cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6085/aa/iposxx_xxxix01xmsr01_20200701.50.1
title: MARINe/PISCO: Intertidal: site temperature data: Post Point, Washington (IPOSXX)

description: This metadata record describes a mix of intertidal seawater and air temperature data collected at Post Point, Washington by MARINe/PISCO. Measurements were collected using HOBO Pendant MX Water Temperature Data Logger (Onset Computer Corp. MX2201) beginning 2020-07-02. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 10 or 15 minute intervals.
DOI: 10.6085/aa/iposxx_xxxix01xmsr01_20200701.40.1
title: (:unkn)
DOI: 10.6085/aa/iposxx_xxxix01xmsr01_20210608.50.1
title: MARINe/PISCO: Intertidal: site temperature data: Post Point, Washington (IPOSXX)

description: This metadata record describes a mix of intertidal seawater and air temperature data collected at Post Point, Washington by MARINe/PISCO. Measurements were collected using HOBO Pendant MX Water Temperature Data Logger (Onset Computer Corp. MX2201) beginning 2021-06-09. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 10 or 15 minute intervals.
DOI: 10.6085/aa/iposxx_xxxix01xmsr01_20210608.40.1
title: (:unkn)
DOI: 10.6084/m9.figshare.19719406
title: Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies

description: Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose a new generalized matrix response regression model, where the observed network is treated as a matrix-valued response and the subject covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the effect of subject covariates through a sparse slope tensor. We develop an efficient alternating gradient descent algorithm for parameter estimation, and establish the nonasymptotic error bound for the actual estimator from the algorithm, which quantifies the interplay between the computational and statistical errors. We further show the strong consistency for graph community recovery, as well as the edge selection consistency. We demonstrate the efficacy of our method through simulations and two brain connectivity studies. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19719406.v1
title: Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies

description: Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose a new generalized matrix response regression model, where the observed network is treated as a matrix-valued response and the subject covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the effect of subject covariates through a sparse slope tensor. We develop an efficient alternating gradient descent algorithm for parameter estimation, and establish the nonasymptotic error bound for the actual estimator from the algorithm, which quantifies the interplay between the computational and statistical errors. We further show the strong consistency for graph community recovery, as well as the edge selection consistency. We demonstrate the efficacy of our method through simulations and two brain connectivity studies. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19651416
title: Analytic Permutation Testing for Functional Data ANOVA

description: Analysis of variance is a cornerstone of statistical hypothesis testing. When data lies beyond the assumption of univariate normality, nonparametric methods including rank based statistics and permutation tests are enlisted. The permutation test is a versatile exact nonparametric significance test that requires drastically fewer assumptions than similar parametric tests. The main downfall of the permutation test is high computational cost making this approach laborious for comparing multiple samples of complex data types and completely infeasible in any application requiring speedy results such as high throughput streaming data. We rectify this problem through application of concentration inequalities and thus propose a computation free permutation test—that is, a permutation-less permutation test. This general framework is applied to multivariate and matrix-valued, but with a special emphasis on functional data. We improve these concentration bounds via a novel incomplete beta transform. Our theory is extended from two-sample to <i>k</i>-sample testing through the use of weakly dependent Rademacher chaoses and modified decoupling inequalities. Our methodology is tested on classic functional datasets including the Berkeley growth curves and the phoneme dataset. We further analyze a novel dataset of 12 spoken vowel sounds that was collected to illustrate to power of the analytic permutation test. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19651416.v2
title: Analytic Permutation Testing for Functional Data ANOVA

description: Analysis of variance is a cornerstone of statistical hypothesis testing. When data lies beyond the assumption of univariate normality, nonparametric methods including rank based statistics and permutation tests are enlisted. The permutation test is a versatile exact nonparametric significance test that requires drastically fewer assumptions than similar parametric tests. The main downfall of the permutation test is high computational cost making this approach laborious for comparing multiple samples of complex data types and completely infeasible in any application requiring speedy results such as high throughput streaming data. We rectify this problem through application of concentration inequalities and thus propose a computation free permutation test—that is, a permutation-less permutation test. This general framework is applied to multivariate and matrix-valued, but with a special emphasis on functional data. We improve these concentration bounds via a novel incomplete beta transform. Our theory is extended from two-sample to <i>k</i>-sample testing through the use of weakly dependent Rademacher chaoses and modified decoupling inequalities. Our methodology is tested on classic functional datasets including the Berkeley growth curves and the phoneme dataset. We further analyze a novel dataset of 12 spoken vowel sounds that was collected to illustrate to power of the analytic permutation test. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19779049
title: Semi-Complete Data Augmentation for Efficient State Space Model Fitting

