cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48331/scielodata.tlkflz
title: Dados de replicação para: Sustentabilidade no ensino superior brasileiro: evolução temática, redes científicas e consolidação do campo (2010–2024)

description: &lt;p&gt;O estudo enfrentou o desafio de compreender, de modo sistêmico, a evolução temporal, as lacunas conceituais e colaborativas e o grau de maturidade da pesquisa sobre sustentabilidade nas Instituições de Ensino Superior (IES) brasileiras. Teve como objetivo analisar a produção científica brasileira publicada entre 2010 e 2024, identificando ciclos temporais, padrões conceituais, redes colaborativas e o processo de consolidação dos &lt;em&gt;clusters&lt;/em&gt; de pesquisa. A investigação, com abordagem quantitativa e caráter descritivo, examinou 149 publicações oriundas de bases como CAPES, Catálogo de Teses e Dissertações, &lt;em&gt;Google&lt;/em&gt; Acadêmico e SciELO, considerando um recorte alinhado ao surgimento do &lt;em&gt;UI GreenMetric World University&lt;/em&gt; &lt;em&gt;Rankings&lt;/em&gt;. Os resultados indicaram três fases evolutivas, caracterizadas como emergente, crescimento e expansão, com fortalecimento de macrotemas e diversificação das redes de colaboração científica. Concluiu-se que o campo apresentou avanços substantivos, embora ainda enfrentasse limitações relacionadas à integração territorial, à operacionalização dos ODS e à mensuração do impacto social.&lt;/p&gt;
DOI: 10.15468/dl.dvwdfc
title: Occurrence Download

description: A dataset containing 658739 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "TaxonKey is Sternula antillarum"
 ]
}

 The dataset includes 658739 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068692-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zhamku
title: Occurrence Download

description: A dataset containing 1647393 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Argentina"
 ]
}

 The dataset includes 1647393 records from 1455 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068699-251120083545085/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.30964699
title: Observational checklist for food safety and hygienic practices.

description: Food safety and hygienic practices among food vendors in the Bibiani Municipality.
DOI: 10.6084/m9.figshare.30964699.v1
title: Observational checklist for food safety and hygienic practices.

description: Food safety and hygienic practices among food vendors in the Bibiani Municipality.
DOI: 10.5281/zenodo.17965267
title: Dataset for the project "Assessment of neurotransmitter-related structural brain changes in borderline personality disorder: an MRI study"

description: This dataset contains the data supporting the findings of the manuscript "Assessment of neurotransmitter-related structural brain changes in borderline personality disorder: an MRI study".

The dataset includes clinical measures, gray matter (GM) measures derived from voxel-based morphometry analyses, and measures of spatial correlation between GM maps and PET/SPECT-derived neurotransmitter atlases using the JuSpace toolbox. Structural MRI data were processed using standard pipelines implemented in SPM12 and CAT12. 

Reuse notes. The data can be reused for secondary analyses, meta-analyses, or methodological validation studies, provided that appropriate citation of the original manuscript and/or this dataset is given.
DOI: 10.5281/zenodo.17965266
title: Dataset for the project "Assessment of neurotransmitter-related structural brain changes in borderline personality disorder: an MRI study"

description: This dataset contains the data supporting the findings of the manuscript "Assessment of neurotransmitter-related structural brain changes in borderline personality disorder: an MRI study".

The dataset includes clinical measures, gray matter (GM) measures derived from voxel-based morphometry analyses, and measures of spatial correlation between GM maps and PET/SPECT-derived neurotransmitter atlases using the JuSpace toolbox. Structural MRI data were processed using standard pipelines implemented in SPM12 and CAT12. 

Reuse notes. The data can be reused for secondary analyses, meta-analyses, or methodological validation studies, provided that appropriate citation of the original manuscript and/or this dataset is given.
DOI: 10.6084/m9.figshare.29537984
title: raw_data_cageEXP_fieldEXP.xlsx

description: Database for the paper "<b>Life-stage niche partitioning and functional strategies promote predatory coccinellids’ co-occurrence</b>".
DOI: 10.6084/m9.figshare.29537984.v1
title: raw_data_cageEXP_fieldEXP.xlsx

description: Database for the paper "<b>Life-stage niche partitioning and functional strategies promote predatory coccinellids’ co-occurrence</b>".
DOI: 10.34740/kaggle/dsv/13838040
title: Grid Stability Assessment Dataset
DOI: 10.25790/bml0cm.192
title: JHU–MNI single-subject atlas
DOI: 10.6084/m9.figshare.30964738
title: Static and Dynamic BART for Rank-Order Data

