cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1280106.10fu
title: SH1280106.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6084/m9.figshare.31049602
title: Structural Identification for Spatio-Temporal Dynamic Models

description: Identifying latent cluster structures in spatial trends constitutes an important yet challenging task in diverse applications. In this paper, we propose a novel method based on a discrepancy measure over small spatial blocks that effectively uncovers heterogeneity within dynamic spatial data. Our approach effectively detects boundaries where structural changes occur, thus allowing for more nuanced insights into underlying spatial patterns. Unlike methods predicated on strong stationarity assumptions, our framework accommodates piecewise-defined parameters and irregular sampling locations, enabling its broad applicability to real-world datasets. We further establish asymptotic properties and limit distributions of the proposed methods by leveraging the notion of spatial physical dependence, accounting for correlations across spatial domains. Simulations and real data analyses confirm the effectiveness of the method, highlighting its robustness and accuracy in identifying complex spatio-temporal structures.
DOI: 10.6084/m9.figshare.31049602.v1
title: Structural Identification for Spatio-Temporal Dynamic Models

description: Identifying latent cluster structures in spatial trends constitutes an important yet challenging task in diverse applications. In this paper, we propose a novel method based on a discrepancy measure over small spatial blocks that effectively uncovers heterogeneity within dynamic spatial data. Our approach effectively detects boundaries where structural changes occur, thus allowing for more nuanced insights into underlying spatial patterns. Unlike methods predicated on strong stationarity assumptions, our framework accommodates piecewise-defined parameters and irregular sampling locations, enabling its broad applicability to real-world datasets. We further establish asymptotic properties and limit distributions of the proposed methods by leveraging the notion of spatial physical dependence, accounting for correlations across spatial domains. Simulations and real data analyses confirm the effectiveness of the method, highlighting its robustness and accuracy in identifying complex spatio-temporal structures.
DOI: 10.15156/bio/sh1280108.10fu
title: SH1280108.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1280110.10fu
title: SH1280110.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6084/m9.figshare.30505807
title: A Factor-Copula Latent-Vine Time Series Model for Extreme Flood Insurance Losses

description: Statistical inference on the dependence of multivariate extremes poses notable challenges, particularly in contexts characterized by large dimensions and sparse extreme observations. While copula models provide flexible parametric methods for dependence modeling, caution is warranted when using them for extremal dependence inference or tail extrapolation. In this article, a novel class of factor-vine copula models is introduced. It is designed for modeling the dependence of extreme insurance losses within the context of the National Flood Insurance Program (NFIP), with broader applicability to space-time dependence modeling in multivariate time series data featuring a clustering structure. The proposed model is a specialized graphical vine dependence model with a latent factor structure. It integrates the advantages of both vine and factor copulas by allowing for great flexibility in tail dependence modeling while maintaining interpretability through a parsimonious latent structure. It is also shown how the incorporation of univariate extreme-value margins and tail-weighted dependence measures within the factor-vine model can address current challenges associated with using parametric copulas for extreme inference. Applications of the proposed model are discussed in the context of the NFIP, focusing on its efficacy in evaluating the risks associated with extreme weather events. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.30505807.v2
title: A Factor-Copula Latent-Vine Time Series Model for Extreme Flood Insurance Losses

description: Statistical inference on the dependence of multivariate extremes poses notable challenges, particularly in contexts characterized by large dimensions and sparse extreme observations. While copula models provide flexible parametric methods for dependence modeling, caution is warranted when using them for extremal dependence inference or tail extrapolation. In this article, a novel class of factor-vine copula models is introduced. It is designed for modeling the dependence of extreme insurance losses within the context of the National Flood Insurance Program (NFIP), with broader applicability to space-time dependence modeling in multivariate time series data featuring a clustering structure. The proposed model is a specialized graphical vine dependence model with a latent factor structure. It integrates the advantages of both vine and factor copulas by allowing for great flexibility in tail dependence modeling while maintaining interpretability through a parsimonious latent structure. It is also shown how the incorporation of univariate extreme-value margins and tail-weighted dependence measures within the factor-vine model can address current challenges associated with using parametric copulas for extreme inference. Applications of the proposed model are discussed in the context of the NFIP, focusing on its efficacy in evaluating the risks associated with extreme weather events. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15156/bio/sh1280111.10fu
title: SH1280111.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6084/m9.figshare.30505807.v1
title: A factor-copula latent-vine time series model for extreme flood insurance losses

