cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.f7rgfx
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-94.58079 18.18063,-94.58913 18.17181,-94.58376 18.15304,-94.56341 18.14152,-94.52746 18.13864,-94.51966 18.13411,-94.4825 18.12693,-94.44606 18.12368,-94.42627 18.12671,-94.42095 18.13683,-94.4201 18.15341,-94.43031 18.15583,-94.46308 18.14977,-94.49798 18.14936,-94.52121 18.15584,-94.54105 18.16086,-94.55692 18.16547,-94.58079 18.18063))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Dermochelys coriacea (Vandelli, 1761), Caretta caretta (Linnaeus, 1758), Eretmochelys imbricata (Linnaeus, 1766), Chelonia mydas (Linnaeus, 1758), Lepidochelys kempii Garman, 1880)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0187668-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.32w3v7
title: Occurrence Download

description: A dataset containing 972 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Orconectes rusticus (Girard, 1852)"
 ]
}

 The dataset includes 972 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0274710-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m23zhd
title: Occurrence Download

description: A dataset containing 251 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Broussonetia papyrifera (L.) L'Hér. ex Vent.",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 251 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0187639-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.waymwr
title: Occurrence Download

description: A dataset containing 1593 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhodnius Stål, 1859"
 ]
}

 The dataset includes 1593 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007295-240425142415019/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.86w9dh
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Coffea arabica L.",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0187663-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q49xap
title: Occurrence Download

description: A dataset containing 935 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation)",
 "StateProvince is one of (putumayo)",
 "TaxonKey is one of (Butorides Blyth, 1852, Butorides striata flyensis Salomonsen, 1966, Butorides striata degens Hartert, 1920)"
 ]
 }
 ]
}

 The dataset includes 935 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0235004-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2vva23
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Leucospis sinensis Walker, 1862",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0202434-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.efac8e
title: Occurrence Download

description: A dataset containing 1089 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hosta ventricosa Stearn"
 ]
}

 The dataset includes 1089 records from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0249028-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sc4sqw
title: Occurrence Download

description: A dataset containing 14129 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Arhopalus rusticus (Linnaeus, 1758)"
 ]
}

 The dataset includes 14129 records from 396 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007876-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9xmbxx
title: Occurrence Download

description: A dataset listing the 34 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((15.32133 47.75658,15.32613 47.75658,15.32613 47.76051,15.32133 47.76051,15.32133 47.75658))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi",
 "Year 1000-2025"
 ]
}

 The dataset's 34 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007867-250402121839773/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.28457229
title: Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning

description: Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic neuronal loss in Alzheimer’s disease. We developed a QSAR model to facilitate the identification of effective butyrylcholinesterase inhibitors. The model employs multi-feature selection and feature learning, improving the in silico screening efficiency and accelerating drug discovery efforts. This study aims to integrate Human Intestinal Absorption (HIA) values of butyrylcholinesterase (BChE) target inhibitors and their 50% inhibitory concentration (IC<sub>50</sub>) with machine learning tools. The model was developed using chemical descriptors in combination with supervised machine learning classification algorithms. Random Forest Classifier algorithm proved to be the ultimate best fit for classification model metrics including log loss probability (0.04225), accuracy score (98.88%) and Matthew’s correlation coefficient (0.98). Furthermore, a subset of the active dataset was used to study the regression based on HIA values using multi-feature selection and feature learning. The models were validated using precision, recall and F1 score for regression modelling. After integrating HIA data with existing machine learning algorithms, we observed a significant reduction of 89.63% in the number of inhibitors. The findings provide valuable pharmacological insights that can help in future design of drug development schemes different from conventional methods.
DOI: 10.6084/m9.figshare.28457229.v1
title: Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning

description: Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic neuronal loss in Alzheimer’s disease. We developed a QSAR model to facilitate the identification of effective butyrylcholinesterase inhibitors. The model employs multi-feature selection and feature learning, improving the in silico screening efficiency and accelerating drug discovery efforts. This study aims to integrate Human Intestinal Absorption (HIA) values of butyrylcholinesterase (BChE) target inhibitors and their 50% inhibitory concentration (IC<sub>50</sub>) with machine learning tools. The model was developed using chemical descriptors in combination with supervised machine learning classification algorithms. Random Forest Classifier algorithm proved to be the ultimate best fit for classification model metrics including log loss probability (0.04225), accuracy score (98.88%) and Matthew’s correlation coefficient (0.98). Furthermore, a subset of the active dataset was used to study the regression based on HIA values using multi-feature selection and feature learning. The models were validated using precision, recall and F1 score for regression modelling. After integrating HIA data with existing machine learning algorithms, we observed a significant reduction of 89.63% in the number of inhibitors. The findings provide valuable pharmacological insights that can help in future design of drug development schemes different from conventional methods.
DOI: 10.6084/m9.figshare.28652570
title: Predictive modelling of peroxisome proliferator-activated receptor gamma (PPARγ) IC50 inhibition by emerging pollutants using light gradient boosting machine