description: Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with latent scores defined as nonlinear Bayesian additive regression tree functions of covariates. To make inferences in the dynamic ARROBART model, closed-form filtering, predictive, and smoothing distributions for the latent time-varying scores are derived. These results are applied in a Gibbs sampler with data augmentation for posterior inference. The proposed methods are shown to outperform existing competitors in simulation studies, static data applications to electoral data, stated preferences for sushi and movies, and dynamic data applications to economic complexity rankings of countries and weekly pollster rankings of NCAA football teams.
DOI: 10.6084/m9.figshare.30964738.v1
title: Static and Dynamic BART for Rank-Order Data

description: Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with latent scores defined as nonlinear Bayesian additive regression tree functions of covariates. To make inferences in the dynamic ARROBART model, closed-form filtering, predictive, and smoothing distributions for the latent time-varying scores are derived. These results are applied in a Gibbs sampler with data augmentation for posterior inference. The proposed methods are shown to outperform existing competitors in simulation studies, static data applications to electoral data, stated preferences for sushi and movies, and dynamic data applications to economic complexity rankings of countries and weekly pollster rankings of NCAA football teams.
DOI: 10.6084/m9.figshare.30891538
title: Apparent expansion of virulent <i>Vibrio parahaemolyticus</i> in humans and sea otters

description: Vibriosis is the most important public health threat from seafood consumption and marine recreation. Pathogenic <i>Vibrio</i> spp. employ virulence factors, including hemolysins and secretion systems, frequently detected in human cases, but virulence data in northern and southern sea otters (<i>Enhydra lutris kenyoni</i> and <i>E. l</i>. nereis, respectively) are limited despite their potential as marine bioindicators. Genomic epidemiology was used to characterize virulence factors of <i>Vibrio</i> spp. genomes (<i>n</i> = 570), including <i>V. alginolyticus</i> (<i>n</i> = 55), <i>V. diabolicus</i> (<i>n</i> = 52), non-O1/O139 <i>V. cholerae</i> (<i>n</i> = 163), and <i>V. parahaemolyticus</i> (<i>n</i> = 287) collected in North America (2000–2019). Virulence factors of <i>V. parahaemolyticus</i> were compared between isolation sources: bivalves, environment, humans, and southern and northern sea otters. Hemolysins (<i>tdh</i>, <i>trh</i>) and type III secretion system 2 (T3SS2) gene prevalences were lowest in environmental isolates, while <i>tdh</i> and T3SS2 gene prevalences were higher in human and northern sea otter isolates than those from southern sea otters. A hemolysin allele (<i>trh1</i>) was detected almost exclusively in human and sea otter isolates. Despite <i>V. parahaemolyticus</i> genomic diversity, detected genomic clusters were comprised of highly related and <i>tdh</i><sup>+</sup>/<i>trh</i><sup>+</sup> genomes from nonenvironmental sources including humans and sea otters. Observed pathology in <i>Vibrio</i> spp. positive sea otters frequently included septicemia, enteritis, and moderate-to-severe melena. Co-occurrence of T3SS2 and T6SS1 in <i>V. parahaemolyticus</i> was associated with pathological findings and ampicillin-susceptible genotypes, suggesting a trade-off between virulence and antimicrobial resistance. Based on these findings, <i>V. parahaemolyticus</i> undergoes selection pressures resulting in apparent expansion, i.e. genomic clustering, of <i>tdh</i><sup>+</sup>/<i>trh</i><sup>+</sup> virulent strains infecting humans and sea otters.
DOI: 10.6084/m9.figshare.30891538.v1
title: Apparent expansion of virulent <i>Vibrio parahaemolyticus</i> in humans and sea otters

description: Vibriosis is the most important public health threat from seafood consumption and marine recreation. Pathogenic <i>Vibrio</i> spp. employ virulence factors, including hemolysins and secretion systems, frequently detected in human cases, but virulence data in northern and southern sea otters (<i>Enhydra lutris kenyoni</i> and <i>E. l</i>. nereis, respectively) are limited despite their potential as marine bioindicators. Genomic epidemiology was used to characterize virulence factors of <i>Vibrio</i> spp. genomes (<i>n</i> = 570), including <i>V. alginolyticus</i> (<i>n</i> = 55), <i>V. diabolicus</i> (<i>n</i> = 52), non-O1/O139 <i>V. cholerae</i> (<i>n</i> = 163), and <i>V. parahaemolyticus</i> (<i>n</i> = 287) collected in North America (2000–2019). Virulence factors of <i>V. parahaemolyticus</i> were compared between isolation sources: bivalves, environment, humans, and southern and northern sea otters. Hemolysins (<i>tdh</i>, <i>trh</i>) and type III secretion system 2 (T3SS2) gene prevalences were lowest in environmental isolates, while <i>tdh</i> and T3SS2 gene prevalences were higher in human and northern sea otter isolates than those from southern sea otters. A hemolysin allele (<i>trh1</i>) was detected almost exclusively in human and sea otter isolates. Despite <i>V. parahaemolyticus</i> genomic diversity, detected genomic clusters were comprised of highly related and <i>tdh</i><sup>+</sup>/<i>trh</i><sup>+</sup> genomes from nonenvironmental sources including humans and sea otters. Observed pathology in <i>Vibrio</i> spp. positive sea otters frequently included septicemia, enteritis, and moderate-to-severe melena. Co-occurrence of T3SS2 and T6SS1 in <i>V. parahaemolyticus</i> was associated with pathological findings and ampicillin-susceptible genotypes, suggesting a trade-off between virulence and antimicrobial resistance. Based on these findings, <i>V. parahaemolyticus</i> undergoes selection pressures resulting in apparent expansion, i.e. genomic clustering, of <i>tdh</i><sup>+</sup>/<i>trh</i><sup>+</sup> virulent strains infecting humans and sea otters.
DOI: 10.6084/m9.figshare.30964744.v1
title: Smoothing Variances Across Time: Adaptive Stochastic Volatility

