cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31314550
title: <b>test</b>

description: This study conducts a controlled salt-marsh .
DOI: 10.6084/m9.figshare.31314550.v2
title: <b>test</b>

description: This study conducts a controlled salt-marsh .
DOI: 10.15468/dl.f8h58p
title: Occurrence Download

description: A dataset containing 120 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Roccellina mollis"
 ]
}

 The dataset includes 120 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008612-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dvsceg
title: Occurrence Download

description: A dataset containing 693 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is one of (Morocco, Algeria, Tunisia)",
 "TaxonKey is Salix"
 ]
}

 The dataset includes 693 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008611-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6rwttx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acineta sella-turcica",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008614-260208012135463/datasets/export for details.
DOI: 10.6084/m9.figshare.30762079
title: Aspirin’s therapeutic mechanism against diesel particulate matter-induced lung injury revealed by network pharmacology and experimental models

description: Occupational exposure to diesel particulate matter (DPM) poses significant risks of pulmonary injury, highlighting an urgent need for effective pharmacological intervention. This study aimed to systematically elucidate the protective mechanism of aspirin against DPM-induced lung injury by integrating computational and experimental approaches. Methodologically, network pharmacology utilizing SwissTargetPrediction and GeneCards identified PTGS2 as the core therapeutic target via Venn diagram analysis and topological screening. Molecular docking and dynamics simulations then confirmed the stable binding of aspirin to the PTGS2 catalytic pocket. Biological validation was conducted via colony formation assay and Western blot in A549 cells, which demonstrated aspirin’s efficacy in mitigating DPM-induced cytotoxicity and suppressing inflammatory and apoptotic pathways. Furthermore, <i>in vivo</i> studies incorporating HE staining and protein analysis in a mouse model corroborated the protective role of aspirin against DPM-induced alveolar damage and inflammatory infiltration. Collectively, our findings validate PTGS2 as a critical therapeutic target of aspirin against DPM-induced lung injury and highlight the promise of pulmonary-targeted aspirin formulations to enhance therapeutic precision while reducing systemic toxicity. Future efforts should focus on advancing the clinical translation of inhalable aspirin and validating its long-term efficacy in high-risk occupational populations.
DOI: 10.6084/m9.figshare.30762079.v1
title: Aspirin’s therapeutic mechanism against diesel particulate matter-induced lung injury revealed by network pharmacology and experimental models

description: Occupational exposure to diesel particulate matter (DPM) poses significant risks of pulmonary injury, highlighting an urgent need for effective pharmacological intervention. This study aimed to systematically elucidate the protective mechanism of aspirin against DPM-induced lung injury by integrating computational and experimental approaches. Methodologically, network pharmacology utilizing SwissTargetPrediction and GeneCards identified PTGS2 as the core therapeutic target via Venn diagram analysis and topological screening. Molecular docking and dynamics simulations then confirmed the stable binding of aspirin to the PTGS2 catalytic pocket. Biological validation was conducted via colony formation assay and Western blot in A549 cells, which demonstrated aspirin’s efficacy in mitigating DPM-induced cytotoxicity and suppressing inflammatory and apoptotic pathways. Furthermore, <i>in vivo</i> studies incorporating HE staining and protein analysis in a mouse model corroborated the protective role of aspirin against DPM-induced alveolar damage and inflammatory infiltration. Collectively, our findings validate PTGS2 as a critical therapeutic target of aspirin against DPM-induced lung injury and highlight the promise of pulmonary-targeted aspirin formulations to enhance therapeutic precision while reducing systemic toxicity. Future efforts should focus on advancing the clinical translation of inhalable aspirin and validating its long-term efficacy in high-risk occupational populations.
DOI: 10.6084/m9.figshare.30646127
title: Network toxicology and machine learning reveal key molecular targets and pathways of mono-2-ethylhexyl phthalate-induced atherosclerosis

