cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13519115
title: Adaptive Bayesian Spectral Analysis of High-Dimensional Nonstationary Time Series

description: This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk toward zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the number and locations of partition points are assumed unknown. Stochastic approximation Monte Carlo techniques are used to accommodate the unknown number of segments, and a conditional Whittle likelihood-based Gibbs sampler is developed for efficient sampling within segments. By averaging over the distribution of partitions, the proposed method can approximate both abrupt and slowly varying changes in spectral matrices. Performance of the proposed model is evaluated by extensive simulations and demonstrated through the analysis of high-density electroencephalography. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13519115.v4
title: Adaptive Bayesian Spectral Analysis of High-Dimensional Nonstationary Time Series

description: This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk toward zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the number and locations of partition points are assumed unknown. Stochastic approximation Monte Carlo techniques are used to accommodate the unknown number of segments, and a conditional Whittle likelihood-based Gibbs sampler is developed for efficient sampling within segments. By averaging over the distribution of partitions, the proposed method can approximate both abrupt and slowly varying changes in spectral matrices. Performance of the proposed model is evaluated by extensive simulations and demonstrated through the analysis of high-density electroencephalography. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13180187
title: Modeling Nonstationary Extreme Dependence With Stationary Max-Stable Processes and Multidimensional Scaling

description: Modeling the joint distribution of extreme events at multiple locations is a challenging task with important applications. In this study, we use max-stable models to study extreme daily precipitation events in Switzerland. The nonstationarity of the spatial process at hand involves important challenges, which are often dealt with by using a stationary model in a so-called climate space, with well-chosen covariates. Here, we instead choose to warp the weather stations under study in a latent space of higher dimension using multidimensional scaling (MDS). Two methods are proposed to define target dissimilarity matrices, based respectively on extremal coefficients and on pairwise likelihoods. Results suggest that the proposed methods allow capturing complex spatial dependences of spatial extreme precipitations, enabling in turn to reliably extrapolate functionals such as extremal coefficients. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.13180187.v3
title: Modeling Nonstationary Extreme Dependence With Stationary Max-Stable Processes and Multidimensional Scaling

description: Modeling the joint distribution of extreme events at multiple locations is a challenging task with important applications. In this study, we use max-stable models to study extreme daily precipitation events in Switzerland. The nonstationarity of the spatial process at hand involves important challenges, which are often dealt with by using a stationary model in a so-called climate space, with well-chosen covariates. Here, we instead choose to warp the weather stations under study in a latent space of higher dimension using multidimensional scaling (MDS). Two methods are proposed to define target dissimilarity matrices, based respectively on extremal coefficients and on pairwise likelihoods. Results suggest that the proposed methods allow capturing complex spatial dependences of spatial extreme precipitations, enabling in turn to reliably extrapolate functionals such as extremal coefficients. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.13519115.v3
title: Adaptive Bayesian Spectral Analysis of High-Dimensional Nonstationary Time Series

description: This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk toward zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the number and locations of partition points are assumed unknown. Stochastic approximation Monte Carlo techniques are used to accommodate the unknown number of segments, and a conditional Whittle likelihood-based Gibbs sampler is developed for efficient sampling within segments. By averaging over the distribution of partitions, the proposed method can approximate both abrupt and slowly varying changes in spectral matrices. Performance of the proposed model is evaluated by extensive simulations and demonstrated through the analysis of high-density electroencephalography. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13143714.v3
title: Particle MCMC With Poisson Resampling: Parallelization and Continuous Time Models

description: We introduce a new version of particle filter in which the number of “children” of a particle at a given time has a Poisson distribution. As a result, the number of particles is random and varies with time. An advantage of this scheme is that descendants of different particles can evolve independently. It makes easy to parallelize computations. Moreover, particle filter with Poisson resampling is readily adapted to the case when a hidden process is a continuous time, piecewise deterministic semi-Markov process. We show that the basic techniques of particle MCMC, namely particle independent Metropolis-Hastings, particle Gibbs sampler and its version with ancestor sampling, work under our Poisson resampling scheme. Our version of particle Gibbs sampler is uniformly ergodic under the same assumptions as its standard counterpart. We present simulation results which indicate that our algorithms can compete with the existing methods. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.20425572
title: Simulated EvoDevo dataset

