cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc253x4c
title: CCDC 1999752: Experimental Crystal Structure Determination

description: Related Article: Catherine M. Aitchison, Michael Sachs, Marc A. Little, Liam Wilbraham, Nick J. Brownbill, Christopher M. Kane, Frédéric Blanc, Martijn A. Zwijnenburg, James R. Durrant, Reiner Sebastian Sprick, Andrew I. Cooper|2020|Chemical Science|11|8744|doi:10.1039/D0SC02675A
DOI: 10.5517/ccdc.csd.cc253x5d
title: CCDC 1999753: Experimental Crystal Structure Determination

description: Related Article: Catherine M. Aitchison, Michael Sachs, Marc A. Little, Liam Wilbraham, Nick J. Brownbill, Christopher M. Kane, Frédéric Blanc, Martijn A. Zwijnenburg, James R. Durrant, Reiner Sebastian Sprick, Andrew I. Cooper|2020|Chemical Science|11|8744|doi:10.1039/D0SC02675A
DOI: 10.5517/ccdc.csd.cc253x6f
title: CCDC 1999754: Experimental Crystal Structure Determination

description: Related Article: Catherine M. Aitchison, Michael Sachs, Marc A. Little, Liam Wilbraham, Nick J. Brownbill, Christopher M. Kane, Frédéric Blanc, Martijn A. Zwijnenburg, James R. Durrant, Reiner Sebastian Sprick, Andrew I. Cooper|2020|Chemical Science|11|8744|doi:10.1039/D0SC02675A
DOI: 10.5517/ccdc.csd.cc253x8h
title: CCDC 1999756: Experimental Crystal Structure Determination

description: Related Article: Catherine M. Aitchison, Michael Sachs, Marc A. Little, Liam Wilbraham, Nick J. Brownbill, Christopher M. Kane, Frédéric Blanc, Martijn A. Zwijnenburg, James R. Durrant, Reiner Sebastian Sprick, Andrew I. Cooper|2020|Chemical Science|11|8744|doi:10.1039/D0SC02675A
DOI: 10.5517/ccdc.csd.cc25dg57
title: CCDC 2007968: Experimental Crystal Structure Determination

description: Related Article: Suraj Sharma, Nibedita Baruah Dutta, Mayurakhi Bhuyan, Babulal Das, Gakul Baishya|2020|Org.Biomol.Chem.|18|6537|doi:10.1039/D0OB01304H
DOI: 10.5517/ccdc.csd.cc25fxcx
title: CCDC 2009369: Experimental Crystal Structure Determination

description: Related Article: Lu Hu, Haixiang Gao, Jean'ne M. Shreeve|2020|J.Mater.Chem.A|8|17411|doi:10.1039/D0TA05933A
DOI: 10.25318/3310025201-eng
title: Percentage of workforce laid off to adapt to COVID-19, by business characteristics

description: Percentage of workforce laid off to adapt to the COVID-19 pandemic, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership.
DOI: 10.25318/3310025201-fra
title: Pourcentage de l'effectif mis à pied afin de s'adapter à la COVID-19, selon les caractéristiques de l'entreprise

description: Pourcentage de l'effectif qui a été mis à pied afin de s'adapter à la COVID-19, selon le code du Système de classification des industries de l'Amérique du Nord (SCIAN), l'effectif de l'entreprise, le type d'entreprise, activités commerciales et le propriétaire majoritaire.
DOI: 10.6084/m9.figshare.12311102
title: Dataset and R-script for preprint of Åsheim et al., 2020, Rapid-warming tolerance correlates with tolerance to slow warming but not growth at non-optimal temperatures in zebrafish

description: Dataset and R-script for analysis of data from preprint of Åsheim et al., 2020, Rapid-warming tolerance correlates with tolerance to slow warming but not growth at non-optimal temperatures in zebrafish<br>See attached description file for more details.
DOI: 10.6084/m9.figshare.12311102.v1
title: Dataset and R-script for preprint of Åsheim et al., 2020, Rapid-warming tolerance correlates with tolerance to slow warming but not growth at non-optimal temperatures in zebrafish

description: Dataset and R-script for analysis of data from preprint of Åsheim et al., 2020, Rapid-warming tolerance correlates with tolerance to slow warming but not growth at non-optimal temperatures in zebrafish<br>See attached description file for more details.
DOI: 10.6084/m9.figshare.12957850
title: Correlation of seasonal precipitation isotopic profile with the modern climatological data: a case study from the western Newfoundland region of Canada

