cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5787693.v2
title: COMRADES Crowdsourced Informativeness Dataset (CCSID)

description: Crowdsourced annotations for informativeness of messages in crisis response. Developed for training an automatic classifier, as part of the EC COMRADES project.
DOI: 10.6084/m9.figshare.5787693.v1
title: COMRADES Crowdsourced Informativeness Datased (CCSID)

description: Crowdsourced annotations for informativeness of messages in crisis response. Developed for training an automatic classifier, as part of the EC COMRADES project.
DOI: 10.6084/m9.figshare.12616766.v1
title: Supplementary Tables

description: Supplementary Tables
DOI: 10.6084/m9.figshare.5787615.v1
title: Dendritic polyglycerol nanoparticles show charge dependent bio-distribution in early human placental explants and reduce hCG secretion

description: A thorough understanding of nanoparticle bio-distribution at the feto-maternal interface will be a prerequisite for their diagnostic or therapeutic application in women of childbearing age and for teratologic risk assessment. Therefore, the tissue interaction of biocompatible dendritic polyglycerol nanoparticles (dPG-NPs) with first- trimester human placental explants were analyzed and compared to less sophisticated trophoblast-cell based models. First-trimester human placental explants, BeWo cells and primary trophoblast cells from human term placenta were exposed to fluorescence labeled, ∼5 nm dPG-NPs, with differently charged surfaces, at concentrations of 1 µM and 10 nM, for 6 and 24 h. Accumulation of dPGs was visualized by fluorescence microscopy. To assess the impact of dPG-NP on trophoblast integrity and endocrine function, LDH, and hCG releases were measured. A dose- and charge-dependent accumulation of dPG-NPs was observed at the early placental barrier and in cell lines, with positive dPG-NP-surface causing deposits even in the mesenchymal core of the placental villi. No signs of plasma membrane damage could be detected. After 24 h we observed a significant reduction of hCG secretion in placental explants, without significant changes in trophoblast apoptosis, at low concentrations of charged dPG-NPs. In conclusion, dPG-NP’s surface charge substantially influences their bio-distribution at the feto-maternal interface, with positive charge facilitating trans-trophoblast passage, and in contrast to more artificial models, the first-trimester placental explant culture model reveals potentially hazardous influences of charged dPG-NPs on early placental physiology.
DOI: 10.6084/m9.figshare.5787612
title: Physical properties and seismic-reflection interpretation of bathyal marine sediments affected by carbonate and silica diagenesis in the Tasman Sea

description: Diagenesis of calcareous and siliceous sediments in northern Zealandia produces characteristic seismic reflections that are a valuable tool for constraining paleoenvironmental and tectonic history. We correlate Deep Sea Drilling Program (DSDP) borehole lithology changes with seismic reflectivity to characterise distinct diagenetic horizons. An increase in reflectivity in calcareous Paleocene to Miocene age sediments marks the depth that ooze alters to chalk. Predominantly occurring at c. 300 m below the seafloor on the Lord Howe Rise, variations are observed in the New Caledonia Trough, and in the Eocene in the Southern Lord Howe Rise due to changes in sediment composition. Paleocene–Eocene silica-rich sediments undergoing diagenesis produced distinct negative–positive polarity paired reflections from unaltered and altered sediments, respectively. Sediments undergoing diagenesis range in age from Paleocene to Miocene, indicating that overburden thickness and temperature are the main controlling factors for the onset of diagenesis, whereas the diagenetic potential of sediments is controlled by composition and burial conditions.
DOI: 10.6084/m9.figshare.5787612.v1
title: Physical properties and seismic-reflection interpretation of bathyal marine sediments affected by carbonate and silica diagenesis in the Tasman Sea

