cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4692367
title: Supplementary files from Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for peatland mapping and virtual reality (VR) visualization

description: Supplementary material including video and digital reconstructions of the Mer Bleue peatland from Unmanned Aerial Vehicle LiDAR and Structure from Motion Photogrammetry. 3D files are provided in .ply format for viewing in virtual or augmented reality.
DOI: 10.5281/zenodo.4692366
title: Supplementary files from Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for peatland mapping and virtual reality (VR) visualization

description: Supplementary material including video and digital reconstructions of the Mer Bleue peatland from Unmanned Aerial Vehicle LiDAR and Structure from Motion Photogrammetry. 3D files are provided in .ply format for viewing in virtual or augmented reality.
DOI: 10.5281/zenodo.4663056
title: SmartAnswer - ESR6 - Acoustic measurements for Rod Vortex Generators.

description: Measurement data of flow control device - Rod Vortex Generators for a wind turbine profile - DU96W180.
DOI: 10.5281/zenodo.4663055
title: SmartAnswer - ESR6 - Acoustic measurements for Rod Vortex Generators.

description: Measurement data of flow control device - Rod Vortex Generators for a wind turbine profile - DU96W180.
DOI: 10.6084/m9.figshare.14420336
title: Antibiotic resistance to ‘AWaRe’ drugs among uropathogens in Nepal: any ‘reserve’ drugs left?

description: sno id sex age specimen repdate year organism trimethops ampi nitrofur oflox cipro xone amika genta aztero colistin polymi tobra pipera ampisal cefta cefep imipe merop clinda erythro oxa peni line vanco doxy Agegroup Gramstain
DOI: 10.6084/m9.figshare.14420336.v1
title: Antibiotic resistance to ‘AWaRe’ drugs among uropathogens in Nepal: any ‘reserve’ drugs left?

description: sno id sex age specimen repdate year organism trimethops ampi nitrofur oflox cipro xone amika genta aztero colistin polymi tobra pipera ampisal cefta cefep imipe merop clinda erythro oxa peni line vanco doxy Agegroup Gramstain
DOI: 10.5281/zenodo.4618233
title: What Tweets and YouTube comments have in common? Sentiment and Graph analysis on data related to US Elections 2020.

description: The presidential elections in the United States on November 3rd 2020 caused extensive discussions on social media. A part of the content on US elections is organic, coming from users discussing their opinions on the candidates, political positions, or relevant content presented on television. Another significant part originates from organized campaigns, both official, including communication campaigns and dissemination, or unofficial, including astroturfing and targeting manipulation of the electorate. In this study, we obtain approximately 19.8M tweets from 4.5M users, based on prevalent hashtags related to the 2020 US election. From these, we mined 28.343 YouTube links tweeted and obtained likes, dislikes and comments of these videos. In this paper, we study the connection between the two social networks. We employ an array of techniques, including volume analysis, exploring the retweet graph, sentiment and graph analysis on the communities formed in YouTube and Twitter. Furthermore, we propose a method to combine the results of community detection on the two social networks and measure the differences between them. Particularly, we study the daily traffic per prevalent hashtags, plot the retweet graph from July to November 2020, highlight the two main entities (‘Biden’ and ‘Trump’) and show how the discussion around those entities grows in the period closer to the elections. Additionally, we perform a sentiment analysis of both the Twitter corpus and the YouTube comments in tweeted videos. We found that 35,2% o the users contained in our Twitter dataset express positive sentiment towards Trump and 28% express positive sentiment towards Biden; while 18% of the users in our YouTube dataset express positive sentiment towards Trump and 12% express positive sentiment towards Biden. Finally, we link the Twitter Retweet graph with the YouTube comment graph using tweeted video links. We measure their similarity and differences and show the interactions and the correlation between the largest communities on YouTube and Twitter.
description: Youtube And Twitter dataset, collected during US 2020 Elections.
DOI: 10.5281/zenodo.4618232
title: What Tweets and YouTube comments have in common? Sentiment and Graph analysis on data related to US Elections 2020.

