cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7639697.v1
title: Inferring Atmospheric Release Characteristics in a Large Computer Experiment using Bayesian Adaptive Splines

description: An atmospheric release of hazardous material, whether accidental or intentional, can be catastrophic for those in the path of the plume. Predicting the path of a plume based on characteristics of the release (location, amount and duration) and meteorological conditions is an active research area highly relevant for emergency and long-term response to these releases. As a result, researchers have developed particle dispersion simulators to provide plume path predictions that incorporate release characteristics and meteorological conditions. However, since release characteristics and meteorological conditions are often unknown, the inverse problem is of great interest, that is, based on all the observations of the plume so far, what can be inferred about the release characteristics? This is the question we seek to answer using plume observations from a controlled release at the Diablo Canyon Nuclear Power Plant in Central California. With access to a large number of evaluations of a computationally expensive particle dispersion simulator that includes continuous and categorical inputs and spatio-temporal output, building a fast statistical surrogate model (or emulator) presents many statistical challenges, but is an essential tool for inverse modeling and sensitivity analysis. We achieve accurate emulation using Bayesian adaptive splines to model weights on empirical orthogonal functions. We use this emulator as well as appropriately identifiable simulator discrepancy and observational error models to calibrate the simulator, thus finding a posterior distribution of characteristics of the release. Since the release was controlled, these characteristics are known, making it possible to compare our findings to the truth.
DOI: 10.6084/m9.figshare.7757150.v1
title: Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions

description: In studying structural inter-connections in the human brain, it is common to first estimate fiber bundles connecting different regions relying on diffusion MRI. These fiber bundles act as highways for neural activity. Current statistical methods reduce the rich information into an adjacency matrix, with the elements containing a count of fibers or a mean diffusion feature along the fibers. The goal of this article is to avoid discarding the rich geometric information of fibers, developing flexible models for characterizing the population distribution of fibers between brain regions of interest within and across different individuals. We start by decomposing each fiber into a rotation matrix, shape and translation from a global reference curve. These components are viewed as data lying on a product space composed of different Euclidean spaces and manifolds. To nonparametrically model the distribution within and across individuals, we rely on a hierarchical mixture of product kernels specific to the component spaces. Taking a Bayesian approach to inference, we develop efficient methods for posterior sampling. The approach automatically produces clusters of fibers within and across individuals. Applying the method to Human Connectome Project data, we find interesting relationships between brain fiber geometry and reading ability. 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.7433033.v1
title: Robust alternatives to ANCOVA for estimating the treatment effect via a randomized comparative study

description: In comparing two treatments via a randomized clinical trial, the analysis of covariance technique is often utilized to estimate an overall treatment effect. The ANCOVA is generally perceived as a more efficient procedure than its simple two sample estimation counterpart. Unfortunately when the ANCOVA model is nonlinear, the resulting estimator is generally not consistent. Recently, various nonparametric alternatives to the ANCOVA, such as the augmentation methods, have been proposed to estimate the treatment effect by adjusting the covariates. However, the properties of these alternatives have not been studied in the presence of treatment allocation imbalance. In this paper, we take a different approach to explore how to improve the precision of the naive two-sample estimate even when the observed distributions of baseline covariates between two groups are dissimilar. Specifically, we derive a bias-adjusted estimation procedure constructed from a conditional inference principle via relevant ancillary statistics from the observed covariates. This estimator is shown to be asymptotically equivalent to an augmentation estimator under the unconditional setting. We utilize the data from a clinical trial for evaluating a combination treatment of cardiovascular diseases to illustrate our findings.
DOI: 10.6084/m9.figshare.7056401.v2
title: Semiparametric Regression Using Variational Approximations

