cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10371016
title: Supplementary material of "Anti-inflammatory boosted QSAR models for predicting Nitric Oxide inhibition by bioactive metabolites from Aspergillus species""

description: The study focuses on employing advanced QSAR modeling techniques to predict NO inhibitory metabolites from Aspergillus. It utilizes decision trees, the Ranker method, and CorrelationAttributeEval for attribute selection, enhanced by Rotation Forest and AdaBoost, with classifiers like Artificial Neural Networks and J48 Trees. The QSAR models, using an ensemble approach with Rotation Forest and AdaBoost.M1 in a KNIME workflow, selected seven molecular descriptors from a dataset of diverse Aspergillus anti-inflammatory metabolites. The RF-enhanced model excelled in capturing complex relationships and enhancing predictive performance. It identified electrotopological state, topological distances, and secondary amides as key contributors to NO inhibition. This model shows promising potential for identifying new anti-inflammatory drugs based on computational techniques.








 
description: The study focuses on employing advanced QSAR modeling techniques to predict NO inhibitory metabolites from Aspergillus. It utilizes decision trees, the Ranker method, and CorrelationAttributeEval for attribute selection, enhanced by Rotation Forest and AdaBoost, with classifiers like Artificial Neural Networks and J48 Trees. The QSAR models, using an ensemble approach with Rotation Forest and AdaBoost.M1 in a KNIME workflow, selected seven molecular descriptors from a dataset of diverse Aspergillus anti-inflammatory metabolites. The RF-enhanced model excelled in capturing complex relationships and enhancing predictive performance. It identified electrotopological state, topological distances, and secondary amides as key contributors to NO inhibition. This model shows promising potential for identifying new anti-inflammatory drugs based on computational techniques.
DOI: 10.5281/zenodo.10371017
title: Supplementary material of "Advancements in QSAR modelling: Decision trees and rotation forest for prediction of Aspergillus anti-inflammatory metabolites"

description: The study focuses on employing advanced QSAR modeling techniques to predict NO inhibitory metabolites from Aspergillus. It utilizes decision trees, the Ranker method, and CorrelationAttributeEval for attribute selection, enhanced by Rotation Forest and AdaBoost, with classifiers like Artificial Neural Networks and J48 Trees. The QSAR models, using an ensemble approach with Rotation Forest and AdaBoost.M1 in a KNIME workflow, selected seven molecular descriptors from a dataset of diverse Aspergillus anti-inflammatory metabolites. The RF-enhanced model excelled in capturing complex relationships and enhancing predictive performance. It identified electrotopological state, topological distances, and secondary amides as key contributors to NO inhibition. This model shows promising potential for identifying new anti-inflammatory drugs based on computational techniques.
DOI: 10.5281/zenodo.10415232
title: Supplementary material of "Advancements in QSAR modelling: Decision trees and rotation forest for prediction of Aspergillus anti-inflammatory metabolites"

description: The study focuses on employing advanced QSAR modeling techniques to predict NO inhibitory metabolites from Aspergillus. It utilizes decision trees, the Ranker method, and CorrelationAttributeEval for attribute selection, enhanced by Rotation Forest and AdaBoost, with classifiers like Artificial Neural Networks and J48 Trees. The QSAR models, using an ensemble approach with Rotation Forest and AdaBoost.M1 in a KNIME workflow, selected seven molecular descriptors from a dataset of diverse Aspergillus anti-inflammatory metabolites. The RF-enhanced model excelled in capturing complex relationships and enhancing predictive performance. It identified electrotopological state, topological distances, and secondary amides as key contributors to NO inhibition. This model shows promising potential for identifying new anti-inflammatory drugs based on computational techniques.
DOI: 10.5281/zenodo.11238407
title: Supplementary material of ""Enhancing QSAR models for anti-inflammatory activity: Using decision trees and rotation forest to predict NO inhibition by Aspergillus metabolites"