description: The presidential elections in the United States on November 3rd 2020 caused extensive discussions on social media. A part of the content on US elections is organic, coming from users discussing their opinions on the candidates, political positions, or relevant content presented on television. Another significant part originates from organized campaigns, both official, including communication campaigns and dissemination, or unofficial, including astroturfing and targeting manipulation of the electorate. In this study, we obtain approximately 19.8M tweets from 4.5M users, based on prevalent hashtags related to the 2020 US election. From these, we mined 28.343 YouTube links tweeted and obtained likes, dislikes and comments of these videos. In this paper, we study the connection between the two social networks. We employ an array of techniques, including volume analysis, exploring the retweet graph, sentiment and graph analysis on the communities formed in YouTube and Twitter. Furthermore, we propose a method to combine the results of community detection on the two social networks and measure the differences between them. Particularly, we study the daily traffic per prevalent hashtags, plot the retweet graph from July to November 2020, highlight the two main entities (‘Biden’ and ‘Trump’) and show how the discussion around those entities grows in the period closer to the elections. Additionally, we perform a sentiment analysis of both the Twitter corpus and the YouTube comments in tweeted videos. We found that 35,2% o the users contained in our Twitter dataset express positive sentiment towards Trump and 28% express positive sentiment towards Biden; while 18% of the users in our YouTube dataset express positive sentiment towards Trump and 12% express positive sentiment towards Biden. Finally, we link the Twitter Retweet graph with the YouTube comment graph using tweeted video links. We measure their similarity and differences and show the interactions and the correlation between the largest communities on YouTube and Twitter.
description: Youtube And Twitter dataset, collected during US 2020 Elections.
DOI: 10.48531/jbru.calmip/buabfj/s29od5
title: Static.plt

description: :unav
DOI: 10.48531/jbru.calmip/buabfj
title: Aerodynamic forces; 2D morphing - numerical simulations - incidence 1.8 deg - static case

description: ASCII file containing aerodynamic forces around an A320 wing. From a 2D numerical simulation performed with the NSMB code. The first four columns give the physical time in seconds, the drag coefficient, the lift coefficient and the pitching moment coefficient for a morphing wing. The trailing edge not vibrating, it is the so-called static case.
DOI: 10.11583/dtu.14185754
title: Resource data from the Sornfelli mast

description: This resource dataset consists of 10-minute statistics of wind speed and wind direction measurements from a met mast at Sornfelli, Faro Island, Denmark. The period includes 1 year of measurements, which starts in 1999.Further site documentation is available here.<br><br><b>Data files:</b>1) Sornf_all.nc includes all periods2) Sornf_concurrent.nc only includes concurrent periods for all channels.
DOI: 10.11583/dtu.14185754.v1
title: Resource data from the Sornfelli mast

description: This resource dataset consists of 10-minute statistics of wind speed and wind direction measurements from a met mast at Sornfelli, Faro Island, Denmark. The period includes 1 year of measurements, which starts in 1999.Further site documentation is available here.<br><br><b>Data files:</b>1) Sornf_all.nc includes all periods2) Sornf_concurrent.nc only includes concurrent periods for all channels.
DOI: 10.5281/zenodo.4568900
title: Turbulent flow induced noise and vibrations

description: This data consists in mat files containing the vibrational and noise radiation of a bare and metamaterial cavity-backed plate due to a grazing flow excitation. To plot the curves, it is just a matter of running the file ' read_files_tbl_NVH.m '
DOI: 10.6084/m9.figshare.14413706
title: Picozoa: initial single gene trees

description: Initial single gene alignments and trees for 320 markers. this still includes paralogs and contamination removed before concatenation<br>
DOI: 10.6084/m9.figshare.14413652
title: Picozoa: PhyloMagnet contigs NR

description: gene-centric assembled contigs and BLAST searches of these contigs against the NR database for core plastid genes<br>
DOI: 10.11583/dtu.14307710
title: Wind resource data and turbulence from the Orkney mast

description: This resource dataset consists of 10-minute wind speed and wind direction measurements from the met mast on Burger Hill, Orkney Island, Scotland. The period includes more than 1 year of measurements, which starts in 2000.Detailed site documentation is available here.<br><br><b>Data files:</b>1) orkney_all.nc2) orkney_concurrent.nc<br>3) Raw time series = orkney.zip (ascii), each with a duration of 600-3600 sec and sampled with 1 Hz.
DOI: 10.11583/dtu.14307710.v1
title: Wind resource data and turbulence from the Orkney mast

