cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3688396
title: CMR for Identifying the Substrate of Ventricular Arrhythmia in Patients with Q1 Normal Echocardiography

description: OBJECTIVES This study sought to determine whether cardiac magnetic resonance (CMR) may identify structural heart<br> disease (SHD) in patients with ventricular arrhythmia who had echocardiography ruled out pathological findings.<br> BACKGROUND Approximately one-half of sudden cardiac deaths are attributable to malignant VA. Echocardiography is<br> commonly used to identify SHD that is the most frequent substrate of VA.<br> Q6 METHODS A single-center prospective study was conducted in consecutive patients with significant VA, categorized as<br> &gt;1,000 but &lt;10,000 ventricular ectopic beats [VEB]s/24 h; $10,000 VEBs/24 h; nonsustained ventricular tachycardia,<br> sustained ventricular tachycardia (SVT), or a history of resuscitated cardiac arrest, and no pathological findings at<br> echocardiography, requiring a clinically indicated CMR. Primary endpoint was CMR detection of SHD. Secondary endpoints<br> were a composite of CMR detection of SHD and abnormal findings not specific for a definite SHD diagnosis.<br> RESULTS A total of 946 patients were enrolled (mean 41 16 years of age; 64% men). CMR studies were used to<br> diagnose SHD in 241 patients (25.5%) and abnormal findings not specific for a definite SHD diagnosis in 187 patients<br> (19.7%). Myocarditis (n ¼ 91) was the more frequent disease, followed by arrhythmogenic cardiomyopathy (n ¼ 55),<br> dilated cardiomyopathy (n ¼ 39), ischemic heart disease (n ¼ 22), hypertrophic cardiomyopathy (n ¼ 13), congenital<br> cardiac disease (n ¼ 10), left ventricle noncompaction (n ¼ 5), and pericarditis (n ¼ 5). The strongest univariate and<br> multivariate predictors of SHD on CMR images were chest pain (odds ratios [OR]: 2.52 and 2.38, respectively) and SVT<br> (ORs: 2.67 and 2.23, respectively).<br> CONCLUSIONS SHD was able to be identified on CMR imaging in a sizable number of patients with significant VA and<br> completely normal echocardiography. Chest pain and SVT were the strongest predictors of positive CMR imaging<br> results.
DOI: 10.5281/zenodo.3688395
title: CMR for Identifying the Substrate of Ventricular Arrhythmia in Patients with Q1 Normal Echocardiography

description: OBJECTIVES This study sought to determine whether cardiac magnetic resonance (CMR) may identify structural heart<br> disease (SHD) in patients with ventricular arrhythmia who had echocardiography ruled out pathological findings.<br> BACKGROUND Approximately one-half of sudden cardiac deaths are attributable to malignant VA. Echocardiography is<br> commonly used to identify SHD that is the most frequent substrate of VA.<br> Q6 METHODS A single-center prospective study was conducted in consecutive patients with significant VA, categorized as<br> &gt;1,000 but &lt;10,000 ventricular ectopic beats [VEB]s/24 h; $10,000 VEBs/24 h; nonsustained ventricular tachycardia,<br> sustained ventricular tachycardia (SVT), or a history of resuscitated cardiac arrest, and no pathological findings at<br> echocardiography, requiring a clinically indicated CMR. Primary endpoint was CMR detection of SHD. Secondary endpoints<br> were a composite of CMR detection of SHD and abnormal findings not specific for a definite SHD diagnosis.<br> RESULTS A total of 946 patients were enrolled (mean 41 16 years of age; 64% men). CMR studies were used to<br> diagnose SHD in 241 patients (25.5%) and abnormal findings not specific for a definite SHD diagnosis in 187 patients<br> (19.7%). Myocarditis (n ¼ 91) was the more frequent disease, followed by arrhythmogenic cardiomyopathy (n ¼ 55),<br> dilated cardiomyopathy (n ¼ 39), ischemic heart disease (n ¼ 22), hypertrophic cardiomyopathy (n ¼ 13), congenital<br> cardiac disease (n ¼ 10), left ventricle noncompaction (n ¼ 5), and pericarditis (n ¼ 5). The strongest univariate and<br> multivariate predictors of SHD on CMR images were chest pain (odds ratios [OR]: 2.52 and 2.38, respectively) and SVT<br> (ORs: 2.67 and 2.23, respectively).<br> CONCLUSIONS SHD was able to be identified on CMR imaging in a sizable number of patients with significant VA and<br> completely normal echocardiography. Chest pain and SVT were the strongest predictors of positive CMR imaging<br> results.
DOI: 10.5281/zenodo.3688363
title: Nepal landslide dataset for semantic segmentation

