cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc1nznz8
title: CCDC 1548821: Experimental Crystal Structure Determination

description: Related Article: Philippe Weis,  Ingo Krossing,  Daniel Kratzert, Clarissa Hettich|2019|Eur.J.Inorg.Chem.|2019|1657|doi:10.1002/ejic.201800743
DOI: 10.5517/ccdc.csd.cc1nrxlx
title: CCDC 1542330: Experimental Crystal Structure Determination

description: Related Article: Philippe Weis,  Ingo Krossing,  Daniel Kratzert, Clarissa Hettich|2019|Eur.J.Inorg.Chem.|2019|1657|doi:10.1002/ejic.201800743
DOI: 10.5517/ccdc.csd.cc1pwwn3
title: CCDC 1575936: Experimental Crystal Structure Determination

description: Related Article: Philippe Weis,  Ingo Krossing,  Daniel Kratzert, Clarissa Hettich|2019|Eur.J.Inorg.Chem.|2019|1657|doi:10.1002/ejic.201800743
DOI: 10.5517/ccdc.csd.cc1nnkd8
title: CCDC 1539100: Experimental Crystal Structure Determination

description: Related Article: Philippe Weis,  Ingo Krossing,  Daniel Kratzert, Clarissa Hettich|2019|Eur.J.Inorg.Chem.|2019|1657|doi:10.1002/ejic.201800743
DOI: 10.5517/ccdc.csd.cc210lrf
title: CCDC 1877414: Experimental Crystal Structure Determination

description: Related Article: Julia Krüger,  Christoph Wölper,  Lukas John,  Lijuan Song,  Peter R. Schreiner, Stephan Schulz|2019|Eur.J.Inorg.Chem.|2019|1669|doi:10.1002/ejic.201900167
DOI: 10.5517/ccdc.csd.cc210lsg
title: CCDC 1877415: Experimental Crystal Structure Determination

description: Related Article: Julia Krüger,  Christoph Wölper,  Lukas John,  Lijuan Song,  Peter R. Schreiner, Stephan Schulz|2019|Eur.J.Inorg.Chem.|2019|1669|doi:10.1002/ejic.201900167
DOI: 10.5517/ccdc.csd.cc210lth
title: CCDC 1877416: Experimental Crystal Structure Determination

description: Related Article: Julia Krüger,  Christoph Wölper,  Lukas John,  Lijuan Song,  Peter R. Schreiner, Stephan Schulz|2019|Eur.J.Inorg.Chem.|2019|1669|doi:10.1002/ejic.201900167
DOI: 10.5517/ccdc.csd.cc210lvj
title: CCDC 1877417: Experimental Crystal Structure Determination

description: Related Article: Julia Krüger,  Christoph Wölper,  Lukas John,  Lijuan Song,  Peter R. Schreiner, Stephan Schulz|2019|Eur.J.Inorg.Chem.|2019|1669|doi:10.1002/ejic.201900167
DOI: 10.5517/ccdc.csd.cc210lwk
title: CCDC 1877418: Experimental Crystal Structure Determination

description: Related Article: Julia Krüger,  Christoph Wölper,  Lukas John,  Lijuan Song,  Peter R. Schreiner, Stephan Schulz|2019|Eur.J.Inorg.Chem.|2019|1669|doi:10.1002/ejic.201900167
DOI: 10.5517/ccdc.csd.cc212ld4
title: CCDC 1879325: Experimental Crystal Structure Determination

description: Related Article: Adhir Majumder,  Rajat Naskar,  Pallabi Roy, Ramananda Maity|2019|Eur.J.Inorg.Chem.|2019|1810|doi:10.1002/ejic.201801570
DOI: 10.5517/ccdc.csd.cc21867s
title: CCDC 1884714: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc21868t
title: CCDC 1884715: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc21869v
title: CCDC 1884716: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc2186bw
title: CCDC 1884717: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc2186cx
title: CCDC 1884718: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc2186dy
title: CCDC 1884719: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc2186fz
title: CCDC 1884720: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc2186h1
title: CCDC 1884722: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc2186k3
title: CCDC 1884724: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc2186j2
title: CCDC 1884723: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc2186g0
title: CCDC 1884721: Experimental Crystal Structure Determination

