cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.e7pwhh
title: Occurrence Download

description: A dataset containing 21507 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (January, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Phalacrocorax sulcirostris (Brandt, 1837)",
 "Year 1998-2009"
 ]
}

 The dataset includes 21507 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059630-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kecdzw
title: Occurrence Download

description: A dataset containing 198 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron anthosphaerum Diels"
 ]
}

 The dataset includes 198 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059631-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.btbraj
title: Occurrence Download

description: A dataset containing 321 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (April, May, June)",
 "OccurrenceStatus is Present",
 "TaxonKey is Phalacrocorax pygmeus (Pallas, 1773)"
 ]
}

 The dataset includes 321 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059633-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7df62g
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((149.395832 -33.986208, 149.402569 -33.986208, 149.402569 -33.983962, 149.404815 -33.983962, 149.404815 -33.977225, 149.400324 -33.977225, 149.400324 -33.979471, 149.395832 -33.979471, 149.395832 -33.986208)), ((149.395832 -33.986208, 149.393586 -33.986208, 149.393586 -33.992946, 149.400324 -33.992946, 149.400324 -33.9907, 149.395832 -33.9907, 149.395832 -33.986208)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059634-240626123714530/datasets/export for details.
DOI: 10.15468/dl.7544hw
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (April, May, June)",
 "TaxonKey is Catharacta lonnbergi Mathews, 1912"
 ]
}

 The dataset includes 45 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059635-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4gqa48
title: Occurrence Download

description: A dataset containing 12767 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panthera leo (Linnaeus, 1758)"
 ]
}

 The dataset includes 12767 records from 171 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059632-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.byg5n4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.025275 -33.981717, 149.025275 -33.983962, 149.027521 -33.983962, 149.027521 -33.981717, 149.025275 -33.981717))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059637-240626123714530/datasets/export for details.
DOI: 10.15468/dl.rmjpms
title: Occurrence Download

description: A dataset containing 6037 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phalacrocorax punctatus (Sparrman, 1786)",
 "Year 1991-2015"
 ]
}

 The dataset includes 6037 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059636-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6gkwq3
title: Occurrence Download

description: A dataset containing 184 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (January, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Phalacrocorax pygmeus (Pallas, 1773)"
 ]
}

 The dataset includes 184 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059638-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nek5ny
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (April, May, June)",
 "TaxonKey is Catharacta maccormicki (H.Saunders, 1893)"
 ]
}

 The dataset includes 9 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059639-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tnc7dn
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((148.964639 -33.983962, 148.964639 -33.9907, 148.966884 -33.9907, 148.966884 -33.983962, 148.964639 -33.983962))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059640-240626123714530/datasets/export for details.
DOI: 10.15468/dl.9h2edd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.009554 -33.983962, 149.009554 -33.986208, 149.0118 -33.986208, 149.0118 -33.983962, 149.009554 -33.983962))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059641-240626123714530/datasets/export for details.
DOI: 10.6084/m9.figshare.26531350
title: eDNA data of “Seasonal changes of plankton community and its influencing factors in subtropical coastal marine areas revealed by eDNA-based network analysis”

description: Coastal marine ecosystems are among the most ecologically and socio-economically dynamic regions on Earth, and the study of eukaryotic planktonic communities is an important aspect of aquatic ecology. It is essential to monitor plankton biodiversity and identify influencing factors in order to measure human effects on coastal waters and help contribute to ecosystem preservation. Environmental DNA (eDNA) metabarcoding analysis is an advanced toolfor detecting the presence of aquatic organisms, which has revolutionary significance for biodiversity monitoring and real-time detection of wild environments. In this study, eDNA metabarcoding was used to study seasonal changes in eukaryotic plankton communities in subtropical coastal waters of the northern South China Sea, to explore how various trophic groups affect the complexity and stability of the species network. The results showed that the summer interaction network is looser and had has reduce biodiversitycompared to the spring interaction network. As an important part of the planktonic community, the abundance and diversity of algae reflect the interactions between different species and the influence of environmental factors on the dominant species, particularly temperature and nutrients. In addition, producer diversity set the basis for species network complexity and indirectly affected network stability, while consumer diversity and its flexible predation strategies may directly lead to differences in network stability across seasons. There were close correlations among species interaction, environmental factors and species diversity. This study can help us understand the ecosystem functions throughout the year and the complex interactions of planktonic communities from a multiple trophic level perspective, and provide effective methods for sustainable resource utilization and ecological environmental protection in the future.
DOI: 10.6084/m9.figshare.26531350.v1
title: eDNA data of “Seasonal changes of plankton community and its influencing factors in subtropical coastal marine areas revealed by eDNA-based network analysis”

