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

description: A dataset containing 3042 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-76.51057 -57.50961,-60.52263 -57.50961,-60.52263 -52.72793,-76.51057 -52.72793,-76.51057 -57.50961))",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 3042 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017376-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gtyvj3
title: Occurrence Download

description: A dataset containing 5520 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sicyos angulatus L., Sicyos angulatus f. ohtanus Asai, Micrampelis echinata Raf., Elaterium trifoliatum L., Sicyos acutus Raf., Sicyos schimperi Naudin, Sicyoides angulata (L.) Medik., Sicyoides angulata (L.) Moench)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 5520 records from 156 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017378-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10902347
title: D1.4 LITERATURE REVIEW ON SOCIAL NETWORK ANALYSIS RELATED TO TRUST IN SCIENCE

description: This document constitutes a part of the D1.4 Social Network Analysis and includes the literature review that was conducted to investigate the methodologies used for addressing the topic of trust in science in Online Social Networks (OSNs). This review contains studies that have approached the topic of trust in science from different perspectives in OSNs examining both data from OSNs and suveys related to OSNs providing useful insights about the factors that influence public trust in science. Important findings are derived from the literature review that affect public’s trust in science, such as the political ideology, educational level, and cultural factors. Also, different methods of the studies are described such as the analysis of the text of the messages, the reactions of users, and deep learning techniques. The findings of the literature review are provided to the final document of D1.4 as they address the further analysis of the Task 1.4 Social Network Analysis
DOI: 10.5281/zenodo.10902348
title: D1.4 LITERATURE REVIEW ON SOCIAL NETWORK ANALYSIS RELATED TO TRUST IN SCIENCE

description: This document constitutes a part of the D1.4 Social Network Analysis and includes the literature review that was conducted to investigate the methodologies used for addressing the topic of trust in science in Online Social Networks (OSNs). This review contains studies that have approached the topic of trust in science from different perspectives in OSNs examining both data from OSNs and suveys related to OSNs providing useful insights about the factors that influence public trust in science. Important findings are derived from the literature review that affect public’s trust in science, such as the political ideology, educational level, and cultural factors. Also, different methods of the studies are described such as the analysis of the text of the messages, the reactions of users, and deep learning techniques. The findings of the literature review are provided to the final document of D1.4 as they address the further analysis of the Task 1.4 Social Network Analysis
DOI: 10.5281/zenodo.10906805
title: D1.4 LITERATURE REVIEW ON SOCIAL NETWORK ANALYSIS RELATED TO TRUST IN SCIENCE

description: This document constitutes a part of the D1.4 Social Network Analysis and includes the literature review that was conducted to investigate the methodologies used for addressing the topic of trust in science in Online Social Networks (OSNs). This review contains studies that have approached the topic of trust in science from different perspectives in OSNs examining both data from OSNs and suveys related to OSNs providing useful insights about the factors that influence public trust in science. Important findings are derived from the literature review that affect public’s trust in science, such as the political ideology, educational level, and cultural factors. Also, different methods of the studies are described such as the analysis of the text of the messages, the reactions of users, and deep learning techniques. The findings of the literature review are provided to the final document of D1.4 as they address the further analysis of the Task 1.4 Social Network Analysis
DOI: 10.5281/zenodo.14276181
title: D1.4 LITERATURE REVIEW ON SOCIAL NETWORK ANALYSIS RELATED TO TRUST IN SCIENCE

description: This document constitutes a part of the D1.4 Social Network Analysis and includes the literature review that was conducted to investigate the methodologies used for addressing the topic of trust in science in Online Social Networks (OSNs). This review contains studies that have approached the topic of trust in science from different perspectives in OSNs examining both data from OSNs and suveys related to OSNs providing useful insights about the factors that influence public trust in science. Important findings are derived from the literature review that affect public’s trust in science, such as the political ideology, educational level, and cultural factors. Also, different methods of the studies are described such as the analysis of the text of the messages, the reactions of users, and deep learning techniques. The findings of the literature review are provided to the final document of D1.4 as they address the further analysis of the Task 1.4 Social Network Analysis
DOI: 10.5281/zenodo.14276940
title: D1.4 LITERATURE REVIEW ON SOCIAL NETWORK ANALYSIS RELATED TO TRUST IN SCIENCE