description: This database contains images used for the semantic segmentation of landslide scars from a fully convolutional neural network U-Net. 1. <strong>Training dataset: </strong>it contains 230 GeoTIFF 8 bits images and associated PNG masks (scars indicated in white and background in black color). 2. <strong>Validation dataset</strong>: it contains 35 GeoTIFF 8 bits images and associated PNG masks used for U-Net validation step. 3. <strong>Test dataset: </strong>it contains 10 GeoTIFF 8 bits images and associated PNG masks for testing. Also, the "SHAPEFILES_LANDSLIDES.rar" file contains the vector layers of the masked images in .shp format.
DOI: 10.25345/c5xd5x
title: MassIVE MSV000085012 - UGA-independent Mis-incorporation of Selenocysteine throughout Escherichia coli Proteome
title: LC-MS/MS based shotgun proteomics data of E.coli grew in high selenium-to-sulfur ratio medium. Files beginning with letter M are data sets of the control group cultured in standard M9 minimal medium. Files beginning with letter S are data sets of the experimental group cultured in high selenium-to-sulfur ratio medium. There are 4 biological replicates in each group, and 3 technical replicates in each biological replicate.
DOI: 10.5287/bodleian:8gjyyv74b
title: Reprehenderit deseru
DOI: 10.15490/fairdomhub.1.datafile.3363.1
title: /reports/SupplementaryTableS6

description: Supplementary Table S6 - EvidentialGene Summary Statistics for PacBio sequences. Layer _p_stRT/_I_STRT/_S_03_stCuSTr/_A_02.2_assembly-contribution-count/reports/
DOI: 10.5257/imf/ifs/2020-02
title: International Finance Statistics (IFS) (February 2020 Edition)

description: The International Finance Statistics (IFS) is one of the International Monetary Fund's principal statistical datasets and has been available since January 1948. The IFS contains country data for most Fund members, as well as for Anguilla, Aruba, the Central African Economic and Monetary Community (CEMAC), Curacao, the currency union of Curacao and Sint Maarten, the Eastern Caribbean Currency Union (ECCU), the Euro Area, Montserrat, the former Netherlands Antilles, Sint Maarten, the West African Economic Monetary Union (WAEMU), West Bank and Gaza, and some non-sovereign territorial entities for which statistics are provided internationally on a separate basis.
DOI: 10.25345/c5nx13
title: MassIVE MSV000085013 - UGA-independent Mis-incorporation of Selenocysteine throughout Escherichia coli Proteome
title: LC-MS/MS based shotgun proteomics data of E.coli grew in high selenium-to-sulfur ratio medium. Files beginning with letter M are data sets of the control group cultured in standard M9 minimal medium. Files beginning with letter S are data sets of the experimental group cultured in high selenium-to-sulfur ratio medium. There are 4 biological replicates in each group, and 3 technical replicates in each biological replicate.
DOI: 10.25345/c5sq5s
title: MassIVE MSV000085014 - GNPS_Pseudomonas_Streptomycetes_Nocardia_spp_Pos
title: Protegens_Pf5, P_putida_BW11M1, P_fluorescens_Pf0-1_LK194_Gac+, P_aeruginosa_TUEPA were cultivated in DMBgly except P_aurantiaca_PB-St2 was cultured in DMBgly and KsB Noc_IFM0406 was cultivated in MM9 S_SW4 and DH12 in R4
DOI: 10.5281/zenodo.3688391
title: Crowdsourcing public perceptions of urban green space quality: A case study of Rembrandt park in Amsterdam

