cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14669487
title: Sawyer Mill Dam Removal Project Vegetation Area Drone Flight Paths

description: These are the automated drone flight paths for the vegetation area of the Evans et al. Sawyer Mill dam removal study in Dover, New Hampshire, USA. These flight paths are specific to the reservoir response manuscript. A DJI Phantom 3 Professional drone with its original RGB camera equipped with a polarizing filter was used for the study.<br>All flight paths are available as csv files that were exported out of the Litchi mission hub. The pdf file contains screenshots of the paths in DJIFlightPlanner and Litchi. For information on the Litchi flight app, please see their website here: https://flylitchi.com/. The same set of flight paths were flown in both 2019 and 2020 to keep imagery collection consistent. Nadir flight paths had 80% side image overlap and 90% forward image overlap set in DJIFlightPlanner. The imagery angle for the angled flight paths was set to 20 degrees off nadir (-70 gimbal pitch in Litchi), and the paths of the angled flights were manually drawn in Litchi to supplement the nadir flight paths that were designed in DJIFlightPlanner.<br>For the vegetation area, the altitude of the drone was set to 150 feet above ground level. The angled flight paths had to be traced and executed manually due to an error in Litchi (“waypoint distance too close”). This was not fixed for the 2020 flight date, so the angled flights were manually flown in 2020, as well.<br>These materials were made using resources from an NSF EPSCoR funded project “RII Track-2 FEC: Strengthening the scientific basis for decision-making about dams: Multi-scale, coupled-systems research on ecological, social, and economic trade-offs” (a.k.a. "Future of Dams"). Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
DOI: 10.6084/m9.figshare.14672178
title: scz2021

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.34619/3w3t-hj8s
title: OntoAndalus

description: OntoAndalus is an ontology about pottery artefacts of al-Andalus, a relevant topic in Islamic archaeological studies in Spain and Portugal. The purpose of OntoAndalus is to further knowledge in the domain and to allow for the development of a multilingual terminological resource in the Semantic Web, based on formal definitions or descriptions of concepts and other units of knowledge in the domain.\r\nOntoAndalus is being developed in OWL and is presently aligned with the DOLCE+DnS Ultralite top-level ontology.
description: Almeida, Bruno (2019). OntoAndalus [Dataset]. CLUNL, Faculdade de Ciências Sociais e Humanas, Universidade Nova de Lisboa.
DOI: 10.18743/data.00130
title: Interview with Bairbre McDonagh recorded in Leeds on 25.06.2019

description: Bairbre was born in Dublin in 1971 and grew up in Limerick, the second of four siblings. She describes how she was compared to her older sister and how both at school and at home she was made to feel not clever enough. Outside school she learned music and was good at sport. She reflects on family life and the role of her mother as homemaker. After her Leaving Certificate exams she attended Dublin Institute of Technology and then completed her studies in London. She describes how she was keen to leave Limerick but also enjoyed meeting up with friends on her return and going clubbing in Limerick. While studying in London she made some good friends and met her husband. Her first holiday in Ireland with him prompted an awkward conversation about sex with her mother which she tried found embarrassing. Once her course finished she started working in London and eventually recruited her boyfriend (now husband) to the same company.
description: Tá Úna Gan A Gúna Irish Women’s Digital History Project Interviews with a range of women born in Ireland during the twentieth century or those with Irish connections. The aim was to gather the stories, memories and opinions of Irish women born in each decade of the twentieth century, about their experiences between the approximate ages of 15 and 25. The sample was found simply through women with whom volunteer interviewers had a connection or through Irish organisations. Interviews were recorded and participants checked and approved the transcripts and recordings. See website https://unaganaguna.org/
DOI: 10.6084/m9.figshare.14672187
title: sud2018-alc

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.34619/ctho-dqqk
title: MIGRANTE.PT
title: MIGRANTE.PT

description: Resulting from the project EXPRIMI, MIGRANTE.PT is an European Portuguese corpus for specific purposes with around 1,5 million tokens, of institutional texts concerning the integration of migrants in Portugal and directed to these migrants, collected from sites and materials freely available online.\r\nIt comprises different types of texts from different sources. Those include the two most relevant migrant and refugees managing and integration Portuguese institutions – ACM Portugal: The High Commissariat for Migrations, CPR: Portuguese Refugees Council –, governmental and non-governmental institutions such as Caritas, ERS: National Entity for Health Regulation, OM: Migrations Observatory; SEF: Foreign and Boarders Services; MAI: Ministry of Internal Administration; Social Security; AR: Portuguese Parliament and several municipalities; as well as media texts from several sources, collected by these organizations according to their informative and/or promotional character.
description: Resulting from the project EXPRIMI, MIGRANTE.PT is an European Portuguese corpus for specific purposes with around 1,5 million tokens, of institutional texts concerning the integration of migrants in Portugal and directed to these migrants, collected from sites and materials freely available online.\r\nIt comprises different types of texts from different sources. Those include the two most relevant migrant and refugees managing and integration Portuguese institutions – ACM Portugal: The High Commissariat for Migrations, CPR: Portuguese Refugees Council –, governmental and non-governmental institutions such as Caritas, ERS: National Entity for Health Regulation, OM: Migrations Observatory; SEF: Foreign and Boarders Services; MAI: Ministry of Internal Administration; Social Security; AR: Portuguese Parliament and several municipalities; as well as media texts from several sources, collected by these organizations according to their informative and/or promotional character.
description: Amaro, Raquel; Correia, Susana; Gonçalves, Matilde (2021). MIGRANTE.PT [Dataset]. CLUNL, Faculdade de Ciências Sociais e Humanas, Universidade Nova de Lisboa. 
description: Amaro, Raquel; Correia, Susana; Gonçalves, Matilde (2021). MIGRANTE.PT [Dataset]. CLUNL, Faculdade de Ciências Sociais e Humanas, Universidade Nova de Lisboa.
DOI: 10.6084/m9.figshare.7874177.v2
title: Smoothing With Couplings of Conditional Particle Filters