description: Appendix 1: Measured oxygen and hydrogen isotopes of precipitation for this study, averaged in triplicate and reported to one decimal place
DOI: 10.6084/m9.figshare.12957850.v1
title: Correlation of seasonal precipitation isotopic profile with the modern climatological data: a case study from the western Newfoundland region of Canada

description: Appendix 1: Measured oxygen and hydrogen isotopes of precipitation for this study, averaged in triplicate and reported to one decimal place
DOI: 10.6084/m9.figshare.12957853
title: Correlation of seasonal precipitation isotopic profile with the modern climatological data: a case study from the western Newfoundland region of Canada

description: Appendix 2: Modern meteorological data collected at the weather station housed in the Grenfell Campus of Memorial University of Newfoundland. Data collected every 30 minutes, presented as average for 24-hour period.
DOI: 10.6084/m9.figshare.12957853.v1
title: Correlation of seasonal precipitation isotopic profile with the modern climatological data: a case study from the western Newfoundland region of Canada

description: Appendix 2: Modern meteorological data collected at the weather station housed in the Grenfell Campus of Memorial University of Newfoundland. Data collected every 30 minutes, presented as average for 24-hour period.
DOI: 10.6084/m9.figshare.12957860
title: Supplementary Material for: Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients

description: <b><i>Background:</i></b> The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance. <b><i>Objective:</i></b> The aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis. <b><i>Methods:</i></b> A single-center cohort of patients with COVID-19<b><i></i></b>from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients. <b><i>Results:</i></b> One hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.<b><i></i></b>u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097–0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916–0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death. <b><i>Conclusions:</i></b> This model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.
DOI: 10.6084/m9.figshare.12957860.v1
title: Supplementary Material for: Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients

description: <b><i>Background:</i></b> The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance. <b><i>Objective:</i></b> The aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis. <b><i>Methods:</i></b> A single-center cohort of patients with COVID-19<b><i></i></b>from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients. <b><i>Results:</i></b> One hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.<b><i></i></b>u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097–0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916–0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death. <b><i>Conclusions:</i></b> This model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.
DOI: 10.5281/zenodo.3776506
title: Inflow dynamics in weakly stratified lakes subject to large isopycnal displacements (figure data)

description: Data displayed in the figures of the article entitled "Inflow dynamics in weakly stratified lakes subject to large isopycnal displacements". Water Resources Research, 56, e2019WR026578. https://doi.org/10.1029/2019WR026578
DOI: 10.5281/zenodo.3776505
title: Inflow dynamics in weakly stratified lakes subject to large isopycnal displacements (figure data)

description: Data displayed in the figures of the article entitled "Inflow dynamics in weakly stratified lakes subject to large isopycnal displacements". Water Resources Research, 56, e2019WR026578. https://doi.org/10.1029/2019WR026578
DOI: 10.25416/edgehill.12847133
title: System Resilience Through Communities Of Practice.pptx

description: This presentation I began my thinking about how Communities of Practice can aid to a system's resilience. This is a thought piece--a beginning of an idea.
DOI: 10.25416/edgehill.12847133.v1
title: System Resilience Through Communities Of Practice.pptx

description: This presentation I began my thinking about how Communities of Practice can aid to a system's resilience. This is a thought piece--a beginning of an idea.
DOI: 10.21227/e2g3-c633
title: Transcranial Doppler Ultrasound Database (Philips CX50 ultrasound system)

description: Transcranial Doppler (TCD) echo data was recorded from healthy adults and neurocritical care adult patients. The insonated cerebral vessels were the middle cerebral artery (MCA) and the internal carotid artery (ICA). The ultrasound system used in this study was the Philips CX50.
DOI: 10.6084/m9.figshare.12957955
title: Well penetration data: The integration of public domain lithostratigraphic data into a series of cross-border North Sea well penetration maps

description: Well penetration data, North Sea
DOI: 10.6084/m9.figshare.12957952.v1
title: Public domain shapefiles: The integration of public domain lithostratigraphic data into a series of cross-border North Sea well penetration maps

description: Public domain shapefiles
DOI: 10.6084/m9.figshare.12957952
title: Public domain shapefiles: The integration of public domain lithostratigraphic data into a series of cross-border North Sea well penetration maps

description: Public domain shapefiles
DOI: 10.6084/m9.figshare.12957955.v1
title: Well penetration data: The integration of public domain lithostratigraphic data into a series of cross-border North Sea well penetration maps

description: Well penetration data, North Sea
DOI: 10.21227/axc7-1p55
title: Transcranial Doppler Ultrasound Database (Philips CX50 ultrasound system)

