cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19232834.v2
title: Composite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects

description: Modeling and estimating autocorrelated discrete data can be challenging. In this article, we use an autoregressive panel ordered probit model where the serial correlation in the discrete variable is driven by the autocorrelation in the latent variable. In such a nonlinear model, the presence of a lagged latent variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods that involve a much lower order of integration. However, parameter identification might potentially become problematic since the information employed in lower dimensional distributions may not be rich enough for identification. Therefore, we characterize types of composite likelihoods that are valid for this model and study conditions under which the parameters can be identified. Moreover, we provide consistency and asymptotic normality results for two different composite likelihood estimators and conduct Monte Carlo studies to assess their finite-sample performances. Finally, we apply our method to analyze corporate bond ratings. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27629391
title: Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil

description: We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time-varying forecast uncertainty and risk for the real price of oil over the period 1974–2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk.
DOI: 10.6084/m9.figshare.27629391.v1
title: Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil

description: We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time-varying forecast uncertainty and risk for the real price of oil over the period 1974–2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk.
DOI: 10.6084/m9.figshare.19232834.v1
title: Composite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects

description: Modeling and estimating autocorrelated discrete data can be challenging. In this paper, we use an autoregressive panel ordered probit model where the serial correlation in the discrete variable is driven by the autocorrelation in the latent variable. In such a nonlinear model, the presence of a lagged latent variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods that involve a much lower order of integration. However, parameter identification might potentially become problematic since the information employed in lower dimensional distributions may not be rich enough for identification. Therefore, we characterize types of composite likelihoods that are valid for this model and study conditions under which the parameters can be identified. Moreover, we provide consistency and asymptotic normality results for two different composite likelihood estimators and conduct Monte Carlo studies to assess their finite-sample performances. Finally, we apply our method to analyze corporate bond ratings.
DOI: 10.15468/dl.fg8avq
title: Occurrence Download

description: A dataset containing 176942 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "OccurrenceStatus is Present",
 "TaxonKey is Bubulcus ibis (Linnaeus, 1758)"
 ]
}

 The dataset includes 176942 records from 344 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000430-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14052007
title: Supp Table 1. Comparative Analysis of Drug Interactions Identified by ChatGPT-4o with Dermatologist Agreement
DOI: 10.5281/zenodo.14052008
title: Supp Table 1. Comparative Analysis of Drug Interactions Identified by ChatGPT-4o with Dermatologist Agreement
DOI: 10.5281/zenodo.13325667
title: UAV_SegetalFlora

description: Data and models trained to reproduce the results of the manuscript Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning. Available at: https://doi.org/10.1016/j.ecolind.2024.112780.

If you use the data and trained models please cite the original article as: Barrasso, C. et al., (2024) ‘Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning’, Ecological Indicators. Available at: https://doi.org/10.1016/j.ecolind.2024.112780.
DOI: 10.5281/zenodo.13983340
title: UAV_SegetalFlora

description: Data and models trained to reproduce the results of the manuscript Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning. Available at: https://doi.org/10.1016/j.ecolind.2024.112780.

If you use the data and trained models please cite the original article as: Barrasso, C. et al., (2024) ‘Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning’, Ecological Indicators. Available at: https://doi.org/10.1016/j.ecolind.2024.112780.
DOI: 10.15468/dl.2vcnxv
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyparrhenia andongensis (Rendle) Stapf",
 "Continent is Africa"
 ]
}

 The dataset includes 7 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000484-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13138753
title: Raw MRI imaging for "In Vivo Neuroprotection in Ischemic Stroke by Activated Protein C Requires β-Arrestin 2"

description: The protease, activated protein C (APC) and its variants provide pharmacological benefits in many murine preclinical injury models, including neuroprotection for ischemic stroke and mortality reduction for sepsis.  The in vivo mechanism of action for APC in ischemic stroke and sepsis injury models, similar to extensive in vitro studies using cultured cells, involves protease activated receptor (PAR) 1-mediated biased signaling. Cell culture studies showed that APC/PAR1 signaling requires β-Arrestin 2, an intracellular scaffold protein, and that β-Arrestin 2-dependent, APC-initiated signaling can distinctly alter a wide variety of intracellular signaling pathways.  It is unclear whether in vitro studies can be extrapolated to in vivo mechanisms of action.  This study employed a proximal transient Middle Cerebral Artery Occlusion model to study the neuroprotective actions of the signaling-selective APC variant, 3K3A-APC, in β-arrestin 2 deficient (Arrb2-/-) mice. Based on the size and shape of brain injury areas, 3K3A-APC significantly limited brain injury in control mice to a relatively small, localized area whereas these protective effects were lost in Arrb2-/- mice. Thus, these data indicate that the major in vitro mechanism of action that requires β-arrestin 2 for APC/PAR1 biased signaling is central to the in vivo mechanism of action for APC’s neuroprotection.
DOI: 10.15468/dl.dx5fzy
title: Occurrence Download

