cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9373604
title: Health effects of particulate matter in major Indian cities

description: <b>Background</b>: Particulate matter (PM) is one among the crucial air pollutants and has the potential to cause a wide range of health effects. Indian cities ranked top places in the World Health Organization list of most polluted cities by PM. <b>Objectives</b>: Present study aims to assess the trends, short- and long-term health effects of PM in major Indian cities. <b>Methods</b>: PM-induced hospital admissions and mortality are quantified using AirQ+ software. <b>Results</b>: Annual PM concentration in most of the cities is higher than the National Ambient Air Quality Standards of India. Trend analysis showed peak PM concentration during post-monsoon and winter seasons. The respiratory and cardiovascular hospital admissions in the male (female) population are estimated to be 31,307 (28,009) and 5460 (4882) cases, respectively. PM<sub>2.5</sub> has accounted for a total of 1,27,014 deaths in 2017. <b>Conclusion</b>: Cities with high PM concentration and exposed population are more susceptible to mortality and hospital admissions.
DOI: 10.6084/m9.figshare.14533353
title: pathashkar1.amw

description: path analysis of a model of burnout in nurses<br>
DOI: 10.6084/m9.figshare.14533353.v1
title: pathashkar1.amw

description: path analysis of a model of burnout in nurses<br>
DOI: 10.6084/m9.figshare.14533548
title: Arab Barometer Data_W5

description: Arab Barometer survey data Wave 5<br>
DOI: 10.6084/m9.figshare.14533548.v1
title: Arab Barometer Data_W5

description: Arab Barometer survey data Wave 5<br>
DOI: 10.5517/ccdc.csd.cc27461n
title: CCDC 2059610: Experimental Crystal Structure Determination

description: Related Article: Gustav J. Wørmer, Nikolaj L. Villadsen, Peter Nørby, Thomas B. Poulsen|2021|Angew.Chem.,Int.Ed.|60|10521|doi:10.1002/anie.202101439
DOI: 10.5517/ccdc.csd.cc27462p
title: CCDC 2059611: Experimental Crystal Structure Determination

description: Related Article: Gustav J. Wørmer, Nikolaj L. Villadsen, Peter Nørby, Thomas B. Poulsen|2021|Angew.Chem.,Int.Ed.|60|10521|doi:10.1002/anie.202101439
DOI: 10.25505/fiz.icsd.cc27w2z6
title: CSD 2081618: Experimental Crystal Structure Determination

description: Related Article: C.N.DIPUNADAS|2021|ICSD Communication|||
DOI: 10.5281/zenodo.4662869
title: Machine-Learning-Based High-Resolution Earthquake Catalog For the 2016-2017 Central Italy Sequence

description: This is the earthquake catalog published as part of the manuscript Tan et al. (2021), Machine-Learning-Based High-Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated During the 2016-2017 Central Italy Sequence, <em>The Seismic Record</em>. The meaning of column headers: ml_n: number of amplitude measurements that went into the local magnitude estimate ml_mean: local magnitude taken as mean of all stations ml_std: local magnitude sample standard deviation ml_median: local magnitude taken as median of all stations mw: moment magnitude converted from ml_median split: 1 are potential split events, identified for having similar origin time, location, and magnitude as another event but with significantly fewer associated arrival time picks
DOI: 10.6084/m9.figshare.14527746
title: Python notebooks used in this study from Methods for analysing lineage tracing datasets

description: Compressed file with notebooks for performing ABC and MLE analysis.
DOI: 10.6084/m9.figshare.14527746.v1
title: Python notebooks used in this study from Methods for analysing lineage tracing datasets

description: Compressed file with notebooks for performing ABC and MLE analysis.
DOI: 10.6084/m9.figshare.14479229
title: Supplementary Data Table from MRI-based quantification of ophthalmic changes in healthy volunteers during acute 15° head-down tilt as an analogue to microgravity