description: <strong><em>City-dwellers are realizing the benefits of green spaces and are flocking to urban parks. City planners face the challenge of ensuring that urban green spaces are functional for all citizens. To make informed choices they need the right information and that is where the Mijn Park app can help. </em></strong> Research shows that when considering the social functions of urban green spaces, quality is just as important as quantity. It is easy enough to map how much green spaces there are, but how do we measure their quality? How do city planners ensure that the city’s green areas are attractive, accessible and inclusive – for everyone? The Vrije Universiteit Amsterdam in collaboration with the International Institute for Applied Systems Analysis developed Mijn Park, a mobile application that will help city planners do just that. As part of the LandSense Citizen Observatory, the ‘Mijn Park’ (My Park) app asks respondents to go to several locations in a park and give subjective responses to those locations. They are then further questioned about how they use the whole park and how much they would like to see certain changes made in the park. This information provides information that can help to inform decisions about any renovations or improvements to the park. A pilot campaign was conducted in the summer of 2018 in Rembrandt park in Amsterdam and insights from the citizen-driven observations were shared with the Department of Planning and Sustainability of Amsterdam. This dataset includes responses and photographs collected by 129 unique volunteers providing 377 observations in select locations across Rembrandt Park. The following files are available: _Preview MijnPark-Amsterdam-LandSense.png Attributes-MijnPark-Amsterdam-LandSense.csv MijnPark-Amsterdam-LandSense.csv MijnPark-Amsterdam-LandSense.geoJSON README.txt This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the LandSense Citizen Observatory, Vrije Universiteit (VU), Amsterdam and the International Institute for Applied Systems Analysis (IIASA). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.
DOI: 10.5281/zenodo.3688392
title: Crowdsourcing public perceptions of urban green space quality: A case study of Rembrandt park in Amsterdam

description: <strong><em>City-dwellers are realizing the benefits of green spaces and are flocking to urban parks. City planners face the challenge of ensuring that urban green spaces are functional for all citizens. To make informed choices they need the right information and that is where the Mijn Park app can help. </em></strong> Research shows that when considering the social functions of urban green spaces, quality is just as important as quantity. It is easy enough to map how much green spaces there are, but how do we measure their quality? How do city planners ensure that the city’s green areas are attractive, accessible and inclusive – for everyone? The Vrije Universiteit Amsterdam in collaboration with the International Institute for Applied Systems Analysis developed Mijn Park, a mobile application that will help city planners do just that. As part of the LandSense Citizen Observatory, the ‘Mijn Park’ (My Park) app asks respondents to go to several locations in a park and give subjective responses to those locations. They are then further questioned about how they use the whole park and how much they would like to see certain changes made in the park. This information provides information that can help to inform decisions about any renovations or improvements to the park. A pilot campaign was conducted in the summer of 2018 in Rembrandt park in Amsterdam and insights from the citizen-driven observations were shared with the Department of Planning and Sustainability of Amsterdam. This dataset includes responses and photographs collected by 129 unique volunteers providing 377 observations in select locations across Rembrandt Park. The following files are available: _Preview MijnPark-Amsterdam-LandSense.png Attributes-MijnPark-Amsterdam-LandSense.csv MijnPark-Amsterdam-LandSense.csv MijnPark-Amsterdam-LandSense.geoJSON README.txt This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the LandSense Citizen Observatory, Vrije Universiteit (VU), Amsterdam and the International Institute for Applied Systems Analysis (IIASA). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.
DOI: 10.25345/c5j69z
title: MassIVE MSV000085015 - UGA-independent Mis-incorporation of Selenocysteine throughout Escherichia coli Proteome
title: LC-MS/MS based shotgun proteomics data of E.coli grew in high selenium-to-sulfur ratio medium. Files beginning with letter M are data sets of the control group cultured in standard M9 minimal medium. Files beginning with letter S are data sets of the experimental group cultured in high selenium-to-sulfur ratio medium. There are 4 biological replicates in each group, and 3 technical replicates in each biological replicate.
DOI: 10.21227/3245-9950
title: ERA Dataset

description: Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance. In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales. Moreover, to offer a benchmark for this task, we extensively validate existing deep networks. We expect that the ERA dataset will facilitate further progress in automatic aerial video comprehension. The website is \url{https://lcmou.github.io/ERA_Dataset/}.
DOI: 10.21227/845w-3c43
title: ERA Dataset

description: Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance. In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales. Moreover, to offer a benchmark for this task, we extensively validate existing deep networks. We expect that the ERA dataset will facilitate further progress in automatic aerial video comprehension. The website is \url{https://lcmou.github.io/ERA_Dataset/}.
DOI: 10.5281/zenodo.3688405
title: Software Variability Tools SMS and Survey: List of References and Tools

description: Software Variability Tools SMS and Survey: List of References and Tools
description: This research was funded by Brazilian funding agencies FAPESP (grants: 2016/05919-0, 2017/06195-9, 2018/20882-1, 2019/19730-5) and CNPq (grant: 312634/2018-8), and Spanish research network MCIU-AEI TIN2017-90664-REDT.
DOI: 10.5281/zenodo.3688406
title: Software Variability Tools SMS and Survey: List of References and Tools