description: Mono-2-ethylhexyl phthalate (MEHP), a major metabolite of diethylhexyl phthalate, is increasingly recognized as an environmental contaminant with potential cardiovascular toxicity. However, the molecular mechanisms underlying MEHP-induced atherosclerosis (AS) remain poorly understood. This study aimed to investigate the toxicological targets and pathways through which MEHP contributes to AS development using network toxicology approaches. DEGs associated with AS were identified from the GSE100927 dataset. MEHP targets were predicted using multiple databases including SEA, SwissTargetPrediction, and TargetNet. Common toxicological targets were identified through intersection analysis. Functional enrichment analysis, GSVA, and ssGSEA were performed. Machine learning algorithms including LASSO regression, RF, and SVM were employed to identify key targets. A nomogram model was constructed for AS risk prediction, and molecular docking analysis was conducted to validate protein-ligand interactions. Analysis identified 13,905 DEGs in AS, with 172 potential MEHP targets yielding 92 common toxicological targets. Enrichment analysis revealed involvement in calcium signaling, PPAR signaling, inflammatory response, and immune pathways. Machine learning identified three key targets: PDPK1, HDAC10, and HRH1. The nomogram model based on HDAC10 and HRH1 demonstrated excellent predictive performance. ssGSEA analysis revealed significant associations between key targets and immune pathways. Molecular docking confirmed strong binding affinities, with HRH1-MEHP showing the highest affinity. MEHP may promote AS via coordinated effects on calcium handling, receptor-mediated and transcriptional signaling, oxidative stress, apoptosis, and immune activation. HRH1, HDAC10, and PDPK1 emerge as mechanistic mediators and potential biomarkers, with an HDAC10/HRH1 nomogram offering translational utility for AS risk stratification; docking results provide testable hypotheses for mechanistic validation.
DOI: 10.6084/m9.figshare.30646127.v1
title: Network toxicology and machine learning reveal key molecular targets and pathways of mono-2-ethylhexyl phthalate-induced atherosclerosis

description: Mono-2-ethylhexyl phthalate (MEHP), a major metabolite of diethylhexyl phthalate, is increasingly recognized as an environmental contaminant with potential cardiovascular toxicity. However, the molecular mechanisms underlying MEHP-induced atherosclerosis (AS) remain poorly understood. This study aimed to investigate the toxicological targets and pathways through which MEHP contributes to AS development using network toxicology approaches. DEGs associated with AS were identified from the GSE100927 dataset. MEHP targets were predicted using multiple databases including SEA, SwissTargetPrediction, and TargetNet. Common toxicological targets were identified through intersection analysis. Functional enrichment analysis, GSVA, and ssGSEA were performed. Machine learning algorithms including LASSO regression, RF, and SVM were employed to identify key targets. A nomogram model was constructed for AS risk prediction, and molecular docking analysis was conducted to validate protein-ligand interactions. Analysis identified 13,905 DEGs in AS, with 172 potential MEHP targets yielding 92 common toxicological targets. Enrichment analysis revealed involvement in calcium signaling, PPAR signaling, inflammatory response, and immune pathways. Machine learning identified three key targets: PDPK1, HDAC10, and HRH1. The nomogram model based on HDAC10 and HRH1 demonstrated excellent predictive performance. ssGSEA analysis revealed significant associations between key targets and immune pathways. Molecular docking confirmed strong binding affinities, with HRH1-MEHP showing the highest affinity. MEHP may promote AS via coordinated effects on calcium handling, receptor-mediated and transcriptional signaling, oxidative stress, apoptosis, and immune activation. HRH1, HDAC10, and PDPK1 emerge as mechanistic mediators and potential biomarkers, with an HDAC10/HRH1 nomogram offering translational utility for AS risk stratification; docking results provide testable hypotheses for mechanistic validation.
DOI: 10.6084/m9.figshare.30800323.v1
title: Decabromodiphenyl ethane as a neurotoxicant in Alzheimer’s disease: unraveling its mechanisms through the integration of multiple computational toxicology methods

description: The role of environmental pollutants as risk factors for Alzheimer’s disease (AD) and related neurodegenerative pathologies necessitates mechanistic investigation. Evidence implicates brominated flame retardants (BFRs)—decabromodiphenyl ethane (DBDPE)—in AD pathogenesis, though their molecular mechanisms remain inadequately elucidated. To address this challenge, we combined multiple cross-disciplinary methods (network toxicology, machine learning [ML], molecular docking, molecular dynamics [MD] simulations, and Mendelian randomization [MR] analysis) to systematically delineate DBDPE-induced AD pathogenesis. Initial screening of the SwissTargetPrediction database and GSE132903 dataset identified 47 overlapping DBDPE-AD targets. Subsequent protein-protein interaction (PPI) network analysis refined these to 42 high-confidence targets. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed association of core targets with metabolic pathways and neuroactive ligand-receptor interactions. Three core targets were prioritized using ML framework. Molecular docking confirmed strong binding affinities between DBDPE and the core targets. Given PLAU’s exceptional binding energy, we conducted MD simulations to validate complex stability and characterize binding-site interactions. Finally, MR analysis established causal links between PLAU and AD susceptibility. In summary, this study establishes a comprehensive theoretical framework for understanding the molecular mechanisms of DBDPE-induced AD and provides valuable insights for developing preventive and therapeutic strategies targeting AD associated with DBDPE exposure.
DOI: 10.6084/m9.figshare.30800323
title: Decabromodiphenyl ethane as a neurotoxicant in Alzheimer’s disease: unraveling its mechanisms through the integration of multiple computational toxicology methods

