cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/kytjbk47mx.1
title: Pharos ceramic LS001

description: Examination of Pharos ceramic sample by thin section, SEM-EDS and pXRF.
DOI: 10.6084/m9.figshare.14588394
title: Bayesian Hierarchical Model for Change Point Detection in Multivariate Sequences

description: Motivated by the wind turbine anomaly detection, we propose a Bayesian hierarchical model (BHM) for the mean-change detection in multivariate sequences. By combining the exchange random order distribution induced from the Poisson–Dirichlet process and nonlocal priors, BHM exhibits satisfactory performance for mean-shift detection with multivariate sequences under different error distributions. In particular, BHM yields the smallest detection error compared with other competitive methods considered in the article. We use a local scan procedure to accelerate the computation, while the anomaly locations are determined by maximizing the posterior probability through dynamic programming. We establish consistency of the estimated number and locations of the change points and conduct extensive simulations to evaluate the BHM approach. Among the popular change point detection algorithms, BHM yields the best performance for most of the datasets in terms of the F1 score for the wind turbine anomaly detection.
DOI: 10.6084/m9.figshare.14588394.v1
title: Bayesian Hierarchical Model for Change Point Detection in Multivariate Sequences

description: Motivated by the wind turbine anomaly detection, we propose a Bayesian hierarchical model (BHM) for the mean-change detection in multivariate sequences. By combining the exchange random order distribution induced from the Poisson–Dirichlet process and nonlocal priors, BHM exhibits satisfactory performance for mean-shift detection with multivariate sequences under different error distributions. In particular, BHM yields the smallest detection error compared with other competitive methods considered in the article. We use a local scan procedure to accelerate the computation, while the anomaly locations are determined by maximizing the posterior probability through dynamic programming. We establish consistency of the estimated number and locations of the change points and conduct extensive simulations to evaluate the BHM approach. Among the popular change point detection algorithms, BHM yields the best performance for most of the datasets in terms of the F1 score for the wind turbine anomaly detection.
DOI: 10.6084/m9.figshare.14696562
title: Sequential Design of Multi-Fidelity Computer Experiments: Maximizing the Rate of Stepwise Uncertainty Reduction

description: This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study. Accurate simulations usually entail a high computational effort, while coarse simulations are obtained at a lower cost. The cost can be measured, for example, by the run time of the simulator or the financial cost of the computing resources. In this setting, simulation results obtained at several levels of fidelity can be combined in order to estimate quantities of interest (the optimal value of the output, the probability that the output exceeds a given threshold, etc.) in an efficient manner. We propose a new Bayesian sequential strategy called maximal rate of stepwise uncertainty reduction (MR-SUR), that selects additional simulations to be performed by maximizing the ratio between the expected reduction of uncertainty and the cost of simulation. This generic strategy unifies several existing methods, and provides a principled approach to develop new ones. We assess its performance on several examples, including a computationally intensive problem of fire safety analysis where the quantity of interest is the probability of exceeding a tenability threshold during a building fire.
DOI: 10.6084/m9.figshare.14696562.v2
title: Sequential Design of Multi-Fidelity Computer Experiments: Maximizing the Rate of Stepwise Uncertainty Reduction

description: This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study. Accurate simulations usually entail a high computational effort, while coarse simulations are obtained at a lower cost. The cost can be measured, for example, by the run time of the simulator or the financial cost of the computing resources. In this setting, simulation results obtained at several levels of fidelity can be combined in order to estimate quantities of interest (the optimal value of the output, the probability that the output exceeds a given threshold, etc.) in an efficient manner. We propose a new Bayesian sequential strategy called maximal rate of stepwise uncertainty reduction (MR-SUR), that selects additional simulations to be performed by maximizing the ratio between the expected reduction of uncertainty and the cost of simulation. This generic strategy unifies several existing methods, and provides a principled approach to develop new ones. We assess its performance on several examples, including a computationally intensive problem of fire safety analysis where the quantity of interest is the probability of exceeding a tenability threshold during a building fire.
DOI: 10.6084/m9.figshare.14939291
title: Bayesian Dynamic Feature Partitioning in High-Dimensional Regression With Big Data

