cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28473768.v1
title: Statistical inference for high-dimensional convoluted rank regression

description: High-dimensional penalized rank regression is a powerful tool for modeling high-dimensional data due to its robustness and estimation efficiency. However, the non-smoothness of the rank loss brings great challenges to the computation. To solve this critical issue, high-dimensional convoluted rank regression has been recently proposed, introducing penalized convoluted rank regression estimators. However, these developed estimators cannot be directly used to make inference. In this paper, we investigate the statistical inference problem of high-dimensional convoluted rank regression. The use of U-statistic in convoluted rank loss function presents challenges for the analysis. We begin by establishing estimation error bounds of the penalized convoluted rank regression estimators under weaker conditions on the predictors. Building on this, we further introduce a debiased estimator and provide its Bahadur representation. Subsequently, a high-dimensional Gaussian approximation for the maximum deviation of the debiased estimator is derived, which allows us to construct simultaneous confidence intervals. For implementation, a novel bootstrap procedure is proposed and its theoretical validity is also established. Finally, simulation and real data analysis are conducted to illustrate the merits of our proposed methods.
DOI: 10.6084/m9.figshare.28590394
title: Kernel Meets Sieve: Transformed Hazards Models with Sparse Longitudinal Covariates

description: We study the transformed hazards model with time-dependent covariates observed intermittently for the censored outcome. Existing work assumes the availability of the whole trajectory of the time-dependent covariates, which is unrealistic. We propose combining kernel-weighted log-likelihood and sieve maximum log-likelihood estimation to conduct statistical inference. The method is robust and easy to implement. We establish the asymptotic properties of the proposed estimator and contribute to a rigorous theoretical framework for general kernel-weighted sieve M-estimators. Numerical studies corroborate our theoretical results and show that the proposed method performs favorably over competing methods. The analysis of a dataset from a COVID-19 study in Wuhan identifies clinical predictors that otherwise cannot be obtained using existing methods. 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.28590394.v2
title: Kernel Meets Sieve: Transformed Hazards Models with Sparse Longitudinal Covariates

description: We study the transformed hazards model with time-dependent covariates observed intermittently for the censored outcome. Existing work assumes the availability of the whole trajectory of the time-dependent covariates, which is unrealistic. We propose combining kernel-weighted log-likelihood and sieve maximum log-likelihood estimation to conduct statistical inference. The method is robust and easy to implement. We establish the asymptotic properties of the proposed estimator and contribute to a rigorous theoretical framework for general kernel-weighted sieve M-estimators. Numerical studies corroborate our theoretical results and show that the proposed method performs favorably over competing methods. The analysis of a dataset from a COVID-19 study in Wuhan identifies clinical predictors that otherwise cannot be obtained using existing methods. 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.28590394.v1
title: Kernel meets sieve: transformed hazards models with sparse longitudinal covariates

description: We study the transformed hazards model with time-dependent covariates observed intermittently for the censored outcome. Existing work assumes the availability of the whole trajectory of the time-dependent covariates, which is unrealistic. We propose combining kernel-weighted log-likelihood and sieve maximum log-likelihood estimation to conduct statistical inference. The method is robust and easy to implement. We establish the asymptotic properties of the proposed estimator and contribute to a rigorous theoretical framework for general kernel-weighted sieve M-estimators. Numerical studies corroborate our theoretical results and show that the proposed method performs favorably over competing methods. The analysis of a data set from a COVID-19 study in Wuhan identifies clinical predictors that otherwise cannot be obtained using existing methods.
DOI: 10.6084/m9.figshare.28620083
title: Frequency Domain Statistical Inference for High-Dimensional Time Series

description: Analyzing time series in the frequency domain enables the development of powerful tools for investigating the second-order characteristics of multivariate processes. Parameters like the spectral density matrix and its inverse, the coherence or the partial coherence, encode comprehensively the complex linear relations between the component processes of the multivariate system. In this article, we develop inference procedures for such parameters in a high-dimensional, time series setup. Toward this goal, we first focus on the derivation of consistent estimators of the coherence and, more importantly, of the partial coherence which possess manageable limiting distributions that are suitable for testing purposes. Statistical tests of the hypothesis that the maximum over frequencies of the coherence, respectively, of the partial coherence, do not exceed a prespecified threshold value are developed. Our approach allows for testing hypotheses for individual coherences and/or partial coherences as well as for multiple testing of large sets of such parameters. In the latter case, a consistent procedure to control the false discovery rate is developed. The finite sample performance of the inference procedures introduced is investigated by means of simulations and applications to the construction of graphical interaction models for brain connectivity based on EEG data are presented.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.28620083.v1
title: Frequency Domain Statistical Inference for High-Dimensional Time Series