description: Diagenesis of calcareous and siliceous sediments in northern Zealandia produces characteristic seismic reflections that are a valuable tool for constraining paleoenvironmental and tectonic history. We correlate Deep Sea Drilling Program (DSDP) borehole lithology changes with seismic reflectivity to characterise distinct diagenetic horizons. An increase in reflectivity in calcareous Paleocene to Miocene age sediments marks the depth that ooze alters to chalk. Predominantly occurring at c. 300 m below the seafloor on the Lord Howe Rise, variations are observed in the New Caledonia Trough, and in the Eocene in the Southern Lord Howe Rise due to changes in sediment composition. Paleocene–Eocene silica-rich sediments undergoing diagenesis produced distinct negative–positive polarity paired reflections from unaltered and altered sediments, respectively. Sediments undergoing diagenesis range in age from Paleocene to Miocene, indicating that overburden thickness and temperature are the main controlling factors for the onset of diagenesis, whereas the diagenetic potential of sediments is controlled by composition and burial conditions.
DOI: 10.6084/m9.figshare.5787558
title: Who Wants to be a Statistician? An Analysis of ACT-Tested Public School Students

description: This study examines predictors of statistics as occupation choice while in high school. The overall rate of choosing statistics was 1 per 1,681 students. Females, Asian students, students from the southern United States, and students from rural schools were less likely to choose statistics, and there was an increase in statistics choice rates between 2014 and 2017. Differences across other socio-demographic groups were small after accounting for other predictors. The strongest predictors of statistics choice were ACT Mathematics score and a measure of vocational interests corresponding to Holland's Conventional personality type. The results of the study can be used to identify high school students with interest and achievement profiles that are common among prospective statisticians, and to gain a better understanding of factors that affect statistics occupation choice. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5787558.v2
title: Who Wants to be a Statistician? An Analysis of ACT-Tested Public School Students

description: This study examines predictors of statistics as occupation choice while in high school. The overall rate of choosing statistics was 1 per 1,681 students. Females, Asian students, students from the southern United States, and students from rural schools were less likely to choose statistics, and there was an increase in statistics choice rates between 2014 and 2017. Differences across other socio-demographic groups were small after accounting for other predictors. The strongest predictors of statistics choice were ACT Mathematics score and a measure of vocational interests corresponding to Holland's Conventional personality type. The results of the study can be used to identify high school students with interest and achievement profiles that are common among prospective statisticians, and to gain a better understanding of factors that affect statistics occupation choice. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5787558.v1
title: Who wants to be a statistician? An analysis of ACT-tested public school students

description: This study examines predictors of statistics as occupation choice while in high school. The overall rate of choosing statistics was 1 per 1,681 students. Females, Asian students, students from the southern United States, and students from rural schools were less likely to choose statistics, and there was an increase in statistics choice rates between 2014 and 2017. Differences across other socio-demographic groups were small after accounting for other predictors. The strongest predictors of statistics choice were ACT Mathematics score and a measure of vocational interests corresponding to Holland's Conventional personality type. The results of the study can be used to identify high school students with interest and achievement profiles that are common among prospective statisticians, and to gain a better understanding of factors that affect statistics occupation choice.
DOI: 10.6084/m9.figshare.5787555.v1
title: Modeling spatial processes with unknown extremal dependence class

description: Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as it can have a large impact upon extrapolation, i.e., the estimated probabilities of events more extreme than those observed. In this work, we present a single spatial model that is able to capture both dependence classes in a parsimonious manner, and with a smooth transition between the two cases. The model covers a wide range of possibilities from asymptotic independence through to complete dependence, and permits weakening dependence of extremes even under asymptotic dependence. Censored likelihood-based inference for the implied copula is feasible in moderate dimensions due to closed-form margins. The model is applied to oceanographic datasets with ambiguous true limiting dependence structure.
DOI: 10.6084/m9.figshare.5787555.v4
title: Modeling Spatial Processes with Unknown Extremal Dependence Class

description: Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as it can have a large impact upon extrapolation, that is, the estimated probabilities of events more extreme than those observed. In this work, we present a single spatial model that is able to capture both dependence classes in a parsimonious manner, and with a smooth transition between the two cases. The model covers a wide range of possibilities from asymptotic independence through to complete dependence, and permits weakening dependence of extremes even under asymptotic dependence. Censored likelihood-based inference for the implied copula is feasible in moderate dimensions due to closed-form margins. The model is applied to oceanographic datasets with ambiguous true limiting dependence structure. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5787555.v3
title: Modeling Spatial Processes with Unknown Extremal Dependence Class