description: A dataset containing 31207 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corbicula fluminea (O.F.Müller, 1774)",
 "Country is United States of America",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 31207 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000483-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.ty68ubfo
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2907 records)
DOI: 10.5281/zenodo.14051709
title: Qualitative Data on Effects of Early and Prolonged Parent-Child Separation: Understanding Mental Health of Separated-Reunited Chinese American Children

description: Early and prolonged parent-child separation due to parental migration or immigration may result in attachment disruption that can threaten the long-term mental health and functioning of affected children, and these risks can persist following reunification and through adulthood. Although sending infants back to the home country for rearing is often practiced among Chinese immigrants, especially low-income families, research has been sparse in understanding the long-term impact of early and prolonged parent-child separation and reunification on disparities in mental health and functioning among separated-reunited children and the mechanism through which such relationships may operate.

Funded by National Institute on Minority Health and Health Disparities (NIMH), we collected semi-structured interview data from 24 parent-child dyads who have experienced separation. The data included interview scrpits with primary coding to understand the mental health impacts, risk/protective factors, and service needs among separated-reunited Chinese American children.
DOI: 10.5281/zenodo.14051710
title: Qualitative Data on Effects of Early and Prolonged Parent-Child Separation: Understanding Mental Health of Separated-Reunited Chinese American Children

description: Early and prolonged parent-child separation due to parental migration or immigration may result in attachment disruption that can threaten the long-term mental health and functioning of affected children, and these risks can persist following reunification and through adulthood. Although sending infants back to the home country for rearing is often practiced among Chinese immigrants, especially low-income families, research has been sparse in understanding the long-term impact of early and prolonged parent-child separation and reunification on disparities in mental health and functioning among separated-reunited children and the mechanism through which such relationships may operate.

Funded by National Institute on Minority Health and Health Disparities (NIMH), we collected semi-structured interview data from 24 parent-child dyads who have experienced separation. The data included interview scrpits with primary coding to understand the mental health impacts, risk/protective factors, and service needs among separated-reunited Chinese American children.
DOI: 10.15468/dl.5v22ym
title: Occurrence Download

description: A dataset containing 931779 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "PublishingCountry is Belgium"
 ]
}

 The dataset includes 931779 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000377-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h2ax39
title: Occurrence Download

description: A dataset containing 644 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyparrhenia bracteata (Humb. & Bonpl. ex Willd.) Stapf",
 "Continent is Africa"
 ]
}

 The dataset includes 644 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000486-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hcev7a
title: Occurrence Download

description: A dataset containing 9411 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.33_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Solidago L."
 ]
}

 The dataset includes 9411 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000487-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3wc5du
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Haematopus ostralegus Linnaeus, 1758",
 "Year is 38",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007866-241107131044228/datasets/export for details.
DOI: 10.15468/dl.mk9cvs
title: Occurrence Download

description: A dataset containing 126 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Bulgaria, Romania, Türkiye)",
 "TaxonKey is Crocus flavus Weston"
 ]
}

 The dataset includes 126 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007868-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zxtuab
title: Occurrence Download

description: A dataset containing 1147 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bauhinia aculeata L."
 ]
}