description: The role of environmental pollutants as risk factors for Alzheimer’s disease (AD) and related neurodegenerative pathologies necessitates mechanistic investigation. Evidence implicates brominated flame retardants (BFRs)—decabromodiphenyl ethane (DBDPE)—in AD pathogenesis, though their molecular mechanisms remain inadequately elucidated. To address this challenge, we combined multiple cross-disciplinary methods (network toxicology, machine learning [ML], molecular docking, molecular dynamics [MD] simulations, and Mendelian randomization [MR] analysis) to systematically delineate DBDPE-induced AD pathogenesis. Initial screening of the SwissTargetPrediction database and GSE132903 dataset identified 47 overlapping DBDPE-AD targets. Subsequent protein-protein interaction (PPI) network analysis refined these to 42 high-confidence targets. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed association of core targets with metabolic pathways and neuroactive ligand-receptor interactions. Three core targets were prioritized using ML framework. Molecular docking confirmed strong binding affinities between DBDPE and the core targets. Given PLAU’s exceptional binding energy, we conducted MD simulations to validate complex stability and characterize binding-site interactions. Finally, MR analysis established causal links between PLAU and AD susceptibility. In summary, this study establishes a comprehensive theoretical framework for understanding the molecular mechanisms of DBDPE-induced AD and provides valuable insights for developing preventive and therapeutic strategies targeting AD associated with DBDPE exposure.
DOI: 10.15468/dl.w8zcyg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-72.03506 -34.37987, -72.03739 -34.42221, -71.98684 -34.42392, -71.98679 -34.38224, -72.03506 -34.37987))",
 "ClassKey is one of (Reptilia)",
 "HasCoordinate is true",
 "EventDate is greater than or equal to 2021-01-01",
 "EventDate is less than or equal to 2026-02-11",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008616-260208012135463/datasets/export for details.
DOI: 10.15468/dl.n4tb4e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-72.03506 -34.37987, -72.03739 -34.42221, -71.98684 -34.42392, -71.98679 -34.38224, -72.03506 -34.37987))",
 "ClassKey is one of (Reptilia)",
 "HasCoordinate is true",
 "EventDate is greater than or equal to 2025-01-01",
 "EventDate is less than or equal to 2026-02-11",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008615-260208012135463/datasets/export for details.
DOI: 10.5281/zenodo.18610507
title: SARS-CoV-2 Sequenzdaten aus Deutschland

description: Im Datensatz "SARS-CoV-2-Sequenzdaten aus Deutschland" umfasst vollständige Virusgenomsequenzen sowie zugehörige Metadaten aus bundesweit erhobenen Proben. Die Proben werden in Zusammenarbeit vom IMSSC2-Labornetzwerk, dem Nationalen Referenzzentrum für Coronaviren an der Charité sowie dem RKI sequenziert und bioinformatisch analysiert. Der Datensatz ermöglicht eine fundierte molekular-epidemiologische Analyse der SARS-CoV-2-Ausbreitung in Deutschland und stellt eine zentrale Ressource für Forschung und öffentliche Gesundheitsüberwachung dar.
DOI: 10.15468/tz2rfa
title: Telmatophilus typhae (Fallén, 1802) (Coleoptera: Cryptophagidae), new species for Portugal

description: This dataset contains the digitized treatments in Plazi based on the original journal article Gil, Francisco, Grosso-Silva, José Manuel (2023): Telmatophilus typhae (Fallén, 1802) (Coleoptera: Cryptophagidae), new species for Portugal. Arquivos Entomolóxicos 27: 59-60, DOI: 10.5281/zenodo.12812964
DOI: 10.15468/dl.tsj5gb
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-72.03506 -34.37987, -72.03739 -34.42221, -71.98684 -34.42392, -71.98679 -34.38224, -72.03506 -34.37987))",
 "ClassKey is one of (Reptilia, Mammalia)",
 "HasCoordinate is true",
 "EventDate is greater than or equal to 2021-01-01",
 "EventDate is less than or equal to 2026-02-11",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation)"
 ]
}

 The dataset includes 20 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008618-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48580/dgm6h.v4
title: Telmatophilus typhae (Fallén, 1802) (Coleoptera: Cryptophagidae), new species for Portugal
DOI: 10.15468/dl.65896c
title: Occurrence Download

description: A dataset containing 459 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Verbesina glabrata",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 459 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008619-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18610313
title: Testcase ShyBFM v1.0

description: This repository contains the numerical grid, the forcing dataset and the model set-up needed to run the testcase simulation for ShyBFM v1.0 (https://zenodo.org/records/17750906).

Scripts used to run the model and convert the output to NetCDF are also provided.

