A.K., Dang, T.D., Bagchi, A., and Galelli, S., (2020a). Expected benefits of Laos' hydropower development curbed by hydro-climatic variability and limited transmission capacity—opportunities to reform. Journal of Water Resources Planning and Management, DOI: 10.1061/(ASCE)WR.1943-5452.0001279, (preprint). Chowdhury, A.K., Kern, J., Dang, T.D. and Galelli, S., (2020b). PowNet: A Network-Constrained Unit Commitment/Economic Dispatch Model for Large-Scale Power Systems Analysis. Journal of Open Research Software, 8(1), p.5. DOI: http://doi.org/10.5334/jors.302
description: {"references": ["Chowdhury, A.K., Dang, T.D., Bagchi, A., and Galelli, S., (2020a). Expected benefits of Laos' hydropower development curbed by hydro-climatic variability and limited transmission capacity\u2014opportunities to reform. Journal of Water Resources Planning and Management (in review).", "Chowdhury, A.K., Kern, J., Dang, T.D. and Galelli, S., (2020b). PowNet: A Network-Constrained Unit Commitment/Economic Dispatch Model for Large-Scale Power Systems Analysis. Journal of Open Research Software, 8(1), p.5. DOI: http://doi.org/10.5334/jors.302"]}
DOI: 10.5281/zenodo.3756647
title: Analysis of well water level response to atmospheric loading from low- to high-frequency band

description: Data used in manuscript "Analysis of well water level response to atmospheric loading from low- to high-frequency band"
DOI: 10.5281/zenodo.3756648
title: Analysis of well water level response to atmospheric loading from low- to high-frequency band

description: Data used in manuscript "Analysis of well water level response to atmospheric loading from low- to high-frequency band"
DOI: 10.5281/zenodo.3756657
title: EMUE-D6-4-MobileOpticalMeasurement

description: Evaluation of measurement uncertainty in the calibration of a mobile optical measurement system This example illustrates the evaluation of measurement uncertainty related to the calibration of a mobile optical measurement system, based on the use of an SI-traceable reference standard bar measured in specific spatial positions. The measurement system studied (Krypton, model K610) contains three linear CCD cameras, in different spatial positions and orientations, with overlapping fields of view, permitting the simultaneous observation of an infrared LED located in a region of interest. By applying triangulation techniques, the measurement system can determine the static and dynamic spatial position of a set of observed LEDs. Files contained in the dataset are: - Example_MU_A244_MobileOpticalMeasSystem.pdf: Report “Evaluation of measurement uncertainty in the calibration of a mobile optical measurement system”; - MobileOpticalMeasSystem data.xlsx: File containing the input data required for the measurement uncertainty evaluation (calibration results, estimates and standard uncertainties of the input quantities).
DOI: 10.5281/zenodo.3756658
title: EMUE-D6-4-MobileOpticalMeasurement

description: Evaluation of measurement uncertainty in the calibration of a mobile optical measurement system This example illustrates the evaluation of measurement uncertainty related to the calibration of a mobile optical measurement system, based on the use of an SI-traceable reference standard bar measured in specific spatial positions. The measurement system studied (Krypton, model K610) contains three linear CCD cameras, in different spatial positions and orientations, with overlapping fields of view, permitting the simultaneous observation of an infrared LED located in a region of interest. By applying triangulation techniques, the measurement system can determine the static and dynamic spatial position of a set of observed LEDs. Files contained in the dataset are: - Example_MU_A244_MobileOpticalMeasSystem.pdf: Report “Evaluation of measurement uncertainty in the calibration of a mobile optical measurement system”; - MobileOpticalMeasSystem data.xlsx: File containing the input data required for the measurement uncertainty evaluation (calibration results, estimates and standard uncertainties of the input quantities).
DOI: 10.5281/zenodo.3754180
title: HpTonB(30-285) simulation Amber ff03ws

description: MD simulation of periplasmic part of TonB protein from <em>Helicobacter Pylori</em> HpTonB(30-285) with Amber ff03ws force field without additional ions. Trajectory contains the last 1000ns of the total 1171ns simulation. run1microsend.xtc (10 ps saving frequency), onlyPROTEIN.tpr, and confPROT.gro contain only protein. run6-8skip.xtc (10 ps saving frequency), run6.tpr and confEND.gro contain also solvent. Force field parameters are downloaded from https://github.com/bestlab/force_fields
DOI: 10.5281/zenodo.3754179
title: HpTonB(30-285) simulation Amber ff03ws