description: Statistical inference on the dependence of multivariate extremes poses notable challenges, particularly in contexts characterized by large dimensions and sparse extreme observations. While copula models provide flexible parametric methods for dependence modeling, caution is warranted when using them for extremal dependence inference or tail extrapolation. In this article, a novel class of factor-vine copula models is introduced. It is designed for modeling the dependence of extreme insurance losses within the context of the National Flood Insurance Program (NFIP), with broader applicability to space-time dependence modeling in multivariate time series data featuring a clustering structure. The proposed model is a specialized graphical vine dependence model with a latent factor structure. It integrates the advantages of both vine and factor copulas by allowing for great flexibility in tail dependence modeling while maintaining interpretability through a parsimonious latent structure. It is also shown how the incorporation of univariate extreme-value margins and tail-weighted dependence measures within the factor-vine model can address current challenges associated with using parametric copulas for extreme inference. Applications of the proposed model are discussed in the context of the NFIP, focusing on its efficacy in evaluating the risks associated with extreme weather events.
DOI: 10.15156/bio/sh1280112.10fu
title: SH1280112.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6084/m9.figshare.31049608
title: Optimal Differentially Private Ranking from Pairwise Comparisons*

description: Data privacy is a central concern in many applications involving ranking from incomplete and noisy pairwise comparisons, such as recommendation systems, educational assessments, and opinion surveys on sensitive topics. In this work, we propose differentially private algorithms for ranking based on pairwise comparisons. Specifically, we develop and analyze ranking methods under two privacy notions: edge differential privacy, which protects the confidentiality of individual comparison outcomes, and individual differential privacy, which safeguards potentially many comparisons contributed by a single individual. Our algorithms–including a perturbed maximum likelihood estimator and a noisy count-based method–are shown to achieve minimax optimal rates of convergence under the respective privacy constraints. We further demonstrate the practical effectiveness of our methods through experiments on both simulated and real-world data.
DOI: 10.6084/m9.figshare.31049608.v1
title: Optimal Differentially Private Ranking from Pairwise Comparisons*

description: Data privacy is a central concern in many applications involving ranking from incomplete and noisy pairwise comparisons, such as recommendation systems, educational assessments, and opinion surveys on sensitive topics. In this work, we propose differentially private algorithms for ranking based on pairwise comparisons. Specifically, we develop and analyze ranking methods under two privacy notions: edge differential privacy, which protects the confidentiality of individual comparison outcomes, and individual differential privacy, which safeguards potentially many comparisons contributed by a single individual. Our algorithms–including a perturbed maximum likelihood estimator and a noisy count-based method–are shown to achieve minimax optimal rates of convergence under the respective privacy constraints. We further demonstrate the practical effectiveness of our methods through experiments on both simulated and real-world data.
DOI: 10.15156/bio/sh1280114.10fu
title: SH1280114.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1280115.10fu
title: SH1280115.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1280116.10fu
title: SH1280116.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1280117.10fu
title: SH1280117.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6084/m9.figshare.30505865
title: Posterior Risk of Modular and Semi-Modular Bayesian Inference

description: Modular Bayesian methods perform inference in models that are specified through a collection of coupled sub-models, known as modules. These modules often arise from modeling different data sources or from combining domain knowledge from different disciplines. “Cutting feedback” is a Bayesian inference method that ensures misspecification of one module does not affect inferences for parameters in other modules, and produces what is known as the cut posterior. However, choosing between the cut posterior and the standard Bayesian posterior is challenging. When misspecification is not severe, cutting feedback can greatly increase posterior uncertainty without a large reduction of estimation bias, leading to a bias-variance tradeoff. This tradeoff motivates semi-modular posteriors, which interpolate between standard and cut posteriors based on a tuning parameter. In this work, we provide the first precise formulation of the bias-variance tradeoff that is present in cutting feedback, and we propose a new semi-modular posterior that takes advantage of it. Under general regularity conditions, we prove that this semi-modular posterior is more accurate than the cut posterior according to a notion of posterior risk. An important implication of this result is that point inferences made under the cut posterior are <i>inadmissable</i>. The new method is demonstrated in a number of examples. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.30505865.v2
title: Posterior Risk of Modular and Semi-Modular Bayesian Inference

description: Modular Bayesian methods perform inference in models that are specified through a collection of coupled sub-models, known as modules. These modules often arise from modeling different data sources or from combining domain knowledge from different disciplines. “Cutting feedback” is a Bayesian inference method that ensures misspecification of one module does not affect inferences for parameters in other modules, and produces what is known as the cut posterior. However, choosing between the cut posterior and the standard Bayesian posterior is challenging. When misspecification is not severe, cutting feedback can greatly increase posterior uncertainty without a large reduction of estimation bias, leading to a bias-variance tradeoff. This tradeoff motivates semi-modular posteriors, which interpolate between standard and cut posteriors based on a tuning parameter. In this work, we provide the first precise formulation of the bias-variance tradeoff that is present in cutting feedback, and we propose a new semi-modular posterior that takes advantage of it. Under general regularity conditions, we prove that this semi-modular posterior is more accurate than the cut posterior according to a notion of posterior risk. An important implication of this result is that point inferences made under the cut posterior are <i>inadmissable</i>. The new method is demonstrated in a number of examples. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15156/bio/sh1280120.10fu
title: SH1280120.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.