description: Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as it can have a large impact upon extrapolation, that is, the estimated probabilities of events more extreme than those observed. In this work, we present a single spatial model that is able to capture both dependence classes in a parsimonious manner, and with a smooth transition between the two cases. The model covers a wide range of possibilities from asymptotic independence through to complete dependence, and permits weakening dependence of extremes even under asymptotic dependence. Censored likelihood-based inference for the implied copula is feasible in moderate dimensions due to closed-form margins. The model is applied to oceanographic datasets with ambiguous true limiting dependence structure. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5787552.v1
title: Power and Sample Size for Fixed-Effects Inference in Reversible Linear Mixed Models

description: Despite the popularity of the general linear mixed model for data analysis, power and sample size methods and software are not generally available for commonly used test statistics and reference distributions. Statisticians resort to simulations with homegrown and uncertified programs or rough approximations which are misaligned with the data analysis. For a wide range of designs with longitudinal and clustering features, we provide accurate power and sample size approximations for inference about fixed effects in linear models we call reversible. We show that under widely applicable conditions, the general linear mixed-model Wald test has non-central distributions equivalent to well-studied multivariate tests. In turn, exact and approximate power and sample size results for the multivariate Hotelling-Lawley test provide exact and approximate power and sample size results for the mixed-model Wald test. The calculations are easily computed with a free, open-source product that requires only a web browser to use. Commercial software can be used for a smaller range of reversible models. Simple approximations allow accounting for modest amounts of missing data. A real-world example illustrates the methods. Sample size results are presented for a multicenter study on pregnancy. The proposed study, an extension of a funded project, has clustering within clinic. Exchangeability among participants allows averaging across them to remove the clustering structure. The resulting simplified design is a single level longitudinal study. Multivariate methods for power provide an approximate sample size. All proofs and inputs for the example are in the Supplementary Materials (available online).
DOI: 10.6084/m9.figshare.5787540
title: On the Long-Run Volatility of Stocks

description: In this article, we investigate whether or not the volatility per period of stocks is lower over longer horizons. Taking the perspective of an investor, we evaluate the predictive variance of <i>k</i>-period returns under different model and prior specifications. We adopt the state-space framework of Pástor and Stambaugh to model the dynamics of expected returns and evaluate the effects of prior elicitation in the resulting volatility estimates. Part of the developments includes an extension that incorporates time-varying volatilities and covariances in a constrained prior information set-up. Our conclusion for the U.S. market, under plausible prior specifications, is that stocks are less volatile in the long run. Model assessment exercises demonstrate the models and priors supporting our main conclusions are in accordance with the data. To assess the generality of the results, we extend our analysis to a number of international equity indices. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5787540.v2
title: On the Long-Run Volatility of Stocks

description: In this article, we investigate whether or not the volatility per period of stocks is lower over longer horizons. Taking the perspective of an investor, we evaluate the predictive variance of <i>k</i>-period returns under different model and prior specifications. We adopt the state-space framework of Pástor and Stambaugh to model the dynamics of expected returns and evaluate the effects of prior elicitation in the resulting volatility estimates. Part of the developments includes an extension that incorporates time-varying volatilities and covariances in a constrained prior information set-up. Our conclusion for the U.S. market, under plausible prior specifications, is that stocks are less volatile in the long run. Model assessment exercises demonstrate the models and priors supporting our main conclusions are in accordance with the data. To assess the generality of the results, we extend our analysis to a number of international equity indices. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5787540.v1
title: On the Long Run Volatility of Stocks

description: In this paper we investigate whether or not the volatility per period of stocks is lower over longer horizons. Taking the perspective of an investor, we evaluate the predictive variance of <i>k</i>-period returns under different model and prior specifications. We adopt the state space framework of Pástor and Stambaugh (2012) to model the dynamics of expected returns and evaluate the effects of prior elicitation in the resulting volatility estimates. Part of the developments includes an extension that incorporates time-varying volatilities and covariances in a constrained prior information set up. Our conclusion for the U.S. market, under plausible prior specifications, is that stocks are less volatile in the long run. Model assessment exercises demonstrate the models and priors supporting our main conclusions are in accordance with the data. To assess the generality of the results, we extend our analysis to a number of international equity indices.
DOI: 10.6084/m9.figshare.5787537
title: Minimum Mean Squared Error Estimation of the Radius of Gyration in Small-Angle X-Ray Scattering Experiments