description: This document constitutes a part of the D1.4 Social Network Analysis and includes the literature review that was conducted to investigate the methodologies used for addressing the topic of trust in science in Online Social Networks (OSNs). This review contains studies that have approached the topic of trust in science from different perspectives in OSNs examining both data from OSNs and suveys related to OSNs providing useful insights about the factors that influence public trust in science. Important findings are derived from the literature review that affect public’s trust in science, such as the political ideology, educational level, and cultural factors. Also, different methods of the studies are described such as the analysis of the text of the messages, the reactions of users, and deep learning techniques. The findings of the literature review are provided to the final document of D1.4 as they address the further analysis of the Task 1.4 Social Network Analysis
DOI: 10.15468/dl.4uq8zc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sebastodes owstoni Jordan & Thompson, 1914",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017379-241126133413365/datasets/export for details.
DOI: 10.15468/dl.4ev3g2
title: Occurrence Download

description: A dataset containing 1649 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-139.14403 51.15609,-108.99029 11.45229,-82.53968 11.49716,-71.10831 14.81374,-63.60702 16.88593,-52.02864 51.31006,-139.14403 51.15609))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Tardigrada"
 ]
}

 The dataset includes 1649 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017380-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xbvqsz
title: Occurrence Download

description: A dataset containing 3487 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "TaxonKey is Autographa precationis Guenée, 1852"
 ]
}

 The dataset includes 3487 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017381-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hyt3g8
title: Occurrence Download

description: A dataset containing 104353 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "Geometry POLYGON((38.33719 16.86357,52.77687 16.86357,52.77687 23.47259,38.33719 23.47259,38.33719 16.86357))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 104353 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017338-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14276832
title: The data used in simulation
DOI: 10.5281/zenodo.14276833
title: The data used in simulation
DOI: 10.15468/dl.mdseyu
title: Occurrence Download

description: A dataset containing 2986 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grampus griseus (G.Cuvier, 1812)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2986 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017382-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2qr5g3
title: Occurrence Download

description: A dataset containing 859 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dentex gibbosus (Rafinesque, 1810)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 859 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017383-241126133413365/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.27960804
title: <b>Mineral and microbial properties drive the formation of mineral-associated organic matter and its response to increased temperature</b>

description: Mineral associated organic matter (MAOM) formation efficiency based on isotopically labelled laboratory incubations (at 15°C and 25°C) and a series of potential drivers, including soil, mineral, and microbial properties.
DOI: 10.6084/m9.figshare.27960804.v2
title: <b>Mineral and microbial properties drive the formation of mineral-associated organic matter and its response to increased temperature</b>

description: Mineral associated organic matter (MAOM) formation efficiency based on isotopically labelled laboratory incubations (at 15°C and 25°C) and a series of potential drivers, including soil, mineral, and microbial properties.
DOI: 10.6084/m9.figshare.27960804.v3
title: <b>Mineral and microbial properties drive the formation of mineral-associated organic matter and its response to increased temperature</b>

description: Mineral associated organic matter (MAOM) formation efficiency based on isotopically labelled laboratory incubations (at 15°C and 25°C) and a series of potential drivers, including soil, mineral, and microbial properties.
DOI: 10.15468/dl.qbchdu
title: Occurrence Download

description: A dataset containing 807768736 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters is less than or equal to 5059.0",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 807768736 records from 21301 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017347-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1078443.10fu
title: SH1078443.10FU

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/sh1078444.10fu
title: SH1078444.10FU

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/sh1078452.10fu
title: SH1078452.10FU

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/sh1078457.10fu
title: SH1078457.10FU

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/sh1078459.10fu
title: SH1078459.10FU

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/sh1078460.10fu
title: SH1078460.10FU

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/sh1078463.10fu
title: SH1078463.10FU

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/sh1078465.10fu
title: SH1078465.10FU

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/sh1078468.10fu
title: SH1078468.10FU

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/sh1078470.10fu
title: SH1078470.10FU

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.