cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1481267.v1
title: Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data

description: Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online.
DOI: 10.6084/m9.figshare.1481266
title: Engineering-Driven Statistical Adjustment and Calibration

description: Engineering model development involves several simplifying assumptions for the purpose of mathematical tractability, which are often not realistic in practice. This leads to discrepancies in the model predictions. A commonly used statistical approach to overcome this problem is to build a statistical model for the discrepancies between the engineering model and observed data. In contrast, an engineering approach would be to find the causes of discrepancy and fix the engineering model using first principles. However, the engineering approach is time consuming, whereas the statistical approach is fast. The drawback of the statistical approach is that it treats the engineering model as a black box and therefore, the statistically adjusted models lack physical interpretability. This article proposes a new framework for model calibration and statistical adjustment. It tries to open up the black box using simple main effects analysis and graphical plots and introduces statistical models inside the engineering model. This approach leads to simpler adjustment models that are physically more interpretable. The approach is illustrated using a model for predicting the cutting forces in a laser-assisted mechanical micro-machining process. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1481262
title: The Uncertainty of Storm Season Changes: Quantifying the Uncertainty of Autocovariance Changepoints

description: In oceanography, there is interest in determining storm season changes for logistical reasons such as equipment maintenance scheduling. In particular, there is interest in capturing the uncertainty associated with these changes in terms of the number and location of them. Such changes are associated with autocovariance changes. This article proposes a framework to quantify the uncertainty of autocovariance changepoints in time series motivated by this oceanographic application. More specifically, the framework considers time series under the locally stationary wavelet (LSW) framework, deriving a joint density for scale processes in the raw wavelet periodogram. By embedding this density within a hidden Markov model (HMM) framework, we consider changepoint characteristics under this multiscale setting. Such a methodology allows us to model changepoints and their uncertainty for a wide range of models, including piecewise second-order stationary processes, for example, piecewise moving average processes.
DOI: 10.6084/m9.figshare.1481265.v1
title: Cook’s Distance Measures for Varying Coefficient Models With Functional Responses

description: The aim of this article is to develop Cook’s distance measures for assessing the influence of both atypical curves and observations under varying coefficient model with functional responses. Our Cook’s distance measures include Cook’s distances for deleting multiple curves and for deleting multiple grid points, and their scaled Cook’s distances. We systematically investigate some theoretical properties of these diagnostic measures. Simulation studies are conducted to evaluate the finite sample properties of these Cook’s distances under different scenarios. A real diffusion tensor tract dataset is analyzed to illustrate the use of our diagnostic measures.
DOI: 10.6084/m9.figshare.1481266.v1
title: Engineering-Driven Statistical Adjustment and Calibration

description: Engineering model development involves several simplifying assumptions for the purpose of mathematical tractability, which are often not realistic in practice. This leads to discrepancies in the model predictions. A commonly used statistical approach to overcome this problem is to build a statistical model for the discrepancies between the engineering model and observed data. In contrast, an engineering approach would be to find the causes of discrepancy and fix the engineering model using first principles. However, the engineering approach is time consuming, whereas the statistical approach is fast. The drawback of the statistical approach is that it treats the engineering model as a black box and therefore, the statistically adjusted models lack physical interpretability. This article proposes a new framework for model calibration and statistical adjustment. It tries to open up the black box using simple main effects analysis and graphical plots and introduces statistical models inside the engineering model. This approach leads to simpler adjustment models that are physically more interpretable. The approach is illustrated using a model for predicting the cutting forces in a laser-assisted mechanical micro-machining process. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1481262.v1
title: The Uncertainty of Storm Season Changes: Quantifying the Uncertainty of Autocovariance Changepoints

description: In oceanography, there is interest in determining storm season changes for logistical reasons such as equipment maintenance scheduling. In particular, there is interest in capturing the uncertainty associated with these changes in terms of the number and location of them. Such changes are associated with autocovariance changes. This article proposes a framework to quantify the uncertainty of autocovariance changepoints in time series motivated by this oceanographic application. More specifically, the framework considers time series under the locally stationary wavelet (LSW) framework, deriving a joint density for scale processes in the raw wavelet periodogram. By embedding this density within a hidden Markov model (HMM) framework, we consider changepoint characteristics under this multiscale setting. Such a methodology allows us to model changepoints and their uncertainty for a wide range of models, including piecewise second-order stationary processes, for example, piecewise moving average processes.
DOI: 10.6084/m9.figshare.1481260
title: Bayesian Estimation of Density via Multiple Sequential Inversions of Two-Dimensional Images With Application to Electron Microscopy