description: Bayesian computation of high-dimensional linear regression models using Markov chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive since these methods perform costly computations at each iteration of the sampling chain. Furthermore, this computational cost cannot usually be efficiently divided across a parallel architecture. These problems are aggravated if the data size is large or data arrive sequentially over time (streaming or online settings). This article proposes a novel dynamic feature partitioned regression (DFP) for efficient online inference for high-dimensional linear regressions with large or streaming data. DFP constructs a <i>pseudo posterior density</i> of the parameters at every time point, and quickly updates the pseudo posterior when a new block of data (data shard) arrives. DFP updates the pseudo posterior at every time point suitably and partitions the set of parameters to exploit parallelization for efficient posterior computation. The proposed approach is applied to high-dimensional linear regression models with Gaussian scale mixture priors and spike-and-slab priors on large parameter spaces, along with large data, and is found to yield state-of-the-art inferential performance. The algorithm enjoys theoretical support with pseudoposterior densities over time being arbitrarily close to the full posterior as the data size grows, as shown in the supplementary material. Supplementary material also contains details of the DFP algorithm applied to different priors. Package to implement DFP is available in https://github.com/Rene-Gutierrez/DynParRegReg. The dataset is available in https://github.com/Rene-Gutierrez/DynParRegReg\_Implementation.
DOI: 10.6084/m9.figshare.14696562.v1
title: Sequential design of multi-fidelity computer experiments: maximizing the rate of stepwise uncertainty reduction

description: This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study. Accurate simulations usually entail a high computational effort, while coarse simulations are obtained at a lower cost. The cost can be measured, e.g., by the run time of the simulator or the financial cost of the computing resources. In this setting, simulation results obtained at several levels of fidelity can be combined in order to estimate quantities of interest (the optimal value of the output, the probability that the output exceeds a given threshold…) in an efficient manner. We propose a new Bayesian sequential strategy called Maximal Rate of Stepwise Uncertainty Reduction (MR-SUR), that selects additional simulations to be performed by maximizing the ratio between the expected reduction of uncertainty and the cost of simulation. This generic strategy unifies several existing methods, and provides a principled approach to develop new ones. We assess its performance on several examples, including a computationally intensive problem of fire safety analysis where the quantity of interest is the probability of exceeding a tenability threshold during a building fire.
DOI: 10.6084/m9.figshare.14823587
title: PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models

description: Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. It is common to carry out Bayesian inference for these models via Markov chain Monte Carlo (MCMC). Each iteration of the MCMC algorithm is computationally expensive due to costly matrix operations. In addition, the MCMC algorithm needs to be run for more iterations because the strong cross-correlations among the spatial latent variables result in slow mixing Markov chains. To address these computational challenges, we propose a projection-based intrinsic conditional autoregression (PICAR) approach, which is a discretized and dimension-reduced representation of the underlying spatial random field using empirical basis functions on a triangular mesh. Our approach exhibits fast mixing as well as a considerable reduction in computational cost per iteration. PICAR is computationally efficient and scales well to high dimensions. It is also automated and easy to implement for a wide array of user-specified hierarchical spatial models. We show, via simulation studies, that our approach performs well in terms of parameter inference and prediction. We provide several examples to illustrate the applicability of our method, including (i) a high-dimensional cloud cover dataset that showcases its computational efficiency, (ii) a spatially varying coefficient model that demonstrates the ease of implementation of PICAR in the probabilistic programming languages stan and nimble, and (iii) a watershed survey example that illustrates how PICAR applies to models that are not amenable to efficient inference via existing methods.
DOI: 10.6084/m9.figshare.14823587.v1
title: PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models

description: Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. It is common to carry out Bayesian inference for these models via Markov chain Monte Carlo (MCMC). Each iteration of the MCMC algorithm is computationally expensive due to costly matrix operations. In addition, the MCMC algorithm needs to be run for more iterations because the strong cross-correlations among the spatial latent variables result in slow mixing Markov chains. To address these computational challenges, we propose a projection-based intrinsic conditional autoregression (PICAR) approach, which is a discretized and dimension-reduced representation of the underlying spatial random field using empirical basis functions on a triangular mesh. Our approach exhibits fast mixing as well as a considerable reduction in computational cost per iteration. PICAR is computationally efficient and scales well to high dimensions. It is also automated and easy to implement for a wide array of user-specified hierarchical spatial models. We show, via simulation studies, that our approach performs well in terms of parameter inference and prediction. We provide several examples to illustrate the applicability of our method, including (i) a high-dimensional cloud cover dataset that showcases its computational efficiency, (ii) a spatially varying coefficient model that demonstrates the ease of implementation of PICAR in the probabilistic programming languages stan and nimble, and (iii) a watershed survey example that illustrates how PICAR applies to models that are not amenable to efficient inference via existing methods.
DOI: 10.6084/m9.figshare.14588391.v1
title: Class maps for visualizing classification results