description: Related Article: Matthias Gawron,  Bastian Nayyar,  Christina Krabbe,  Michael Lutter, Klaus Jurkschat|2019|Eur.J.Inorg.Chem.|2019|1799|doi:10.1002/ejic.201801567
DOI: 10.5517/ccdc.csd.cc274r6c
title: CCDC 2060142: Experimental Crystal Structure Determination

description: Related Article: James Kaduk|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc274r7d
title: CCDC 2060143: Experimental Crystal Structure Determination

description: Related Article: James Kaduk|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc274r8f
title: CCDC 2060144: Experimental Crystal Structure Determination

description: Related Article: James Kaduk|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc274vpy
title: CCDC 2060250: Experimental Crystal Structure Determination

description: Related Article: James Kaduk|2021|CSD Communication|||
DOI: 10.34740/kaggle/dsv/3105721
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.21227/h1pa-nx72
title: Critical Current Densities through Josephson Junctions in Low Magnetic Fields

description: Understanding the properties of grain boundaries in polycrystalline superconductors is essential for optimizing their critical current density. Here, we provide computational simulations of 2D Josephson junctions (JJs) in low magnetic fields using time--dependent Ginzburg--Landau theory, since they can be considered a proxy for a grain boundary between two grains. We present data for junctions with a wide range of superconducting electrodes of different Ginzburg--Landau parameter (κ) values and geometries, as well as normal barriers with different strengths of pair--breaking --- characterized by the thickness of the junction and the junction condensation parameter (αn). We describe our results using analytic solutions, and hence provide a detailed description of Josephson junctions in low fields up to that required for a single fluxon to penetrate the junction.
DOI: 10.5281/zenodo.5913573
title: AndyData-lab-prescientTeleopICub

description: This dataset contains the reference trajectoires for the humanoid robot iCub, teleoperated by a human operator.<br> The operator controlled the robot while performing different tasks: reaching a low/mid-height target with the right hand, reaching a high target with the right hand, picking up a box located at different locations and placing the box at different locations.<br> The operator's whole-body kinematics was recorded with inertial motion capture systems and retargeted on the robot.<br> All data files are in csv format. Training and test data were used to learn movement primitives for the whole-body trajectories and were acquired when teleoperating the robot in a local network with ideally no delay.<br> A detailed description of how the data were collected and/or generated is available in the paper associated with the dataset: "Prescient teleoperation of humanoid robots" (Penco et al., hal preprint) https://hal.archives-ouvertes.fr/hal-?/document
DOI: 10.5281/zenodo.4906335
title: AndyData-lab-prescientTeleopICub

description: This dataset contains the reference trajectoires for the humanoid robot iCub, teleoperated by a human operator.<br> The operator controlled the robot while performing different tasks: reaching a low/mid-height target with the right hand, reaching a high target with the right hand, picking up a box located at different locations and placing the box at different locations.<br> The operator's whole-body kinematics was recorded with inertial motion capture systems and retargeted on the robot.<br> All data files are in csv format. Training and test data were used to learn movement primitives for the whole-body trajectories and were acquired when teleoperating the robot in a local network with ideally no delay.<br> A detailed description of how the data were collected and/or generated is available in the paper associated with the dataset: "Prescient teleoperation of humanoid robots" (Penco et al., hal preprint) https://hal.archives-ouvertes.fr/hal-?/document
DOI: 10.6084/m9.figshare.16802158.v6
title: Good Fences Make Good Neighbours? On the Impact of Cultural and Geographical Dispersion on Community Smells

description: Appendix and Replication Package of the paper titled <b>Good Fences Make Good Neighbours! On the Impact of Cultural and Geographical Dispersion on Community Smells</b>.
DOI: 10.6084/m9.figshare.19086305
title: Impacts of Surface Exchange Coefficients on Simulations of Super Typhoon Megi (2010) Using a Coupled Ocean-Atmosphere-Wave Model

