cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5807388.v2
title: Adaptive Bayesian Time–Frequency Analysis of Multivariate Time Series

description: This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood-based penalized spline models of modified Cholesky components, which provide flexible nonparametric estimates that preserve positive definite structures of spectral matrices. The approach is formulated in a Bayesian framework, in which the number and location of partitions are random, and relies on reversible jump Markov chain and Hamiltonian Monte Carlo methods that can adapt to the unknown number of segments and parameters. By averaging over the distribution of partitions, the approach can approximate both abrupt and slowly varying changes in spectral matrices. Empirical performance is evaluated in simulation studies and illustrated through analyses of electroencephalography during sleep and of the El Niño-Southern Oscillation. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5807388.v1
title: Adaptive Bayesian Time-Frequency Analysis of Multivariate Time Series

description: This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood based penalized spline models of modified Cholesky components, which provide flexible nonparametric estimates that preserve positive definite structures of spectral matrices. The approach is formulated in a Bayesian framework, in which the number and location of partitions are random, and relies on reversible jump Markov chain and Hamiltonian Monte Carlo methods that can adapt to the unknown number of segments and parameters. By averaging over the distribution of partitions, the approach can approximate both abrupt and slow-varying changes in spectral matrices. Empirical performance is evaluated in simulation studies and illustrated through analyses of electroencephalography during sleep and of the El Niño-Southern Oscillation.
DOI: 10.6084/m9.figshare.5807379
title: Bayesian Semiparametric Mixed Effects Markov Models With Application to Vocalization Syntax

description: Studying the neurological, genetic, and evolutionary basis of human vocal communication mechanisms using animal vocalization models is an important field of neuroscience. The datasets typically comprise structured sequences of syllables or “songs” produced by animals from different genotypes under different social contexts. It has been difficult to come up with sophisticated statistical methods that appropriately model animal vocal communication syntax. We address this need by developing a novel Bayesian semiparametric framework for inference in such datasets. Our approach is built on a novel class of mixed effects Markov transition models for the songs that accommodate exogenous influences of genotype and context as well as animal-specific heterogeneity. Crucial advantages of the proposed approach include its ability to provide insights into key scientific queries related to global and local influences of the exogenous predictors on the transition dynamics via automated tests of hypotheses. The methodology is illustrated using simulation experiments and the aforementioned motivating application in neuroscience. 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.5807379.v2
title: Bayesian Semiparametric Mixed Effects Markov Models With Application to Vocalization Syntax

description: Studying the neurological, genetic, and evolutionary basis of human vocal communication mechanisms using animal vocalization models is an important field of neuroscience. The datasets typically comprise structured sequences of syllables or “songs” produced by animals from different genotypes under different social contexts. It has been difficult to come up with sophisticated statistical methods that appropriately model animal vocal communication syntax. We address this need by developing a novel Bayesian semiparametric framework for inference in such datasets. Our approach is built on a novel class of mixed effects Markov transition models for the songs that accommodate exogenous influences of genotype and context as well as animal-specific heterogeneity. Crucial advantages of the proposed approach include its ability to provide insights into key scientific queries related to global and local influences of the exogenous predictors on the transition dynamics via automated tests of hypotheses. The methodology is illustrated using simulation experiments and the aforementioned motivating application in neuroscience. 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.5807379.v1
title: Bayesian Semiparametric Mixed Effects Markov Models with Application to Vocalization Syntax

description: Studying the neurological, genetic and evolutionary basis of human vocal communication mechanisms using animal vocalization models is an important field of neuroscience. The data sets typically comprise structured sequences of syllables or ‘songs’ produced by animals from different genotypes under different social contexts. It has been difficult to come up with sophisticated statistical methods that appropriately model animal vocal communication syntax. We address this need by developing a novel Bayesian semiparametric framework for inference in such data sets. Our approach is built on a novel class of mixed effects Markov transition models for the songs that accommodate exogenous influences of genotype and context as well as animal-specific heterogeneity. Crucial advantages of the proposed approach include its ability to provide insights into key scientific queries related to global and local influences of the exogenous predictors on the transition dynamics via automated tests of hypotheses. The methodology is illustrated using simulation experiments and the aforementioned motivating application in neuroscience.
DOI: 10.6084/m9.figshare.5807361.v2
title: To Wait or Not to Wait: Two-Way Functional Hazards Model for Understanding Waiting in Call Centers