description: The study focuses on employing advanced QSAR modeling techniques to predict NO inhibitory metabolites from Aspergillus. It utilizes decision trees, the Ranker method, and CorrelationAttributeEval for attribute selection, enhanced by Rotation Forest and AdaBoost, with classifiers like Artificial Neural Networks and J48 Trees. The QSAR models, using an ensemble approach with Rotation Forest and AdaBoost.M1 in a KNIME workflow, selected seven molecular descriptors from a dataset of diverse Aspergillus anti-inflammatory metabolites. The RF-enhanced model excelled in capturing complex relationships and enhancing predictive performance. It identified electrotopological state, topological distances, and secondary amides as key contributors to NO inhibition. This model shows promising potential for identifying new anti-inflammatory drugs based on computational techniques.








 
description: The study focuses on employing advanced QSAR modeling techniques to predict NO inhibitory metabolites from Aspergillus. It utilizes decision trees, the Ranker method, and CorrelationAttributeEval for attribute selection, enhanced by Rotation Forest and AdaBoost, with classifiers like Artificial Neural Networks and J48 Trees. The QSAR models, using an ensemble approach with Rotation Forest and AdaBoost.M1 in a KNIME workflow, selected seven molecular descriptors from a dataset of diverse Aspergillus anti-inflammatory metabolites. The RF-enhanced model excelled in capturing complex relationships and enhancing predictive performance. It identified electrotopological state, topological distances, and secondary amides as key contributors to NO inhibition. This model shows promising potential for identifying new anti-inflammatory drugs based on computational techniques.
DOI: 10.5281/zenodo.11242548
title: Supplementary material of ""Enhancing QSAR models for anti-inflammatory activity: Using decision trees and rotation forest to predict NO inhibition by Aspergillus metabolites"

description: The study focuses on employing advanced QSAR modeling techniques to predict NO inhibitory metabolites from Aspergillus. It utilizes decision trees, the Ranker method, and CorrelationAttributeEval for attribute selection, enhanced by Rotation Forest and AdaBoost, with classifiers like Artificial Neural Networks and J48 Trees. The QSAR models, using an ensemble approach with Rotation Forest and AdaBoost.M1 in a KNIME workflow, selected seven molecular descriptors from a dataset of diverse Aspergillus anti-inflammatory metabolites. The RF-enhanced model excelled in capturing complex relationships and enhancing predictive performance. It identified electrotopological state, topological distances, and secondary amides as key contributors to NO inhibition. This model shows promising potential for identifying new anti-inflammatory drugs based on computational techniques.








 
description: The study focuses on employing advanced QSAR modeling techniques to predict NO inhibitory metabolites from Aspergillus. It utilizes decision trees, the Ranker method, and CorrelationAttributeEval for attribute selection, enhanced by Rotation Forest and AdaBoost, with classifiers like Artificial Neural Networks and J48 Trees. The QSAR models, using an ensemble approach with Rotation Forest and AdaBoost.M1 in a KNIME workflow, selected seven molecular descriptors from a dataset of diverse Aspergillus anti-inflammatory metabolites. The RF-enhanced model excelled in capturing complex relationships and enhancing predictive performance. It identified electrotopological state, topological distances, and secondary amides as key contributors to NO inhibition. This model shows promising potential for identifying new anti-inflammatory drugs based on computational techniques.
DOI: 10.5281/zenodo.11652165
title: The Delayed Box: Biphenyl Bisimide Cyclophane, a Supramolecular Nano‒environment for Efficient Generation of Delayed Fluorescence

description: Additional data to report https://doi.org/10.1021/jacs.4c07730:

