cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5787495.v1
title: Modeling Random Effects Using Global-Local Shrinkage Priors in Small Area Estimation

description: Small area estimation is becoming increasingly popular for survey statisticians. One very important program is Small Area Income and Poverty Estimation undertaken by the United States Bureau of the Census, which aims at providing estimates related to income and poverty based on American Community Survey data at the state level and even at lower levels of geography. This article introduces global-local shrinkage priors for random effects in small area estimation to capture wide area level variation when the number of small areas is very large. These priors employ two levels of parameters, global and local parameters, to express variances of area-specific random effects so that both small and large random effects can be captured properly. We show via simulations and data analysis that use of the global-local priors can improve estimation results in most cases.
DOI: 10.6084/m9.figshare.5787492
title: A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models

description: Inference for spatial generalized linear mixed models (SGLMMs) for high-dimensional non-Gaussian spatial data is computationally intensive. The computational challenge is due to the high-dimensional random effects and because Markov chain Monte Carlo (MCMC) algorithms for these models tend to be slow mixing. Moreover, spatial confounding inflates the variance of fixed effect (regression coefficient) estimates. Our approach addresses both the computational and confounding issues by replacing the high-dimensional spatial random effects with a reduced-dimensional representation based on random projections. Standard MCMC algorithms mix well and the reduced-dimensional setting speeds up computations per iteration. We show, via simulated examples, that Bayesian inference for this reduced-dimensional approach works well both in terms of inference as well as prediction; our methods also compare favorably to existing reduced-rank approaches. We also apply our methods to two real world data examples, one on bird count data and the other classifying rock types.
DOI: 10.6084/m9.figshare.5787492.v1
title: A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models

description: Inference for spatial generalized linear mixed models (SGLMMs) for high-dimensional non-Gaussian spatial data is computationally intensive. The computational challenge is due to the high-dimensional random effects and because Markov chain Monte Carlo (MCMC) algorithms for these models tend to be slow mixing. Moreover, spatial confounding inflates the variance of fixed effect (regression coefficient) estimates. Our approach addresses both the computational and confounding issues by replacing the high-dimensional spatial random effects with a reduced-dimensional representation based on random projections. Standard MCMC algorithms mix well and the reduced-dimensional setting speeds up computations per iteration. We show, via simulated examples, that Bayesian inference for this reduced-dimensional approach works well both in terms of inference as well as prediction; our methods also compare favorably to existing reduced-rank approaches. We also apply our methods to two real world data examples, one on bird count data and the other classifying rock types.
DOI: 10.6084/m9.figshare.5787486
title: An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations

description: In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations, and statistical modeling. In this article, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as simplifying assumptions for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplifications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations. 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.5787486.v2
title: An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations

description: In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations, and statistical modeling. In this article, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as simplifying assumptions for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplifications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations. 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.5787486.v1
title: An efficient surrogate model for emulation and physics extraction of large eddy simulations

description: In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. In this paper, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as simplifying assumptions for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplifications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations.
DOI: 10.6084/m9.figshare.5787474
title: FSEM: Functional Structural Equation Models for Twin Functional Data

description: The aim of this article is to develop a novel class of functional structural equation models (FSEMs) for dissecting functional genetic and environmental effects on twin functional data, while characterizing the varying association between functional data and covariates of interest. We propose a three-stage estimation procedure to estimate varying coefficient functions for various covariates (e.g., gender) as well as three covariance operators for the genetic and environmental effects. We develop an inference procedure based on weighted likelihood ratio statistics to test the genetic/environmental effect at either a fixed location or a compact region. We also systematically carry out the theoretical analysis of the estimated varying functions, the weighted likelihood ratio statistics, and the estimated covariance operators. We conduct extensive Monte Carlo simulations to examine the finite-sample performance of the estimation and inference procedures. We apply the proposed FSEM to quantify the degree of genetic and environmental effects on twin white matter tracts obtained from the UNC early brain development study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5787474.v1
title: FSEM: Functional Structural Equation Models for Twin Functional Data

description: The aim of this article is to develop a novel class of functional structural equation models (FSEMs) for dissecting functional genetic and environmental effects on twin functional data, while characterizing the varying association between functional data and covariates of interest. We propose a three-stage estimation procedure to estimate varying coefficient functions for various covariates (e.g., gender) as well as three covariance operators for the genetic and environmental effects. We develop an inference procedure based on weighted likelihood ratio statistics to test the genetic/environmental effect at either a fixed location or a compact region. We also systematically carry out the theoretical analysis of the estimated varying functions, the weighted likelihood ratio statistics, and the estimated covariance operators. We conduct extensive Monte Carlo simulations to examine the finite-sample performance of the estimation and inference procedures. We apply the proposed FSEM to quantify the degree of genetic and environmental effects on twin white matter tracts obtained from the UNC early brain development study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12685937.v2
title: Daily updated dataset of national and global CO2 emissions from fossil fuel and cement production