description: Data used directly in the study for figures and tables are the output of the coupled COAWST model system.
DOI: 10.6084/m9.figshare.19086305.v1
title: Impacts of Surface Exchange Coefficients on Simulations of Super Typhoon Megi (2010) Using a Coupled Ocean-Atmosphere-Wave Model

description: Data used directly in the study for figures and tables are the output of the coupled COAWST model system.
DOI: 10.34740/kaggle/dsv/3105778
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.5913733
title: Investor Sentiment and Information Efficiency: Evidence from the Bitcoin Market.

description: This dataset contains information about the volume of Google searches for the term "bitcoin," the values of the Twitter happiness index and the Fear Index, the number of page views for the term "Bitcoin" on Wikipedia, and the online participation of the Bitcointalk.org online forum. Additionally, bitcoin prices and historical returns for the period July 2015 to June 2021 are included.
DOI: 10.5281/zenodo.5913732
title: Investor Sentiment and Information Efficiency: Evidence from the Bitcoin Market.

description: This dataset contains information about the volume of Google searches for the term "bitcoin," the values of the Twitter happiness index and the Fear Index, the number of page views for the term "Bitcoin" on Wikipedia, and the online participation of the Bitcointalk.org online forum. Additionally, bitcoin prices and historical returns for the period July 2015 to June 2021 are included.
DOI: 10.5281/zenodo.5913755
title: Conspiracy, Government trust, and Vaccination

description: Datasets
DOI: 10.5281/zenodo.5518878
title: Conspiracy, Government trust, and Vaccination

description: Datasets
DOI: 10.6084/m9.figshare.19086368
title: Simulated Training Dataset for Chen et al. Communications Biology

description: Simulated Training Dataset for Chen et al. Communications Biology
DOI: 10.6084/m9.figshare.19086368.v1
title: Simulated Training Dataset for Chen et al. Communications Biology

description: Simulated Training Dataset for Chen et al. Communications Biology
DOI: 10.6084/m9.figshare.17099939
title: Streamflow trends during recent decades at some near-natural catchments in Iran

description: In this study, we assessed the impacts of climate variations on streamflow in 28 near-natural catchments in Iran. To this end, we analysed the trend of annual streamflow, precipitation, and temperatures using the Mann-Kendall test and Sen’s slope between water years 1982 and 2011. We evaluated the frequency of precipitation in different classes, i.e. below 5, 5–10, 10–15, and above 15 mm/day. Our results indicate a decline in streamflow at 25 catchments, at a rate of −5.66 to −0.19 mm/year. The annual precipitation amount did not decrease significantly, while the frequency of light precipitation events (&lt;5 mm/day) increased. About 60% of the upward trend was significant. Mean temperature increased at all studied catchments, at an average rate of 0.055°C/year. In short, our results indicate that increases in temperature and the frequency of light precipitation events are two leading factors in streamflow reduction across studied catchments.
DOI: 10.6084/m9.figshare.17099939.v1
title: Streamflow trends during recent decades at some near-natural catchments in Iran

description: In this study, we assessed the impacts of climate variations on streamflow in 28 near-natural catchments in Iran. To this end, we analysed the trend of annual streamflow, precipitation, and temperatures using the Mann-Kendall test and Sen’s slope between water years 1982 and 2011. We evaluated the frequency of precipitation in different classes, i.e. below 5, 5–10, 10–15, and above 15 mm/day. Our results indicate a decline in streamflow at 25 catchments, at a rate of −5.66 to −0.19 mm/year. The annual precipitation amount did not decrease significantly, while the frequency of light precipitation events (&lt;5 mm/day) increased. About 60% of the upward trend was significant. Mean temperature increased at all studied catchments, at an average rate of 0.055°C/year. In short, our results indicate that increases in temperature and the frequency of light precipitation events are two leading factors in streamflow reduction across studied catchments.
DOI: 10.5281/zenodo.5913708
title: Supplementary data for the publication of Characterization of Emissions in Fab Labs: an Additive Manu-facturing Environment Issue