description: We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with Dynamic Shrinkage Processes (DSP) in log-variances. Unlike the classical Stochastic Volatility (SV) or GARCH-type models with restrictive parametric stationarity assumptions, our proposed Adaptive Stochastic Volatility (ASV) model provides smooth yet dynamically adaptive estimates of evolving volatility and its uncertainty. We further enhance the model by incorporating a nugget effect, allowing it to flexibly capture small-scale variability while preserving smoothness elsewhere. We derive the theoretical properties of the global-local shrinkage prior DSP. Simulation studies demonstrate that ASV is highly robust to misspecification, consistently recovering the latent volatility structure across a wide range of data-generating processes. Furthermore, ASV’s capacity to yield locally smooth and interpretable estimates facilitates a clearer understanding of the underlying patterns and trends in volatility. As an extension, we develop the Bayesian Trend Filter with ASV (BTF-ASV) which allows joint modeling of the mean and volatility with abrupt changes. Finally, our proposed models are applied to time series data from finance, econometrics, and environmental science, highlighting their flexibility and broad applicability.
DOI: 10.6084/m9.figshare.30964744
title: Smoothing Variances Across Time: Adaptive Stochastic Volatility

description: We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with Dynamic Shrinkage Processes (DSP) in log-variances. Unlike the classical Stochastic Volatility (SV) or GARCH-type models with restrictive parametric stationarity assumptions, our proposed Adaptive Stochastic Volatility (ASV) model provides smooth yet dynamically adaptive estimates of evolving volatility and its uncertainty. We further enhance the model by incorporating a nugget effect, allowing it to flexibly capture small-scale variability while preserving smoothness elsewhere. We derive the theoretical properties of the global-local shrinkage prior DSP. Simulation studies demonstrate that ASV is highly robust to misspecification, consistently recovering the latent volatility structure across a wide range of data-generating processes. Furthermore, ASV’s capacity to yield locally smooth and interpretable estimates facilitates a clearer understanding of the underlying patterns and trends in volatility. As an extension, we develop the Bayesian Trend Filter with ASV (BTF-ASV) which allows joint modeling of the mean and volatility with abrupt changes. Finally, our proposed models are applied to time series data from finance, econometrics, and environmental science, highlighting their flexibility and broad applicability.
DOI: 10.6084/m9.figshare.30964742
title: Invalid Proxies and Volatility Changes

description: When in proxy-SVARs the covariance matrix of VAR disturbances is subject to exogenous, permanent breaks that cause IRFs to change across volatility regimes, even strong, exogenous external instruments yield inconsistent estimates of the dynamic causal effects. However, if these volatility shifts are properly incorporated into the analysis through (testable) “stability restrictions”, we demonstrate that the target IRFs are point-identified and can be estimated consistently under a necessary and sufficient rank condition. If the shifts in volatility are sufficiently informative, standard asymptotic inference remains valid even with (i) local-to-zero covariance between the proxies and the instrumented structural shocks, and (ii) potential failures of instrument exogeneity. Intuitively, shifts in volatility act similarly to strong instruments that are correlated with both the target and non-target shocks. We illustrate the effectiveness of our approach by revisiting a seminal fiscal proxy-SVAR for the US economy. We detect a sharp change in the size of the tax multiplier when the narrative tax instrument is complemented with the decline in unconditional volatility observed during the transition from the Great Inflation to the Great Moderation. The narrative tax instrument contributes to identify the tax shock in both regimes, although our empirical analysis raises concerns about its statistical validity.
DOI: 10.6084/m9.figshare.30964742.v1
title: Invalid Proxies and Volatility Changes