description: Small-angle X-ray scattering (SAXS) is a technique that yields low-resolution structural information of biological macromolecules by exposing a large ensemble of molecules in solution to a powerful X-ray beam. The beam interacts with the molecules and the intensity of the scattered beam is recorded on a detector plate. The radius of gyration for a molecule, which is a measure of the spread of its mass, can be estimated from the lowest scattering angles of SAXS data. This estimation method requires specification of a window of scattering angles. Under a local polynomial model with autoregressive errors, we develop methodology and supporting asymptotic theory for selection of an optimal window, minimum mean square error estimation of the radius of gyration, and estimation of its variance. Simulation studies confirm the quality of our asymptotic approximations and the superior performance of the proposed methodology relative to the accepted standard. Our semi-automated methodology makes it feasible to estimate the radius of gyration many times, from replicated SAXS data under various experimental conditions, in an objective and reproducible manner. This in turn allows for secondary analyses of the dataset of estimates, as we demonstrate with a split–split plot analysis for 357 SAXS intensity curves. Supplementary materials for this article are available online.
DOI: 10.25377/sussex.5787534
title: Data for research article "Neonicotinoids Thiamethoxam and Clothianidin Adversely Affect the Colonisation of Freshwater Microcosms"

description: Raw data from counting the colonisation of invertebrate populations in aquatic microcosms. Each column is total number of that invertebrate in that microscosm.<br>Effects of both thiamethoxam and clothianidin were investigated.<b><br></b><b>Abstract from article</b><br>Surface waters are sometimes contaminated with neonicotinoids: a widespread, persistent, systemic class of insecticide with leaching potential. Previous ecotoxicological investigations of this chemical class in aquatic ecosystems have largely focused on the impacts of the neonicotinoid imidacloprid; few empirical, manipulative studies have investigated the effect on invertebrate abundances of two other neonicotinoids which are now more widely used: clothianidin and thiamethoxam. In this study, we employ a simple microcosm semi-field design, incorporating a one-off contamination event, to investigate the effect of these pesticides at field-realistic levels (ranging from 0 to 15 ppb) on invertebrate colonisation and survival in small ephemeral ponds. In line with previous research on neonicotinoid impacts on aquatic invertebrates, significant negative effects of both neonicotinoids were found. There were clear differences between the two chemicals, with thiamethoxam generally producing stronger negative effects than clothianidin. Populations of Chironomids (Diptera) and Ostracoda were negatively affected by both chemicals, while Culicidae appeared to be unaffected by clothianidin at the doses used. Our data demonstrate that field-realistic concentrations of neonicotinoids are likely to reduce populations of invertebrates found in ephemeral ponds, which may have knock on effects up the the food chain.We highlight the importance of developing pesticide monitoring schemes for European surface waters.<br><br><br>
DOI: 10.6084/m9.figshare.5787528
title: Linear Non-Gaussian Component Analysis Via Maximum Likelihood

description: Independent component analysis (ICA) is popular in many applications, including cognitive neuroscience and signal processing. Due to computational constraints, principal component analysis (PCA) is used for dimension reduction prior to ICA (PCA+ICA), which could remove important information. The problem is that interesting independent components (ICs) could be mixed in several principal components that are discarded and then these ICs cannot be recovered. We formulate a linear non-Gaussian component model with Gaussian noise components. To estimate the model parameters, we propose likelihood component analysis (LCA), in which dimension reduction and latent variable estimation are achieved simultaneously. Our method orders components by their marginal likelihood rather than ordering components by variance as in PCA. We present a parametric LCA using the logistic density and a semiparametric LCA using tilted Gaussians with cubic B-splines. Our algorithm is scalable to datasets common in applications (e.g., hundreds of thousands of observations across hundreds of variables with dozens of latent components). In simulations, latent components are recovered that are discarded by PCA+ICA methods. We apply our method to multivariate data and demonstrate that LCA is a useful data visualization and dimension reduction tool that reveals features not apparent from PCA or PCA+ICA. We also apply our method to a functional magnetic resonance imaging experiment from the Human Connectome Project and identify artifacts missed by PCA+ICA. We present theoretical results on identifiability of the linear non-Gaussian component model and consistency of LCA. Supplementary materials for this article are available online.