description: Datasets for the publication of the article "Characterization of Emissions in Fab Labs: an Additive Manufacturing Environment Issue": - Ultrafine Particles: UFP per Zone and mode; - VOC emissions: VOC per Zone and mode.
DOI: 10.5281/zenodo.5913707
title: Supplementary data for the publication of Characterization of Emissions in Fab Labs: an Additive Manu-facturing Environment Issue

description: Datasets for the publication of the article "Characterization of Emissions in Fab Labs: an Additive Manufacturing Environment Issue": - Ultrafine Particles: UFP per Zone and mode; - VOC emissions: VOC per Zone and mode.
DOI: 10.5281/zenodo.5913827
title: Dataset related to the article "Feasibility and Accuracy of the Automated Software for Dynamic Quantification of Left Ventricular and Atrial Volumes and Function in a Large Unselected Population"

description: This record contains raw data related to the article “Feasibility and Accuracy of the Automated Software for Dynamic Quantification of Left Ventricular and Atrial Volumes and Function in a Large Unselected Population” Abstract We aimed to evaluate the feasibility and accuracy of machine learning-based automated dynamic quantification of left ventricular (LV) and left atrial (LA) volumes in an unselected population. We enrolled 600 unselected patients (12% in atrial fibrillation) clinically referred for transthoracic echocardiography (2DTTE), who also underwent 3D echocardiography (3DE) imaging. LV ejection fraction (EF), LV, and LA volumes were obtained from 2D images; 3D images were analyzed using dynamic heart model (DHM) software (Philips) resulting in LV and LA volume-time curves. A subgroup of 140 patients also underwent cardiac magnetic resonance (CMR) imaging. Average time of analysis, feasibility, and image quality were recorded, and results were compared between 2DTTE, DHM, and CMR. The use of DHM was feasible in 522/600 cases (87%). When feasible, the boundary position was considered accurate in 335/522 patients (64%), while major (n = 38) or minor (n = 149) border corrections were needed. The overall time required for DHM datasets was approximately 40 seconds. As expected, DHM LV volumes were larger than 2D ones (end-diastolic volume: 173 ± 64 vs. 142 ± 58 mL, respectively), while no differences were found for LV EF and LA volumes (EF: 55% ± 12 vs. 56% ± 14; LA volume 89 ± 36 vs. 89 ± 38 mL, respectively). The comparison between DHM and CMR values showed a high correlation for LV volumes (r = 0.70 and r = 0.82, <em>p</em> &lt; 0.001 for end-diastolic and end-systolic volume, respectively) and an excellent correlation for EF (r = 0.82, <em>p</em> &lt; 0.001) and LA volumes. The DHM software is feasible, accurate, and quick in a large series of unselected patients, including those with suboptimal 2D images or in atrial fibrillation.
DOI: 10.5281/zenodo.5913826
title: Dataset related to the article "Feasibility and Accuracy of the Automated Software for Dynamic Quantification of Left Ventricular and Atrial Volumes and Function in a Large Unselected Population"