description: Coastal marine ecosystems are among the most ecologically and socio-economically dynamic regions on Earth, and the study of eukaryotic planktonic communities is an important aspect of aquatic ecology. It is essential to monitor plankton biodiversity and identify influencing factors in order to measure human effects on coastal waters and help contribute to ecosystem preservation. Environmental DNA (eDNA) metabarcoding analysis is an advanced toolfor detecting the presence of aquatic organisms, which has revolutionary significance for biodiversity monitoring and real-time detection of wild environments. In this study, eDNA metabarcoding was used to study seasonal changes in eukaryotic plankton communities in subtropical coastal waters of the northern South China Sea, to explore how various trophic groups affect the complexity and stability of the species network. The results showed that the summer interaction network is looser and had has reduce biodiversitycompared to the spring interaction network. As an important part of the planktonic community, the abundance and diversity of algae reflect the interactions between different species and the influence of environmental factors on the dominant species, particularly temperature and nutrients. In addition, producer diversity set the basis for species network complexity and indirectly affected network stability, while consumer diversity and its flexible predation strategies may directly lead to differences in network stability across seasons. There were close correlations among species interaction, environmental factors and species diversity. This study can help us understand the ecosystem functions throughout the year and the complex interactions of planktonic communities from a multiple trophic level perspective, and provide effective methods for sustainable resource utilization and ecological environmental protection in the future.
DOI: 10.15468/dl.dhdgup
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron anwheiense E.H.Wilson"
 ]
}

 The dataset includes 79 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059642-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5sjgb3
title: Occurrence Download

description: A dataset containing 4187 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bonasa bonasia (Linnaeus, 1758)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 4187 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059643-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r6dvqg
title: Occurrence Download

description: A dataset containing 2329 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Prionailurus bengalensis (Kerr, 1792)"
 ]
}

 The dataset includes 2329 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059644-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8001487
title: HiDy: A Large-scale Hierarchical Dynamic Financial Knowledge Base

description: In recent years, domain-specific knowledge bases (KBs) have been increasingly popular due to their expertise and in-depth representation in a specific domain. Among these domain-specific KBs, financial KBs have attracted considerable attention from academics and industries due to their broad spectrum of downstream applications, such as stock movement prediction, financial fraud detection, and supply chain management. Until now, no datasets including open-domain and domain-specific KBs provide dynamic, up-to-date, and comprehensive financial knowledge, which leads to a fair comparison and broader research among popular financial task models impossible or at least very difficult. We, therefore, construct a dynamic financial KB, HiDy, to address the current limitations discussed above. HiDy is a hierarchical, dynamic, robust, diverse, and large-scale financial KB that aims to provide various valuable financial knowledge as critical benchmarking data for fair model testing in different financial tasks. Specifically, HiDy currently contains 34 relation types, more than 495,000 relations, 17 entity types, and more than 51,000 entities. The scale of HiDy is steadily growing due to its continuous updates. To make HiDy easily accessible and retrieved, HiDy is organized in a well-formed financial hierarchy with four branches, <em>Macro</em>, <em>Meso</em>,<em> Micro</em>, and<em> Others</em>. Moreover, the robustness of HiDy is a result of the state-of-the-art knowledge extraction and fusion techniques as well as the manual cleaning. For temporality, HiDy's dynamic knowledge is extracted from various resources under different time-granularity to ensure each branch's knowledge is the most up-to-date.
DOI: 10.5281/zenodo.8038851
title: HiDy: A Large-scale Hierarchical Dynamic Financial Knowledge Base

