cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14329092.v1
title: Participation Measures for Infants and Toddlers Aged Birth to 23 Months: A Systematic Review

description: Participation is a key health outcome; however little is known specifically about how to measure participation of infants and toddlers. The aim of this review was to identify and examine the psychometric properties of participation measures for infants and toddlers aged birth to 23 months according to family of Participation-Related Constructs (fPRC). Four electronic databases were searched to identify (i) measures available in English, (ii) that quantify at least one participation concept of ‘attendance’ and/or ‘involvement’ according to the fPRC, (iii) with psychometric data for infants or toddlers aged from birth to 23 months published in a full text, peer reviewed journal. Psychometric quality was examined using the COSMIN checklist. Four measures met the inclusion criteria: Child Engagement in Daily Life (CEDL) measure, Daily Activities of Infants Scale (DAIS), Test of Playfulness (ToP), and Young Children’s Participation and Environment Measure (YC-PEM). All measured ‘attendance’ and three also measured ‘involvement’. Measures showed unknown to moderate validity and unknown to moderate reliability, with the CEDL reporting the strongest psychometric properties. There are few measures that evaluate infant and toddler participation. Further research is needed to develop psychometrically sound participation measures that evaluate ‘attendance’ and ‘involvement’ for this population.
DOI: 10.5281/zenodo.5529238
title: Neural Network Radiation Emulator (KMA/NIMS), March

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712. (March)
DOI: 10.5281/zenodo.5529237
title: Neural Network Radiation Emulator (KMA/NIMS), March

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712. (March)
DOI: 10.5281/zenodo.5529244
title: Neural Network Radiation Emulator (KMA/NIMS), April

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712. (April)
DOI: 10.5281/zenodo.5529243
title: Neural Network Radiation Emulator (KMA/NIMS), April

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712. (April)
DOI: 10.5281/zenodo.5529254
title: Neural Network Radiation Emulator (KMA/NIMS), May

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (May)
DOI: 10.5281/zenodo.5529253
title: Neural Network Radiation Emulator (KMA/NIMS), May

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (May)
DOI: 10.5281/zenodo.5529256
title: Neural Network Radiation Emulator (KMA/NIMS), June

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (June)
DOI: 10.5281/zenodo.5529255
title: Neural Network Radiation Emulator (KMA/NIMS), June

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (June)
DOI: 10.5281/zenodo.5529262
title: Neural Network Radiation Emulator (KMA/NIMS), July

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (July)
DOI: 10.5281/zenodo.5529261
title: Neural Network Radiation Emulator (KMA/NIMS), July

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (July)
DOI: 10.5281/zenodo.5529268
title: Neural Network Radiation Emulator (KMA/NIMS), August

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (August)
DOI: 10.5281/zenodo.5529267
title: Neural Network Radiation Emulator (KMA/NIMS), August

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (August)
DOI: 10.5281/zenodo.5529272
title: Neural Network Radiation Emulator (KMA/NIMS), September

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (September)
DOI: 10.5281/zenodo.5529271
title: Neural Network Radiation Emulator (KMA/NIMS), September

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (September)
DOI: 10.5281/zenodo.5529274
title: Neural Network Radiation Emulator (KMA/NIMS), October

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (February)
DOI: 10.5281/zenodo.5529273
title: Neural Network Radiation Emulator (KMA/NIMS), October

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (February)
DOI: 10.5281/zenodo.5529280
title: Neural Network Radiation Emulator (KMA/NIMS), December

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (December)
DOI: 10.5281/zenodo.5529279
title: Neural Network Radiation Emulator (KMA/NIMS), December

description: The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (December)
DOI: 10.5281/zenodo.5536408
title: A comprehensive dataset for global, regional and national greenhouse gas emissions by sector 1970-2019

description: Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO<sub>2</sub> emissions from fossil fuel combustion and industry (FFI), CH<sub>4</sub> emissions, N<sub>2</sub>O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO<sub>2</sub> emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.
DOI: 10.6084/m9.figshare.8021066
title: Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty

description: Bronchiolitis (inflammation of the lower respiratory tract) in infants is primarily due to viral infection and is the single most common cause of infant hospitalization in the United States. To increase epidemiological understanding of bronchiolitis (and, subsequently, develop better prevention strategies), this research analyzes data on infant bronchiolitis cases from the U.S. Military Health System between the years 2003–2013 in Norfolk, Virginia, USA. For privacy reasons, child home addresses, birth dates, and diagnosis dates were randomized (jittered) creating spatio-temporal uncertainty in the geographic location and timing of bronchiolitis incidents. Using spatio-temporal point patterns, we created a modeling strategy that accounts for the jittering to estimate and quantify the uncertainty for the incidence proportion (IP) of bronchiolitis. Additionally, we regress the IP onto key covariates including pollution where we adequately account for uncertainty in the pollution levels (i.e., covariate uncertainty) using a land use regression model. Our analysis results indicate that the IP is positively associated with sulfur dioxide and population density. Further, we demonstrate how scientific conclusions may change if various sources of uncertainty (either spatio-temporal or covariate uncertainty) are not accounted for. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.6084/m9.figshare.8021066.v3
title: Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty

description: Bronchiolitis (inflammation of the lower respiratory tract) in infants is primarily due to viral infection and is the single most common cause of infant hospitalization in the United States. To increase epidemiological understanding of bronchiolitis (and, subsequently, develop better prevention strategies), this research analyzes data on infant bronchiolitis cases from the U.S. Military Health System between the years 2003–2013 in Norfolk, Virginia, USA. For privacy reasons, child home addresses, birth dates, and diagnosis dates were randomized (jittered) creating spatio-temporal uncertainty in the geographic location and timing of bronchiolitis incidents. Using spatio-temporal point patterns, we created a modeling strategy that accounts for the jittering to estimate and quantify the uncertainty for the incidence proportion (IP) of bronchiolitis. Additionally, we regress the IP onto key covariates including pollution where we adequately account for uncertainty in the pollution levels (i.e., covariate uncertainty) using a land use regression model. Our analysis results indicate that the IP is positively associated with sulfur dioxide and population density. Further, we demonstrate how scientific conclusions may change if various sources of uncertainty (either spatio-temporal or covariate uncertainty) are not accounted for. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.6084/m9.figshare.8023385.v3
title: Demand Models With Random Partitions

description: Many economic models of consumer demand require researchers to partition sets of products or attributes prior to the analysis. These models are common in applied problems when the product space is large or spans multiple categories. While the partition is traditionally fixed a priori, we let the partition be a model parameter and propose a Bayesian method for inference. The challenge is that demand systems are commonly multivariate models that are not conditionally conjugate with respect to partition indices, precluding the use of Gibbs sampling. We solve this problem by constructing a new location-scale partition distribution that can generate random-walk Metropolis–Hastings proposals and also serve as a prior. Our method is illustrated in the context of a store-level category demand model, where we find that allowing for partition uncertainty is important for preserving model flexibility, improving demand forecasts, and learning about the structure of demand. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.8028866
title: Prediction and Inference With Missing Data in Patient Alert Systems

description: We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-intensive care unit patients using ∼100 variables (vitals, lab results, assessments, etc.). There are several missing predictor values for most patients, which in the health sciences is the norm, rather than the exception. A Bayesian approach is presented that addresses many of the shortcomings to standard approaches to missing predictors: (i) treatment of the uncertainty due to imputation is straight-forward in the Bayesian paradigm, (ii) the predictor distribution is flexibly modeled as an infinite normal mixture with latent variables to explicitly account for discrete predictors (i.e., as in multivariate probit regression models), and (iii) certain missing not at random situations can be handled effectively by allowing the indicator of missingness into the predictor distribution only to inform the distribution of the missing variables. The proposed approach also has the benefit of providing a distribution for the prediction, including the uncertainty inherent in the imputation. Therefore, we can ask questions such as: is it possible this individual is at high risk but we are missing too much information to know for sure? How much would we reduce the uncertainty in our risk prediction by obtaining a particular missing value? This approach is applied to the BPR problem resulting in excellent predictive capability to identify deteriorating patients. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.8028866.v4
title: Prediction and Inference With Missing Data in Patient Alert Systems

description: We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-intensive care unit patients using ∼100 variables (vitals, lab results, assessments, etc.). There are several missing predictor values for most patients, which in the health sciences is the norm, rather than the exception. A Bayesian approach is presented that addresses many of the shortcomings to standard approaches to missing predictors: (i) treatment of the uncertainty due to imputation is straight-forward in the Bayesian paradigm, (ii) the predictor distribution is flexibly modeled as an infinite normal mixture with latent variables to explicitly account for discrete predictors (i.e., as in multivariate probit regression models), and (iii) certain missing not at random situations can be handled effectively by allowing the indicator of missingness into the predictor distribution only to inform the distribution of the missing variables. The proposed approach also has the benefit of providing a distribution for the prediction, including the uncertainty inherent in the imputation. Therefore, we can ask questions such as: is it possible this individual is at high risk but we are missing too much information to know for sure? How much would we reduce the uncertainty in our risk prediction by obtaining a particular missing value? This approach is applied to the BPR problem resulting in excellent predictive capability to identify deteriorating patients. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7458809
title: From Distance Correlation to Multiscale Graph Correlation