description: In state–space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly informative observations, and for a realistic Lotka–Volterra model with an intractable transition density. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7869365.v2
title: Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models

description: We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combine the ensemble Kalman filter and smoother, developed in the geophysics literature, with state-space algorithms from the statistics literature. Our algorithms address a variety of estimation scenarios, including online and off-line state and parameter estimation. We take a Bayesian perspective, for which the goal is to generate samples from the joint posterior distribution of states and parameters. The key benefit of our approach is the use of ensemble Kalman methods for dimension reduction, which allows inference for high-dimensional state vectors. We compare our methods to existing ones, including ensemble Kalman filters, particle filters, and particle MCMC. Using a real data example of cloud motion and data simulated under a number of nonlinear and non-Gaussian scenarios, we show that our approaches outperform these existing methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7874159.v1
title: Non-negative Matrix Factorization via Archetypal Analysis

description: Given a collection of data points, non-negative matrix factorization (NMF) suggests expressing them as convex combinations of a small set of ‘archetypes’ with non-negative entries. This decomposition is unique only if the true archetypes are non-negative and sufficiently sparse (or the weights are sufficiently sparse), a regime that is captured by the separability condition and its generalizations. In this paper, we study an approach to NMF that can be traced back to the work of Cutler and Breiman [18] and does not require the data to be separable, while providing a generally unique decomposition. We optimize a trade-off between two objectives: we minimize the distance of the data points from the convex envelope of the archetypes (which can be interpreted as an empirical risk), while also minimizing the distance of the archetypes from the convex envelope of the data (which can be interpreted as a data-dependent regularization). The archetypal analysis method of [18] is recovered as the limiting case in which the last term is given infinite weight. We introduce a ‘uniqueness condition’ on the data which is necessary for identifiability. We prove that, under uniqueness (plus additional regularity conditions on the geometry of the archetypes), our estimator is robust. While our approach requires solving a non-convex optimization problem, we find that standard optimization methods succeed in finding good solutions both for real and synthetic data.
DOI: 10.6084/m9.figshare.8069495.v2
title: A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis

description: Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the classical general linear model (GLM), a “massive univariate” approach. Here, we propose a spatial Bayesian GLM for cs-fMRI, which employs a class of sophisticated spatial processes to model latent activation fields. We make several advances compared with existing spatial Bayesian models for volumetric fMRI. First, we use integrated nested Laplacian approximations, a highly accurate and efficient Bayesian computation technique, rather than variational Bayes. To identify regions of activation, we utilize an excursions set method based on the joint posterior distribution of the latent fields, rather than the marginal distribution at each location. Finally, we propose the first multi-subject spatial Bayesian modeling approach, which addresses a major gap in the existing literature. The methods are very computationally advantageous and are validated through simulation studies and two task fMRI studies from the Human Connectome Project. 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.7881014.v1
title: Individualized Multilayer Tensor Learning with An Application in Imaging Analysis

description: This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles (TMVs) are randomly distributed with heterogeneous patterns. This imposes a significant challenge for conventional imaging regression and dimension reduction models assuming a homogeneous feature structure. We develop an innovative multilayer tensor learning method to incorporate heterogeneity to a higher-order tensor decomposition and predict disease status effectively through utilizing subject-wise imaging features and multimodality information. Specifically, we construct a multilayer decomposition which leverages an individualized imaging layer in addition to a modality-specific tensor structure. One major advantage of our approach is that we are able to efficiently capture the heterogeneous spatial features of signals that are not characterized by a population structure as well as integrating multimodality information simultaneously. To achieve scalable computing, we develop a new bi-level block improvement algorithm. In theory, we investigate both the algorithm convergence property, tensor signal recovery error bound and asymptotic consistency for prediction model estimation. We also apply the proposed method for simulated and human breast cancer imaging data. Numerical results demonstrate that the proposed method outperforms other existing competing methods.
DOI: 10.6084/m9.figshare.7819544.v1
title: Category-Adaptive Variable Screening for Ultra-high Dimensional Heterogeneous Categorical Data