description: In recent years, domain-specific knowledge bases (KBs) have been increasingly popular due to their expertise and in-depth representation in a specific domain. Among these domain-specific KBs, financial KBs have attracted considerable attention from academics and industries due to their broad spectrum of downstream applications, such as stock movement prediction, financial fraud detection, and supply chain management. Until now, no datasets including open-domain and domain-specific KBs provide dynamic, up-to-date, and comprehensive financial knowledge, which leads to a fair comparison and broader research among popular financial task models impossible or at least very difficult. We, therefore, construct a dynamic financial KB, HiDy, to address the current limitations discussed above. HiDy is a hierarchical, dynamic, robust, diverse, and large-scale financial KB that aims to provide various valuable financial knowledge as critical benchmarking data for fair model testing in different financial tasks. Specifically, HiDy currently contains 34 relation types, more than 492,600 relations, 17 entity types, and more than 51,000 entities. The scale of HiDy is steadily growing due to its continuous updates. To make HiDy easily accessible and retrieved, HiDy is organized in a well-formed financial hierarchy with four branches, <em>Macro</em>, <em>Meso</em>,<em> Micro</em>, and<em> Others</em>. Moreover, the robustness of HiDy is a result of the state-of-the-art knowledge extraction and fusion techniques as well as the manual cleaning. For temporality, HiDy's dynamic knowledge is extracted from various resources under different time-granularity to ensure each branch's knowledge is the most up-to-date.
DOI: 10.5281/zenodo.8117217
title: HiDy: A Large-scale Hierarchical Dynamic Financial Knowledge Base

description: In recent years, domain-specific knowledge bases (KBs) have been increasingly popular due to their expertise and in-depth representation in a specific domain. Among these domain-specific KBs, financial KBs have attracted considerable attention from academics and industries due to their broad spectrum of downstream applications, such as stock movement prediction, financial fraud detection, and supply chain management. Until now, no datasets including open-domain and domain-specific KBs provide dynamic, up-to-date, and comprehensive financial knowledge, which leads to a fair comparison and broader research among popular financial task models impossible or at least very difficult. We, therefore, construct a dynamic financial KB, HiDy, to address the current limitations discussed above. HiDy is a hierarchical, dynamic, robust, diverse, and large-scale financial KB that aims to provide various valuable financial knowledge as critical benchmarking data for fair model testing in different financial tasks. Specifically, HiDy currently contains 34 relation types, more than 501,500 relations, 17 entity types, and more than 51,000 entities. The scale of HiDy is steadily growing due to its continuous updates. To make HiDy easily accessible and retrieved, HiDy is organized in a well-formed financial hierarchy with four branches, <em>Macro</em>, <em>Meso</em>,<em> Micro</em>, and<em> Others</em>. Moreover, the robustness of HiDy is a result of the state-of-the-art knowledge extraction and fusion techniques as well as the manual cleaning. For temporality, HiDy's dynamic knowledge is extracted from various resources under different time-granularity to ensure each branch's knowledge is the most up-to-date.
DOI: 10.5281/zenodo.8312333
title: HiDy: A Large-scale Hierarchical Dynamic Financial Knowledge Base

description: HiDy is a hierarchical, dynamic, robust, diverse, and large-scale financial KB that aims to provide various valuable financial knowledge as critical benchmarking data for fair model testing in different financial tasks. Specifically, HiDy currently contains 34 relation types, more than 502,495 relations, 17 entity types, and more than 51,095 entities. The scale of HiDy is steadily growing due to its continuous updates. To make HiDy easily accessible and retrieved, HiDy is organized in a well-formed financial hierarchy with four branches, <em>Macro</em>, <em>Meso</em>,<em> Micro</em>, and<em> Others</em>. We then give explanations on the various csv files as follows. "hidy.nodes.entity_type.csv" includes a mapping dictionary of entities with a specific entity type. The meta data is ID, name, (code), Label. For example, "hidy.nodes.company.csv" includes "0,东诚药业,002675.SZ,company", "1,大庆华科,000985.SZ,company". "hidy.relationships.relation_type.csv" includes quadruple knowledge with a specific relation type. The meta data is START_ID, END_ID, TYPE, time. For example, "hidy.relationships.cooperate.csv" includes "2197,245,cooperate,2019/9/17 12:00, "1165,756,cooperate,2020/8/2 17:10". For details of HiDy, please refer to our GitHub.
DOI: 10.5281/zenodo.8404681
title: HiDy: A Large-scale Hierarchical Dynamic Financial Knowledge Base