description: Table including all relevant processed data and demographic information analyzed in the present study.
DOI: 10.5281/zenodo.4736135
title: OSeMOSYS configuration files for "Three stages in the co-transformation of the energy and mobility sector"

description: Find data.txt and model.txt for all scenarios in the zip archive. Please note that E30P39 is the one analyzed in the publication.
DOI: 10.5281/zenodo.4736134
title: OSeMOSYS configuration files for "Three stages in the co-transformation of the energy and mobility sector"

description: Find data.txt and model.txt for all scenarios in the zip archive. Please note that E30P39 is the one analyzed in the publication.
DOI: 10.5281/zenodo.4660133
title: Measuring Law Over Time (Dataset)

description: This is the dataset accompanying the following paper: Corinna Coupette, Janis Beckedorf, Dirk Hartung, Michael Bommarito, and Daniel Martin Katz, Measuring Law Over Time, <em>Frontiers in Physics</em> (2021), https://www.frontiersin.org/articles/10.3389/fphy.2021.658463.
DOI: 10.5281/zenodo.4660132
title: Measuring Law Over Time (Dataset)

description: This is the dataset accompanying the following paper: Corinna Coupette, Janis Beckedorf, Dirk Hartung, Michael Bommarito, and Daniel Martin Katz, Measuring Law Over Time, <em>Frontiers in Physics</em> (2021), https://www.frontiersin.org/articles/10.3389/fphy.2021.658463.
DOI: 10.6084/m9.figshare.9989702
title: An MCMC computational approach for a continuous time state-dependent regime switching diffusion process

description: State-dependent regime switching diffusion processes or hybrid switching diffusion (HSD) processes are hard to simulate with classical methods which leads us to adopt a Markov chain Monte Carlo (MCMC) Bayesian approach very convenient to estimate complicated models such as the HSD one. In the HSD, the diffusion component is dependent on the switching discrete hidden regimes and the transition rates of the regime switching are dependent on the diffusion observations. Since in reality phenomena are only observed in discrete times, data imputation is called for to create more observations so as to have good approximations for the density of the diffusion process. Three categories of entities will be computed in a Bayesian context: The latent imputed observations, the regime switching states, and the parameters of the models. The latent imputed data is updated at random time intervals in block using a Metropolis Hastings algorithm. The switching states are computed by an adaptation of a forward filtering backward smoothing algorithm to the HSD model. The parameters are estimated after prior specifications and conditional posterior densities formulation using Gibbs sampler or Metropolis Hastings algorithm.
DOI: 10.6084/m9.figshare.9989702.v1
title: An MCMC computational approach for a continuous time state-dependent regime switching diffusion process

description: State-dependent regime switching diffusion processes or hybrid switching diffusion (HSD) processes are hard to simulate with classical methods which leads us to adopt a Markov chain Monte Carlo (MCMC) Bayesian approach very convenient to estimate complicated models such as the HSD one. In the HSD, the diffusion component is dependent on the switching discrete hidden regimes and the transition rates of the regime switching are dependent on the diffusion observations. Since in reality phenomena are only observed in discrete times, data imputation is called for to create more observations so as to have good approximations for the density of the diffusion process. Three categories of entities will be computed in a Bayesian context: The latent imputed observations, the regime switching states, and the parameters of the models. The latent imputed data is updated at random time intervals in block using a Metropolis Hastings algorithm. The switching states are computed by an adaptation of a forward filtering backward smoothing algorithm to the HSD model. The parameters are estimated after prior specifications and conditional posterior densities formulation using Gibbs sampler or Metropolis Hastings algorithm.
DOI: 10.6084/m9.figshare.10075259.v1
title: Modified likelihood ratio tests for unit gamma regressions