description: Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age. In this paper, we establish a new framework that generalizes distance correlation (Dcorr)—a correlation measure that was recently proposed and shown to be universally consistent for dependence testing against all joint distributions of finite moments—to the multiscale graph correlation (MGC). By using the characteristic functions and incorporating the nearest neighbor machinery, we formalize the population version of local distance correlations, define the optimal scale in a given dependency, and name the optimal local correlation as MGC. The new theoretical framework motivates a theoretically sound sample MGC and allows a number of desirable properties to be proved, including the universal consistency, convergence, and almost unbiasedness of the sample version. The advantages of MGC are illustrated via a comprehensive set of simulations with linear, nonlinear, univariate, multivariate, and noisy dependencies, where it loses almost no power in monotone dependencies while achieving better performance in general dependencies, compared to Dcorr and other popular methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7458809.v3
title: From Distance Correlation to Multiscale Graph Correlation

description: Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age. In this paper, we establish a new framework that generalizes distance correlation (Dcorr)—a correlation measure that was recently proposed and shown to be universally consistent for dependence testing against all joint distributions of finite moments—to the multiscale graph correlation (MGC). By using the characteristic functions and incorporating the nearest neighbor machinery, we formalize the population version of local distance correlations, define the optimal scale in a given dependency, and name the optimal local correlation as MGC. The new theoretical framework motivates a theoretically sound sample MGC and allows a number of desirable properties to be proved, including the universal consistency, convergence, and almost unbiasedness of the sample version. The advantages of MGC are illustrated via a comprehensive set of simulations with linear, nonlinear, univariate, multivariate, and noisy dependencies, where it loses almost no power in monotone dependencies while achieving better performance in general dependencies, compared to Dcorr and other popular methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7461740.v2
title: A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments

description: We propose a class of subspace ascent methods for computing optimal approximate designs that covers existing algorithms as well as new and more efficient ones. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to that of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality, which also has applications beyond experimental design, such as the construction of the minimum-volume ellipsoid containing a given set of data points. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality, which enable one to use REX and some other algorithms for computing A-optimal and I-optimal designs. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8023382
title: Penalized and Constrained Optimization: An Application to High-Dimensional Website Advertising

description: Firms are increasingly transitioning advertising budgets to Internet display campaigns, but this transition poses new challenges. These campaigns use numerous potential metrics for success (e.g., <i>reach</i> or <i>click rate</i>), and because each website represents a separate advertising opportunity, this is also an inherently high-dimensional problem. Further, advertisers often have constraints they wish to place on their campaign, such as targeting specific sub-populations or websites. These challenges require a method flexible enough to accommodate thousands of websites, as well as numerous metrics and campaign constraints. Motivated by this application, we consider the general constrained high-dimensional problem, where the parameters satisfy linear constraints. We develop the <i>Penalized and Constrained</i> optimization method (PaC) to compute the solution path for high-dimensional, linearly constrained criteria. PaC is extremely general; in addition to internet advertising, we show it encompasses many other potential applications, such as portfolio estimation, monotone curve estimation, and the generalized lasso. Computing the PaC coefficient path poses technical challenges, but we develop an efficient algorithm over a grid of tuning parameters. Through extensive simulations, we show PaC performs well. Finally, we apply PaC to a proprietary dataset in an exemplar Internet advertising case study and demonstrate its superiority over existing methods in this practical setting. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7461740
title: A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments

description: We propose a class of subspace ascent methods for computing optimal approximate designs that covers existing algorithms as well as new and more efficient ones. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to that of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality, which also has applications beyond experimental design, such as the construction of the minimum-volume ellipsoid containing a given set of data points. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality, which enable one to use REX and some other algorithms for computing A-optimal and I-optimal designs. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7461740.v1
title: A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments

description: We propose a class of subspace ascent methods for computing optimal approximate designs that covers existing algorithms as well as new and more efficient ones. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to that of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality, which also has applications beyond experimental design, such as the construction of the minimum-volume ellipsoid containing a given set of data points. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality, which enable one to use REX and some other algorithms for computing A-optimal and I-optimal designs. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8021066.v2
title: Modeling Bronchiolitis Incidence Proportions in the Presence of Spatio-Temporal Uncertainty

description: Bronchiolitis (inflammation of the lower respiratory tract) in infants is primarily due to viral infection and is the single most common cause of infant hospitalization in the United States. To increase epidemiological understanding of bronchiolitis (and, subsequently, develop better prevention strategies), this research analyzes data on infant bronchiolitis cases from the U.S. Military Health System between the years 2003–2013 in Norfolk, Virginia, USA. For privacy reasons, child home addresses, birth dates, and diagnosis dates were randomized (jittered) creating spatio-temporal uncertainty in the geographic location and timing of bronchiolitis incidents. Using spatio-temporal point patterns, we created a modeling strategy that accounts for the jittering to estimate and quantify the uncertainty for the incidence proportion (IP) of bronchiolitis. Additionally, we regress the IP onto key covariates including pollution where we adequately account for uncertainty in the pollution levels (i.e., covariate uncertainty) using a land use regression model. Our analysis results indicate that the IP is positively associated with sulfur dioxide and population density. Further, we demonstrate how scientific conclusions may change if various sources of uncertainty (either spatio-temporal or covariate uncertainty) are not accounted for. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.