description: Timely, comprehensive and accurate dataset of CO2 emissions from fossil fuel combustion and cement production are fundamental prerequisites to understanding the global carbon cycle and designing evidenced-based policies for reducing carbon emissions. Here we provided daily CO2 emission data record started from Jan. 1<sup>st</sup> 2019. The dataset uncovered the daily CO2 dynamics through daily, weekly and seasonal variations as well as the change of daily CO2 due to the holiday events and the impact of COVID-19 pandemic.Such near-real-time CO2 dataset would be of great advantage to further monitoring the human activities and to capture the impacts of COVID-19 for long term.
DOI: 10.6084/m9.figshare.5787321
title: Example Figure (Fig. 2)

description: These data contain the information needed to replicate the Methods Figure 2.<br>The .mat file contains two variables, 'dat' and 'hilb', where 'dat.powspctrm' contains the time-frequency information needed to replicate Fig. 2A.<br>'hilb.trial{1}' contains the amplitude envelopes for the alpha-band (8-12 Hz), which is needed in order to replicate the panels showing DFA estimates. <br>
DOI: 10.6084/m9.figshare.5787219
title: Physicochemical analysis and determination of di- and tri-saccharide content in Longan, Litchi and Siam weed honeys of Thailand

description: Honeys produced by bees from the nectars of longan, litchi and Siam weed growing in Thailand were analyzed for physicochemical profiles (moisture, Brix%, soluble solids, density, viscosity, pH, free acid, electrical conductivity, HMF, DN, proline, color, flavonoid and antioxidant activities) and their carbohydrates quantified as monosaccharides, disaccharides and trisaccharides. All physicochemical parameters met the regulation requirements under the Codex Alimentarius Standard, EU Council and directives legislation of the Ministry of Public Health of Thailand, except the HMF value of one sample. The conductivity, color [Abs]<sub>450</sub> and antioxidant activity were parameters that showed that there are significant differences in means between the groups of honey samples based on one way analysis of variance (ANOVA) (<i>p</i> p
DOI: 10.6084/m9.figshare.5787219.v1
title: Physicochemical analysis and determination of di- and tri-saccharide content in Longan, Litchi and Siam weed honeys of Thailand

description: Honeys produced by bees from the nectars of longan, litchi and Siam weed growing in Thailand were analyzed for physicochemical profiles (moisture, Brix%, soluble solids, density, viscosity, pH, free acid, electrical conductivity, HMF, DN, proline, color, flavonoid and antioxidant activities) and their carbohydrates quantified as monosaccharides, disaccharides and trisaccharides. All physicochemical parameters met the regulation requirements under the Codex Alimentarius Standard, EU Council and directives legislation of the Ministry of Public Health of Thailand, except the HMF value of one sample. The conductivity, color [Abs]<sub>450</sub> and antioxidant activity were parameters that showed that there are significant differences in means between the groups of honey samples based on one way analysis of variance (ANOVA) (<i>p</i> p
DOI: 10.6084/m9.figshare.5787195.v1
title: Map of Engabreen and the Svartisen Subglacial Laboratory

description: Map of Engabreen, the Svartisen Subglacial Laboratory and the underground hydropower tunnel network located in Northern Norway, adapted from Lefeuvre et al. (2015). The inset shows the extent of the glacier catchment. Rainwater and meltwater is collected by several intakes at the surface and under the glacier. Discharge measured in the tunnel network at Fonndal and Sediment Chamber stations is used to calculate subglacial discharge collected by the subglacial intakes. The load cells, that measure pressure at the base of the glacier, are installed near the research shaft. The background satellite image is from summer 2009 (source: Statens Kartverk). <br>This figure is a high-resolution of figure 1 published in Lefeuvre P-M, Zwinger T, Jackson M, Gagliardini O, Lappegard G and Hagen JO (2018) Stress Redistribution Explains Anti-correlated Subglacial Pressure Variations. <i>Front. Earth Sci</i>. 5:110. https://doi.org/10.3389/feart.2017.00110Cite this paper if you use the figure<br>
DOI: 10.6084/m9.figshare.5787195.v2
title: Map of Engabreen and the Svartisen Subglacial Laboratory, Northern Norway