description: Simulated gene expression dataset for EvoDevo.
DOI: 10.6084/m9.figshare.20425572.v1
title: Simulated EvoDevo dataset

description: Simulated gene expression dataset for EvoDevo.
DOI: 10.48704/cosb2
title: CoSb2
DOI: 10.6084/m9.figshare.16934420
title: Bayesian Shrinkage for Functional Network Models, With Applications to Longitudinal Item Response Data

description: Longitudinal item response data are common in social science, educational science, and psychology, among other disciplines. Studying the time-varying relationships between items is crucial for educational assessment or designing marketing strategies from survey questions. Although dynamic network models have been widely developed, we cannot apply them directly to item response data because there are multiple systems of nodes with various types of local interactions among items, resulting in multiplex network structures. We propose a new model to study these temporal interactions among items by embedding the functional parameters within the exponential random graph model framework. Inference on such models is difficult because the likelihood functions contain intractable normalizing constants. Furthermore, the number of functional parameters grows exponentially as the number of items increases. Variable selection for such models is not trivial because standard shrinkage approaches do not consider temporal trends in functional parameters. To overcome these challenges, we develop a novel Bayes approach by combining an auxiliary variable MCMC algorithm and a recently developed functional shrinkage method. We apply our algorithm to survey and review datasets, illustrating that the proposed approach can avoid the evaluation of intractable normalizing constants as well as the detection of significant temporal interactions among items. Through a simulation study under different scenarios, we examine the performance of our algorithm. Our method is, to our knowledge, the first attempt to select functional variables for models with intractable normalizing constants. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16934420.v2
title: Bayesian Shrinkage for Functional Network Models, With Applications to Longitudinal Item Response Data

description: Longitudinal item response data are common in social science, educational science, and psychology, among other disciplines. Studying the time-varying relationships between items is crucial for educational assessment or designing marketing strategies from survey questions. Although dynamic network models have been widely developed, we cannot apply them directly to item response data because there are multiple systems of nodes with various types of local interactions among items, resulting in multiplex network structures. We propose a new model to study these temporal interactions among items by embedding the functional parameters within the exponential random graph model framework. Inference on such models is difficult because the likelihood functions contain intractable normalizing constants. Furthermore, the number of functional parameters grows exponentially as the number of items increases. Variable selection for such models is not trivial because standard shrinkage approaches do not consider temporal trends in functional parameters. To overcome these challenges, we develop a novel Bayes approach by combining an auxiliary variable MCMC algorithm and a recently developed functional shrinkage method. We apply our algorithm to survey and review datasets, illustrating that the proposed approach can avoid the evaluation of intractable normalizing constants as well as the detection of significant temporal interactions among items. Through a simulation study under different scenarios, we examine the performance of our algorithm. Our method is, to our knowledge, the first attempt to select functional variables for models with intractable normalizing constants. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16942936
title: Leave-One-Out Kernel Density Estimates for Outlier Detection

description: This article introduces <i>lookout</i>, a new approach to detect outliers using leave-one-out kernel density estimates and extreme value theory. Outlier detection methods that use kernel density estimates generally employ a user defined parameter to determine the bandwidth. Lookout uses persistent homology to construct a bandwidth suitable for outlier detection without any user input. We demonstrate the effectiveness of lookout on an extensive data repository by comparing its performance with other outlier detection methods based on extreme value theory. Furthermore, we introduce <i>outlier persistence</i>, a useful concept that explores the birth and the cessation of outliers with changing bandwidth and significance levels. The R package lookout implements this algorithm. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.16942936.v2
title: Leave-One-Out Kernel Density Estimates for Outlier Detection

description: This article introduces <i>lookout</i>, a new approach to detect outliers using leave-one-out kernel density estimates and extreme value theory. Outlier detection methods that use kernel density estimates generally employ a user defined parameter to determine the bandwidth. Lookout uses persistent homology to construct a bandwidth suitable for outlier detection without any user input. We demonstrate the effectiveness of lookout on an extensive data repository by comparing its performance with other outlier detection methods based on extreme value theory. Furthermore, we introduce <i>outlier persistence</i>, a useful concept that explores the birth and the cessation of outliers with changing bandwidth and significance levels. The R package lookout implements this algorithm. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.16934420.v1
title: Bayesian Shrinkage for Functional Network Models, with Applications to Longitudinal Item Response Data