description: Semiparametric regression offers a flexible framework for modeling nonlinear relationships between a response and covariates. A prime example are generalized additive models (GAMs) where splines (say) are used to approximate nonlinear functional components in conjunction with a quadratic penalty to control for overfitting. Estimation and inference are then generally performed based on the penalized likelihood, or under a mixed model framework. The penalized likelihood framework is fast but potentially unstable, and choosing the smoothing parameters needs to be done externally using cross-validation, for instance. The mixed model framework tends to be more stable and offers a natural way for choosing the smoothing parameters, but for nonnormal responses involves an intractable integral. In this article, we introduce a new framework for semiparametric regression based on variational approximations (VA). The approach possesses the stability and natural inference tools of the mixed model framework, while achieving computation times comparable to using penalized likelihood. Focusing on GAMs, we derive fully tractable variational likelihoods for some common response types. We present several features of the VA framework for inference, including a variational information matrix for inference on parametric components, and a closed-form update for estimating the smoothing parameter. We demonstrate the consistency of the VA estimates, and an asymptotic normality result for the parametric component of the model. Simulation studies show the VA framework performs similarly to and sometimes better than currently available software for fitting GAMs. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7780640.v2
title: Scaled von Mises–Fisher Distributions and Regression Models for Paleomagnetic Directional Data

description: We propose a new distribution for analyzing paleomagnetic directional data, that is, a novel transformation of the von Mises–Fisher distribution. The new distribution has ellipse-like symmetry, as does the Kent distribution; however, unlike the Kent distribution the normalizing constant in the new density is easy to compute and estimation of the shape parameters is straightforward. To accommodate outliers, the model also incorporates an additional shape parameter, which controls the tail-weight of the distribution. We also develop a general regression model framework that allows both the mean direction and the shape parameters of the error distribution to depend on covariates. The proposed regression procedure is shown to be equivariant with respect to the choice of coordinate system for the directional response. To illustrate, we analyses paleomagnetic directional data from the GEOMAGIA50.v3 database. We predict the mean direction at various geological time points and show that there is significant heteroscedasticity present. It is envisaged that the regression structures and error distribution proposed here will also prove useful when covariate information is available with (i) other types of directional response data; and (ii) square-root transformed compositional data of general dimension. Supplementary materials for this article are available online. Code submitted with this article was checked by an Associate Editor for Reproducibility and is available as an online supplement.
DOI: 10.15468/93hpzn
title: A review of the mud-dauber wasps of genus Sceliphron Klug (Hymenoptera Sphecidae) from India

description: This dataset contains the digitized treatments in Plazi based on the original journal article Anagha, S., Kumar, P. Girish, Binoy, C., Mazumdar, P. C., Sureshan, P. M. (2021): A review of the mud-dauber wasps of genus Sceliphron Klug (Hymenoptera Sphecidae) from India. Zootaxa 4969 (1): 61-85, DOI: https://doi.org/10.11646/zootaxa.4969.1.3
DOI: 10.5281/zenodo.4812960
title: Long document similarity dataset, Wikipedia excerptions for wine collections

description: Wine-related articles extracted from Wikipedia. For all articles, the figures and tables have been filtered out, as well as the categories and "see also" sections. The article structure, and particularly the sub-titles and paragraphs are kept in these datasets <strong>Wines</strong> Wikipedia wines dataset consists of 1635 articles from the wine domain. The extracted dataset consists of a non-trivial mixture of articles, including different wine categories, brands, wineries, grape types, and more. The ground-truth recommendations were crafted by a human sommelier, which annotated 92 source articles with ~10 ground-truth recommendations for each sample. Examples for ground-truth expert-based recommendations are Dom Pérignon - Moët & Chandon Pinot Meunier - Chardonnay
DOI: 10.5281/zenodo.4812959
title: Long document similarity dataset, Wikipedia excerptions for wine collections

description: Wine-related articles extracted from Wikipedia. For all articles, the figures and tables have been filtered out, as well as the categories and "see also" sections. The article structure, and particularly the sub-titles and paragraphs are kept in these datasets <strong>Wines</strong> Wikipedia wines dataset consists of 1635 articles from the wine domain. The extracted dataset consists of a non-trivial mixture of articles, including different wine categories, brands, wineries, grape types, and more. The ground-truth recommendations were crafted by a human sommelier, which annotated 92 source articles with ~10 ground-truth recommendations for each sample. Examples for ground-truth expert-based recommendations are Dom Pérignon - Moët & Chandon Pinot Meunier - Chardonnay
DOI: 10.6084/m9.figshare.14672103
title: ocd2018