description: Map of Engabreen, the Svartisen Subglacial Laboratory and the underground hydropower tunnel network located in Northern Norway, adapted from Lefeuvre et al. (2015). The inset shows the extent of the glacier catchment. Rainwater and meltwater is collected by several intakes at the surface and under the glacier. Discharge measured in the tunnel network at Fonndal and Sediment Chamber stations is used to calculate subglacial discharge collected by the subglacial intakes. The load cells, that measure pressure at the base of the glacier, are installed near the research shaft. The background satellite image is from summer 2009 (source: Statens Kartverk). <br>This figure is a high-resolution of figure 1 published in Lefeuvre P-M, Zwinger T, Jackson M, Gagliardini O, Lappegard G and Hagen JO (2018) Stress Redistribution Explains Anti-correlated Subglacial Pressure Variations. <i>Front. Earth Sci</i>. 5:110. https://doi.org/10.3389/feart.2017.00110Cite this paper if you use the figure<br>
DOI: 10.6084/m9.figshare.5787195.v3
title: Map of Engabreen and the Svartisen Subglacial Laboratory, Northern Norway. Lefeuvre et al., 2018, Frontiers.

description: Map of Engabreen, the Svartisen Subglacial Laboratory and the underground hydropower tunnel network located in Northern Norway, adapted from Lefeuvre et al. (2015). The inset shows the extent of the glacier catchment. Rainwater and meltwater is collected by several intakes at the surface and under the glacier. Discharge measured in the tunnel network at Fonndal and Sediment Chamber stations is used to calculate subglacial discharge collected by the subglacial intakes. The load cells, that measure pressure at the base of the glacier, are installed near the research shaft. The background satellite image is from summer 2009 (source: Statens Kartverk). <br>This figure is a high-resolution of figure 1 published in Lefeuvre P-M, Zwinger T, Jackson M, Gagliardini O, Lappegard G and Hagen JO (2018) Stress Redistribution Explains Anti-correlated Subglacial Pressure Variations. <i>Front. Earth Sci</i>. 5:110. https://doi.org/10.3389/feart.2017.00110Cite this paper if you use the figure<br>
DOI: 10.6084/m9.figshare.5787180.v2
title: Morte enceflica Conceitos essenciais, diagnóstico e atualização - Brain death: Essentials concepts, diagnosis and updates

description: Medical Review article in portuguese about Brain Death<br>ABSTRACT<br>Brain death represents the irreversible medical condition in which the brain functions (telencephalon and diencephalons) and the brainstem are hopelessly compromised. Three prerequisites are necessary to define it, that is: irreversible coma with known cause; absence of hypothermia, hypotension or severe metabolic disorder; exclusion of exogenous intoxication, or psychotropic drugs effect. It is based on the concomitant presence of coma unresponsive to external stimuli, absence of brain stem reflexes, and the presence of apnea. The diagnosis is established after two clinical tests with an interval of at least six hours, carried out by two different professionals not linked to the transplant team. It is mandatory to prove, through complementary exams, the absence of central nervous system perfusion, or electric activity or metabolism. Brain death means death both legally and scientifically. It is necessary for all health care professionals, especially the physician, to be familiar with the concept of brain death, so that the application of life support technology is beneficial, individually and socially committed, not only to promote inadequate intervention, extension of suffering and familiar distress and futile and artificial prolongation of life<br><br>RESUMO<br>A morte encefálica representa o estado clínico irreversível em que as funções cerebrais (telencéfalo e diencéfalo) e do tronco encefálico estão irremediavelmente comprometidas. São necessários três pré-requisitos para defini-la: coma com causa conhecida e irreversível; ausência de hipotermia, hipotensão ou distúrbio metabólico grave; exclusão de intoxicação exógena ou efeito de medicamentos psicotrópicos. Baseia-se na presença concomitante de coma sem resposta ao estímulo externo, inexistência de reflexos do tronco encefálico e apneia. O diagnóstico é estabelecido após dois exames clínicos, com intervalo de no mínimo seis horas entre eles, realizados por profissionais diferentes e não vinculados à equipe de transplantes. É obrigatória a comprovação, por intermédio de exames complementares, de ausência no sistema nervoso central de perfusão ou atividade elétrica ou metabolismo. Morte encefálica significa morte tanto legal quanto cientificamente. É necessário que todo profissional de saúde, especialmente o médico, esteja familiarizado com o conceito de morte encefálica, para que a aplicação da tecnologia na sustentação da vida seja benéfica, individual e socialmente comprometida, e não apenas promovedora de intervenção inadequada, extensão do sofrimento e angústia familiar e prolongamento inútil e artificial da vida.<br><br>
DOI: 10.6084/m9.figshare.5787171
title: NGS reads of sunflower line HA89