description: We propose a novel efficient model-fitting algorithm for state space models. State space models are an intuitive and flexible class of models, frequently used due to the combination of their natural separation of the different mechanisms acting on the system of interest: the latent underlying system process; and the observation process. This flexibility, however, often comes at the price of more complicated model-fitting algorithms due to the associated analytically intractable likelihood. For the general case a Bayesian data augmentation approach is often employed, where the true unknown states are treated as auxiliary variables and imputed within the MCMC algorithm. However, standard “vanilla” MCMC algorithms may perform very poorly due to high correlation between the imputed states and/or parameters, often leading to model-specific bespoke algorithms being developed that are nontransferable to alternative models. The proposed method addresses the inefficiencies of traditional approaches by combining data augmentation with numerical integration in a Bayesian hybrid approach. This approach permits the use of standard “vanilla” updating algorithms that perform considerably better than the traditional approach in terms of improved mixing and lower autocorrelation, and has the potential to be incorporated into bespoke model-specific algorithms. To demonstrate the ideas, we apply our semi-complete data augmentation algorithm to different application areas and models, leading to distinct implementation schemes and improved mixing and demonstrating improved mixing of the model parameters. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19779049.v2
title: Semi-Complete Data Augmentation for Efficient State Space Model Fitting

description: We propose a novel efficient model-fitting algorithm for state space models. State space models are an intuitive and flexible class of models, frequently used due to the combination of their natural separation of the different mechanisms acting on the system of interest: the latent underlying system process; and the observation process. This flexibility, however, often comes at the price of more complicated model-fitting algorithms due to the associated analytically intractable likelihood. For the general case a Bayesian data augmentation approach is often employed, where the true unknown states are treated as auxiliary variables and imputed within the MCMC algorithm. However, standard “vanilla” MCMC algorithms may perform very poorly due to high correlation between the imputed states and/or parameters, often leading to model-specific bespoke algorithms being developed that are nontransferable to alternative models. The proposed method addresses the inefficiencies of traditional approaches by combining data augmentation with numerical integration in a Bayesian hybrid approach. This approach permits the use of standard “vanilla” updating algorithms that perform considerably better than the traditional approach in terms of improved mixing and lower autocorrelation, and has the potential to be incorporated into bespoke model-specific algorithms. To demonstrate the ideas, we apply our semi-complete data augmentation algorithm to different application areas and models, leading to distinct implementation schemes and improved mixing and demonstrating improved mixing of the model parameters. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20263823.v2
title: Deep Learning With Functional Inputs

description: We present a methodology for integrating functional data into deep neural networks. The model is defined for scalar responses with multiple functional and scalar covariates. A by-product of the method is a set of dynamic functional weights that can be visualized during the optimization process. This visualization leads to a greater interpretability of the relationship between the covariates and the response relative to conventional neural networks. The model is shown to perform well in a number of contexts including prediction of new data and recovery of the true underlying relationship between the functional covariate and scalar response; these results were confirmed through real data applications and simulation studies. An R package (FuncNN) has also been developed on top of Keras, a popular deep learning library—this allows for general use of the approach. A supplemental document, the data and R codes are available online.
DOI: 10.6084/m9.figshare.20263823
title: Deep Learning With Functional Inputs

description: We present a methodology for integrating functional data into deep neural networks. The model is defined for scalar responses with multiple functional and scalar covariates. A by-product of the method is a set of dynamic functional weights that can be visualized during the optimization process. This visualization leads to a greater interpretability of the relationship between the covariates and the response relative to conventional neural networks. The model is shown to perform well in a number of contexts including prediction of new data and recovery of the true underlying relationship between the functional covariate and scalar response; these results were confirmed through real data applications and simulation studies. An R package (FuncNN) has also been developed on top of Keras, a popular deep learning library—this allows for general use of the approach. A supplemental document, the data and R codes are available online.
DOI: 10.6084/m9.figshare.19651416.v1
title: Analytic Permutation Testing for functional data ANOVA