Activating delayed fluorescence emission in a dilute solution via a non-covalent approach is a formidable challenge. In this report, we propose a strategy for efficient delayed fluorescence generation in dilute solution using a non-covalent approach via supramolecularly engineered cyclophane-based nanoenvironments that provide sufficient binding strength to π-conjugated guests and that can stabilize triplet excitons by reducing vibrational dissipation and lowering the singlet–triplet energy gap for efficient delayed fluorescence emission. Toward this goal, a novel biphenyl bisimide-derived cyclophane is introduced as an electron-deficient and efficient triplet-generating host. Upon encapsulation of various carbazole-derived guests inside the nanocavity of this cyclophane, emissive charge transfer (CT) states close to the triplet energy level of the biphenyl bisimide are generated. The experimental results of host–guest studies manifest high association constants up to 104 M–1 as the prerequisite for inclusion complex formation, the generation of emissive CT states, and triplet-state stabilization in a diluted solution state. By means of different carbazole guest molecules, we could realize tunable delayed fluorescence emission in this carbazole-encapsulated biphenyl bisimide cyclophane in methylcyclohexane/carbon tetrachloride solutions with a quantum yield (QY) of up to 15.6%. Crystal structure analyses and solid-state photophysical studies validate the conclusions from our solution studies and provide insights into the delayed fluorescence emission mechanism.
description: Original data to report:  https://doi.org/10.1021/jacs.4c07730
DOI: 10.5281/zenodo.11652166
title: The Delayed Box: Biphenyl Bisimide Cyclophane, a Supramolecular Nano‒environment for Efficient Generation of Delayed Fluorescence

description: Additional data to report https://doi.org/10.1021/jacs.4c07730:

Activating delayed fluorescence emission in a dilute solution via a non-covalent approach is a formidable challenge. In this report, we propose a strategy for efficient delayed fluorescence generation in dilute solution using a non-covalent approach via supramolecularly engineered cyclophane-based nanoenvironments that provide sufficient binding strength to π-conjugated guests and that can stabilize triplet excitons by reducing vibrational dissipation and lowering the singlet–triplet energy gap for efficient delayed fluorescence emission. Toward this goal, a novel biphenyl bisimide-derived cyclophane is introduced as an electron-deficient and efficient triplet-generating host. Upon encapsulation of various carbazole-derived guests inside the nanocavity of this cyclophane, emissive charge transfer (CT) states close to the triplet energy level of the biphenyl bisimide are generated. The experimental results of host–guest studies manifest high association constants up to 104 M–1 as the prerequisite for inclusion complex formation, the generation of emissive CT states, and triplet-state stabilization in a diluted solution state. By means of different carbazole guest molecules, we could realize tunable delayed fluorescence emission in this carbazole-encapsulated biphenyl bisimide cyclophane in methylcyclohexane/carbon tetrachloride solutions with a quantum yield (QY) of up to 15.6%. Crystal structure analyses and solid-state photophysical studies validate the conclusions from our solution studies and provide insights into the delayed fluorescence emission mechanism.
description: Original data to report:  https://doi.org/10.1021/jacs.4c07730
DOI: 10.5281/zenodo.12801744
title: Supplementary material of "Anti-inflammatory boosted QSAR models for predicting Nitric Oxide inhibition by bioactive metabolites from Aspergillus species""

description: The study focuses on employing advanced QSAR modeling techniques to predict NO inhibitory metabolites from Aspergillus. It utilizes decision trees, the Ranker method, and CorrelationAttributeEval for attribute selection, enhanced by Rotation Forest and AdaBoost, with classifiers like Artificial Neural Networks and J48 Trees. The QSAR models, using an ensemble approach with Rotation Forest and AdaBoost.M1 in a KNIME workflow, selected seven molecular descriptors from a dataset of diverse Aspergillus anti-inflammatory metabolites. The RF-enhanced model excelled in capturing complex relationships and enhancing predictive performance. It identified electrotopological state, topological distances, and secondary amides as key contributors to NO inhibition. This model shows promising potential for identifying new anti-inflammatory drugs based on computational techniques.