description: Semi-raw NGS data (trimmed reads) of three sunflower isonuclear lines: HA89 fertile line and two CMS lines - HA89 (PET1) and HA89 (PET2). There are also some raw data of Sanger sequencing.<br>
DOI: 10.6084/m9.figshare.5787171.v1
title: NGS reads of sunflower line HA89

description: Semi-raw NGS data (trimmed reads) of three sunflower isonuclear lines: HA89 fertile line and two CMS lines - HA89 (PET1) and HA89 (PET2). There are also some raw data of Sanger sequencing.<br>
DOI: 10.6084/m9.figshare.5787156
title: F6037-0164 Inhibition Assay

description: IC50 determination on KAT-II for F6037-7280
DOI: 10.6084/m9.figshare.5787156.v1
title: F6037-0164 Inhibition Assay

description: IC50 determination on KAT-II for F6037-7280
DOI: 10.6084/m9.figshare.5787153
title: F0037-7280 Inhibition Assay

description: IC50 determination on KAT-II for F0037-7280
DOI: 10.6084/m9.figshare.5787153.v1
title: F0037-7280 Inhibition Assay

description: IC50 determination on KAT-II for F0037-7280
DOI: 10.6084/m9.figshare.5787021
title: Transitive Inference in Derived Lists (Order Scrambled) Among Human Participants

description: Data from a TI experiment in which adjacent pairs were trained for five 5-item lists. At test, new "derived" lists were test, consisting of one item from each training list, each maintaining its original rank.<br>SubjectID: Participant ID in order run.Trial: Trial within session.Phase: (1) First Training, (2) Second Training, (3) TestingPhaseType: (1) First Training, (2) Second Training, (3) TD=0, (4) TD=3, (5) TD=5, (6) TD=7, (7) TD=10Subtrial: Trial within phase and list.S1: Left stimulus IDS2: Right stimulus IDDist: Symbolic distanceReactionTime: RT in miliseconds.Correct: Whether the stimulus with the lower rank was selected.<br>
DOI: 10.6084/m9.figshare.5787021.v1
title: Transitive Inference in Derived Lists (Order Scrambled) Among Human Participants

description: Data from a TI experiment in which adjacent pairs were trained for five 5-item lists. At test, new "derived" lists were test, consisting of one item from each training list, each maintaining its original rank.<br>SubjectID: Participant ID in order run.Trial: Trial within session.Phase: (1) First Training, (2) Second Training, (3) TestingPhaseType: (1) First Training, (2) Second Training, (3) TD=0, (4) TD=3, (5) TD=5, (6) TD=7, (7) TD=10Subtrial: Trial within phase and list.S1: Left stimulus IDS2: Right stimulus IDDist: Symbolic distanceReactionTime: RT in miliseconds.Correct: Whether the stimulus with the lower rank was selected.<br>
DOI: 10.6084/m9.figshare.5787015
title: Transitive Inference in Derived Lists (Order Maintained) Among Human Participants

description: Data from a TI experiment in which adjacent pairs were trained for five 5-item lists. At test, new "derived" lists were test, consisting of one item from each training list, each maintaining its original rank.<br>SubjectID: Participant ID in order run.Trial: Trial within session.Phase: (1) First Training, (2) Second Training, (3) TestingSubtrial: Trial within phase and list.S1: Left stimulus IDS2: Right stimulus IDDist: Symbolic distanceReactionTime: RT in miliseconds.Correct: Whether the stimulus with the lower rank was selected.<br>
DOI: 10.6084/m9.figshare.5787015.v1
title: Transitive Inference in Derived Lists (Order Maintained) Among Human Participants

description: Data from a TI experiment in which adjacent pairs were trained for five 5-item lists. At test, new "derived" lists were test, consisting of one item from each training list, each maintaining its original rank.<br>SubjectID: Participant ID in order run.Trial: Trial within session.Phase: (1) First Training, (2) Second Training, (3) TestingSubtrial: Trial within phase and list.S1: Left stimulus IDS2: Right stimulus IDDist: Symbolic distanceReactionTime: RT in miliseconds.Correct: Whether the stimulus with the lower rank was selected.<br>