description: Analysis of variance is a cornerstone of statistical hypothesis testing. When data lies beyond the assumption of univariate normality, nonparametric methods including rank based statistics and permutation tests are enlisted. The permutation test is a versatile exact nonparametric significance test that requires drastically fewer assumptions than similar parametric tests. The main downfall of the permutation test is high computational cost making this approach laborious for comparing multiple samples of complex data types and completely infeasible in any application requiring speedy results such as high throughput streaming data. We rectify this problem through application of concentration inequalities and thus propose a computation free permutation test—i.e. a permutation-less permutation test. This general framework is applied to multivariate and matrix-valued, but with a special emphasis on functional data. We improve these concentration bounds via a novel incomplete beta transform. Our theory is extended from 2-sample to <i>k</i>-sample testing through the use of weakly dependent Rademacher chaoses and modified decoupling inequalities. Our methodology is tested on classic functional data sets including the Berkeley growth curves and the phoneme dataset. We further analyze a novel dataset of 12 spoken vowel sounds that was collected to illustrate to power of the analytic permutation test.
DOI: 10.6084/m9.figshare.19779049.v1
title: Semi-Complete Data Augmentation for Efficient State Space Model Fitting

description: We propose a novel efficient model-fitting algorithm for state space models. State space models are an intuitive and flexible class of models, frequently used due to the combination of their natural separation of the different mechanisms acting on the system of interest: the latent underlying system process; and the observation process. This flexibility, however, often comes at the price of more complicated model-fitting algorithms due to the associated analytically intractable likelihood. For the general case a Bayesian data augmentation approach is often employed, where the true unknown states are treated as auxiliary variables and imputed within the MCMC algorithm. However, standard “vanilla” MCMC algorithms may perform very poorly due to high correlation between the imputed states and/or parameters, often leading to model-specific bespoke algorithms being developed that are non-transferable to alternative models. The proposed method addresses the inefficiencies of traditional approaches by combining data augmentation with numerical integration in a Bayesian hybrid approach. This approach permits the use of standard “vanilla” updating algorithms that perform considerably better than the traditional approach in terms of improved mixing and lower autocorrelation, and has the potential to be incorporated into bespoke model-specific algorithms. To demonstrate the ideas, we apply our semi-complete data augmentation algorithm to different application areas and models, leading to distinct implementation schemes and improved mixing and demonstrating improved mixing of the model parameters.
DOI: 10.6084/m9.figshare.19831250
title: Enforcing Stationarity through the Prior in Vector Autoregressions

description: Stationarity is a very common assumption in time series analysis. A vector autoregressive process is stable if and only if the roots of its characteristic equation lie outside the unit circle, constraining the autoregressive coefficient matrices to lie in the stationary region. However, the stationary region has a highly complex geometry which impedes specification of a prior distribution. In this work, an unconstrained reparameterization of a stationary vector autoregression is presented. The new parameters are partial autocorrelation matrices, which are interpretable, and can be transformed bijectively to the space of unconstrained square matrices through a simple mapping of their singular values. This transformation preserves various structural forms of the partial autocorrelation matrices and readily facilitates specification of a prior. Properties of this prior are described along with an important special case which is exchangeable with respect to the order of the elements in the observation vector. Posterior inference and computation are described and implemented using Hamiltonian Monte Carlo via Stan. The prior and inferential procedures are illustrated with an application to a macroeconomic time series which highlights the benefits of enforcing stationarity and encouraging shrinkage toward a sensible parametric structure. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20263823.v1
title: Deep Learning with Functional Inputs

description: We present a methodology for integrating functional data into deep neural networks. The model is defined for scalar responses with multiple functional and scalar covariates. A by-product of the method is a set of dynamic functional weights that can be visualized during the optimization process. This visualization leads to a greater interpretability of the relationship between the covariates and the response relative to conventional neural networks. The model is shown to perform well in a number of contexts including prediction of new data and recovery of the true underlying relationship between the functional covariate and scalar response; these results were confirmed through real data applications and simulation studies. An R package (FuncNN) has also been developed on top of Keras, a popular deep learning library – this allows for general use of the approach. A supplemental document, the data and R codes are available online.
DOI: 10.6084/m9.figshare.19831250.v2
title: Enforcing Stationarity through the Prior in Vector Autoregressions