description: This resource dataset consists of 10-minute wind speed and wind direction measurements from the met mast on Burger Hill, Orkney Island, Scotland. The period includes more than 1 year of measurements, which starts in 2000.Detailed site documentation is available here.<br><br><b>Data files:</b>1) orkney_all.nc2) orkney_concurrent.nc<br>3) Raw time series = orkney.zip (ascii), each with a duration of 600-3600 sec and sampled with 1 Hz.
DOI: 10.5281/zenodo.4670353
title: Dataset documenting article "A longitudinal study of CMT1A using Rasch analysis based CMT neuropathy and examination scores"

description: The dataset contains anonymized diagnosis information on patients evaluated at Fondazione Besta and included in the related article
DOI: 10.5281/zenodo.4670352
title: Dataset documenting article "A longitudinal study of CMT1A using Rasch analysis based CMT neuropathy and examination scores"

description: The dataset contains anonymized diagnosis information on patients evaluated at Fondazione Besta and included in the related article
DOI: 10.5281/zenodo.4686986
title: Exploring the impact of multisensory integration and episodic memory with machine learning: a proof-of-concept study in anorexia nervosa

description: In addition to established body image alterations, abnormal perception and executive functioning in anorexia nervosa (AN), neurocognitive factors including multisensory integration (MSI) and episodic memory (EM) might play a pivotal role in the diagnosis of this disorder. According to the allocentric lock theory, deficits in the updating of distorted memory of body-based episodes through misleading real-time multisensory bodily stimuli could lead to altered body image in AN. In this study, 25 healthy females and nine AN individuals were tested on a set of neurocognitive measures, encompassing unimodal perceptual accuracy and MSI (MSI) ability assessed with the sound-induced flash illusion (SiFI), episodic memory recognition (EMR) evaluated with a remember/know (R/K) task, memory and executive functions tested with the RBMT-3 and the Stroop task. Collected data were analyzed with Bayesian statistics and machine learning algorithms. In the SiFI task, we found that AN compared to control group had lower discrimination accuracy for unimodal visual and auditory stimuli, for bimodal (visual and auditory) stimuli and disrupted MSI ability. Further, we found on the EMR task and the RBMT-3 that AN individuals had higher proportions of false memories than healthy females. Additionally, we found lower inhibition at the Stroop task for the patient group compared to controls. The importance of the considered neurocognitive measures was confirmed by a machine learning feasibility analysis, which showed that SiFI, RBMT-3, Stroop and EMR had more weight than classic eating disorder risk scales of the EDI-3 when computing classification between the AN and control individuals. In conclusion, MSI along with memory could be crucial factors for improving diagnosis and consequently design innovative therapeutic solutions that tap critical bodily and cognitive elements altered in AN with new technologies such as virtual reality.
DOI: 10.5281/zenodo.4686985
title: Exploring the impact of multisensory integration and episodic memory with machine learning: a proof-of-concept study in anorexia nervosa

description: In addition to established body image alterations, abnormal perception and executive functioning in anorexia nervosa (AN), neurocognitive factors including multisensory integration (MSI) and episodic memory (EM) might play a pivotal role in the diagnosis of this disorder. According to the allocentric lock theory, deficits in the updating of distorted memory of body-based episodes through misleading real-time multisensory bodily stimuli could lead to altered body image in AN. In this study, 25 healthy females and nine AN individuals were tested on a set of neurocognitive measures, encompassing unimodal perceptual accuracy and MSI (MSI) ability assessed with the sound-induced flash illusion (SiFI), episodic memory recognition (EMR) evaluated with a remember/know (R/K) task, memory and executive functions tested with the RBMT-3 and the Stroop task. Collected data were analyzed with Bayesian statistics and machine learning algorithms. In the SiFI task, we found that AN compared to control group had lower discrimination accuracy for unimodal visual and auditory stimuli, for bimodal (visual and auditory) stimuli and disrupted MSI ability. Further, we found on the EMR task and the RBMT-3 that AN individuals had higher proportions of false memories than healthy females. Additionally, we found lower inhibition at the Stroop task for the patient group compared to controls. The importance of the considered neurocognitive measures was confirmed by a machine learning feasibility analysis, which showed that SiFI, RBMT-3, Stroop and EMR had more weight than classic eating disorder risk scales of the EDI-3 when computing classification between the AN and control individuals. In conclusion, MSI along with memory could be crucial factors for improving diagnosis and consequently design innovative therapeutic solutions that tap critical bodily and cognitive elements altered in AN with new technologies such as virtual reality.
DOI: 10.5281/zenodo.4655974
title: Dataset documenting article "Amyotrophic lateral sclerosis patients' and caregivers' distress and loneliness during COVID-19 lockdown"