description: Peroxisome proliferator-activated receptor gamma (PPARγ), a critical nuclear receptor, plays a pivotal role in regulating metabolic and inflammatory processes. However, various environmental contaminants can disrupt PPARγ function, leading to adverse health effects. This study introduces a novel approach to predict the inhibitory activity (IC50 values) of 140 chemical compounds across 13 categories, including pesticides, organochlorines, dioxins, detergents, flame retardants, and preservatives, on PPARγ. The predictive model, based on the light-gradient boosting machine (LightGBM) algorithm, was trained on a dataset of 1804 molecules showed <i>r</i><sup>2</sup> values of 0.82 and 0.59, Mean Absolute Error (MAE) of 0.38 and 0.58, and Root Mean Square Error (RMSE) of 0.54 and 0.76 for the training and test sets, respectively. This study provides novel insights into the interactions between emerging contaminants and PPARγ, highlighting the potential hazards and risks these chemicals may pose to public health and the environment. The ability to predict PPARγ inhibition by these hazardous contaminants demonstrates the value of this approach in guiding enhanced environmental toxicology research and risk assessment.
DOI: 10.6084/m9.figshare.28652570.v1
title: Predictive modelling of peroxisome proliferator-activated receptor gamma (PPARγ) IC50 inhibition by emerging pollutants using light gradient boosting machine

description: Peroxisome proliferator-activated receptor gamma (PPARγ), a critical nuclear receptor, plays a pivotal role in regulating metabolic and inflammatory processes. However, various environmental contaminants can disrupt PPARγ function, leading to adverse health effects. This study introduces a novel approach to predict the inhibitory activity (IC50 values) of 140 chemical compounds across 13 categories, including pesticides, organochlorines, dioxins, detergents, flame retardants, and preservatives, on PPARγ. The predictive model, based on the light-gradient boosting machine (LightGBM) algorithm, was trained on a dataset of 1804 molecules showed <i>r</i><sup>2</sup> values of 0.82 and 0.59, Mean Absolute Error (MAE) of 0.38 and 0.58, and Root Mean Square Error (RMSE) of 0.54 and 0.76 for the training and test sets, respectively. This study provides novel insights into the interactions between emerging contaminants and PPARγ, highlighting the potential hazards and risks these chemicals may pose to public health and the environment. The ability to predict PPARγ inhibition by these hazardous contaminants demonstrates the value of this approach in guiding enhanced environmental toxicology research and risk assessment.
DOI: 10.6084/m9.figshare.28740488
title: Modelling of intrinsic membrane permeability of drug molecules by explainable ML-based q-RASPR approach towards better pharmacokinetics and toxicokinetics properties

description: Drug discovery’s success lies in potent inhibition against a target and optimum pharmacokinetic and toxicokinetic properties of drug molecules. Membrane permeability is a crucial factor in determining the absorption, distribution, metabolism, and excretion of drug molecules, thereby determining the pharmacokinetic and toxicokinetic properties important for drug development. Intrinsic permeability (P<sub>0</sub>) is more crucial than apparent permeability (Papp) in assessing the transport of drug molecules across a membrane. It gives more consistent results due to its non-dependency on external/site-specific factors. In the present work, our focus is on the construction of a machine learning (ML)-based quantitative read-across structure–property relationship (q-RASPR) model of intrinsic permeability of drug molecules by utilizing both linear and non-linear algorithms. The Support Vector Regression (SVR) q-RASPR model was found to be the best model having superior predictive ability (<i>Q</i><sup>2</sup><sub>F1</sub> = 0.788, <i>Q</i><sup>2</sup><sub>F2</sub> = 0.785, <i>MAE</i><sub>test</sub> = 0.637). The contribution of important descriptors in the final model is explained to get a mechanistic interpretation of intrinsic permeability. Overall, the present study unveils the application of the q-RASPR framework for significant improvement of the external predictivity of the traditional QSPR model in the case of intrinsic permeability to get a better assessment of the total permeability of drug molecules.
DOI: 10.6084/m9.figshare.28740488.v1
title: Modelling of intrinsic membrane permeability of drug molecules by explainable ML-based q-RASPR approach towards better pharmacokinetics and toxicokinetics properties