description: Longitudinal item response data are common in social science, educational science, and psychology, among other disciplines. Studying the time-varying relationships between items is crucial for educational assessment or designing marketing strategies from survey questions. Although dynamic network models have been widely developed, we cannot apply them directly to item response data because there are multiple systems of nodes with various types of local interactions among items, resulting in multiplex network structures. We propose a new model to study these temporal interactions among items by embedding the functional parameters within the exponential random graph model framework. Inference on such models is difficult because the likelihood functions contain intractable normalizing constants. Furthermore, the number of functional parameters grows exponentially as the number of items increases. Variable selection for such models is not trivial because standard shrinkage approaches do not consider temporal trends in functional parameters. To overcome these challenges, we develop a novel Bayes approach by combining an auxiliary variable MCMC algorithm and a recently-developed functional shrinkage method. We apply our algorithm to survey and review data sets, illustrating that the proposed approach can avoid the evaluation of intractable normalizing constants as well as the detection of significant temporal interactions among items. Through a simulation study under different scenarios, we examine the performance of our algorithm. Our method is, to our knowledge, the first attempt to select functional variables for models with intractable normalizing constants.
DOI: 10.6084/m9.figshare.16942936.v1
title: Leave-one-out kernel density estimates for outlier detection

description: This paper introduces <i>lookout</i>, a new approach to detect outliers using leave-one-out kernel density estimates and extreme value theory. Outlier detection methods that use kernel density estimates generally employ a user defined parameter to determine the bandwidth. Lookout uses persistent homology to construct a bandwidth suitable for outlier detection without any user input. We demonstrate the effectiveness of lookout on an extensive data repository by comparing its performance with other outlier detection methods based on extreme value theory. Furthermore, we introduce <i>outlier persistence</i>, a useful concept that explores the birth and the cessation of outliers with changing bandwidth and significance levels. The R package lookout implements this algorithm.
DOI: 10.5281/zenodo.6942796
title: Gesport_Women on sports boards in NSFs of five countries: Italy, Portugal, Spain, Turkey and the United Kingdom

description: This database has been built by the authors with the support of the Erasmus+ programme of the European Union under Grant number 590521-EPP-1-2017-1-ES-SPO-SC. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain, and the United Kingdom in 2018 and in 2022.
description: With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP
DOI: 10.6084/m9.figshare.20039144
title: Dataset 1

description: Types of environmental documents held by participant hotels and pumping test status.
DOI: 10.6084/m9.figshare.20039282
title: Dataset 2

description: Samples of hotel water sources and data
DOI: 10.57745/y3jltv/cggyz9
title: Rocco_and_al_SupplementarySCRIPTS.Rmd

description: :unav
DOI: 10.57745/y3jltv/sfthwo
title: Rocco_and_al_BDD_TBZ__TPs_molFiles.zip

description: :unav
DOI: 10.57745/y3jltv/dw0xdp
title: Rocco_and_al_BDD_TBZ_TPs.sdf

description: :unav
DOI: 10.57745/y3jltv/g9igum
title: Rocco_and_al_BDD_TBZ_spreadsheet.csv

description: :unav
DOI: 10.5281/zenodo.6959274
title: TS minimum database

description: Minimum dataset of TS data in CNB, Panama
DOI: 10.5281/zenodo.6959273
title: TS minimum database

description: Minimum dataset of TS data in CNB, Panama
DOI: 10.5281/zenodo.6959275
title: Gesport_Women on sports boards in NSFs of five countries: Italy, Portugal, Spain, Turkey and the United Kingdom

description: This database has been built by the authors with the support of the Erasmus+ programme of the European Union under Grant number 590521-EPP-1-2017-1-ES-SPO-SC. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain, and the United Kingdom in 2018 and in 2022.
description: With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP
DOI: 10.5281/zenodo.6942795
title: Gesport_Women on sports boards in NSFs of five countries: Italy, Portugal, Spain, Turkey and the United Kingdom