description: Stationarity is a very common assumption in time series analysis. A vector autoregressive process is stable if and only if the roots of its characteristic equation lie outside the unit circle, constraining the autoregressive coefficient matrices to lie in the stationary region. However, the stationary region has a highly complex geometry which impedes specification of a prior distribution. In this work, an unconstrained reparameterization of a stationary vector autoregression is presented. The new parameters are partial autocorrelation matrices, which are interpretable, and can be transformed bijectively to the space of unconstrained square matrices through a simple mapping of their singular values. This transformation preserves various structural forms of the partial autocorrelation matrices and readily facilitates specification of a prior. Properties of this prior are described along with an important special case which is exchangeable with respect to the order of the elements in the observation vector. Posterior inference and computation are described and implemented using Hamiltonian Monte Carlo via Stan. The prior and inferential procedures are illustrated with an application to a macroeconomic time series which highlights the benefits of enforcing stationarity and encouraging shrinkage toward a sensible parametric structure. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20181806.v1
title: Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices

description: We present new methodologies for Bayesian inference on the rate parameters of a discretely observed continuous-time Markov jump process with a countably infinite statespace. The usual method of choice for inference, particle Markov chain Monte Carlo (particle MCMC), struggles when the observation noise is small. We consider the most challenging regime of exact observations and provide two new methodologies for inference in this case: the minimal extended statespace algorithm (MESA) and the nearly minimal extended statespace algorithm (nMESA). By extending the Markov chain Monte Carlo statespace, both MESA and nMESA use the exponentiation of finite rate matrices to perform exact Bayesian inference on the Markov jump process even though its statespace is countably infinite. Numerical experiments show improvements over particle MCMC of between a factor of three and several orders of magnitude.
DOI: 10.6084/m9.figshare.19831250.v1
title: Enforcing stationarity through the prior in vector autoregressions

description: Stationarity is a very common assumption in time series analysis. A vector autoregressive process is stationary if and only if the roots of its characteristic equation lie outside the unit circle, constraining the autoregressive coefficient matrices to lie in the stationary region. However, the stationary region has a highly complex geometry which impedes specification of a prior distribution. In this work, an unconstrained reparameterization of a stationary vector autoregression is presented. The new parameters are partial autocorrelation matrices, which are interpretable, and can be transformed bijectively to the space of unconstrained square matrices through a simple mapping of their singular values. This transformation preserves various structural forms of the partial autocorrelation matrices and readily facilitates specification of a prior. Properties of this prior are described along with an important special case which is exchangeable with respect to the order of the elements in the observation vector. Posterior inference and computation are described and implemented using Hamiltonian Monte Carlo via Stan. The prior and inferential procedures are illustrated with an application to a macroeconomic time series which highlights the benefits of enforcing stationarity and encouraging shrinkage towards a sensible parametric structure. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.7473978
title: Chiral Conglomerate Crystals in the CSD (v5.43; Nov 2021)

description: The list of chiral conglomerate crystals found by manual search of the CSD (Version 5.43; November 2021) from 2020-2021. Associated with the following preprint:https://doi.org/10.26434/chemrxiv-2023-jt4jf
description: {"references": ["https://doi.org/10.26434/chemrxiv-2023-jt4jf"]}
DOI: 10.5281/zenodo.7473979
title: Chiral Conglomerate Crystals in the CSD (v5.43; Nov 2021)

description: The list of chiral conglomerate crystals found by manual search of the CSD (Version 5.43; November 2021) from 2020-2021.
DOI: 10.5281/zenodo.7693828
title: Chiral Conglomerate Crystals in the CSD (v5.43; Nov 2021)

description: The list of chiral conglomerate crystals found by manual search of the CSD (Version 5.43; November 2021) from 2020-2021. Associated with the following preprint:https://doi.org/10.26434/chemrxiv-2023-jt4jf
description: {"references": ["https://doi.org/10.26434/chemrxiv-2023-jt4jf"]}
DOI: 10.6084/m9.figshare.22207693
title: Scaled peptide and protein level data

description: These datasets include scaled peptide and protein intensities post normalization within and across batches
DOI: 10.6084/m9.figshare.22207693.v1
title: Scaled peptide and protein level data

description: These datasets include scaled peptide and protein intensities post normalization within and across batches