description: We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a scanning electron microscope. An image results from a sequence of projections of the convolution of the density function with the unknown microscopy correction function that we also learn from the data; thus, learning of the unknowns demands multiple inversions. We invoke a novel design of experiment, involving imaging at multiple values of the parameter that controls the subsurface depth from which information about the density structure is carried, to result in the image. Real-life material density functions are characterized by high-density contrasts and are highly discontinuous, implying that they exhibit correlation structures that do not vary smoothly. In the absence of training data, modeling such correlation structures of real material density functions is not possible. So we discretize the material sample and treat values of the density function at chosen locations inside it as independent and distribution-free parameters. Resolution of the available image dictates the discretization length of the model; three models pertaining to distinct resolution classes (at micrometer to nanometer scale lengths) are developed. We develop priors on the material density, such that these priors adapt to the sparsity inherent in the density function. The likelihood is defined in terms of the distance between the convolution of the unknown functions and the image data. The posterior probability density of the unknowns given the data is expressed using the developed priors on the density and priors on the microscopy correction function as elicited from the microscopy literature. We achieve posterior samples using an adaptive Metropolis-within-Gibbs inference scheme. The method is applied to learn the material density of a three-dimensional sample of a nano-structure, using real image data. Illustrations on simulated image data of alloy samples are also included.
DOI: 10.6084/m9.figshare.1481260.v1
title: Bayesian Estimation of Density via Multiple Sequential Inversions of Two-Dimensional Images With Application to Electron Microscopy

description: We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a scanning electron microscope. An image results from a sequence of projections of the convolution of the density function with the unknown microscopy correction function that we also learn from the data; thus, learning of the unknowns demands multiple inversions. We invoke a novel design of experiment, involving imaging at multiple values of the parameter that controls the subsurface depth from which information about the density structure is carried, to result in the image. Real-life material density functions are characterized by high-density contrasts and are highly discontinuous, implying that they exhibit correlation structures that do not vary smoothly. In the absence of training data, modeling such correlation structures of real material density functions is not possible. So we discretize the material sample and treat values of the density function at chosen locations inside it as independent and distribution-free parameters. Resolution of the available image dictates the discretization length of the model; three models pertaining to distinct resolution classes (at micrometer to nanometer scale lengths) are developed. We develop priors on the material density, such that these priors adapt to the sparsity inherent in the density function. The likelihood is defined in terms of the distance between the convolution of the unknown functions and the image data. The posterior probability density of the unknowns given the data is expressed using the developed priors on the density and priors on the microscopy correction function as elicited from the microscopy literature. We achieve posterior samples using an adaptive Metropolis-within-Gibbs inference scheme. The method is applied to learn the material density of a three-dimensional sample of a nano-structure, using real image data. Illustrations on simulated image data of alloy samples are also included.
DOI: 10.6084/m9.figshare.1481147
title: Raw Data: What do contrast threshold equivalent noise studies actually measure? Noise vs. nonlinearity in different masking paradigms.

description: Data from pedestal and noise masking experiments to be published in PLoS One under the title: What do contrast threshold equivalent noise studies actually measure? Noise vs. nonlinearity in different masking paradigms.
DOI: 10.6084/m9.figshare.1481147.v1
title: Raw Data: What do contrast threshold equivalent noise studies actually measure? Noise vs. nonlinearity in different masking paradigms.

description: Data from pedestal and noise masking experiments to be published in PLoS One under the title: What do contrast threshold equivalent noise studies actually measure? Noise vs. nonlinearity in different masking paradigms.
DOI: 10.6084/m9.figshare.1481100
title: Selectively remove progesterone receptor from cells expressing Mx1

description: Figures for PlosOne submission PONE-D-15-18456R1 - [EMID:6b6f820df4a16e15]
DOI: 10.6084/m9.figshare.1481102
title: Enabling Complex Analysis of Large Scale Digital Collections - Phase II Pitch

description: A pitch from UCLDH and the British Library on 14th July 2015 as part of the Jisc Research Data Spring program, phase II: reporting on our pilot project, and where we see the project going forward: Lots of money has been spent digitising heritage collections. Digitised heritage collections are data. But non-computationally trained scholars don't know what to ask of large quantities of data. Often they do not have access to high performance computing facilities and they don’t know how to use them. We have addressed this fundamental problem by extending research data management processes in order to enable novel research in the arts, humanities, and social and historical sciences and a deeper understanding of emerging research needs. In our first phase, we have successfully implemented large scale, complex search of a digitised collection: now we scale up…
DOI: 10.6084/m9.figshare.1481102.v1
title: Enabling Complex Analysis of Large Scale Digital Collections - Phase II Pitch