description: Regression analyses are commonly performed with doubly limited continuous dependent variables; for instance, when modeling the behavior of rates, proportions and income concentration indices. Several models are available in the literature for use with such variables, one of them being the unit gamma regression model. In all such models, parameter estimation is typically performed using the maximum likelihood method and testing inferences on the model's parameters are usually based on the likelihood ratio test. Such a test can, however, deliver quite imprecise inferences when the sample size is small. In this paper, we propose two modified likelihood ratio test statistics for use with the unit gamma regressions that deliver much more accurate inferences when the number of data points in small. Numerical (i.e. simulation) evidence is presented for both fixed dispersion and varying dispersion models, and also for tests that involve nonnested models. We also present and discuss two empirical applications.
DOI: 10.6084/m9.figshare.10075259
title: Modified likelihood ratio tests for unit gamma regressions

description: Regression analyses are commonly performed with doubly limited continuous dependent variables; for instance, when modeling the behavior of rates, proportions and income concentration indices. Several models are available in the literature for use with such variables, one of them being the unit gamma regression model. In all such models, parameter estimation is typically performed using the maximum likelihood method and testing inferences on the model's parameters are usually based on the likelihood ratio test. Such a test can, however, deliver quite imprecise inferences when the sample size is small. In this paper, we propose two modified likelihood ratio test statistics for use with the unit gamma regressions that deliver much more accurate inferences when the number of data points in small. Numerical (i.e. simulation) evidence is presented for both fixed dispersion and varying dispersion models, and also for tests that involve nonnested models. We also present and discuss two empirical applications.
DOI: 10.6084/m9.figshare.11906991
title: Latin hypercube designs based on strong orthogonal arrays and Kriging modelling to improve the payload distribution of trains

description: Nowadays, computer experiments are used increasingly more to solve complex engineering and technological issues. Computer experiments are analysed through suitable metamodels acting as statistical interpolators of the simulated input-output data: Kriging is the most appropriate and widely used one. We optimise the braking performance of freight trains through computer experiments and Kriging modelling by focussing on the payload distribution along the train, so as to reduce the effects of in-train forces among wagons during a train emergency braking. One contribution of this manuscript is that to improve the freight train efficiency in terms of braking performance, we consider that the train is composed of several train sections with each one characterised by its own overall payload. A suitable Latin hypercube design is planned for the computer experiment that achieves excellent space-filling properties with a relatively low number of experimental runs. Kriging models with anisotropic covariance function are subsequently applied to assess which is the best payload distribution capable of reducting the in-train forces according to the specific train-set arrangement considered. The results are very satisfactory and confirm that our approach represents a valid method to be successfully applied by interested Railway Undertakings.
DOI: 10.6084/m9.figshare.11906991.v1
title: Latin hypercube designs based on strong orthogonal arrays and Kriging modelling to improve the payload distribution of trains

description: Nowadays, computer experiments are used increasingly more to solve complex engineering and technological issues. Computer experiments are analysed through suitable metamodels acting as statistical interpolators of the simulated input-output data: Kriging is the most appropriate and widely used one. We optimise the braking performance of freight trains through computer experiments and Kriging modelling by focussing on the payload distribution along the train, so as to reduce the effects of in-train forces among wagons during a train emergency braking. One contribution of this manuscript is that to improve the freight train efficiency in terms of braking performance, we consider that the train is composed of several train sections with each one characterised by its own overall payload. A suitable Latin hypercube design is planned for the computer experiment that achieves excellent space-filling properties with a relatively low number of experimental runs. Kriging models with anisotropic covariance function are subsequently applied to assess which is the best payload distribution capable of reducting the in-train forces according to the specific train-set arrangement considered. The results are very satisfactory and confirm that our approach represents a valid method to be successfully applied by interested Railway Undertakings.
DOI: 10.6084/m9.figshare.14533680
title: Wang et al._Hailiutu River_Low flow period data

description: Wang et al._Hailiutu River_Low flow period data
DOI: 10.6084/m9.figshare.14533680.v1
title: Wang et al._Hailiutu River_Low flow period data