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.5281/zenodo.4792685
title: Heat stress responses of transposon insertion lines

description: The image dataset from lines carrying transposable element insertions and their response to heat stress. The images were taken using the Raspberry Pi camera. The plants were sown in the soil on 28th of December 2020, and exposed to increasing heat stress (ramp from 24C to 36C over 2h, followed by 36C for 6h) on 20th of January 2021. The pictures contain a time stamp - where first three characters represent month and day of the month (e.g. 126 - 26th of January), the following two characters represent hour of the day (e.g. 13 - is 1 PM), the following two characters represent minutes, and the last two characters represent the seconds. The images are taken every 30 minutes, and images taken during the lights-off period (from 10:00 PM to 7:00 AM) have been discarded from the current dataset.
DOI: 10.5281/zenodo.4792684
title: Heat stress responses of transposon insertion lines

description: The image dataset from lines carrying transposable element insertions and their response to heat stress. The images were taken using the Raspberry Pi camera. The plants were sown in the soil on 28th of December 2020, and exposed to increasing heat stress (ramp from 24C to 36C over 2h, followed by 36C for 6h) on 20th of January 2021. The pictures contain a time stamp - where first three characters represent month and day of the month (e.g. 126 - 26th of January), the following two characters represent hour of the day (e.g. 13 - is 1 PM), the following two characters represent minutes, and the last two characters represent the seconds. The images are taken every 30 minutes, and images taken during the lights-off period (from 10:00 PM to 7:00 AM) have been discarded from the current dataset.
DOI: 10.6084/m9.figshare.14672106
title: ptsd2018

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.5281/zenodo.4788227
title: Replication package for: Roman Transport Network Connectivity and Economic Integration

description: The package contains data and code to reproduce the results reported in Flückiger, Hornung, Larch, Ludwig, and Mees (forthcoming): "Roman Transport Network Connectivity and Economic Integration", Review of Economic Studies
DOI: 10.5281/zenodo.4749576
title: Replication package for: Roman Transport Network Connectivity and Economic Integration

description: The package contains data and code to reproduce the results reported in Flückiger, Hornung, Larch, Ludwig, and Mees (forthcoming): "Roman Transport Network Connectivity and Economic Integration", Review of Economic Studies
DOI: 10.6084/m9.figshare.14672133
title: ptsd2019

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.6084/m9.figshare.14672151
title: scz2011

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.5281/zenodo.4773516
title: Replication package for: Skill-Biased Structural Change

description: The package contains all the code and files necessary to reproduce the figures, numbers, and tables in Buera, Kaboski, Rogerson, and Vizcaino (forthcoming). "Skill-Biased Structural Change". Review of Economic Studies. Detailed instructions are also given about accessing the raw data.
DOI: 10.5281/zenodo.4750983
title: Replication package for: Skill-Biased Structural Change

description: The package contains all the code and files necessary to reproduce the figures, numbers, and tables in Buera, Kaboski, Rogerson, and Vizcaino (forthcoming). "Skill-Biased Structural Change". Review of Economic Studies. Detailed instructions are also given about accessing the raw data.
DOI: 10.6084/m9.figshare.14672154
title: scz2013sweden

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.5255/ukda-sn-852721
title: Crossing the Mediterranean Sea by Boat: Mapping and Documenting Migratory Journeys and Experiences, 2015-2016

description: Data includes an overview of demographic details of research participants, including the sites and dates of interviews. For ethical reasons, interview transcripts and visual data are excluded from this data set.
DOI: 10.6084/m9.figshare.8021066.v1
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.8028866.v2
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.8023382.v1
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.7458809.v1
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: 10.15468/9gjgbe
title: Myrmecotypus haddadi sp. nov. - a new species of ant resembling sac spider from the Bolivian orocline (Araneae: Corinnidae: Castianeirinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Perger, Robert, Rubio, Gonzalo D. (2021): Myrmecotypus haddadi sp. nov. - a new species of ant resembling sac spider from the Bolivian orocline (Araneae: Corinnidae: Castianeirinae). Zootaxa 4969 (1): 54-60, DOI: https://doi.org/10.11646/zootaxa.4969.1.2