description: When in proxy-SVARs the covariance matrix of VAR disturbances is subject to exogenous, permanent breaks that cause IRFs to change across volatility regimes, even strong, exogenous external instruments yield inconsistent estimates of the dynamic causal effects. However, if these volatility shifts are properly incorporated into the analysis through (testable) “stability restrictions”, we demonstrate that the target IRFs are point-identified and can be estimated consistently under a necessary and sufficient rank condition. If the shifts in volatility are sufficiently informative, standard asymptotic inference remains valid even with (i) local-to-zero covariance between the proxies and the instrumented structural shocks, and (ii) potential failures of instrument exogeneity. Intuitively, shifts in volatility act similarly to strong instruments that are correlated with both the target and non-target shocks. We illustrate the effectiveness of our approach by revisiting a seminal fiscal proxy-SVAR for the US economy. We detect a sharp change in the size of the tax multiplier when the narrative tax instrument is complemented with the decline in unconditional volatility observed during the transition from the Great Inflation to the Great Moderation. The narrative tax instrument contributes to identify the tax shock in both regimes, although our empirical analysis raises concerns about its statistical validity.
DOI: 10.6084/m9.figshare.30948301
title: Giardiavirus rewires host translation and glycolytic metabolism to support its replication in <i>Giardia duodenalis</i>

description: <i>Giardia duodenalis</i> is an intestinal protozoan parasite responsible for giardiasis, a disease primarily characterized by diarrhea and associated with long-term complications such as malnutrition and growth impairment in children. The presence of Giardiavirus (GLV) has been shown to attenuate pathological damage in <i>G. duodenalis</i>-infected murine models and modulate distinct pro-inflammatory responses in host cells stimulated by <i>Giardia</i>. However, the understanding of the impact of the GLV on the <i>G. duodenalis</i> itself remains limited. Here, we found that GLV infection interfered with the host protein expression system by reducing both mRNA and protein levels of <i>Giardia</i> genes, while paradoxically enhancing mRNA translation efficiency. Additionally, GLV infection induced energy metabolic reprogramming in <i>Giardia</i>, as evidenced by the identification of 21 significantly altered energy metabolites. KEGG enrichment analysis revealed glycolysis/gluconeogenesis as the most prominently enriched metabolic pathway in GLV-infected <i>Giardia</i>. Notably, glycolysis continued to be upregulated with successive passages of GLV infection, even after the GLV load plateaued. The glycolytic enzyme enolase was found to be closely associated with GLV infection within <i>Giardia</i>, and morpholino-mediated knockdown of enolase expression resulted in a significant reduction in GLV replication. Overall, these findings demonstrate that GLV infection manipulates host translation and energy metabolic pathways to facilitate its persistence in <i>G. duodenalis</i>, and reveal both GLV and host metabolic targets as promising research subjects for developing drugs and vaccines for the prevention and treatment of giardiasis.
DOI: 10.6084/m9.figshare.30948301.v1
title: Giardiavirus rewires host translation and glycolytic metabolism to support its replication in <i>Giardia duodenalis</i>

description: <i>Giardia duodenalis</i> is an intestinal protozoan parasite responsible for giardiasis, a disease primarily characterized by diarrhea and associated with long-term complications such as malnutrition and growth impairment in children. The presence of Giardiavirus (GLV) has been shown to attenuate pathological damage in <i>G. duodenalis</i>-infected murine models and modulate distinct pro-inflammatory responses in host cells stimulated by <i>Giardia</i>. However, the understanding of the impact of the GLV on the <i>G. duodenalis</i> itself remains limited. Here, we found that GLV infection interfered with the host protein expression system by reducing both mRNA and protein levels of <i>Giardia</i> genes, while paradoxically enhancing mRNA translation efficiency. Additionally, GLV infection induced energy metabolic reprogramming in <i>Giardia</i>, as evidenced by the identification of 21 significantly altered energy metabolites. KEGG enrichment analysis revealed glycolysis/gluconeogenesis as the most prominently enriched metabolic pathway in GLV-infected <i>Giardia</i>. Notably, glycolysis continued to be upregulated with successive passages of GLV infection, even after the GLV load plateaued. The glycolytic enzyme enolase was found to be closely associated with GLV infection within <i>Giardia</i>, and morpholino-mediated knockdown of enolase expression resulted in a significant reduction in GLV replication. Overall, these findings demonstrate that GLV infection manipulates host translation and energy metabolic pathways to facilitate its persistence in <i>G. duodenalis</i>, and reveal both GLV and host metabolic targets as promising research subjects for developing drugs and vaccines for the prevention and treatment of giardiasis.
DOI: 10.15468/dl.2dccy2
title: Occurrence Download

description: A dataset containing 26512 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material citation)",
 "Country is Venezuela (Bolivarian Republic of)",
 "OccurrenceStatus is Present",
 "TaxonKey is Diptera"
 ]
}

 The dataset includes 26512 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068703-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14334879
title: Peer-to-Peer Boliviano (BOB) Exchange Data