cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4uytps
title: Occurrence Download

description: A dataset containing 351793 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-91.01633 33.70092,-84.31111 33.82235,-81.63006 35.37547,-75.9157 40.74231,-74.74121 42.95556,-76.00898 43.53324,-76.07654 43.57536,-78.83032 42.55633,-82.387 41.38047,-84.77756 41.25457,-88.80069 41.21072,-91.01633 33.70092))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Odonata"
 ]
}

 The dataset includes 351793 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040278-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q264nf
title: Occurrence Download

description: A dataset containing 250134 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((143.83768 -38.23062,147.57089 -38.23062,147.57089 -36.40258,143.83768 -36.40258,143.83768 -38.23062))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta",
 "Year 2022-2025"
 ]
}

 The dataset includes 250134 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040299-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bvw6jp
title: Occurrence Download

description: A dataset containing 949 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Echium plantagineum",
 "Country is Argentina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 949 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040306-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5qc9k4
title: Occurrence Download

description: A dataset containing 993542 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Month is October",
 "PublishingCountry is Portugal"
 ]
}

 The dataset includes 993542 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040286-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bgfm8q
title: Occurrence Download

description: A dataset containing 106 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Echium plantagineum",
 "Country is Austria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 106 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040308-250811113504898/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.29976169
title: data_3.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 48 , FIN, TRA , A , ≥ 4.5]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976166
title: data_0.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [A , 48 , TRA, NET , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976169.v1
title: data_3.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 48 , FIN, TRA , A , ≥ 4.5]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976166.v1
title: data_0.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [A , 48 , TRA, NET , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976172
title: data_4.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 48 , TRA, DOC , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976172.v1
title: data_4.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 48 , TRA, DOC , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976175
title: data_5.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 48 , FIN, NET , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976175.v1
title: data_5.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 48 , FIN, NET , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976181
title: data_8.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [D , 51 , TRA, NET , B , ≥ 7]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976181.v1
title: data_8.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [D , 51 , TRA, NET , B , ≥ 7]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976178
title: data_1.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 48 , TRA, NET , A , ≥ 4.5]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976178.v1
title: data_1.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 48 , TRA, NET , A , ≥ 4.5]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976184
title: data_6.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [C , 48 , TRA, NET , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976184.v1
title: data_6.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [C , 48 , TRA, NET , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976187
title: data_9.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 49 , TRA, NET , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976187.v1
title: data_9.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 49 , TRA, NET , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976193.v1
title: data_7.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [C , 48 , TRA, NET , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976190
title: data_2.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 48 , TRA, NET , A , ≥ 4.5]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976190.v1
title: data_2.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [B , 48 , TRA, NET , A , ≥ 4.5]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976193
title: data_7.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [C , 48 , TRA, NET , A , ≥ 4]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976196
title: data_11.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [F , 85 , TRA, NET , B , ≥ 7]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976199
title: data_10.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [E , 85 , TRA, NET , B , ≥ 7]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976199.v1
title: data_10.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [E , 85 , TRA, NET , B , ≥ 7]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>
DOI: 10.6084/m9.figshare.29976196.v1
title: data_11.txt

description: Constructed dataset used in project on AI in criminal justice based on a predictive approach to threat detection. The parameters used to construct this dataset are as follows:[Dataset , Cardinality , (Number of Samples) , 2-Dimensions , Construction Rules , Classification , Threat Criterion , LEN(X)]: [F , 85 , TRA, NET , B , ≥ 7]Where: FIN = Financial transactions: Unusual financial activities; TRA = Travel patterns: Frequent travel to regions known for drug production or terrorist activity; NET = Associations and networks: Connections with individuals or organizations known for drug trafficking or terrorist affiliations; DOC = Document authenticity: Possession of forged or altered travel documents. All parameters are described in the study paper under review for publication.<br>