 
description: The study focuses on employing advanced QSAR modeling techniques to predict NO inhibitory metabolites from Aspergillus. It utilizes decision trees, the Ranker method, and CorrelationAttributeEval for attribute selection, enhanced by Rotation Forest and AdaBoost, with classifiers like Artificial Neural Networks and J48 Trees. The QSAR models, using an ensemble approach with Rotation Forest and AdaBoost.M1 in a KNIME workflow, selected seven molecular descriptors from a dataset of diverse Aspergillus anti-inflammatory metabolites. The RF-enhanced model excelled in capturing complex relationships and enhancing predictive performance. It identified electrotopological state, topological distances, and secondary amides as key contributors to NO inhibition. This model shows promising potential for identifying new anti-inflammatory drugs based on computational techniques.
DOI: 10.18419/darus-1111/9
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DOI: 10.18419/darus-1111/10
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DOI: 10.18419/darus-1111/15
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DOI: 10.18419/darus-1111/16
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DOI: 10.18419/darus-1111/17
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DOI: 10.18419/darus-1111/18
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DOI: 10.18419/darus-1111/19
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DOI: 10.5281/zenodo.12818308
title: Data for: Coarse-Grained Simulations of Columnar Ionic Liquid Crystals: Comparison with Experiments

description: This repository contains the python scripts required for running coarse-grained simulations of columnar ionic liquid crystals, as detailed in the corresponding manuscript. The scripts have been tested with HOOMD-blue v.2.9.7 and python v.3.9.



The script "equilibration_run_npt.py" performs the initial equilibration in the NPT ensemble using an isotropic barostat at atmospheric pressure.

The script "production_run_nvt.py" performs the production run in the NVT ensemble.


Both scripts take two input parameters from the command line, that is the name of the start configuration and the temperature T. For example, use the command "python3 production_run_nvt.py ILC8_303.0K_end_production.gsd 303" to load the file "ILC8_303.0K_end_production.gsd" and perform a production run at T=303K.

In addition, we have included the final snapshots of our simulations in the .gsd format (https://gsd.readthedocs.io/). The file name indicates the specific ILC molecule and temperature, at which it was simulated. For example, the file "ILC8_303.0K_end_production.gsd" contains the final frame of the NVT simulations of the molecule ILC-8 at temperature T=303K.
DOI: 10.5281/zenodo.12818309
title: Data for: Coarse-Grained Simulations of Columnar Ionic Liquid Crystals: Comparison with Experiments

description: This repository contains the python scripts required for running coarse-grained simulations of columnar ionic liquid crystals, as detailed in the corresponding manuscript. The scripts have been tested with HOOMD-blue v.2.9.7 and python v.3.9.



The script "equilibration_run_npt.py" performs the initial equilibration in the NPT ensemble using an isotropic barostat at atmospheric pressure.

The script "production_run_nvt.py" performs the production run in the NVT ensemble.


Both scripts take two input parameters from the command line, that is the name of the start configuration and the temperature T. For example, use the command "python3 production_run_nvt.py ILC8_303.0K_end_production.gsd 303" to load the file "ILC8_303.0K_end_production.gsd" and perform a production run at T=303K.

In addition, we have included the final snapshots of our simulations in the .gsd format (https://gsd.readthedocs.io/). The file name indicates the specific ILC molecule and temperature, at which it was simulated. For example, the file "ILC8_303.0K_end_production.gsd" contains the final frame of the NVT simulations of the molecule ILC-8 at temperature T=303K.
DOI: 10.18419/darus-1111/20
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DOI: 10.18419/darus-1111/21
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DOI: 10.18419/darus-1111/22
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DOI: 10.5281/zenodo.12820417
title: Voices from Cabinda: Audio files of the examples in the paper "Você + 2SG in Cabindan Portuguese (Angola): Two Effects of Language Contact" (LaborHistórico)

description: Audio files of the examples presented throughout the paper "Você + 2SG in Cabindan Portuguese (Angola): Two Effects of Language Contact" (accepted for publication in LaborHistórico). The audio excerpts are taken from my corpus of interviews in Cabinda, which will be made available soon. This fieldwork, conducted in compliance with ethical standards, was made possible through the assistance of the NGO Ajuda de Desenvolvimento de Povo para Povo and with the authorization of the Secretaria Provincial da Cultura in Cabinda. It received funding from the University of Augsburg (Förderung des wissenschaftlichen Nachwuchses).
DOI: 10.5281/zenodo.12820418
title: Voices from Cabinda: Audio files of the examples in the paper "Você + 2SG in Cabindan Portuguese (Angola): Two Effects of Language Contact" (LaborHistórico)