description: Classification is a major tool of statistics and machine learning. A classification method first processes a training set of objects with given classes (labels), with the goal of afterward assigning new objects to one of these classes. When running the resulting prediction method on the training data or on test data, it can happen that an object is predicted to lie in a class that differs from its given label. This is sometimes called label bias, and raises the question whether the object was mislabeled. The proposed class map reflects the probability that an object belongs to an alternative class, how far it is from the other objects in its given class, and whether some objects lie far from all classes. The goal is to visualize aspects of the classification results to obtain insight in the data. The display is constructed for discriminant analysis, the k-nearest neighbor classifier, support vector machines, logistic regression, and coupling pairwise classifications. It is illustrated on several benchmark datasets, including some about images and texts.
DOI: 10.6084/m9.figshare.14939291.v1
title: Bayesian Dynamic Feature Partitioning in High-Dimensional Regression with Big Data

description: Bayesian computation of high dimensional linear regression models using Markov Chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive since these methods perform costly computations at each iteration of the sampling chain. Furthermore, this computational cost cannot usually be efficiently divided across a parallel architecture. These problems are aggravated if the data size is large or data arrive sequentially over time (streaming or online settings). This article proposes a novel dynamic feature partitioned regression (DFP) for efficient online inference for high dimensional linear regressions with large or streaming data. DFP constructs a <i>pseudo posterior density</i> of the parameters at every time point, and quickly updates the pseudo posterior when a new block of data (data shard) arrives. DFP updates the pseudo posterior at every time point suitably and partitions the set of parameters to exploit parallelization for efficient posterior computation. The proposed approach is applied to high dimensional linear regression models with Gaussian scale mixture priors and spike and slab priors on large parameter spaces, along with large data, and is found to yield state-of-the-art inferential performance. The algorithm enjoys theoretical support with pseudo posterior densities over time being arbitrarily close to the full posterior as the data size grows, as shown in the supplementary material. Supplementary material also contains details of the DFP algorithm applied to different priors. Package to implement DFP is available in https://github.com/Rene-Gutierrez/DynParRegReg. The dataset is available in https://github.com/Rene-Gutierrez/DynParRegReg_Implementation.
DOI: 10.17632/86xc5zgn59.1
title: Issues and Perspectives of Critical Thinking Assessment in Children:  Computerized Performance-based Approach

description: The data that support the findings of the study "Issues and Perspectives of Critical Thinking Assessment in Children:  Computerized Performance-based Approach".
The data file includes two sheets. The first sheet contains the data. The second sheet is the codebook. 
Missing variables were marked NA.
The dataset includes:
Indicators. 14 indicators for the AI and 10 indicators for the MI. 
Log data. Twenty-nine variables that extracted from log data.
Gender
Math score
Language literacy score
Level of the AI and the MI based on the BN analysis results
Class membership based on the results of LCA
DOI: 10.17632/848nfnn2m5.1
title: Data availability - Influence of aluminium powder aging on Directed Energy deposition

description: This is the supplementary material related to the paper "Influence of aluminium powder aging on Directed Energy deposition" published in Materials & Design (2022). It contains all of the raw data used for the different figures.
DOI: 10.17632/smd3886dmj.1
title: Cuantificación de superficie de las áreas hospitalarias del HIGA "San Roque"

description: Se expone en un archivo con extensión ".dwg" la cuantificación de superficie construida de las distintas áreas hospitalarias que posee el Hospital Interzonal General de Agudos "San Roque". Las áreas hospitalarias se conforman por los siguientes grupos: internación, atención ambulatoria, diagnóstico y tratamiento, servicios auxiliares y de apoyo, cirugía, circulaciones y baños, y administración.
DOI: 10.3886/icpsr08627.v1
title: National Medical Care Expenditure Survey, 1977: Health Insurance/Employer Survey Data
title: Version 1