description: Transcranial Doppler (TCD) echo data was recorded from healthy adults and neurocritical care adult patients. The insonated cerebral vessels were the middle cerebral artery (MCA) and the internal carotid artery (ICA). The ultrasound system used in this study was the Philips CX50.
DOI: 10.21227/qbv8-wb98
title: Transcranial Doppler Ultrasound Database (Philips CX50 ultrasound system)

description: Transcranial Doppler (TCD) echo data was recorded from healthy adults and neurocritical care adult patients. The insonated cerebral vessels were the middle cerebral artery (MCA) and the internal carotid artery (ICA). The ultrasound system used in this study was the Philips CX50.
DOI: 10.6084/m9.figshare.12957950
title: data_and_code.zip

description: Statistical analysis code and raw data<br>
DOI: 10.6084/m9.figshare.12957950.v1
title: data_and_code.zip

description: Statistical analysis code and raw data<br>
DOI: 10.6084/m9.figshare.12563192.v3
title: CovMulNet19.zip

description: CovMulNet19 is a comprehensive network containing all available known interactions involving SARS-CoV-2 proteins, interacting-human proteins, diseases and symptoms that are related to these human proteins, and compounds that can potentially target them. Extensive network analysis methods, based on a bootstrap approach, allow us to prioritise a list of diseases that display a high similarity to \covid and a list of drugs that could potentially be beneficial to treat patients. As a key feature of CovMulNet19, the inclusion of symptoms allows a deeper characterization of the disease pathology, representing a useful proxy for CoVid19-related molecular processes. We recapitulate many of the known symptoms of the disease and we find the most similar diseases to COVID-19 reflect conditions that are risk factors in patients. <br>
DOI: 10.6084/m9.figshare.12563192.v4
title: CovMulNet19.zip

description: CovMulNet19 is a comprehensive network containing all available known interactions involving SARS-CoV-2 proteins, interacting-human proteins, diseases and symptoms that are related to these human proteins, and compounds that can potentially target them. Extensive network analysis methods, based on a bootstrap approach, allow us to prioritise a list of diseases that display a high similarity to \covid and a list of drugs that could potentially be beneficial to treat patients. As a key feature of CovMulNet19, the inclusion of symptoms allows a deeper characterization of the disease pathology, representing a useful proxy for CoVid19-related molecular processes. We recapitulate many of the known symptoms of the disease and we find the most similar diseases to COVID-19 reflect conditions that are risk factors in patients. <br>
DOI: 10.25387/g3.12950441.v1
title: Supplemental Material for Masinas et al., 2020

description: Figure S1: Diagrams illustrating the database schema for each of the clusters: A) core, B) Endocytic Compartment Morphology project, C) CYCLoPs projectTable S1: Number of cells for each class of the BBBC021 dataset included in the analysis, classification accuracy for each class, and the effect of training set size on classification accuracy.<br>
DOI: 10.25387/g3.12950441
title: Supplemental Material for Masinas et al., 2020

description: Figure S1: Diagrams illustrating the database schema for each of the clusters: A) core, B) Endocytic Compartment Morphology project, C) CYCLoPs projectTable S1: Number of cells for each class of the BBBC021 dataset included in the analysis, classification accuracy for each class, and the effect of training set size on classification accuracy.<br>
DOI: 10.24381/cds.dcfb0bf4
title: Eutrophication indicators for the Northwest European Shelf and Mediterranean Sea from 2006 up to 2100
DOI: 10.21227/7wb1-0r93
title: Drone Motion Primitive Dataset

description: Drone technology is one of the largest tackled fields in today’s world, as it can range from pure enjoyment of drone racing to medical use and fighting crime. Several teams are interested in developing improved human machine interfaces for operating drones. This dataset is a collection of different motion primitives commanded using a PS3 joystick to control an Ardrone on Gazebo. This has been conducted using ROS Melodic on Ubuntu 18.04. The PS3 six-axis joystick was used to collect this data, and it was connected to the Gazebo drone by using the ArDrone Helpers libraries.This dataset contains 4 different motion primitives which are: Line, Helix, Sine, and Arc. The testing was conducted on each primitive alone and using a stop indicator, which is specified for each primitive, at the end of each primitive. The stop indicators are as follows:· X for Sine· Circle for Helix· Square for Line· Triangle for ArcThen a collection of every two primitives consecutively was also conducted (i.e. line to helix, arc to line, helix to sine …) using the same stop indictors at the end of every corresponding primitive. Each test has around 300 to 500 samples. It is also important to note that our data is sample and hold, meaning that if the joystick’s analog stick is in a certain position for a few seconds, then it will not send any new samples for the velocities.The data was extracted into a .YAML file, as continuous commands from the joystick which were used to move the drone in Gazebo. They are then parsed (using python 2.7) to extract the velocities of the drone along the x y z axes as well as the pressed buttons (x or circle or square or triangle) into a .CSV file.This parsed data is further processed (using python3) into another .CSV file containing all the velocities as arrays, a histogram to get the average number of samples for every sequence, and a .RTF file which contains some analysis for each primitive or collection of primitives (number of samples taken, min and max sequence size, mean, median, and deviation).N.B: for the sine primitive, the data has been extracted into two categories: one where the sine was along the x-y axes and another where it was along the x-z or y-z axes.The github link for the two codes used is: https://github.com/justinelhalabi/PS3-Joystick-Command-Parsing-into-CSV
DOI: 10.21227/a8db-nm59
title: Drone Motion Primitive Dataset