description: Audio files of the examples presented throughout the paper "Você + 2SG in Cabindan Portuguese (Angola): Two Effects of Language Contact" (accepted for publication in LaborHistórico). The audio excerpts are taken from my corpus of interviews in Cabinda, which will be made available soon. This fieldwork, conducted in compliance with ethical standards, was made possible through the assistance of the NGO Ajuda de Desenvolvimento de Povo para Povo and with the authorization of the Secretaria Provincial da Cultura in Cabinda. It received funding from the University of Augsburg (Förderung des wissenschaftlichen Nachwuchses).
DOI: 10.18419/darus-1111/23
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DOI: 10.18419/darus-1111/24
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DOI: 10.5281/zenodo.12820686
title: Unified EoS within Nambu–Jona-Lasinio description of hadronic matter from a Bayesian approach

description: # Introduction to the Unified Equations of State Dataset for Neutron Star Properties

This dataset comprises three unified equations of state (EOS) for neutron star properties, denoted as eNJL1, eNJL2, and eNJL3, developed based on a microscopic nuclear matter formalism with explicit chiral symmetry. This formalism is grounded in the Nambu Jona-Lasinio (NJL) model and has been specifically designed to accurately describe nuclear matter by introducing four-point and eight-point interactions. These interactions are crucial for reproducing nuclear matter properties at saturation density.

### Background and Methodology

To ensure the robustness and accuracy of the model parameters, a Bayesian inference approach was utilized. This method involved imposing constraints from experimental and {\it ab-initio} nuclear matter calculations, as well as observational data from neutron stars. Key properties such as the effective nucleon mass, radius, and maximum mass of neutron stars were meticulously matched against these constraints. The resulting model parameters provide a comprehensive and reliable description of nuclear matter and neutron star properties.

### Key Findings

- **Effective Nucleon Mass:** The model predicts an effective nucleon mass range between 0.75 and 0.8 times the nucleon mass.- **Neutron Star Radius:** At a 90% confidence level, the radius of a 1.4 $M_\odot$ neutron star varies between 11.48 km and 13.20 km.- **Maximum Mass and Radius:** The model predicts neutron stars with masses up to approximately 2.2 $M_\odot$, with the radius of a 2 $M_\odot$ star being above 10.5 km.- **Speed of Sound:** The speed of sound in the center of massive stars increases monotonically with density, reaching values between $\sqrt{0.7}c$ and $\sqrt{0.8}c$.

### High-Density Constraints and Observational Alignment

The high-density perturbative QCD (pQCD) results impose significant constraints on the EOS, invalidating those predicting larger maximum masses and radii. Interestingly, the EOS that satisfy pQCD constraints also align closely with recent mass-radius measurements, particularly those of PSR J0437-4715. This alignment provides an additional layer of validation against other relativistic mean field model results.

### Dataset Overview

This dataset includes three distinct EOS models (eNJL1, eNJL2, and eNJL3), each offering a unified approach for calculating neutron star properties. These models are detailed in the associated article, where the implications of the findings and the methodology are thoroughly discussed.

Researchers and astrophysicists can utilize this dataset to further explore and model the properties of neutron stars, contributing to a deeper understanding of these fascinating celestial objects. The provided EOS models are essential tools for simulations and theoretical studies aimed at deciphering the complex nature of neutron stars and nuclear matter under extreme conditions.
DOI: 10.18419/darus-1111/25
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DOI: 10.18419/darus-1111/26
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DOI: 10.15468/dl.t3c2q6
title: Occurrence Download

description: A dataset containing 18336 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Linaria supina (L.) Chaz.)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 18336 records from 384 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035975-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-1111/27
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DOI: 10.18419/darus-1111/28
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