description: The Dataset “Consonni_2020_JNeurology” contains demographical, neuropsychological and clinical data of a small sample of patients with Amyotrophic Lateral Sclerosis and their caregivers. These data were obtained by remote follow-ups visits during the first Italian lockdown
description: Sdudy supported by Fondazione Regionale per la Ricerca Biomedica, Regione Lombardia (TRANS-ALS; grant number: 2015-0023).
DOI: 10.5281/zenodo.4655973
title: Dataset documenting article "Amyotrophic lateral sclerosis patients' and caregivers' distress and loneliness during COVID-19 lockdown"

description: The Dataset “Consonni_2020_JNeurology” contains demographical, neuropsychological and clinical data of a small sample of patients with Amyotrophic Lateral Sclerosis and their caregivers. These data were obtained by remote follow-ups visits during the first Italian lockdown
description: Sdudy supported by Fondazione Regionale per la Ricerca Biomedica, Regione Lombardia (TRANS-ALS; grant number: 2015-0023).
DOI: 10.5281/zenodo.4692545
title: Executive Functions Are Associated with Fall Risk but not Balance in Chronic Cerebrovascular Disease

description: Background: Older people’s deficits in executive functions (EF) have been shown to lead to higher fall risk, postural sway, and reduced speed. Crucially, EF impairments are even more pronounced in individuals with chronic cerebrovascular disease (CVD), namely vascular cognitive impairment. Methods: In this retrospective cross-sectional study, we used a complete neuropsychological battery, including the Trail Making Test (TMT) and physical measures, such as the Morse fall and EQUI scales, to assess 66 individuals with chronic CVD. Linear regressions, Bayesian analyses, and model selection were performed to see the impact of EF, global cognition,and vascular parkinsonism/hemiplegia on physical measures (fall risk and balance). Results: The TMTpart B and BA correlated (r=0.44 andr=0.45) with Morse fall scale. Only EF significantly explained fall risk, whereas global cognition and vascular parkinsonism/hemiplegia did not. These findings were confirmed by Bayesian evidence and parsimony model selection. Balance was not significantly correlated with any of the neuropsychological tests. Conclusions: This is the first study investigating the relationship between cognitive and physical measures in a sample of older people with chronic CVD. The results are consistent with previous findings that link EF with fall risk in CVD.
DOI: 10.5281/zenodo.4692544
title: Executive Functions Are Associated with Fall Risk but not Balance in Chronic Cerebrovascular Disease

description: Background: Older people’s deficits in executive functions (EF) have been shown to lead to higher fall risk, postural sway, and reduced speed. Crucially, EF impairments are even more pronounced in individuals with chronic cerebrovascular disease (CVD), namely vascular cognitive impairment. Methods: In this retrospective cross-sectional study, we used a complete neuropsychological battery, including the Trail Making Test (TMT) and physical measures, such as the Morse fall and EQUI scales, to assess 66 individuals with chronic CVD. Linear regressions, Bayesian analyses, and model selection were performed to see the impact of EF, global cognition,and vascular parkinsonism/hemiplegia on physical measures (fall risk and balance). Results: The TMTpart B and BA correlated (r=0.44 andr=0.45) with Morse fall scale. Only EF significantly explained fall risk, whereas global cognition and vascular parkinsonism/hemiplegia did not. These findings were confirmed by Bayesian evidence and parsimony model selection. Balance was not significantly correlated with any of the neuropsychological tests. Conclusions: This is the first study investigating the relationship between cognitive and physical measures in a sample of older people with chronic CVD. The results are consistent with previous findings that link EF with fall risk in CVD.
DOI: 10.11583/dtu.14185277
title: Resource data from the Dumpen mast, Sisimiut

description: This resource dataset consists of 10 minute wind speed and wind direction measurements from a small met mast on Dumpen, Sisimiut,Greenland. The period includes 1 year of measurements, which starts in 2004.Detailed site documentation is available here.<br><b>Data files:</b>1) Sisimiut_m_all.nc includes all periods2) Sisimiut_m_concurrent.nc only includes concurrent periods for all channels.
DOI: 10.11583/dtu.14185277.v1
title: Resource data from the Dumpen mast, Sisimiut

description: This resource dataset consists of 10 minute wind speed and wind direction measurements from a small met mast on Dumpen, Sisimiut,Greenland. The period includes 1 year of measurements, which starts in 2004.Detailed site documentation is available here.<br><b>Data files:</b>1) Sisimiut_m_all.nc includes all periods2) Sisimiut_m_concurrent.nc only includes concurrent periods for all channels.
DOI: 10.11583/dtu.14236703
title: Wind resource data from the Belmont mast.