description: This record contains raw data related to the article “Feasibility and Accuracy of the Automated Software for Dynamic Quantification of Left Ventricular and Atrial Volumes and Function in a Large Unselected Population” Abstract We aimed to evaluate the feasibility and accuracy of machine learning-based automated dynamic quantification of left ventricular (LV) and left atrial (LA) volumes in an unselected population. We enrolled 600 unselected patients (12% in atrial fibrillation) clinically referred for transthoracic echocardiography (2DTTE), who also underwent 3D echocardiography (3DE) imaging. LV ejection fraction (EF), LV, and LA volumes were obtained from 2D images; 3D images were analyzed using dynamic heart model (DHM) software (Philips) resulting in LV and LA volume-time curves. A subgroup of 140 patients also underwent cardiac magnetic resonance (CMR) imaging. Average time of analysis, feasibility, and image quality were recorded, and results were compared between 2DTTE, DHM, and CMR. The use of DHM was feasible in 522/600 cases (87%). When feasible, the boundary position was considered accurate in 335/522 patients (64%), while major (n = 38) or minor (n = 149) border corrections were needed. The overall time required for DHM datasets was approximately 40 seconds. As expected, DHM LV volumes were larger than 2D ones (end-diastolic volume: 173 ± 64 vs. 142 ± 58 mL, respectively), while no differences were found for LV EF and LA volumes (EF: 55% ± 12 vs. 56% ± 14; LA volume 89 ± 36 vs. 89 ± 38 mL, respectively). The comparison between DHM and CMR values showed a high correlation for LV volumes (r = 0.70 and r = 0.82, <em>p</em> &lt; 0.001 for end-diastolic and end-systolic volume, respectively) and an excellent correlation for EF (r = 0.82, <em>p</em> &lt; 0.001) and LA volumes. The DHM software is feasible, accurate, and quick in a large series of unselected patients, including those with suboptimal 2D images or in atrial fibrillation.
DOI: 10.6084/m9.figshare.17124065
title: Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors

description: This article considers linear panel data models where the dependence of the regressors and the unobservables is modeled through a factor structure. The number of time periods and the sample size both go to infinity. Unlike in most existing methods for the estimation of this type of models, nonstrong factors are allowed and the number of factors can grow to infinity with the sample size. We study a class of two-step estimators of the regression coefficients. In the first step, factors and factor loadings are estimated. Then, the second step corresponds to the panel regression of the outcome on the regressors and the estimates of the factors and the factor loadings from the first step. The estimators enjoy double robustness. Different methods can be used in the first step while the second step is unique. We derive sufficient conditions on the first-step estimator and the data generating process under which the two-step estimator is asymptotically normal. Assumptions under which using an approach based on principal components analysis in the first step yields an asymptotically normal estimator are also given. The two-step procedure exhibits good finite sample properties in simulations. The approach is illustrated by an empirical application on fiscal policy.
DOI: 10.6084/m9.figshare.17124065.v2
title: Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors

description: This article considers linear panel data models where the dependence of the regressors and the unobservables is modeled through a factor structure. The number of time periods and the sample size both go to infinity. Unlike in most existing methods for the estimation of this type of models, nonstrong factors are allowed and the number of factors can grow to infinity with the sample size. We study a class of two-step estimators of the regression coefficients. In the first step, factors and factor loadings are estimated. Then, the second step corresponds to the panel regression of the outcome on the regressors and the estimates of the factors and the factor loadings from the first step. The estimators enjoy double robustness. Different methods can be used in the first step while the second step is unique. We derive sufficient conditions on the first-step estimator and the data generating process under which the two-step estimator is asymptotically normal. Assumptions under which using an approach based on principal components analysis in the first step yields an asymptotically normal estimator are also given. The two-step procedure exhibits good finite sample properties in simulations. The approach is illustrated by an empirical application on fiscal policy.
DOI: 10.6084/m9.figshare.17124065.v1
title: Factor and factor loading augmented estimators for panel regression with possibly non-strong factors

description: This paper considers linear panel data models where the dependence of the regressors and the unobservables is modelled through a factor structure. The number of time periods and the sample size both go to infinity. Unlike in most existing methods for the estimation of this type of models, non-strong factors are allowed and the number of factors can grow to infinity with the sample size. We study a class of two-step estimators of the regression coefficients. In the first step, factors and factor loadings are estimated. Then, the second step corresponds to the panel regression of the outcome on the regressors and the estimates of the factors and the factor loadings from the first step. The estimators enjoy double robustness. Different methods can be used in the first step while the second step is unique. We derive sufficient conditions on the first-step estimator and the data generating process under which the two-step estimator is asymptotically normal. Assumptions under which using an approach based on principal components analysis in the first step yields an asymptotically normal estimator are also given. The two-step procedure exhibits good finite sample properties in simulations. The approach is illustrated by an empirical application on fiscal policy.