description: Wang et al._Hailiutu River_Low flow period data
DOI: 10.25345/c5qn6v
title: MassIVE MSV000087359 - Merchant Laboratory UC Berkeley Metabolomics
title: Sample for metabolomic analysis provided by the laboratory of Sabeeha Merchant (UCB).
DOI: 10.14457/cu.the.2010.432
title: ผลของวัสดุทางการเกษตรในการช่วยสลายไพรีนและฟีแนนทรีนโดยกลุ่มแบคทีเรีย RRM-V3
DOI: 10.14457/cu.the.2010.433
title: ผลของอุณหภูมิ เวลา และออกซิเจน ของการอบอ่อนต่อการเกิดผิวออกไซด์ทุติยภูมิและพฤติกรรมการกัดผิวด้วยกรดของเหล็กกล้าไร้สนิม AISI 304 /
DOI: 10.14457/kku.the.2010.226
title: ผลของการใช้กระบวนการกลุ่มเพื่อพัฒนาจิตสาธารณะของนักเรียนชั้นมัธยมศึกษาตอนต้นโรงเรียนบ้านโป่งขุนเพชรพัฒนา สำนักงานเขตพื้นที่การศึกษาชัยภูมิ เขต 3
DOI: 10.6084/m9.figshare.14533698
title: Wang et al._Hailiutu River_High flow period data

description: Wang et al._Hailiutu River_High flow period data
DOI: 10.6084/m9.figshare.14533698.v1
title: Wang et al._Hailiutu River_High flow period data

description: Wang et al._Hailiutu River_High flow period data
DOI: 10.14457/kku.res.2010.59
title: บทบาทของเอนไซม์ NQO1 ต่อความไวต่อมะเร็ง พยากรณ์โรค และเป้าหมายของยารักษามะเร็ง
DOI: 10.6084/m9.figshare.10327322
title: Location-scale mixed models and goodness-of-fit assessment applied to insect ecology

description: Survival models have been extensively used to analyse time-until-event data. There is a range of extended models that incorporate different aspects, such as overdispersion/frailty, mixtures, and flexible response functions through semi-parametric models. In this work, we show how a useful tool to assess goodness-of-fit, the half-normal plot of residuals with a simulated envelope, implemented in the hnp package in R, can be used on a location-scale modelling context. We fitted a range of survival models to time-until-event data, where the event was an insect predator attacking a larva in a biological control experiment. We started with the Weibull model and then fitted the exponentiated-Weibull location-scale model with regressors both for the location and scale parameters. We performed variable selection for each model and, by producing half-normal plots with simulated envelopes for the deviance residuals of the model fits, we found that the exponentiated-Weibull fitted the data better. We then included a random effect in the exponentiated-Weibull model to accommodate correlated observations. Finally, we discuss possible implications of the results found in the case study.
DOI: 10.6084/m9.figshare.10327322.v1
title: Location-scale mixed models and goodness-of-fit assessment applied to insect ecology

description: Survival models have been extensively used to analyse time-until-event data. There is a range of extended models that incorporate different aspects, such as overdispersion/frailty, mixtures, and flexible response functions through semi-parametric models. In this work, we show how a useful tool to assess goodness-of-fit, the half-normal plot of residuals with a simulated envelope, implemented in the hnp package in R, can be used on a location-scale modelling context. We fitted a range of survival models to time-until-event data, where the event was an insect predator attacking a larva in a biological control experiment. We started with the Weibull model and then fitted the exponentiated-Weibull location-scale model with regressors both for the location and scale parameters. We performed variable selection for each model and, by producing half-normal plots with simulated envelopes for the deviance residuals of the model fits, we found that the exponentiated-Weibull fitted the data better. We then included a random effect in the exponentiated-Weibull model to accommodate correlated observations. Finally, we discuss possible implications of the results found in the case study.
DOI: 10.6084/m9.figshare.12130377.v1
title: Identifying influential observations in a Bayesian multi-level mediation model