description: Analyzing time series in the frequency domain enables the development of powerful tools for investigating the second-order characteristics of multivariate processes. Parameters like the spectral density matrix and its inverse, the coherence or the partial coherence, encode comprehensively the complex linear relations between the component processes of the multivariate system. In this article, we develop inference procedures for such parameters in a high-dimensional, time series setup. Toward this goal, we first focus on the derivation of consistent estimators of the coherence and, more importantly, of the partial coherence which possess manageable limiting distributions that are suitable for testing purposes. Statistical tests of the hypothesis that the maximum over frequencies of the coherence, respectively, of the partial coherence, do not exceed a prespecified threshold value are developed. Our approach allows for testing hypotheses for individual coherences and/or partial coherences as well as for multiple testing of large sets of such parameters. In the latter case, a consistent procedure to control the false discovery rate is developed. The finite sample performance of the inference procedures introduced is investigated by means of simulations and applications to the construction of graphical interaction models for brain connectivity based on EEG data are presented.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.28590292
title: Geodesic Mixed Effects Models for Repeatedly Observed/Longitudinal Random Objects

description: Mixed effect modeling for longitudinal data is challenging when the observed data are random objects, which are complex data taking values in a general metric space without either global linear or local linear (Riemannian) structure. In such settings the classical additive error model and distributional assumptions are unattainable. Due to the rapid advancement of technology, longitudinal data containing complex random objects, such as covariance matrices, data on Riemannian manifolds, and probability distributions are becoming more common. Addressing this challenge, we develop a mixed-effects regression for data in geodesic spaces, where the underlying mean response trajectories are geodesics in the metric space and the deviations of the observations from the model are quantified by perturbation maps or transports. A key finding is that the geodesic trajectories assumption for the case of random objects is a natural extension of the linearity assumption in the standard Euclidean scenario to the case of general geodesic metric spaces. Geodesics can be recovered from noisy observations by exploiting a connection between the geodesic path and the path obtained by global Fréchet regression for random objects. The effect of baseline Euclidean covariates on the geodesic paths is modeled by another Fréchet regression step. We study the asymptotic convergence of the proposed estimates and provide illustrations through simulations and real-data applications. 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.28590292.v1
title: Geodesic Mixed Effects Models for Repeatedly Observed/Longitudinal Random Objects

description: Mixed effect modeling for longitudinal data is challenging when the observed data are random objects, which are complex data taking values in a general metric space without either global linear or local linear (Riemannian) structure. In such settings the classical additive error model and distributional assumptions are unattainable. Due to the rapid advancement of technology, longitudinal data containing complex random objects, such as covariance matrices, data on Riemannian manifolds, and probability distributions are becoming more common. Addressing this challenge, we develop a mixed-effects regression for data in geodesic spaces, where the underlying mean response trajectories are geodesics in the metric space and the deviations of the observations from the model are quantified by perturbation maps or transports. A key finding is that the geodesic trajectories assumption for the case of random objects is a natural extension of the linearity assumption in the standard Euclidean scenario to the case of general geodesic metric spaces. Geodesics can be recovered from noisy observations by exploiting a connection between the geodesic path and the path obtained by global Fréchet regression for random objects. The effect of baseline Euclidean covariates on the geodesic paths is modeled by another Fréchet regression step. We study the asymptotic convergence of the proposed estimates and provide illustrations through simulations and real-data applications. 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.28616100.v1
title: Cutting Feedback in Misspecified Copula Models

