cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc14jfrv
title: CCDC 1058487: Experimental Crystal Structure Determination

description: Related Article: Lucile Chatelain, Floriana Tuna, Jacques Pécaut, Marinella Mazzanti|2015|Chem.Commun.|51|11309|doi:10.1039/C5CC02945G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc1j2dss
title: CCDC 1402495: Experimental Crystal Structure Determination

description: Related Article: Angelina Cayuela, Stuart R. Kennedy, M. Laura Soriano, Christopher D. Jones, Miguel Valcárcel, Jonathan W. Steed|2015|Chemical Science|6|6139|doi:10.1039/C5SC01859E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc1j2dvv
title: CCDC 1402497: Experimental Crystal Structure Determination

description: Related Article: Raed M. Al-Zoubi, Hussein Al-Mughaid, Mariam A. Al-Zoubi, Khaled T. Jaradat, Robert McDonald|2015|Eur.J.Org.Chem.|2015|5501|doi:10.1002/ejoc.201500887
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.ghnftk
title: Occurrence Download

description: A dataset containing 7156 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Spiraea chamaedryfolia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7156 records from 195 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051965-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fu698h
title: Occurrence Download

description: A dataset containing 332 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stenocereus thurberi (Engelm.) Buxb.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 332 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009672-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mh8k2f
title: Occurrence Download

description: A dataset containing 321 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stewartia monadelpha Siebold & Zucc.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 321 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009673-241126133413365/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.27915825
title: A Novel Methodological Approach to Structural Integration in Recommender Systems and Improve Interaction Modeling by Graph Neural NetworksA Novel Methodological Approach to Structural Integration in Recommender Systems and Improve Interaction Modeling by Graph Neural Networks

description: A Novel Methodological Approach to StructuralIntegration in Recommender Systems and ImproveInteraction Modeling by Graph Neural NetworksNowadays, recommender applications are necessary in information filtering,
by suggesting priorities to users. Traditional methods often struggle with
implementation defects. Generally, condition inconsistencies and dynamic
variables are ignored in most existing Recommender Systems (RSs). Such
challenges are due to the lack of the holistic, dynamic and integrated
approach in the development process of RSs. In order to deal with such
challenges, key achievements of this research can be expressed from two
dimension. First, this study addresses such limitations by designing a novel
methodological approach toward holistic, dynamic and extensible
development process for RSs. Afterward, in order to express the applicability
of the proposed approach and prove its validity, a novel recommender
framework based on the Graph Neural Network (GNN) has been developed
and evaluated, through major performance metrics. The proposed
framework evaluated via MovieLens real-world dataset, with important
performance metrics. Results present significant improvements over
compared baseline models. The proposed approach outperforming compared traditional models and enhanced the accuracy and
robustness of recommendations. The ability of the proposed framework to
capture the intricate relationships in the user-item interaction data, can lead
to more accurate and personalized recommendations. Moreover, applying the
proposed approach can lead to structural integrity, operational transparency,
dynamism and expandability of RSs.
DOI: 10.6084/m9.figshare.27915825.v1
title: A Novel Methodological Approach to Structural Integration in Recommender Systems and Improve Interaction Modeling by Graph Neural Networks

description: A Novel Methodological Approach to StructuralIntegration in Recommender Systems and ImproveInteraction Modeling by Graph Neural Networks
DOI: 10.6084/m9.figshare.27915825.v2
title: A Novel Methodological Approach to Structural Integration in Recommender Systems and Improve Interaction Modeling by Graph Neural NetworksA Novel Methodological Approach to Structural Integration in Recommender Systems and Improve Interaction Modeling by Graph Neural Networks

description: A Novel Methodological Approach to StructuralIntegration in Recommender Systems and ImproveInteraction Modeling by Graph Neural NetworksNowadays, recommender applications are necessary in information filtering,
by suggesting priorities to users. Traditional methods often struggle with
implementation defects. Generally, condition inconsistencies and dynamic
variables are ignored in most existing Recommender Systems (RSs). Such
challenges are due to the lack of the holistic, dynamic and integrated
approach in the development process of RSs. In order to deal with such
challenges, key achievements of this research can be expressed from two
dimension. First, this study addresses such limitations by designing a novel
methodological approach toward holistic, dynamic and extensible
development process for RSs. Afterward, in order to express the applicability
of the proposed approach and prove its validity, a novel recommender
framework based on the Graph Neural Network (GNN) has been developed
and evaluated, through major performance metrics. The proposed
framework evaluated via MovieLens real-world dataset, with important
performance metrics. Results present significant improvements over
compared baseline models. The proposed approach outperforming compared traditional models and enhanced the accuracy and
robustness of recommendations. The ability of the proposed framework to
capture the intricate relationships in the user-item interaction data, can lead
to more accurate and personalized recommendations. Moreover, applying the
proposed approach can lead to structural integrity, operational transparency,
dynamism and expandability of RSs.
DOI: 10.15468/dl.kecb7y
title: Occurrence Download

description: A dataset containing 515 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stewartia pseudocamellia Maxim.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 515 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009674-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jp8exq
title: Occurrence Download

description: A dataset containing 1373 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United States of America",
 "DatasetKey is iNaturalist Research-grade Observations",
 "License is one of (CC0 1.0, CC-BY 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Fraxinus Tourn. ex L.",
 "Year 2020-2022"
 ]
}

 The dataset includes 1373 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009675-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.66xuju
title: Occurrence Download

description: A dataset containing 4259 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Styrax japonicus Siebold & Zucc.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 4259 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009676-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9ggzht
title: Occurrence Download

description: A dataset containing 4135 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is one of (United States of America, Canada)",
 "TaxonKey is Acer macrophyllum Pursh"
 ]
}

 The dataset includes 4135 records from 171 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009677-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3bdrua
title: Occurrence Download

description: A dataset containing 298 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Syagrus coronata (Mart.) Becc.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 298 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009678-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pnrxjm
title: Occurrence Download

description: A dataset containing 108852 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Vicia villosa Roth"
 ]
}

 The dataset includes 108852 records from 1155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009586-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7peqp5
title: Occurrence Download

description: A dataset containing 468 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Syagrus romanzoffiana (Cham.) Glassman",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 468 records from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009679-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0043399.10fu
title: SH0043399.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0043400.10fu
title: SH0043400.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0043401.10fu
title: SH0043401.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0043402.10fu
title: SH0043402.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0043403.10fu
title: SH0043403.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0043404.10fu
title: SH0043404.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0043405.10fu
title: SH0043405.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0043406.10fu
title: SH0043406.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0043407.10fu
title: SH0043407.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0043408.10fu
title: SH0043408.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.