description: Increasingly complex models are being fit to data these days. This is especially the case for Bayesian modelling making use of Markov chain Monte Carlo methods. Tailored model diagnostics are usually lacking behind. This is also the case for Bayesian mediation models. In this paper, we developed a method for the detection of influential observations for a popular mediation model and its extensions in a Bayesian context. Detection of influential observations is based on the case-deletion principle. Importance sampling with weights which take advantage of the dependence structure in hierarchical models is utilized in order to identify the part of the model which is influenced most. We make use of the variance of log importance sampling weights as the measure of influence. It is demonstrated that this approach is useful when interest lies in the impact of individual observations on a subset of model parameters. The method is illustrated on a three-level data set from the field of nursing research, which was previously used to fit a mediation model of patient satisfaction with care. We focused on influential cases on both the second and the third level of the data.
DOI: 10.6084/m9.figshare.12130377
title: Identifying influential observations in a Bayesian multi-level mediation model

description: Increasingly complex models are being fit to data these days. This is especially the case for Bayesian modelling making use of Markov chain Monte Carlo methods. Tailored model diagnostics are usually lacking behind. This is also the case for Bayesian mediation models. In this paper, we developed a method for the detection of influential observations for a popular mediation model and its extensions in a Bayesian context. Detection of influential observations is based on the case-deletion principle. Importance sampling with weights which take advantage of the dependence structure in hierarchical models is utilized in order to identify the part of the model which is influenced most. We make use of the variance of log importance sampling weights as the measure of influence. It is demonstrated that this approach is useful when interest lies in the impact of individual observations on a subset of model parameters. The method is illustrated on a three-level data set from the field of nursing research, which was previously used to fit a mediation model of patient satisfaction with care. We focused on influential cases on both the second and the third level of the data.
DOI: 10.6084/m9.figshare.14371424
title: Supplementary code from On incorporating forecasts into linear state space model Markov decision processes

description: Numerical implementation of example in section 6
DOI: 10.6084/m9.figshare.14371424.v2
title: Supplementary code from On incorporating forecasts into linear state space model Markov decision processes

description: Numerical implementation of example in section 6
DOI: 10.6084/m9.figshare.14371424.v1
title: Supplementary code from On incorporating forecasts into linear state space model Markov decision processes

description: Numerical implementation of example in section 6
DOI: 10.6084/m9.figshare.14533701
title: Supplementary Material for: Impact of Procedure Time on Clinical Outcomes of Patients Who Underwent Endovascular Therapy for Acute Ischemic Stroke

description: <b><i>Background and Purpose:</i></b> The time from onset to reperfusion is associated with clinical outcomes in acute ischemic stroke due to large vessel occlusion (LVO); nevertheless, the time limit of the continuing procedure remains unclear. We analyzed the relationship between procedure time and clinical outcomes in patients with LVO who underwent endovascular treatment (EVT). <b><i>Methods:</i></b> We assessed 1,247 patients who underwent EVT for LVO. Data were obtained from our multicenter registry, and patients were included if data on procedure time were available. Multivariate analysis was performed to assess the impact of procedure time on clinical outcomes using the following parameters: favorable outcome (the modified Rankin score of 0–2 at 90 days), mortality within 90 days, symptomatic intracranial hemorrhage within 72 h after stroke onset, and procedure-related complications. <b><i>Results:</i></b> The rate of favorable outcomes linearly decreased with increasing procedure time, but there was no linear relationship between procedure time and other outcomes. The adjusted odds ratio for 30-minute delay in procedure time was 0.76 (95% confidence interval, 0.68–0.84) for favorable outcome, 1.15 (0.97–1.36) for mortality, 1.08 (0.87–1.33) for symptomatic intracranial hemorrhage, and 0.92 (0.75–1.16) for complications. Significant interactions in the effect of procedure time on favorable outcome were observed between the subgroups stratified by age (≥75 or &lt;75 years). Younger patients had a greater deleterious effect of delayed reperfusion. <b><i>Conclusions:</i></b> Increasing procedure time was associated with less favorable outcomes, but not with the rate of mortality, symptomatic intracerebral hemorrhage, or complications in our cohort.