description: Telephone call centers offer a convenient communication channel between businesses and their customers. Efficient management of call centers needs accurate modeling of customer waiting behavior, which contains important information about customer patience (how long a customer is willing to wait) and service quality (how long a customer needs to wait to get served). Hazard functions offer dynamic characterization of customer waiting behavior, and provide critical inputs for agent scheduling. Motivated by this application, we develop a two-way functional hazards (tF-Hazards) model to study customer waiting behavior as a function of two timescales, waiting duration and the time of day that a customer calls in. The model stems from a two-way piecewise constant hazard function, and imposes low-rank structure and smoothness on the hazard rates to enhance interpretability. We exploit an alternating direction method of multipliers algorithm to optimize a penalized likelihood function of the model. We carefully analyze the data from a U.S. Bank call center, and provide informative insights about customer patience and service quality patterns along waiting time and across different times of a day. The findings provide primitive inputs for call center agent staffing and scheduling, as well as for call center practitioners to understand the effect of system protocols on customer waiting behavior. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5807361.v1
title: To Wait or Not to Wait: Two-way Functional Hazards Model for Understanding Waiting in Call Centers

description: Telephone call centers offer a convenient communication channel between businesses and their customers. Efficient management of call centers needs accurate modeling of customer waiting behavior, which contains important information about customer patience (how long a customer is willing to wait) and service quality (how long a customer needs to wait to get served). Hazard functions offer dynamic characterization of customer waiting behavior, and provide critical inputs for agent scheduling. Motivated by this application, we develop a two-way functional hazards (tF-Hazards) model to study customer waiting behavior as a function of two timescales, waiting duration and the time of day that a customer calls in. The model stems from a two-way piecewise constant hazard function, and imposes low-rank structure and smoothness on the hazard rates to enhance interpretability. We exploit an alternating direction method of multipliers (ADMM) algorithm to optimize a penalized likelihood function of the model. We carefully analyze the data from a US bank call center, and provide informative insights about customer patience and service quality patterns along waiting time and across different times of a day. The findings provide primitive inputs for call center agent staffing and scheduling, as well as for call center practitioners to understand the effect of system protocols on customer waiting behavior.
DOI: 10.6084/m9.figshare.5807358
title: Constructing Priors that Penalize the Complexity of Gaussian Random Fields

description: Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance structure under in-fill asymptotics. We extend the recent penalized complexity prior framework and develop a principled joint prior for the range and the marginal variance of one-dimensional, two-dimensional, and three-dimensional Matérn GRFs with fixed smoothness. The prior is weakly informative and penalizes complexity by shrinking the range toward infinity and the marginal variance toward zero. We propose guidelines for selecting the hyperparameters, and a simulation study shows that the new prior provides a principled alternative to reference priors that can leverage prior knowledge to achieve shorter credible intervals while maintaining good coverage. We extend the prior to a nonstationary GRF parameterized through local ranges and marginal standard deviations, and introduce a scheme for selecting the hyperparameters based on the coverage of the parameters when fitting simulated stationary data. The approach is applied to a dataset of annual precipitation in southern Norway and the scheme for selecting the hyperparameters leads to conservative estimates of nonstationarity and improved predictive performance over the stationary model. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5807358.v2
title: Constructing Priors that Penalize the Complexity of Gaussian Random Fields

description: Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance structure under in-fill asymptotics. We extend the recent penalized complexity prior framework and develop a principled joint prior for the range and the marginal variance of one-dimensional, two-dimensional, and three-dimensional Matérn GRFs with fixed smoothness. The prior is weakly informative and penalizes complexity by shrinking the range toward infinity and the marginal variance toward zero. We propose guidelines for selecting the hyperparameters, and a simulation study shows that the new prior provides a principled alternative to reference priors that can leverage prior knowledge to achieve shorter credible intervals while maintaining good coverage. We extend the prior to a nonstationary GRF parameterized through local ranges and marginal standard deviations, and introduce a scheme for selecting the hyperparameters based on the coverage of the parameters when fitting simulated stationary data. The approach is applied to a dataset of annual precipitation in southern Norway and the scheme for selecting the hyperparameters leads to conservative estimates of nonstationarity and improved predictive performance over the stationary model. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5807358.v1
title: Constructing Priors that Penalize the Complexity of Gaussian Random Fields

description: Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance structure under in-fill asymptotics. We extend the recent Penalised Complexity prior framework and develop a principled joint prior for the range and the marginal variance of one-dimensional, two-dimensional and three-dimensional Matérn GRFs with fixed smoothness. The prior is weakly informative and penalises complexity by shrinking the range towards infinity and the marginal variance towards zero. We propose guidelines for selecting the hyperparameters, and a simulation study shows that the new prior provides a principled alternative to reference priors that can leverage prior knowledge to achieve shorter credible intervals while maintaining good coverage. We extend the prior to a non-stationary GRF parametrized through local ranges and marginal standard deviations, and introduce a scheme for selecting the hyperparameters based on the coverage of the parameters when fitting simulated stationary data. The approach is applied to a dataset of annual precipitation in southern Norway and the scheme for selecting the hyperparameters leads to concervative estimates of non-stationarity and improved predictive performance over the stationary model.
DOI: 10.23645/epacomptox.5807106
title: Retention Time Prediction 2018 Publication Data

description: Files associated with the publication "A Comparison of Three Liquid Chromatography (LC) Retention Time Prediction Models" published in the journal <i>Talanta</i>.
DOI: 10.6084/m9.figshare.5807103
title: Emerging Global Trends: What is Relevant to Libraries and Why Should it Matter to You.