description: HiDy is a hierarchical, dynamic, robust, diverse, and large-scale financial KB that aims to provide various valuable financial knowledge as critical benchmarking data for fair model testing in different financial tasks. Specifically, HiDy currently contains 34 relation types, more than 502,891 relations, 17 entity types, and more than 51,095 entities. The scale of HiDy is steadily growing due to its continuous updates. To make HiDy easily accessible and retrieved, HiDy is organized in a well-formed financial hierarchy with four branches, <em>Macro</em>, <em>Meso</em>,<em> Micro</em>, and<em> Others</em>. We then give explanations on the various csv files as follows. "hidy.nodes.entity_type.csv" includes a mapping dictionary of entities with a specific entity type. The meta data is ID, name, (code), Label. For example, "hidy.nodes.company.csv" includes "0,东诚药业,002675.SZ,company", "1,大庆华科,000985.SZ,company". "hidy.relationships.relation_type.csv" includes quadruple knowledge with a specific relation type. The meta data is START_ID, END_ID, TYPE, time. For example, "hidy.relationships.cooperate.csv" includes "2197,245,cooperate,2019/9/17 12:00, "1165,756,cooperate,2020/8/2 17:10". For details of HiDy, please refer to our GitHub.
DOI: 10.5281/zenodo.10090898
title: HiDy: A Large-scale Hierarchical Dynamic Financial Knowledge Base

description: HiDy is a hierarchical, dynamic, robust, diverse, and large-scale financial KB that aims to provide various valuable financial knowledge as critical benchmarking data for fair model testing in different financial tasks. Specifically, HiDy currently contains 34 relation types, more than 502,891 relations, 17 entity types, and more than 51,095 entities. The scale of HiDy is steadily growing due to its continuous updates. To make HiDy easily accessible and retrieved, HiDy is organized in a well-formed financial hierarchy with four branches, Macro, Meso, Micro, and Others.


We then give explanations on the various csv files as follows.



	
"hidy.nodes.entity_type.csv" includes a mapping dictionary of entities with a specific entity type. The meta data is ID, name, (code), Label. For example, "hidy.nodes.company.csv" includes "0,东诚药业,002675.SZ,company", "1,大庆华科,000985.SZ,company".
	
"hidy.relationships.relation_type.csv" includes quadruple knowledge with a specific relation type. The meta data is START_ID, END_ID, TYPE, time. For example, "hidy.relationships.cooperate.csv" includes "2197,245,cooperate,2019/9/17 12:00, "1165,756,cooperate,2020/8/2 17:10".



For details of HiDy, please refer to our GitHub.
DOI: 10.5281/zenodo.10294865
title: HiDy: A Large-scale Hierarchical Dynamic Financial Knowledge Base

description: HiDy is a hierarchical, dynamic, robust, diverse, and large-scale financial KB that aims to provide various valuable financial knowledge as critical benchmarking data for fair model testing in different financial tasks. Specifically, HiDy currently contains 34 relation types, more than 503,679 relations, 17 entity types, and more than 51,095 entities. The scale of HiDy is steadily growing due to its continuous updates. To make HiDy easily accessible and retrieved, HiDy is organized in a well-formed financial hierarchy with four branches, Macro, Meso, Micro, and Others.
We then give explanations on the various csv files as follows.

"hidy.nodes.entity_type.csv" includes a mapping dictionary of entities with a specific entity type. The meta data is ID, name, (code), Label. For example, "hidy.nodes.company.csv" includes "0,东诚药业,002675.SZ,company", "1,大庆华科,000985.SZ,company".
"hidy.relationships.relation_type.csv" includes quadruple knowledge with a specific relation type. The meta data is START_ID, END_ID, TYPE, time. For example, "hidy.relationships.cooperate.csv" includes "2197,245,cooperate,2019/9/17 12:00, "1165,756,cooperate,2020/8/2 17:10".
For details of HiDy, please refer to our GitHub.