description: This resource dataset consists of 10-minute statistics of wind speed and wind direction measurements from a small met mast at Belmont, Troyees, France. The period includes 0.5 years of measurements from 2006.Detailed site documentation is available here.<br><br><b>Data files</b>:1) Belmont_all.nc2) Belmont_concurrent.nc
DOI: 10.11583/dtu.14236703.v1
title: Wind resource data from the Belmont mast.

description: This resource dataset consists of 10-minute statistics of wind speed and wind direction measurements from a small met mast at Belmont, Troyees, France. The period includes 0.5 years of measurements from 2006.Detailed site documentation is available here.<br><br><b>Data files</b>:1) Belmont_all.nc2) Belmont_concurrent.nc
DOI: 10.5519/qd.azraff4c
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (61 records)
DOI: 10.5281/zenodo.4692508
title: Enabling Electronic Structure-Based Ab-Initio Molecular Dynamics Simulations with Hundreds of Millions of Atoms

description: Atomic structure of entire HIV capsid (10.2210/pdb3J3Q/pdb) solvated in water. Periodic cell dimensions: 118.389 x 80.045 x 66.781 nm
DOI: 10.5281/zenodo.4692507
title: Enabling Electronic Structure-Based Ab-Initio Molecular Dynamics Simulations with Hundreds of Millions of Atoms

description: Atomic structure of entire HIV capsid (10.2210/pdb3J3Q/pdb) solvated in water. Periodic cell dimensions: 118.389 x 80.045 x 66.781 nm
DOI: 10.11583/dtu.14153210
title: Resource data from the Godhavn met mast

description: This resource dataset consists of 10 minute wind speed and wind direction measurements from a small met mast at Qeqertarsuaq (Godhavn),Greenland. The period includes 10 years of measurements, which starts in 1991.Detailed documentation can be found here.<br>Data files:1) Godhavn_all.nc includes all periods2) Godhavn_concurrent.nc only includes concurrent periods for all channels.
DOI: 10.11583/dtu.14153210.v1
title: Resource data from the Godhavn met mast

description: This resource dataset consists of 10 minute wind speed and wind direction measurements from a small met mast at Qeqertarsuaq (Godhavn),Greenland. The period includes 10 years of measurements, which starts in 1991.Detailed documentation can be found here.<br>Data files:1) Godhavn_all.nc includes all periods2) Godhavn_concurrent.nc only includes concurrent periods for all channels.
DOI: 10.6084/m9.figshare.14420516.v2
title: Impact of Typhoon Chan-hom on sediment dynamics and morphological changes on the East China Sea inner shelf

description: The dataset contains the modeling results of the sediment dynamics over the East China Sea under the impact of Super Typhoon Chan-hom.
DOI: 10.6084/m9.figshare.14420516.v1
title: Erosion and deposition patterns in the ECS.xlsx

description: The dataset contains the modeling results of the sediment dynamics over the East China Sea under the impact of Super Typhoon Chan-hom.
DOI: 10.6084/m9.figshare.14376953
title: NEST - DFAB Energy Demand and User Behaviour Data

description: Measurement data for the DFAB building at NEST. The dataset consists of 42 measurement points with a temporal resolution of 1 minute. The data has been recorded over the duration of one year (July 2019 to July 2020). Metadata for each measurement point is stored in a separate file on this repository.
DOI: 10.6084/m9.figshare.14376953.v1
title: NEST - DFAB Energy Demand and User Behaviour Data

description: Measurement data for the DFAB building at NEST. The dataset consists of 42 measurement points with a temporal resolution of 1 minute. The data has been recorded over the duration of one year (July 2019 to July 2020). Metadata for each measurement point is stored in a separate file on this repository.
DOI: 10.6084/m9.figshare.14376950
title: NEST - SolAce Energy Demand and User Behaviour Data

description: Measurement data for the SolAce building at NEST. The dataset consists of 18 measurement points with a temporal resolution of 1 minute. The data has been recorded over the duration of one year (July 2019 to July 2020). Metadata for each measurement point is stored in a separate file on this repository.