description: A pitch from UCLDH and the British Library on 14th July 2015 as part of the Jisc Research Data Spring program, phase II: reporting on our pilot project, and where we see the project going forward: Lots of money has been spent digitising heritage collections. Digitised heritage collections are data. But non-computationally trained scholars don't know what to ask of large quantities of data. Often they do not have access to high performance computing facilities and they don’t know how to use them. We have addressed this fundamental problem by extending research data management processes in order to enable novel research in the arts, humanities, and social and historical sciences and a deeper understanding of emerging research needs. In our first phase, we have successfully implemented large scale, complex search of a digitised collection: now we scale up…
DOI: 10.6084/m9.figshare.1481068
title: nki_epi_raw

description: raw epi data for one subject
DOI: 10.6084/m9.figshare.1481064
title: Zimmermann soil organic carbon fractionation data from: Soil organic carbon and root distribution in a temperate arable agroforestry system

description: Data described at: https://github.com/maupson/research_data/tree/master/silsoe/silsoe_zimmermann_fractions.
DOI: 10.6084/m9.figshare.1481068.v1
title: nki_epi_raw

description: raw epi data for one subject
DOI: 10.6084/m9.figshare.1481100.v2
title: Selectively remove progesterone receptor from cells expressing Mx1

description: Figures for PlosOne submission PONE-D-15-18456R1 - [EMID:6b6f820df4a16e15]
DOI: 10.6084/m9.figshare.1481062
title: Road Characteristics Best Predict the Probability of Vehicle Collisions with a Non-native Ungulate

description: These datafiles are used in analyses contained in the manuscript Tanner AL, Leroux SJ, and Saunders PW. [submitted]. Road Characteristics Best Predict the Probability of Vehicle Collisions with a Non-native Ungulate. These data include information on environmental variables at 600 moose-vehicle collision locations and 1200 randomly selected locations in Newfoundland, Canada. There is one buffer size (5471m radius) used for all variables that require buffered areas. This file also contains the R code that we used to conduct the analyses. We have included comments throughout the code in order to understand the operations being performed. Be sure to import the new datasets (spatialallmvc.csv or spatialallrandom.csv). Please read Metadata file carefully before using any of this data.
DOI: 10.6084/m9.figshare.1481100.v1
title: Slectively remove progesterone receoptor from cells expressing Mx1

description: Figures for PlosOne submission PONE-D-15-18456R1 - [EMID:6b6f820df4a16e15]
DOI: 10.6084/m9.figshare.1481051
title: Wridding (Preview)

description: Wridding is an exploration in the novel as a liquid genre. Told in an unorthodox aesthetic key, the oneiric quest-for-wholeness narrative reveals the characters’ transition from dark lands toward the territories of the reborn light, from erratic possibilities toward what they might become and / or who they are. Within a phantasmagoric journey through the whirlpools of the dreamscape and a fractured empire of chronos, the identities of the characters fluctuate as always already oscillating loops of disintegration and wholeness.
DOI: 10.6084/m9.figshare.1481050
title: Wridding

description: Wridding is an exploration in the novel as a liquid genre. Told in an unorthodox aesthetic key, the oneiric quest-for-wholeness narrative reveals the characters’ transition from dark lands toward the territories of the reborn light, from erratic possibilities toward what they might become and / or who they are. Within a phantasmagoric journey through the whirlpools of the dreamscape and a fractured empire of chronos, the identities of the characters fluctuate as always already oscillating loops of disintegration and wholeness.
DOI: 10.6084/m9.figshare.1481062.v1
title: Road characteristics best predict the probability of vehicle collisions with a non-native, hyperabundant ungulate

description: These datafiles are used in analyses contained in the manuscript Tanner, A.L., Leroux, S.J., and Saunders, P.W. [submitted]. Road characteristics best predict the probability of vehicle collisions with a non-native, hyperabundant ungulate. These data include information on environmental variables at 600 moose-vehicle collision locations, and 3296 randomly selected locations in Newfoundland, Canada. There are three buffer sizes (500m, 2736m, and 5471m) used for all variables that require buffered areas. This file also contains the R code that we used to conduct the analyses. We have included comments throughout the code in order to understand the operations being performed. When importing a new dataset (either: spatialall.csv or spatialallcorr.csv) while running the R code, be sure to set the Please read Metadata file carefully before using any of this data.
DOI: 10.6084/m9.figshare.1480979
title: Bilingual Eye Movement Data L2