description: The populations of interest in modern studies are very often heterogeneous. The population heterogeneity, the qualitative nature of the outcome variable and the high dimensionality of the predictors pose significant challenge in statistical analysis. In this article, we introduce a category-adaptive screening procedure with high-dimensional heterogeneous data, which is to detect category-specific important covariates. The proposal is a model-free approach without any specification of a regression model and an adaptive procedure in the sense that the set of active variables is allowed to vary across different categories, thus making it more flexible to accommodate heterogeneity. For response-selective sampling data, another main discovery of this paper is that the proposed method works directly without any modification. Under mild regularity conditions, the newly procedure is shown to possess the sure screening and ranking consistency properties. Simulation studies contain supportive evidence that the proposed method performs well under various settings and it is effective to extract category-specific information. Applications are illustrated with two real data sets.
DOI: 10.6084/m9.figshare.8069315.v2
title: A Large-Scale Constrained Joint Modeling Approach for Predicting User Activity, Engagement, and Churn With Application to Freemium Mobile Games

description: We develop a <i>constrained extremely zero inflated joint</i> (CEZIJ) modeling framework for simultaneously analyzing player activity, engagement, and dropouts (churns) in app-based mobile freemium games. Our proposed framework addresses the complex interdependencies between a player’s decision to use a freemium product, the extent of her direct and indirect engagement with the product and her decision to permanently drop its usage. CEZIJ extends the existing class of joint models for longitudinal and survival data in several ways. It not only accommodates extremely zero-inflated responses in a joint model setting but also incorporates domain-specific, convex structural constraints on the model parameters. Longitudinal data from app-based mobile games usually exhibit a large set of potential predictors and choosing the relevant set of predictors is highly desirable for various purposes including improved predictability. To achieve this goal, CEZIJ conducts simultaneous, coordinated selection of fixed and random effects in high-dimensional penalized generalized linear mixed models. For analyzing such large-scale datasets, variable selection and estimation are conducted via a distributed computing based split-and-conquer approach that massively increases scalability and provides better predictive performance over competing predictive methods. Our results reveal codependencies between varied player characteristics that promote player activity and engagement. Furthermore, the predicted churn probabilities exhibit idiosyncratic clusters of player profiles over time based on which marketers and game managers can segment the playing population for improved monetization of app-based freemium games. 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.7895756.v1
title: On Prediction Properties of Kriging: Uniform Error Bounds and Robustness

description: Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simple. However, in many applications one would like to predict for a range of untried points simultaneously. In this work, we obtain some error bounds for the simple and universal kriging predictor under the uniform metric. It works for a scattered set of input points in an arbitrary dimension, and also covers the case where the covariance function of the Gaussian process is misspecified. These results lead to a better understanding of the rate of convergence of kriging under the Gaussian or the Matérn correlation functions, the relationship between space-filling designs and kriging models, and the robustness of the Matérn correlation functions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7869362.v1
title: Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments

description: The Gaussian process is a standard tool for building emulators for both deterministic and stochastic computer experiments. However, application of Gaussian process models is greatly limited in practice, particularly for large-scale and many-input computer experiments that have become typical. We propose a multi-resolution functional ANOVA model as a computationally feasible emulation alternative. More generally, this model can be used for large-scale and many-input non-linear regression problems. An overlapping group lasso approach is used for estimation, ensuring computational feasibility in a large-scale and many-input setting. New results on consistency and inference for the (potentially overlapping) group lasso in a high-dimensional setting are developed and applied to the proposed multi-resolution functional ANOVA model. Importantly, these results allow us to quantify the uncertainty in our predictions. Numerical examples demonstrate that the proposed model enjoys marked computational advantages. Data capabilities, both in terms of sample size and dimension, meet or exceed best available emulation tools while meeting or exceeding emulation accuracy.
DOI: 10.6084/m9.figshare.7999331.v2
title: A Generalized Gaussian Process Model for Computer Experiments With Binary Time Series

description: Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor as well as its predictive distribution are constructed. Their performance is examined via two simulation studies. The methodology is applied to study computer simulations for cell adhesion experiments. The fitted model reveals important biological information in repeated cell bindings, which is not directly observable in lab experiments. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8145704.v2
title: Bayesian Optimal Design for Ordinary Differential Equation Models With Application in Biological Science

description: Bayesian optimal design is considered for experiments where the response distribution depends on the solution to a system of nonlinear ordinary differential equations. The motivation is an experiment to estimate parameters in the equations governing the transport of amino acids through cell membranes in human placentas. Decision-theoretic Bayesian design of experiments for such nonlinear models is conceptually very attractive, allowing the formal incorporation of prior knowledge to overcome the parameter dependence of frequentist design and being less reliant on asymptotic approximations. However, the necessary approximation and maximization of the, typically analytically intractable, expected utility results in a computationally challenging problem. These issues are further exacerbated if the solution to the differential equations is not available in closed-form. This article proposes a new combination of a probabilistic solution to the equations embedded within a Monte Carlo approximation to the expected utility with cyclic descent of a smooth approximation to find the optimal design. A novel precomputation algorithm reduces the computational burden, making the search for an optimal design feasible for bigger problems. The methods are demonstrated by finding new designs for a number of common models derived from differential equations, and by providing optimal designs for the placenta experiment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.