description: This database has been built by the authors with the support of the Erasmus+ programme of the European Union under Grant number 590521-EPP-1-2017-1-ES-SPO-SC. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain, and the United Kingdom in 2018 and in 2022.
description: With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP
DOI: 10.6084/m9.figshare.20422551
title: dummy test

description: dummy test
DOI: 10.6084/m9.figshare.20422551.v2
title: dummy test

description: dummy test
DOI: 10.6084/m9.figshare.20422551.v1
title: so-git experiment

description: So-Git experiment.
DOI: 10.6084/m9.figshare.20425836
title: CI and NH data.xlsx

description: Data contains demographics and results of online testing of cochlear implant users and normal hearing controls.
DOI: 10.6084/m9.figshare.14372058
title: Antimony and arsenic contamination in water from antimonite mineralization: a case study from Turhal (Tokat, Northern Turkey)

description: The sources and fate of antimony (Sb) and arsenic (As) in water resources around antimonite (a.k.a. stibnite) deposits is of great concern due to their adverse effects on the environment and human health. The aim of this study was to determine geochemical compositions of gangue/ore material and water samples collected in and around the antimonite deposits located near Turhal (Tokat, Northern Turkey). Turhal antimonite deposits, recognized as the largest in Turkey, are hosted in pre-Upper Jurassic low-grade metamorphic rocks where the antimonite ore is mainly composed of stibnite and quartz. Quartz is also the dominant gangue mineral, with minor amounts of carbonates. The ore samples contain high concentrations of Sb (27.98 − 60.83 wt.% as Sb<sub>2</sub>O<sub>3</sub>) and As (3453 − 5010 ppm). The gangue material contains significant amounts of As (529 ppm), Cr (664 ppm), and Ti (256 ppm). Water samples collected from the study area are characterized by slightly alkaline pH (6.48–8.40) and low Eh (–46 to –5 mV) with electrical conductivity values ranging between 571 and 1607 µS cm<sup>−1</sup>. There is a significant trace element contamination in the aquifer system, especially in water samples collected from the areas hydrologically downgradient of antimonite deposits, where concentrations (in µg L<sup>−1</sup>) reach up to 271.6 for Sb, 429.4 for As, and 2203 for B. There is a high risk of toxicity for the livestock, as well as the human population consuming their products, since Sb and As contaminated springs in the region are used for watering farm animals.
DOI: 10.6084/m9.figshare.14372058.v1
title: Antimony and arsenic contamination in water from antimonite mineralization: a case study from Turhal (Tokat, Northern Turkey)

description: The sources and fate of antimony (Sb) and arsenic (As) in water resources around antimonite (a.k.a. stibnite) deposits is of great concern due to their adverse effects on the environment and human health. The aim of this study was to determine geochemical compositions of gangue/ore material and water samples collected in and around the antimonite deposits located near Turhal (Tokat, Northern Turkey). Turhal antimonite deposits, recognized as the largest in Turkey, are hosted in pre-Upper Jurassic low-grade metamorphic rocks where the antimonite ore is mainly composed of stibnite and quartz. Quartz is also the dominant gangue mineral, with minor amounts of carbonates. The ore samples contain high concentrations of Sb (27.98 − 60.83 wt.% as Sb<sub>2</sub>O<sub>3</sub>) and As (3453 − 5010 ppm). The gangue material contains significant amounts of As (529 ppm), Cr (664 ppm), and Ti (256 ppm). Water samples collected from the study area are characterized by slightly alkaline pH (6.48–8.40) and low Eh (–46 to –5 mV) with electrical conductivity values ranging between 571 and 1607 µS cm<sup>−1</sup>. There is a significant trace element contamination in the aquifer system, especially in water samples collected from the areas hydrologically downgradient of antimonite deposits, where concentrations (in µg L<sup>−1</sup>) reach up to 271.6 for Sb, 429.4 for As, and 2203 for B. There is a high risk of toxicity for the livestock, as well as the human population consuming their products, since Sb and As contaminated springs in the region are used for watering farm animals.
DOI: 10.5287/bodleian:kekqbgg7y
title: Greek in Contact project