description: Sentence Level Eye movements of late Dutch-English bilinguals reading a complete novel in English. Legend in second sheet.
DOI: 10.6084/m9.figshare.1481050.v1
title: Wridding

description: Wridding is an exploration in the novel as a liquid genre. Told in an unorthodox aesthetic key, the oneiric quest-for-wholeness narrative reveals the characters’ transition from dark lands toward the territories of the reborn light, from erratic possibilities toward what they might become and / or who they are. Within a phantasmagoric journey through the whirlpools of the dreamscape and a fractured empire of chronos, the identities of the characters fluctuate as always already oscillating loops of disintegration and wholeness.
DOI: 10.6084/m9.figshare.1481051.v2
title: Wridding (Preview)

description: Wridding is an exploration in the novel as a liquid genre. Told in an unorthodox aesthetic key, the oneiric quest-for-wholeness narrative reveals the characters’ transition from dark lands toward the territories of the reborn light, from erratic possibilities toward what they might become and / or who they are. Within a phantasmagoric journey through the whirlpools of the dreamscape and a fractured empire of chronos, the identities of the characters fluctuate as always already oscillating loops of disintegration and wholeness.
DOI: 10.6084/m9.figshare.1481064.v4
title: Zimmermann soil organic carbon fractionation data from: Soil organic carbon and root distribution in a temperate arable agroforestry system

description: Data described at: https://github.com/maupson/research_data/tree/master/silsoe/silsoe_zimmermann_fractions.
DOI: 10.6084/m9.figshare.1480978
title: Bilingual Eye Movement Data L1

description: Sentence level eye movement data for late Dutch-English bilinguals readign a complete novel in Dutch. Legend in second sheet.
DOI: 10.6084/m9.figshare.1480979.v1
title: Bilingual Eye Movement Data L2

description: Sentence Level Eye movements of late Dutch-English bilinguals reading a complete novel in English. Legend in second sheet.
DOI: 10.6084/m9.figshare.1481064.v3
title: Zimmermann soil organic carbon fractionation data from: Soil organic carbon and root distribution in a temperate arable agroforestry system

description: Data described at: https://github.com/maupson/research_data/tree/master/silsoe/silsoe_zimmermann_fractions.
DOI: 10.6084/m9.figshare.1480977
title: Monolingual Eye Movement Data

description: Sentence level eye movements of English monolinguals reading an entire novel.
DOI: 10.6084/m9.figshare.1481051.v1
title: Wridding

description: Wridding is an exploration in the novel as a liquid genre. Told in an unorthodox aesthetic key, the oneiric quest-for-wholeness narrative reveals the characters’ transition from dark lands toward the territories of the reborn light, from erratic possibilities toward what they might become and / or who they are. Within a phantasmagoric journey through the whirlpools of the dreamscape and a fractured empire of chronos, the identities of the characters fluctuate as always already oscillating loops of disintegration and wholeness.
DOI: 10.6084/m9.figshare.1480975
title: Dutch Materials

description: Characteristics of the Dutch book material. Legend in second sheet.
DOI: 10.6084/m9.figshare.1480976
title: English Materials

description: Characteristics of the English book material. Legend in second sheet.
DOI: 10.6084/m9.figshare.1480978.v1
title: Bilingual Eye Movement Data L1

description: Sentence level eye movement data for late Dutch-English bilinguals readign a complete novel in Dutch. Legend in second sheet.
DOI: 10.6084/m9.figshare.1481062.v2
title: Road characteristics best predict the probability of vehicle collisions with a non-native, hyperabundant ungulate

description: These datafiles are used in analyses contained in the manuscript Tanner, A.L., Leroux, S.J., and Saunders, P.W. [submitted]. Road characteristics best predict the probability of vehicle collisions with a non-native, hyperabundant ungulate. These data include information on environmental variables at 600 moose-vehicle collision locations, and 3296 randomly selected locations in Newfoundland, Canada. There are three buffer sizes (500m, 2736m, and 5471m) used for all variables that require buffered areas. This file also contains the R code that we used to conduct the analyses. We have included comments throughout the code in order to understand the operations being performed. When importing a new dataset (either: spatialall.csv or spatialallcorr.csv) while running the R code, be sure to set the Please read Metadata file carefully before using any of this data.
DOI: 10.6084/m9.figshare.1480975.v1
title: Dutch Materials

description: Characteristics of the Dutch book material. Legend in second sheet.
DOI: 10.6084/m9.figshare.1480976.v1
title: English Materials

description: Characteristics of the English book material. Legend in second sheet.
DOI: 10.6084/m9.figshare.1480974
title: Participant Information

description: Characteristics of bilingual and monolingual participants.