The README file briefly summarise the directory and subdirectories content.
description: README content:

his file contains information about the structure of the testcasedirectory of ShyBFM v1.0

# brief summary of the content of the subdiretories 

-Grid        --> contains the numerical mesh in .grd format and .bas format.-Input       --> contains initial (IC) and boundary conditions (BC) for physics and                 biogeochemical tracers. -RivLoad     --> contains the river load BC for biogeochemical tracers.-Simulation  --> contains the models namelists and a script to run the model                 namelist-tm.nml is the namelist for the ADR module, gotmturb.nml                  is the namelist of GOTM turbulence model while the other                 *.nml files are the BFM namelists.                 │                 └──> str_scripts --> contains the SHYFEM-MPI namelist (shybfm-ER.str).                  │                 └──> bsub_scripts --> bash script to run the model (run_shybfm.sh).

-post-proc  --> contains a script to convert the SHYFEM-MPI output format to NetCDF                  tm_tracers.stm2nc contains the list of the biogeochemical variables                 needed by the stm2nc excecutable file for the conversion to NetCDF. 

-output     --> contains the seasonal (ShyBFM_Mean_Seas_Out.nc) mean of the 10 year                 simulation for the variables discussed in the companion paper on ShyBFM v1.0
DOI: 10.5281/zenodo.18610314
title: Testcase ShyBFM v1.0

description: This repository contains the numerical grid, the forcing dataset and the model set-up needed to run the testcase simulation for ShyBFM v1.0 (https://zenodo.org/records/17750906).

Scripts used to run the model and convert the output to NetCDF are also provided.

The README file briefly summarise the directory and subdirectories content.
description: README content:

his file contains information about the structure of the testcasedirectory of ShyBFM v1.0

# brief summary of the content of the subdiretories 

-Grid        --> contains the numerical mesh in .grd format and .bas format.-Input       --> contains initial (IC) and boundary conditions (BC) for physics and                 biogeochemical tracers. -RivLoad     --> contains the river load BC for biogeochemical tracers.-Simulation  --> contains the models namelists and a script to run the model                 namelist-tm.nml is the namelist for the ADR module, gotmturb.nml                  is the namelist of GOTM turbulence model while the other                 *.nml files are the BFM namelists.                 │                 └──> str_scripts --> contains the SHYFEM-MPI namelist (shybfm-ER.str).                  │                 └──> bsub_scripts --> bash script to run the model (run_shybfm.sh).

-post-proc  --> contains a script to convert the SHYFEM-MPI output format to NetCDF                  tm_tracers.stm2nc contains the list of the biogeochemical variables                 needed by the stm2nc excecutable file for the conversion to NetCDF. 

-output     --> contains the seasonal (ShyBFM_Mean_Seas_Out.nc) mean of the 10 year                 simulation for the variables discussed in the companion paper on ShyBFM v1.0
DOI: 10.6084/m9.figshare.31315282
title: Determinants of Design with Multilayer Perceptron Neural Networks: A Comparison with Logistic Regression

description: This research focuses on harnessing artificial neural networks (ANNs) to enhance the design of steel structures. The design process encompasses various stages, including defining the building’s geometry, estimating loads, selecting an appropriate structural system, sizing components, and creating detailed plans. Optimizing the weight of these structures is vital for reducing costs, improving efficiency, and minimizing environmental impact. This study specifically investigates multilayer perceptron (MLP) neural networks to optimize steel structure design. It evaluates different ANN configurations with varying numbers of hidden layers and neurons to find the most effective arrangement. Additionally, the performance of MLP networks is compared to that of logistic regression. The results demonstrate that MLP networks deliver superior accuracy in optimizing the design of steel structures compared to logistic regression. The process of designing steel structures at an early stage can reduce the con-sumption of energy and raw materials before the production of the structures themselves begins. This is important from an economic point of view because some costs can be reduced during the design process. When designing steel structures, it is also possible to take into account changing conditions, such as the growing share of re-newable energy sources in the total energy balance in many countries.
DOI: 10.6084/m9.figshare.31315282.v1
title: Determinants of Design with Multilayer Perceptron Neural Networks: A Comparison with Logistic Regression

description: This research focuses on harnessing artificial neural networks (ANNs) to enhance the design of steel structures. The design process encompasses various stages, including defining the building’s geometry, estimating loads, selecting an appropriate structural system, sizing components, and creating detailed plans. Optimizing the weight of these structures is vital for reducing costs, improving efficiency, and minimizing environmental impact. This study specifically investigates multilayer perceptron (MLP) neural networks to optimize steel structure design. It evaluates different ANN configurations with varying numbers of hidden layers and neurons to find the most effective arrangement. Additionally, the performance of MLP networks is compared to that of logistic regression. The results demonstrate that MLP networks deliver superior accuracy in optimizing the design of steel structures compared to logistic regression. The process of designing steel structures at an early stage can reduce the con-sumption of energy and raw materials before the production of the structures themselves begins. This is important from an economic point of view because some costs can be reduced during the design process. When designing steel structures, it is also possible to take into account changing conditions, such as the growing share of re-newable energy sources in the total energy balance in many countries.