description: Software Variability Tools SMS and Survey: List of References and Tools
description: This research was funded by Brazilian funding agencies FAPESP (grants: 2016/05919-0, 2017/06195-9, 2018/20882-1, 2019/19730-5) and CNPq (grant: 312634/2018-8), and Spanish research network MCIU-AEI TIN2017-90664-REDT.
DOI: 10.5281/zenodo.3688429
title: IET GTD supplementary dataset

description: nodal prices data
DOI: 10.5281/zenodo.3688430
title: IET GTD supplementary dataset

description: nodal prices data
DOI: 10.5281/zenodo.3688491
title: stopword

description: stop word in Chinese
DOI: 10.6084/m9.figshare.11900823.v7
title: Simulated zero-crossing wave period (3-hourly, 2015.01-2100.12) from FIO-ESM v2.0 ssp126 experiment r1i1p1f1

description: Simulated zero-crossing wave period (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: 10.5281/zenodo.3688490
title: stopword

description: stop word in Chinese
DOI: 10.5285/4ca242208e904efe830af45f1697f730
title: A simulated Northern Hemisphere terrestrial climate dataset for the past 60,000 years (version 2)

description: We present a continuous land climate reconstruction dataset extending from 60 kyr before present to the pre-industrial period at 0.5deg resolution on a monthly timestep for 0degN to 90degN. It has been generated from 42 discrete snapshot simulations using the HadCM3B-M2.1 coupled general circulation model. We incorporate Dansgaard-Oeschger (DO) and Heinrich events to represent millennial scale variability, based on a temperature reconstruction from Greenland ice-cores, with a spatial fingerprint based on a freshwater hosing simulation with HadCM3B-M2.1. Interannual variability is also added and derived from the initial snapshot simulations. Model output has been downscaled to 0.5deg resolution (using simple bilinear interpolation) and bias corrected using either the University of East Anglia, Climate Research Unit observational data (for temperature, precipitation, windchill, and minimum monthly temperature), or the EWEMBI dataset (for incoming shortwave energy). Here we provide datasets for; surface air temperature, precipitation, incoming shortwave energy, wind-chill, snow depth (as snow water equivalent), number of rainy days per month, minimum monthly temperature, and the land-sea mask and ice fractions used in the simulations. The datasets are in the form of NetCDF files. The variables are represented by a set of 24 files that have been compressed into nine folders: temp, precip, down_sw, wind_chill, snow, rainy_days, tempmonmin, landmask and icefrac. Each file represents 2500 years. The landmask and ice fraction are provided annually, whereas the climate variables are given as monthly files equivalent to 30000 months, between the latitudes 0deg to 90degN at 0.5deg resolution. Each of the climate files therefore have the dimensions 180 (lat) x 720 (lon) x 30000 (month). We also provide an example subset of the temperature dataset, which gives decadal averages for each month for 0-2500 years.
DOI: 10.25345/c5dd58
title: MassIVE MSV000085016 - UGA-independent Mis-incorporation of Selenocysteine throughout Escherichia coli Proteome
title: LC-MS/MS based shotgun proteomics data of E.coli grew in high selenium-to-sulfur ratio medium. Files beginning with letter M are data sets of the control group cultured in standard M9 minimal medium. Files beginning with letter S are data sets of the experimental group cultured in high selenium-to-sulfur ratio medium. There are 4 biological replicates in each group, and 3 technical replicates in each biological replicate.
DOI: 10.21227/6sjd-bv32
title: HITL UAV DoS GPS Spoofing Attacks (MAVLink)

description: Hardware-in-the-loop simulation of MAVLink UAV. Simulation environment follows standard jMAVSim setup (https://dev.px4.io/v1.9.0/en/simulation/hitl.html#jmavsimgazebo-hitl-environment).nbsp;Attacks are conducted against the UAV via serial connection. An autonomous survey flight is conducted over the University (~20min flight time). Full flight plan is located in the dataset package as OTU-Survey.plan.nbsp;PX4 Autopilot (https://px4.io)nbsp;running on Pixhawk 4 flight controller. QGroundControl used for GCS (http://qgroundcontrol.com)Telemetry data is contained in TLOG files (https://ardupilot.org/copter/docs/common-mission-planner-telemetry-logs.html)Full flight data is contained in ULOG files (https://dev.px4.io/v1.9.0/en/log/ulog_file_format.html)CSVs are extracted from TLOGs.nbsp;Examples of these attacks against the UAVs can be seen below (examples, not from recorded flights)Denial of Service:https://www.youtube.com/watch?v=npSBsSkWEHwGPS Spoofing:https://www.youtube.com/watch?v=Qw_Vo3FBw-cnbsp;
DOI: 10.21227/1mm5-7t40
title: HITL UAV DoS GPS Spoofing Attacks (MAVLink)