 The dataset includes 1147 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007870-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ctyzjq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vanessa atalanta (Linnaeus, 1758)",
 "Year is 38",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007871-241107131044228/datasets/export for details.
DOI: 10.15468/dl.qfrx5y
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Polyommatus icarus (von Rottemburg, 1775)",
 "Year is 38",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007873-241107131044228/datasets/export for details.
DOI: 10.25345/c50z71819
title: MassIVE MSV000096374 - Spatial Proteomics by Trapped Ion Mobility supported MALDI MS/MS Imaging: A First Glance into Multiplexed and Spatial Peptide Identification
title: RATIONALE In spatial proteomics, matrix-assisted laser desorption/ionization (MALDI) imaging enables rapid and cost-effective peptide measurement. Yet, in situ peptide identification remains challenging. Therefore, this study aims to integrate the trapped ion mobility spectrometry (TIMS)-based parallel accumulation-serial fragmentation (PASEF) into MALDI imaging of peptides to enable multiplexed MS/MS imaging. METHODS An initial MALDI TIMS MS1 survey measurement is performed, followed by a manual generation of a precursor list containing mass over charge values and ion mobility windows. Inside the dual TIMS system, submitted precursors are trapped, separately eluted by their ion mobility and analyzed in a quadrupole time-of-flight device, thereby enabling multiplexed MALDI MS/MS imaging. Finally, precursors are identified by peptide to spectrum matching. RESULTS This study presents the first multiplexed MALDI TIMS MS/MS imaging (iprm-PASEF) of tryptic peptides. Its applicability is showcased on two histomorphologically distinct tissue specimens in a 4-plex and 5-plex setup. Precursors were successfully identified by the search engine MASCOT in one single MALDI imaging experiment for each respective tissue. Peptide identifications were corroborated by liquid-chromatography tandem mass spectrometry experiments and fragment colocalization analyses. CONCLUSIONS In the present study, we demonstrate the feasibility of TIMS-based MALDI MS/MS imaging for the multiplexed and rapid identification of tryptic peptides in a spatial manner. Hence, it represents a first step towards the integration of MALDI imaging into the emerging field of spatial proteomics. The datasets will be uploaded separately. This data set contains the PDX MS1 data. There are two tissues vissible on the.tif image. The relevant one is on the left side of the slide (away from the barcode).
DOI: 10.15468/dl.fxxyr9
title: Occurrence Download

description: A dataset containing 404964 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rosmarinus officinalis L."
 ]
}

 The dataset includes 404964 records from 802 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007854-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hfp7f5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pelophylax esculentus (Linnaeus, 1758)",
 "Year is 38",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007876-241107131044228/datasets/export for details.
DOI: 10.15468/dl.75gc2v
title: Occurrence Download

description: A dataset containing 32915 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "OccurrenceStatus is Present",
 "TaxonKey is Lycorma delicatula (White, 1845)",
 "Year 2014-2024"
 ]
}

 The dataset includes 32915 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007875-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7274/25541062
title: Preconditioning Hybrid Discontinuous Galerkin Schemes for Incompressible Flow and Linear Elasticity Problems

description: This dissertation presents a study on the development, analysis, and application of advanced numerical methods for solving partial differential equations (PDEs), focusing primarily on the Hybrid Discontinuous Galerkin (HDG) schemes. The research introduces innovative HDG schemes, including a monolithic divergence-conforming HDG (H(div)-HDG) scheme for linear fluid-structure interaction (FSI) problems, and proposes robust block-diagonal preconditioners and geometric multigrid algorithms to address the computational challenges associated with these methods.
The work begins by reviewing H(div)-HDG schemes for Stokes and linear elasticity equations, subsequently proposing a novel monolithic scheme for FSI problems that ensures exactly divergence-free fluid velocity approximations and maintains energy stability. An optimal a priori error analysis and convergence rates are validated through numerical experiments. A significant part of the dissertation is devoted to overcoming the computational inefficiency in solving the condensed symmetric and indefinite global linear systems arising from these schemes. A uniform block-diagonal preconditioner is developed, demonstrating robustness with respect to mesh size and model parameters.
Further, the dissertation extends to the construction of optimal geometric multigrid algorithms for the lowest order HDG schemes with numerical integration, emphasizing the equivalence between the global linear system and modified Crouzeix-Raviart discretizations. This foundational work facilitates the development of an hp-multigrid preconditioner for higher-order H(div)-HDG schemes applied to generalized Stokes and Navier-Stokes equations, showcasing robustness across mesh sizes and augmented Lagrangian parameters, and insensitivity to polynomial order variations.
The research findings underscore the efficiency and adaptability of HDG methods in handling complex PDEs, contributing to the field of computational mathematics and numerical analysis. The dissertation concludes with discussions on future research directions, including the exploration of efficient parallel implementations and the extension of robust preconditioning techniques to more complex equations.