description: Keynote Presentation at <b>“International Conference on Reshaping Libraries: Emerging Global Technologies and Trends” (ICRL 2018)</b> February 1-3, 2018. ORganized by DELNET Ambedkar University Delhi, Special Libraries Association (SLA)-Asian Chapter and Society for Library Professionals (SLP) at Hotel Royal Orchid, Jaipur, Rajasthan, India.
DOI: 10.6084/m9.figshare.5807088
title: Timelapse 7 - Maximum intensity projections - fused

description: See accompanying word documents<br><br>
DOI: 10.6084/m9.figshare.5807088.v1
title: Timelapse 7 - Maximum intensity projections - fused

description: See accompanying word documents<br><br>
DOI: 10.6084/m9.figshare.5807088.v2
title: Timelapse 7 - Maximum intensity projections - fused

description: See accompanying word documents<br><br>
DOI: 10.6084/m9.figshare.5807088.v4
title: Timelapse 7 - Maximum intensity projections - fused

description: See accompanying word documents<br><br>
DOI: 10.6084/m9.figshare.5807088.v3
title: Timelapse 7 - Maximum intensity projections - fused

description: See accompanying word documents<br><br>
DOI: 10.6084/m9.figshare.5806743
title: BIAJS Brief Index of Affective Job Satisfaction

description: The current data set includes the assessment of the Brief Index of Affective Job Satisfaction (BIAJS) in a first sample of workers over 40, and in a second sample of workers over 44.
DOI: 10.6084/m9.figshare.5803266
title: Supporting data for Beckles, Z., Gray, S., Hiom, D., Merrett, K., Snow, K., & Steer, D. (2018) 'Disciplinary data publication guides'

description: Supporting data for Beckles, Z., Gray, S., Hiom, D., Merret, K., Snow, K., & Steer, D. (2018) 'Disciplinary data publication guides'. Practice paper presented at 13th International Digital Curation Conference, Barcelona, Spain.<br>File inventory: Data management review template v11.dotm Engineering Faculty intranet research data page.pdf figure 01 - ontology diagram v01.png figure 02 - modelling-study-v04.png figure 03 - physical-experiment-v02.png figure 04 - systematic-review-v02.png Full decision tree v01.png Monitoring and publication data analysis.xlsx
DOI: 10.6084/m9.figshare.5803266.v1
title: figure 01ontology diagram v01.png

description: Supporting data for Beckles, Z., Gray, S., Hiom, D., Merret, K., Snow, K., & Steer, D. (2018) 'Disciplinary data publication guides'. Practice paper presented at 13th International Digital Curation Conference, Barcelona, Spain.
DOI: 10.6084/m9.figshare.5803266.v2
title: Supporting data for Beckles, Z., Gray, S., Hiom, D., Merret, K., Snow, K., & Steer, D. (2018) 'Disciplinary data publication guides'.

description: Supporting data for Beckles, Z., Gray, S., Hiom, D., Merret, K., Snow, K., & Steer, D. (2018) 'Disciplinary data publication guides'. Practice paper presented at 13th International Digital Curation Conference, Barcelona, Spain.<br>File inventory: Data management review template v11.dotm Engineering Faculty intranet research data page.pdf figure 01 - ontology diagram v01.png figure 02 - modelling-study-v04.png figure 03 - physical-experiment-v02.png figure 04 - systematic-review-v02.png Full decision tree v01.png Monitoring and publication data analysis.xlsx
DOI: 10.6084/m9.figshare.5803266.v4
title: Supporting data for Beckles, Z., Gray, S., Hiom, D., Merrett, K., Snow, K., & Steer, D. (2018) 'Disciplinary data publication guides'

description: Supporting data for Beckles, Z., Gray, S., Hiom, D., Merret, K., Snow, K., & Steer, D. (2018) 'Disciplinary data publication guides'. Practice paper presented at 13th International Digital Curation Conference, Barcelona, Spain.<br>File inventory: Data management review template v11.dotm Engineering Faculty intranet research data page.pdf figure 01 - ontology diagram v01.png figure 02 - modelling-study-v04.png figure 03 - physical-experiment-v02.png figure 04 - systematic-review-v02.png Full decision tree v01.png Monitoring and publication data analysis.xlsx
DOI: 10.6084/m9.figshare.5803266.v3
title: Supporting data for Beckles, Z., Gray, S., Hiom, D., Merret, K., Snow, K., & Steer, D. (2018) 'Disciplinary data publication guides'