description: In copula models the marginal distributions and copula function are specified separately. We treat these as two modules in a modular Bayesian inference framework, and propose conducting modified Bayesian inference by “cutting feedback”. Cutting feedback limits the influence of potentially misspecified modules in posterior inference. We consider two types of cuts. The first limits the influence of a misspecified copula on inference for the marginals, which is a Bayesian analogue of the popular Inference for Margins (IFM) estimator. The second limits the influence of misspecified marginals on inference for the copula parameters by using a pseudo likelihood of the ranks to define the cut model. We establish that if only one of the modules is misspecified, then the appropriate cut posterior gives accurate uncertainty quantification asymptotically for the parameters in the other module. Computation of the cut posteriors is difficult, and new variational inference methods to do so are proposed. The efficacy of the new methodology is demonstrated using both simulated data and a substantive multivariate time series application from macroeconomic forecasting. In the latter, cutting feedback from misspecified marginals to a 1096 dimension copula improves posterior inference and predictive accuracy greatly, compared to conventional Bayesian inference.
DOI: 10.5281/zenodo.18146843
title: Warunki brzegowe UMIM

description: EIVA
DOI: 10.5281/zenodo.18146844
title: Warunki brzegowe UMIM

description: EIVA
DOI: 10.15468/dl.earpdf
title: Occurrence Download

description: A dataset containing 45457 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Momordica"
 ]
}

 The dataset includes 45457 records from 824 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011926-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/dans-xvb-zbmy
title: Provincie Utrecht
title: archeologisch onderzoek in het kader van ontgronding 'De Niënhof' te Bunnik

description: &lt;p&gt;onderzoeksrapport&lt;/p&gt;
DOI: 10.15468/dl.rxzrkn
title: Occurrence Download

description: A dataset containing 90547 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Europe",
 "Country is Sweden",
 "GadmGid is SWE",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hepatica nobilis"
 ]
}

 The dataset includes 90547 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011906-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18345895
title: A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI: Rapid Literature Review Results

description: The datasets available here provide supporting material for our ACM TOSEM paper on “A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI”. 

Generative AI (GenAI) is rapidly transforming software engineering (SE) practices, influencing how SE processes are executed, as well as how software systems are developed, operated, and evolved. This paper applies design science research to build a roadmap for GenAI-augmented SE. The process consists of three cycles that incrementally integrate multiple sources of evidence, including collaborative discussions from the FSE 2025 “Software Engineering 2030” workshop, rapid literature reviews (RLRs – with the detailed results capture in these datasets), and external feedback sessions involving peers. McLuhan’s tetrads were used as a conceptual instrument to systematically capture the transforming effects of GenAI on SE processes and software products. The resulting roadmap identifies four fundamental forms of GenAI augmentation in SE and systematically characterizes their related research challenges and opportunities. These insights are then consolidated into a set of future research directions. By grounding the roadmap in a rigorous multi-cycle process and cross-validating it among independent author teams and peers, the study provides a transparent and reproducible foundation for analyzing how GenAI affects SE processes, methods and tools, and for framing future research within this rapidly evolving area.
DOI: 10.5281/zenodo.18345896
title: A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI: Rapid Literature Review Results

description: The datasets available here provide supporting material for our ACM TOSEM paper on “A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI”. 

Generative AI (GenAI) is rapidly transforming software engineering (SE) practices, influencing how SE processes are executed, as well as how software systems are developed, operated, and evolved. This paper applies design science research to build a roadmap for GenAI-augmented SE. The process consists of three cycles that incrementally integrate multiple sources of evidence, including collaborative discussions from the FSE 2025 “Software Engineering 2030” workshop, rapid literature reviews (RLRs – with the detailed results capture in these datasets), and external feedback sessions involving peers. McLuhan’s tetrads were used as a conceptual instrument to systematically capture the transforming effects of GenAI on SE processes and software products. The resulting roadmap identifies four fundamental forms of GenAI augmentation in SE and systematically characterizes their related research challenges and opportunities. These insights are then consolidated into a set of future research directions. By grounding the roadmap in a rigorous multi-cycle process and cross-validating it among independent author teams and peers, the study provides a transparent and reproducible foundation for analyzing how GenAI affects SE processes, methods and tools, and for framing future research within this rapidly evolving area.
DOI: 10.15468/dl.msyqgm
title: Occurrence Download

description: A dataset containing 8169589 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is UK Butterfly Monitoring Scheme (UKBMS)"
 ]
}