description: Drone technology is one of the largest tackled fields in today’s world, as it can range from pure enjoyment of drone racing to medical use and fighting crime. Several teams are interested in developing improved human machine interfaces for operating drones. This dataset is a collection of different motion primitives commanded using a PS3 joystick to control an Ardrone on Gazebo. This has been conducted using ROS Melodic on Ubuntu 18.04. The PS3 six-axis joystick was used to collect this data, and it was connected to the Gazebo drone by using the ArDrone Helpers libraries.This dataset contains 4 different motion primitives which are: Line, Helix, Sine, and Arc. The testing was conducted on each primitive alone and using a stop indicator, which is specified for each primitive, at the end of each primitive. The stop indicators are as follows:· X for Sine· Circle for Helix· Square for Line· Triangle for ArcThen a collection of every two primitives consecutively was also conducted (i.e. line to helix, arc to line, helix to sine …) using the same stop indictors at the end of every corresponding primitive. Each test has around 300 to 500 samples. It is also important to note that our data is sample and hold, meaning that if the joystick’s analog stick is in a certain position for a few seconds, then it will not send any new samples for the velocities.The data was extracted into a .YAML file, as continuous commands from the joystick which were used to move the drone in Gazebo. They are then parsed (using python 2.7) to extract the velocities of the drone along the x y z axes as well as the pressed buttons (x or circle or square or triangle) into a .CSV file.This parsed data is further processed (using python3) into another .CSV file containing all the velocities as arrays, a histogram to get the average number of samples for every sequence, and a .RTF file which contains some analysis for each primitive or collection of primitives (number of samples taken, min and max sequence size, mean, median, and deviation).N.B: for the sine primitive, the data has been extracted into two categories: one where the sine was along the x-y axes and another where it was along the x-z or y-z axes.The github link for the two codes used is: https://github.com/justinelhalabi/PS3-Joystick-Command-Parsing-into-CSV
DOI: 10.21227/z20q-sp70
title: Drone Motion Primitive Dataset

description: Drone technology is one of the largest tackled fields in today’s world, as it can range from pure enjoyment of drone racing to medical use and fighting crime. Several teams are interested in developing improved human machine interfaces for operating drones. This dataset is a collection of different motion primitives commanded using a PS3 joystick to control an Ardrone on Gazebo. This has been conducted using ROS Melodic on Ubuntu 18.04. The PS3 six-axis joystick was used to collect this data, and it was connected to the Gazebo drone by using the ArDrone Helpers libraries.This dataset contains 4 different motion primitives which are: Line, Helix, Sine, and Arc. The testing was conducted on each primitive alone and using a stop indicator, which is specified for each primitive, at the end of each primitive. The stop indicators are as follows:· X for Sine· Circle for Helix· Square for Line· Triangle for ArcThen a collection of every two primitives consecutively was also conducted (i.e. line to helix, arc to line, helix to sine …) using the same stop indictors at the end of every corresponding primitive. Each test has around 300 to 500 samples. It is also important to note that our data is sample and hold, meaning that if the joystick’s analog stick is in a certain position for a few seconds, then it will not send any new samples for the velocities.The data was extracted into a .YAML file, as continuous commands from the joystick which were used to move the drone in Gazebo. They are then parsed (using python 2.7) to extract the velocities of the drone along the x y z axes as well as the pressed buttons (x or circle or square or triangle) into a .CSV file.This parsed data is further processed (using python3) into another .CSV file containing all the velocities as arrays, a histogram to get the average number of samples for every sequence, and a .RTF file which contains some analysis for each primitive or collection of primitives (number of samples taken, min and max sequence size, mean, median, and deviation).N.B: for the sine primitive, the data has been extracted into two categories: one where the sine was along the x-y axes and another where it was along the x-z or y-z axes. The github link for the two codes used is: https://github.com/justinelhalabi/PS3-Joystick-Command-Parsing-into-CSV