description: MD simulation of periplasmic part of TonB protein from <em>Helicobacter Pylori</em> HpTonB(30-285) with Amber ff03ws force field without additional ions. Trajectory contains the last 1000ns of the total 1171ns simulation. run1microsend.xtc (10 ps saving frequency), onlyPROTEIN.tpr, and confPROT.gro contain only protein. run6-8skip.xtc (10 ps saving frequency), run6.tpr and confEND.gro contain also solvent. Force field parameters are downloaded from https://github.com/bestlab/force_fields
DOI: 10.5281/zenodo.3756677
title: Database for "A new perspective for charactering the spatio-temporal patterns of error in GPM IMERG over China Mainland in the past time from 2012 to 2018"

description: This file contains the <strong>dataset</strong> accompanying the manuscript '<strong>2020EA001232-T'</strong> submitted to the <strong>ESS</strong> journal (https://earthandspacescience-submit.agu.org). The data used in this study can be download from public website, and the generated dataset is listed in the order of figures with detail information in ReadMe file. China Merged Precipitation Analysis data (CMPA, hourly, with the resolution of , as validation data) for China Mainland is available at website http://data.cma.cn. Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrieval data (IMERG, half-hourly, with the resolution of , as the observed data) is available at https://pmm.nasa.gov/data-access/downloads/gpm. The Shuttle Radar Topography Mission data (SRTM, with a 90-m spatial resolution) could be accessed at http://srtm.csi.cgiar.org.
DOI: 10.5281/zenodo.3756664
title: MD simulation of HpTonB(30-285) in 40mM NaCl with Amber ff03ws force field

description: MD simulation of periplasmic part of TonB protein from <em>Helicobacter Pylori</em> HpTonB(30-285) with Amber ff03ws force field with 40 mM NaCl. Trajectory contains the last 1000ns of the simulation. nojump_nowater.xtc (10 ps saving frequency), md_noSOL.tpr, and confENDprot.gro contain only protein. micro_40mMNaClskip.xtc (100 ps saving frequency), md.tpr and confEND.gro contain also solvent. Force field parameters for proteins and counterions are dowloaded from https://github.com/bestlab/force_fields, for NaCl from https://bitbucket.org/hseara/ions
DOI: 10.5281/zenodo.3756663
title: MD simulation of HpTonB(30-285) in 40mM NaCl with Amber ff03ws force field

description: MD simulation of periplasmic part of TonB protein from <em>Helicobacter Pylori</em> HpTonB(30-285) with Amber ff03ws force field with 40 mM NaCl. Trajectory contains the last 1000ns of the simulation. nojump_nowater.xtc (10 ps saving frequency), md_noSOL.tpr, and confENDprot.gro contain only protein. micro_40mMNaClskip.xtc (100 ps saving frequency), md.tpr and confEND.gro contain also solvent. Force field parameters for proteins and counterions are dowloaded from https://github.com/bestlab/force_fields, for NaCl from https://bitbucket.org/hseara/ions
DOI: 10.15468/x8jp9h
title: Hill forest dweller: a new cryptic species of Rhinolophus in the ‘ pusillus group’ (Chiroptera: Rhinolophidae) from Thailand and Lao PDR

description: This dataset contains the digitized treatments in Plazi based on the original journal article Soisook, Pipat, Karapan, Sunate, Srikrachang, Mattana, Dejtaradol, Ariya, Nualcharoen, Kwan, Bumrungsri, Sara, Lin, Sai Sein, Aung, Moe Moe, Bates, Paul J. J., Harutyunyan, Margarita, Buś, Magdalena M., Bogdanowicz, Iesław (2016): Hill forest dweller: a new cryptic species of Rhinolophus in the ‘ pusillus group’ (Chiroptera: Rhinolophidae) from Thailand and Lao PDR. Acta Chiropterologica 18 (1): 117-139, DOI: 10.3161/15081109ACC2016.18.1.005
DOI: 10.5281/zenodo.3756784
title: RTPR_rnrdb_tara_full-length_amino_acid_sequences

description: RTPR amino acid sequences used as reference for framshift corrections
DOI: 10.5281/zenodo.3756785
title: RTPR_rnrdb_tara_full-length_amino_acid_sequences

description: RTPR amino acid sequences used as reference for framshift corrections
DOI: 10.21227/7k24-xe21
title: Dataset for Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering

description: Controlling the quality of tertiary structures computed for a protein molecule remains a central challenge in de-novo protein structure prediction. The rule of thumb is to generate as many structures as can be afforded, effectively acknowledging that having more structures increases the likelihood that some will reside near the sought biologically-active structure. A major drawback with this approach is that computing a large number of structures imposes time and space costs. This dataset is associated with our paper, "Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering", where we propose a novel clustering-based approach which we demonstrate to significantly reduce an ensemble of generated structures without sacrificing quality. This dataset provides the biologically-active structures of the protein targets used for evaluation, necessary data (sequence, fragment files) for generating structure ensembles, generated ensembles, reduced ensembles, and data necessary for generating and plotting the results in the paper. The paper is under review and we will update the link to the paper once it is published. The codes associated with this dataset can be found in, https://github.com/ahmed0804/ReducedDecoyPool
DOI: 10.5281/zenodo.3749302
title: 3D models for the ligaments of the Interosseous Membrane of 5 forearms with their biomechanical simulation scenes

description: This dataset contains a group of 15 ligaments corresponding to the five specimens (3 per forearm) modeled as 3D tetrahedral meshes. In addition, 15 simulation scenes written in SOFA framework (INRIA) are supplied to implement stretch experiments. Details about the study are provided in the technical report.
description: In order to run the simulations, you need to install SOFA framework (https://www.sofa-framework.org). In addition, you need to get prosup plugin available on github https://github.com/PROSUP-Project
DOI: 10.5281/zenodo.3749301
title: 3D models for the ligaments of the Interosseous Membrane of 5 forearms with their biomechanical simulation scenes

description: This dataset contains a group of 15 ligaments corresponding to the five specimens (3 per forearm) modeled as 3D tetrahedral meshes. In addition, 15 simulation scenes written in SOFA framework (INRIA) are supplied to implement stretch experiments. Details about the study are provided in the technical report.
description: In order to run the simulations, you need to install SOFA framework (https://www.sofa-framework.org). In addition, you need to get prosup plugin available on github https://github.com/PROSUP-Project
DOI: 10.21227/tedz-0h93
title: Twitter Sentiment Analysis Data

description: Collect Corona Virus related Tweets from here.Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live[1]. The last column, viz. sentiment score, is not the score estimated by the model. The LSTM model is still in the pre-alpha phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. The sentiment scores produced by our model will be made public after the project is completely open-sourced.##################References:[1]https://sentiment.live/ [2]https://textblob.readthedocs.io/en/dev/
DOI: 10.21227/6hpp-vc62
title: Twitter Sentiment Analysis Data

description: Collect Corona Virus related Tweets from here.Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live[1]. The last column, viz. sentiment score, is not the score estimated by the model. The LSTM model is still in the pre-alpha phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. The sentiment scores produced by our model will be made public after the project is completely open-sourced.##################References:[1]https://sentiment.live/ [2]https://textblob.readthedocs.io/en/dev/
DOI: 10.21227/pawv-fd74
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live[1]. The last column, viz. sentiment score, is not the score estimated by the model. The LSTM model is still in the pre-alpha phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. The sentiment scores produced by our model will be made public after the project is completely open-sourced.#####################################Collect Corona Virus related Tweets from here.#####################################References:[1]https://sentiment.live/ [2]https://textblob.readthedocs.io/en/dev/
DOI: 10.5281/zenodo.3756791
title: Data_Effects of input modality on vocal effector prioritization in manual-vocal dual tasks

description: Dual-task costs in RTs and error rates compared between effector systems in vocal-manual dual-tasks using either only auditory or only visual stimuli for both responses
DOI: 10.21227/20dz-4159
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.#####################################Collect Corona Virus related Tweets from here.#####################################The tweets have been collected by the LSTM model deployed here at sentiment.live[1]. The last column, viz. sentiment score, is not the score estimated by the model. The LSTM model is still in the pre-alpha phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. The sentiment scores produced by our model will be made public after the project is completely open-sourced.References:[1]https://sentiment.live/ [2]https://textblob.readthedocs.io/en/dev/
DOI: 10.5281/zenodo.3756790
title: Data_Effects of input modality on vocal effector prioritization in manual-vocal dual tasks

description: Dual-task costs in RTs and error rates compared between effector systems in vocal-manual dual-tasks using either only auditory or only visual stimuli for both responses