description: Drug discovery’s success lies in potent inhibition against a target and optimum pharmacokinetic and toxicokinetic properties of drug molecules. Membrane permeability is a crucial factor in determining the absorption, distribution, metabolism, and excretion of drug molecules, thereby determining the pharmacokinetic and toxicokinetic properties important for drug development. Intrinsic permeability (P<sub>0</sub>) is more crucial than apparent permeability (Papp) in assessing the transport of drug molecules across a membrane. It gives more consistent results due to its non-dependency on external/site-specific factors. In the present work, our focus is on the construction of a machine learning (ML)-based quantitative read-across structure–property relationship (q-RASPR) model of intrinsic permeability of drug molecules by utilizing both linear and non-linear algorithms. The Support Vector Regression (SVR) q-RASPR model was found to be the best model having superior predictive ability (<i>Q</i><sup>2</sup><sub>F1</sub> = 0.788, <i>Q</i><sup>2</sup><sub>F2</sub> = 0.785, <i>MAE</i><sub>test</sub> = 0.637). The contribution of important descriptors in the final model is explained to get a mechanistic interpretation of intrinsic permeability. Overall, the present study unveils the application of the q-RASPR framework for significant improvement of the external predictivity of the traditional QSPR model in the case of intrinsic permeability to get a better assessment of the total permeability of drug molecules.
DOI: 10.6084/m9.figshare.8117945
title: General and specified vulnerability to extreme temperatures among older adults

description: Extreme temperatures pose significant risks to human health and well-being. Older adults are particularly at risk and their susceptibility is a function of vulnerability to general daily life circumstances and to specified events or threats. For the first time, this paper develops a combined general and specified approach to understand the determinants of vulnerability. The findings show that most participants exhibit high levels of heat-related vulnerability, followed by cold-related vulnerability and lastly, general vulnerability. General vulnerability was shown to be primarily shaped by financial, physical and social assets. Whilst, specified vulnerability was found to be mainly shaped by human, physical and placed based assets. Such findings present opportunities to focus on the types of assets that contribute to reducing vulnerability. These findings also suggest that the role assets play in shaping vulnerability must be attended to if we are to fully understand and effectively implement strategies to reduce vulnerability.
DOI: 10.6084/m9.figshare.8117945.v1
title: General and specified vulnerability to extreme temperatures among older adults

description: Extreme temperatures pose significant risks to human health and well-being. Older adults are particularly at risk and their susceptibility is a function of vulnerability to general daily life circumstances and to specified events or threats. For the first time, this paper develops a combined general and specified approach to understand the determinants of vulnerability. The findings show that most participants exhibit high levels of heat-related vulnerability, followed by cold-related vulnerability and lastly, general vulnerability. General vulnerability was shown to be primarily shaped by financial, physical and social assets. Whilst, specified vulnerability was found to be mainly shaped by human, physical and placed based assets. Such findings present opportunities to focus on the types of assets that contribute to reducing vulnerability. These findings also suggest that the role assets play in shaping vulnerability must be attended to if we are to fully understand and effectively implement strategies to reduce vulnerability.
DOI: 10.15468/dl.9rhrxe
title: Occurrence Download

description: A dataset containing 13932 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pleurodema Tschudi, 1838"
 ]
}

 The dataset includes 13932 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006078-160526112335914/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gsf2d9
title: Occurrence Download

description: A dataset containing 613 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Colección de reptiles del Museo de Historia Natural, Universidad de Caldas"
 ]
}

 The dataset includes 613 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041334-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eysqt9
title: Occurrence Download

description: A dataset containing 115290 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dipodomys Gray, 1841"
 ]
}

 The dataset includes 115290 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006078-160623103540712/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kjq2kq
title: Occurrence Download

description: A dataset containing 381 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Human Observation, Occurrence evidence, Observation)",
 "GadmGid is MKD",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 1000-1958"
 ]
}

 The dataset includes 381 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033446-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xegtlq
title: Occurrence Download

description: A dataset containing 242 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aeschynomene marginata Benth.",
 "Country is Brazil",
 {
 "or" : [
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Literature Occurrence"
 ]
 }
 ]
}

 The dataset includes 242 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006078-160822134323880/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7dxbry
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crinum verdoorniae Lehmiller"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0152343-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yefmkq
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 2) Quantitative parameters of the Emiliania huxleyi population at the section along 70 m isobath carried out on 26 and 27 August 2001"
 ]
}

 The dataset includes 60 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006078-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.967k9v
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crinum virgineum Mart. ex Schult. & Schult.f."
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0152344-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9ya89v
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crinum wattii Baker"
 ]
}

 The dataset includes 14 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0152347-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.puawtk
title: Occurrence Download

description: A dataset containing 20689 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Genista sagittalis L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 20689 records from 116 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006078-170714134226665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gwxl3f
title: Occurrence Download

description: A dataset containing 27282 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Micropterus salmoides (Lacepède, 1802)"
 ]
}

 The dataset includes 27282 records from 141 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006078-170826194755519/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.