description: Supporting data for Beckles, Z., Gray, S., Hiom, D., Merret, K., Snow, K., & Steer, D. (2018) 'Disciplinary data publication guides'. Practice paper presented at 13th International Digital Curation Conference, Barcelona, Spain.<br>File inventory: Data management review template v11.dotm Engineering Faculty intranet research data page.pdf figure 01 - ontology diagram v01.png figure 02 - modelling-study-v04.png figure 03 - physical-experiment-v02.png figure 04 - systematic-review-v02.png Full decision tree v01.png Monitoring and publication data analysis.xlsx
DOI: 10.6084/m9.figshare.5803221
title: Toward hydrophobic carminic acid derivatives and their inclusion in polyacrylates.

description: Carminic acid, a natural hydrophilic dye extensively used in food and cosmetic industries, is converted in hydrophobic dyes by acetylation or pivaloylation. These derivatives are successfully used as biocolorants for polyacrylate objects. Spectroscopic properties of the carminic acid derivatives in DMSO and in polybutylacrylate are studied by means of Photoluminescence and Time Resolved Photoluminescence decays, revealing an hypsochromic effect due to presence of bulky substituents as the acetyl or pivaloyl groups. Molecular Mechanics and Density Functional Theory (DFT) calculations confirm the disruption of planarity between the sugar ring and the anthraquinoid system determined by the esterification.
DOI: 10.6084/m9.figshare.5803221.v1
title: Toward hydrophobic carminic acid derivatives and their inclusion in polyacrylates.

description: Carminic acid, a natural hydrophilic dye extensively used in food and cosmetic industries, is converted in hydrophobic dyes by acetylation or pivaloylation. These derivatives are successfully used as biocolorants for polyacrylate objects. Spectroscopic properties of the carminic acid derivatives in DMSO and in polybutylacrylate are studied by means of Photoluminescence and Time Resolved Photoluminescence decays, revealing an hypsochromic effect due to presence of bulky substituents as the acetyl or pivaloyl groups. Molecular Mechanics and Density Functional Theory (DFT) calculations confirm the disruption of planarity between the sugar ring and the anthraquinoid system determined by the esterification.
DOI: 10.6084/m9.figshare.5803176
title: HELCATS WP3 CME KINEMATICS CATALOGUE

description: The FP7 Heliospheric Cataloguing, Analysis and Technique Service (HELCATS) project capitalises on European expertise in heliospheric imaging built up over the last decade, particularly by lead involvement in NASA’s STEREO mission, whilst also exploiting the vast wealth of long established European expertise in such areas as solar and coronal imaging, as well as the interpretation of in-situ and radio measurements of solar wind phenomena.<br><br>The CME kinematics catalogue HIGeoCat builds on the HCME_WP2 identified CME list https://doi.org/10.6084/m9.figshare.5803152.v1.Where possible, time elongation tracks areextracted for each CME along a defined position angle corresponding approximately to the apex of the CME. Single spacecraft geometric fitting techniques (see Davies et al., 2012; doi:10.1088/0004-637X/777/2/167) are used to determine the kinemtatic properties (direction, speed and launch time) for three di􀃖erent simple assumptions of the CME morphology. The result is asuper-set of the HCME_WP2 catalogue containing both the observed parmeters and the derived kinematics. <br>This is version: 05 of the catalogue, released 2018-01-19. (DOI: 10.6084/m9.figshare.5803176). The catalogue is based on version 5 of the WP2 catalog (DOI: 10.6084/m9.figshare.5803152) . The latest version includes events to end of September 2017 on Stereo-A. Note that there is an approximately 1 year gap in Stereo-A data from October 2014 while the spacecraft was behind the Sun. Data from Stereo-B is not available after Septmeber 2014 since contact with the spacecraft has not yet been re-established.
DOI: 10.6084/m9.figshare.5803170
title: In Run Forecast V1.3 Template.xlsx

description: “MiSeq In-Run Forecast” tool: an Excel spreadsheet which can be populated with the SAV run metrics from any MiSeq run using a 500-cycle MiSeq Reagent V2 kit with prepared Nextera XT libraries. Our spreadsheet calculations accurately predict whether a given MiSeq run has performed optimally and how it compares to previous runs.
DOI: 10.6084/m9.figshare.5803170.v1
title: In Run Forecast V1.3 Template.xlsx

description: “MiSeq In-Run Forecast” tool: an Excel spreadsheet which can be populated with the SAV run metrics from any MiSeq run using a 500-cycle MiSeq Reagent V2 kit with prepared Nextera XT libraries. Our spreadsheet calculations accurately predict whether a given MiSeq run has performed optimally and how it compares to previous runs.