description: The purpose of the National Medical Care Expenditure Survey (NMCES) was to assess the cost and extent of health insurance in the United States. Information on health insurance coverage was obtained in a household survey, then verified and supplemented with information from the Health Insurance/Employer Survey (HIES). Insurance companies, employers, unions, and other organizations identified in the household survey as sources of private insurance coverage were asked to verify reported coverage, to provide information on each subscriber's coverage and its cost, the parties responsible for payment of premiums, and the availability of alternative or optional plans. This release includes two distinct datasets. One contains information for each individual respondent in the NMCES component on private health insurance status as verified in the HIES, and includes data on premiums and sources of premium payment. The other contains information for each individual respondent 65 years or older with private insurance as verified in the HIES, as well as information on the services covered by their private insurance benefits.
description: The National Medical Care Expenditure Survey household sample was a national area probability sample of about 14,000 households selected through a four-stage process. Two national area probability samples were drawn independently and the final sampling units were drawn from them.
description: Datasets: DS0: Study-Level Files DS1: Private Insurance Status and Premium Data DS2: Private Insurance Benefit Data for the Population Age 65 and Older
description: The civilian, noninstitutionalized population of the fifty states and the District of Columbia.
description: This is the third set of public use data to be released from the 1977 National Medical Care Expenditure Survey. Two previous data sets (see ICPSR 8325) have provided: (1) person-level data on sociodemographic characteristics, health status, access to care, household-reported health insurance coverage and use, expenditures, and sources of payment for medical and related services, and (2) event-level data on use and related conditions, expenditures, and sources of payment for this population. A fourth dataset, ICPSR 9076, contains data describing the private insurance benefits of each privately insured person under 65 in the sample. Data collection was done by Research Triangle Institute, North Carolina, and its subcontractors, National Opinion Research Center of the University of Chicago, and Abt Associates, Inc., of Cambridge, Massachusetts.
DOI: 10.17632/cn2z2wh8dd.1
title: Image processing Python

description: The background context for this systematic review is image processing in Python through the two more known libraries in literature, OpenCV and Scikit-image.
DOI: 10.5281/zenodo.5751294
title: "Agricultural trade and its impacts on cropland use and the global loss of species habitat." - Supplementary data

description: This dataset and code is part of the following publication:<br> Schwarzmueller, F. & Kastner, T (2022), Agricultural trade and its impact on cropland use<br> and the global loss of species' habitats. Sustainability Science, doi: 10.1007/s11625-022-01138-7<br> There are three zip-folders accompanying this publication: Code.zip contains all the R-Scripts and input files neccessary for the calculation that were written by the authors. Data.zip contains the FAO-input data (as dowloaded in 2021). This exact data is not available anymore from the FAOSTAT website, which is why we included it in this repository. TradeMatrixFeed_import_dry_matter_1986-2013.zip contains the results from the calculation as shown in the paper.
DOI: 10.5281/zenodo.5751293
title: "Agricultural trade and its impacts on cropland use and the global loss of species habitat." - Supplementary data

description: This dataset and code is part of the following publication:<br> Schwarzmueller, F. & Kastner, T (2022), Agricultural trade and its impact on cropland use<br> and the global loss of species' habitats. Sustainability Science, doi: 10.1007/s11625-022-01138-7<br> There are three zip-folders accompanying this publication: Code.zip contains all the R-Scripts and input files neccessary for the calculation that were written by the authors. Data.zip contains the FAO-input data (as dowloaded in 2021). This exact data is not available anymore from the FAOSTAT website, which is why we included it in this repository. TradeMatrixFeed_import_dry_matter_1986-2013.zip contains the results from the calculation as shown in the paper.
DOI: 10.6084/m9.figshare.19632060
title: Tracking the contribution of inductive bias to individualized internal models -- experimental data

description: Code for inference for internal models can be found at https://github.com/mzperix/asrt-beamsampling <br>
DOI: 10.6084/m9.figshare.19632060.v1
title: Tracking the contribution of inductive bias to individualized internal models -- experimental data

description: Code for inference for internal models can be found at https://github.com/mzperix/asrt-beamsampling <br>
DOI: 10.3886/icpsr08629.v2
title: National Health Interview Surveys, 1969-1981: Cumulative Core File
title: Version 2

description: The basic purpose of the Health Interview Survey is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kind of health services people receive. The Health Interview Survey, 1969-1981 is a cumulative core dataset. Criteria for selection were the following: respondent's age greater than or equal to 30 at the time of the interview, variables of specific substantive interest, information on a given variable being available for at least 8 years, variables selected only once (though parallel entries exist on the input files that were merged), health conditions that were defined as chronic by NHIS and caused or contributed to limitation of activity.
description: Multi-stage probability sample
description: Datasets: DS1: National Health Interview Surveys, 1969-1981: Cumulative Core File
description: Civilian, noninstitutionalized population of the United States and the District of Columbia from 1,900 geographically defined Primary Sampling Units (PSU).
description: The data contain dashes (-). Sample age distribution for the 734,741 respondents was: 55-64 (N=137,605), 65-74 (N=98,518), 75+ (N=49,068).