description: Hardware-in-the-loop simulation of MAVLink UAV. Simulation environment follows standard jMAVSim setup (https://dev.px4.io/v1.9.0/en/simulation/hitl.html#jmavsimgazebo-hitl-environment).nbsp;Attacks are conducted against the UAV via serial connection.nbsp;PX4 Autopilot (https://px4.io)nbsp;running on Pixhawk 4 flight controller. QGroundControl used for GCS (http://qgroundcontrol.com)nbsp;Telemetry data is contained in TLOG files (https://ardupilot.org/copter/docs/common-mission-planner-telemetry-logs.html)Full flight data is contained in ULOG files (https://dev.px4.io/v1.9.0/en/log/ulog_file_format.html)CSVs are extracted from TLOGs.nbsp;Examples of these attacks against the UAVs can be seen below (examples, not from recorded flights)nbsp;Denial of Service:https://www.youtube.com/watch?v=npSBsSkWEHwnbsp;GPS Spoofing:https://www.youtube.com/watch?v=Qw_Vo3FBw-c
DOI: 10.25345/c54x36
title: MassIVE MSV000085018 - GNPS - Dataset Creation from GNPS Molcular Networking - Pseudomonas, streptomycetes, and Nocardia spp.
title: Protegens_Pf5, P_putida_BW11M1, P_fluorescens_Pf0-1_LK194_Gac+, P_aeruginosa_TUEPA were cultivated in DMBgly except P_aurantiaca_PB-St2 was cultured in DMBgly and KsB Noc_IFM0406 was cultivated in MM9 S_SW4 and DH12 in R4
DOI: 10.25345/c5wd7c
title: MassIVE MSV000085020 - GNPS - Dataset Creation from GNPS Molcular Networking - Pseudomonas, streptomycetes, and Nocardia spp.
title: Protegens_Pf5, P_putida_BW11M1, P_fluorescens_Pf0-1_LK194_Gac+, P_aeruginosa_TUEPA were cultivated in DMBgly except P_aurantiaca_PB-St2 was cultured in DMBgly and KsB Noc_IFM0406 was cultivated in MM9 S_SW4 and DH12 in R4
DOI: 10.5519/qd.pvwotzei
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.25345/c51699
title: MassIVE MSV000085019 - UGA-independent Mis-incorporation of Selenocysteine throughout Escherichia coli Proteome
title: LC-MS/MS based shotgun proteomics data of E.coli grew in high selenium-to-sulfur ratio medium. Files beginning with letter M are data sets of the control group cultured in standard M9 minimal medium. Files beginning with letter S are data sets of the experimental group cultured in high selenium-to-sulfur ratio medium. There are 4 biological replicates in each group, and 3 technical replicates in each biological replicate.
DOI: 10.5519/qd.zwicfgf5
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.21227/z3tt-t067
title: HITL UAV DoS GPS Spoofing Attacks (MAVLink)

description: Hardware-in-the-loop simulation of MAVLink UAV. Simulation environment follows standard jMAVSim setup (https://dev.px4.io/v1.9.0/en/simulation/hitl.html#jmavsimgazebo-hitl-environment).nbsp;Attacks are conducted against the UAV via serial connection.PX4 Autopilot (https://px4.io)nbsp;running on Pixhawk 4 flight controller. QGroundControl used for GCS (http://qgroundcontrol.com)Telemetry data is contained in TLOG files (https://ardupilot.org/copter/docs/common-mission-planner-telemetry-logs.html)Full flight data is contained in ULOG files (https://dev.px4.io/v1.9.0/en/log/ulog_file_format.html)CSVs are extracted from TLOGs.nbsp;Examples of these attacks against the UAVs can be seen below (examples, not from recorded flights)Denial of Service:https://www.youtube.com/watch?v=npSBsSkWEHwGPS Spoofing:https://www.youtube.com/watch?v=Qw_Vo3FBw-c