 The dataset includes 8169589 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011934-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30283386
title: Second-order group contribution method for <i>T</i><sub>c</sub>, <i>P</i><sub>c</sub>, <i>ω</i>, ΔGf0, ΔHf0 and liquid densities of linear and branched alkanes

description: Accurate prediction of thermodynamic properties of hydrocarbons is essential for chemical process modelling. Conventional group contribution methods often are used to predict these properties. However, these methods often require extensive parameter sets to handle structural complexities. A refined group contribution method for predicting thermodynamic properties of hydrocarbon isomers with reduced complexity and improved accuracy is presented and discussed. By combining the structural framework of Constantinou and Gani (CG94) with a sensitivity-based selection of second-order groups, a reduced yet highly effective set of twelve second-order groups is identified. This reduced set retains the predictive power comparable to more complex models while significantly reducing the number of parameters. Linear regression is applied to model enthalpies and Gibbs free energies of formation for a wide temperature range. To test broader applicability, the model is further extended to properties that require nonlinear regression, including critical temperatures, critical pressures, acentric factors, and liquid densities. For all cases, the proposed model achieves high predictive accuracy, demonstrating its robustness and generalizability. This methodology balances interpretability, efficiency, and performance, making it suitable for both research and industrial thermodynamic modelling.
DOI: 10.6084/m9.figshare.30283386.v1
title: Second-order group contribution method for <i>T</i><sub>c</sub>, <i>P</i><sub>c</sub>, <i>ω</i>, ΔGf0, ΔHf0 and liquid densities of linear and branched alkanes

description: Accurate prediction of thermodynamic properties of hydrocarbons is essential for chemical process modelling. Conventional group contribution methods often are used to predict these properties. However, these methods often require extensive parameter sets to handle structural complexities. A refined group contribution method for predicting thermodynamic properties of hydrocarbon isomers with reduced complexity and improved accuracy is presented and discussed. By combining the structural framework of Constantinou and Gani (CG94) with a sensitivity-based selection of second-order groups, a reduced yet highly effective set of twelve second-order groups is identified. This reduced set retains the predictive power comparable to more complex models while significantly reducing the number of parameters. Linear regression is applied to model enthalpies and Gibbs free energies of formation for a wide temperature range. To test broader applicability, the model is further extended to properties that require nonlinear regression, including critical temperatures, critical pressures, acentric factors, and liquid densities. For all cases, the proposed model achieves high predictive accuracy, demonstrating its robustness and generalizability. This methodology balances interpretability, efficiency, and performance, making it suitable for both research and industrial thermodynamic modelling.
DOI: 10.15468/dl.jn2394
title: Occurrence Download

description: A dataset containing 21220 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myricaria"
 ]
}

 The dataset includes 21220 records from 381 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011928-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/r8bm47
title: On the subgenus Eurysunius Reitter of Turkey IX. Three new species from central northern Anatolia, with an identification key to Turkish species (Coleoptera: Staphylinidae: Paederinae, Astenus)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Anlaş, Sinan (2024): On the subgenus Eurysunius Reitter of Turkey IX. Three new species from central northern Anatolia, with an identification key to Turkish species (Coleoptera: Staphylinidae: Paederinae, Astenus). Zootaxa 5474 (1): 68-76, DOI: 10.11646/zootaxa.5474.1.6, URL: http://dx.doi.org/10.11646/zootaxa.5474.1.6AbstractIn this study, three new species belonging to the subgenus Eurysunius Reitter of the genus Astenus Dejean (Coleoptera: Staphylinidae: Paederinae) are described and illustrated from central northern Anatolia, in Turkey, and compared with the related species: Astenus corumensis sp. n. and A. hititicus sp. n. (Çorum province), and A. kaspareki sp. n. (Amasya province). A improved key to Turkish species of Eurysunius is provided as well. The distributions of 28 species in Turkey are mapped.
DOI: 10.5281/zenodo.18373777
title: Navigating turbid waters: cumulative effects of turbulence and suspended particulate matter on ingestion and defecation of the calanoid copepod Acartia tonsa
DOI: 10.5281/zenodo.18373776
title: Navigating turbid waters: cumulative effects of turbulence and suspended particulate matter on ingestion and defecation of the calanoid copepod Acartia tonsa
DOI: 10.5281/zenodo.18373814
title: ArcMol Enables Task-Adaptive Spherical Representation Learning for Molecular Property Prediction

description: ArcMol, a task-adaptive spherical representation learning framework for molecular property prediction. ArcMol integrates multiple molecular representations and adaptively selects and fuses them in a task-driven manner, organizing molecular embeddings within a hyperspherical latent space to support angular-based task discrimination.