cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.rh2ddf
title: Occurrence Download

description: A dataset containing 103 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Material sample",
 "Continent is South America",
 "Geometry POLYGON((-78.42925 -58.15586,-41.4349 -58.15586,-41.4349 9.46296,-78.42925 9.46296,-78.42925 -58.15586))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Daphnia O.F.Müller, 1785",
 "Year 2000-2025"
 ]
}

 The dataset includes 103 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006208-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15106073
title: It's P=NP

description: About Cook - Levin it's P=NP
DOI: 10.5281/zenodo.15106074
title: It's P=NP

description: About Cook - Levin it's P=NP
DOI: 10.15468/dl.mqr289
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Sticherus lanuginosus (Fée) Nakai"
 ]
}

 The dataset includes 17 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006210-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8bgxqw
title: Occurrence Download

description: A dataset containing 189 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is South America",
 "Geometry POLYGON((-78.42925 -58.15586,-41.4349 -58.15586,-41.4349 9.46296,-78.42925 9.46296,-78.42925 -58.15586))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Daphnia O.F.Müller, 1785",
 "Year 1990-2025"
 ]
}

 The dataset includes 189 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006212-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5angtd
title: Occurrence Download

description: A dataset containing 60541 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "TaxonKey is Arundo donax L."
 ]
}

 The dataset includes 60541 records from 498 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006209-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h7pejh
title: Occurrence Download

description: A dataset containing 18037 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "PublishingCountry is Mauritania"
 ]
}

 The dataset includes 18037 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006213-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r4x5ge
title: Occurrence Download

description: A dataset containing 80904 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "Year 2010-2010"
 ]
}

 The dataset includes 80904 records from 342 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006197-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z2g5u9
title: Occurrence Download

description: A dataset listing the 32 species recorded in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Records of rare species of birds in the Luhansk Nature Reserve and in the surrounding areas",
 "Year is less than or equal to 3000"
 ]
}

 The dataset's 32 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006211-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8qd8at
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Tanzania, United Republic of, Kenya)",
 "Geometry POLYGON((35.07395 -1.82437,34.7741 -7.55009,38.34375 -7.69287,39.94295 -2.65252,35.07395 -1.82437))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Afrocarpus usambarensis (Pilg.) C.N.Page",
 "Year 1970-2025"
 ]
}

 The dataset includes 21 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006217-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4hpg67
title: Occurrence Download

description: A dataset containing 553 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Graphium nomius (Esper, 1798)"
 ]
}

 The dataset includes 553 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006219-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y4e7gx
title: Occurrence Download

description: A dataset containing 98 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "GadmGid is AUS.9_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Galaxias pedderensis Frankenberg, 1968",
 "Year 1970-2025"
 ]
}

 The dataset includes 98 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006218-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14715406
title: A global mapping for all-sized microplastics occurrence in seafloor

description: These files contain critical data obtained through experiments or analyses, which serve as the foundational data for this study. 





Supplementary Data Table 1 provides detailed sampling information for 155 marine sediment samples globally, including coordinates, sampling depth, date of collection, research area, depth zones, cruise details, research vessel, and sampling tools and models.

Supplementary Data Table 2 presents the abundance of microplastics in marine sediments, both in terms of number and mass, including the abundance of 21 microplastic categorys.

Supplementary Data Table 3 presents the basic morphological characteristics of the monitored microplastics, including abundance data for 19 color types, 7 shape categories, 87 detailed polymer types, and 6 size ranges.

Supplementary Data Table 4 provides the average carbon concentration and carbon ratio in microplastics from each sediment sample.

Supplementary Data Table 5 provides environmental parameters for each sediment sampling site, including physical parameters describing surface waves, Stokes drift rate of particles, TOM and TOC content in sediments, and the corresponding particle size of microplastics.

Supplementary Data Table 6 provides global ocean 2D latitude-longitude grid data at a resolution of 0.5° and the corresponding SRTM15+V2.6 seabed depth data at a resolution of 1 arc minute.

Supplementary Data Table 7, Table MP Extraction Process presents key information extracted from 95 published microplastic studies, including basic sediment sampling information, the microplastic extraction process, and abundance data for microplastics. Hence, table MP Correction Process presents microplastic abundance data corrected to full size based on the selected extraction solutions, digestion solutions, and identification tools from the literature.

Supplementary Data Table 8 provides physical parameters for surface waves and seabed currents at all sampling points, valid literature points, and global ocean 2D latitude-longitude grid points.

Supplementary Data Table 9, Table Three Prediction Methods presents the performance of three machine learning models applied to microplastic abundance at sampling points and literature points, with data split into training and testing sets. Table Keras for Predicting Abundancd presents the predicted abundance of microplastics in marine sediments at global ocean 2D latitude-longitude grid points, based on the BP neural network model implemented in the Keras framework.
DOI: 10.5281/zenodo.15106077
title: A global mapping for all-sized microplastics occurrence in seafloor

description: These files contain critical data obtained through experiments or analyses, which serve as the foundational data for this study. 





Supplementary Data Table 1 provides detailed sampling information for 155 marine sediment samples globally, including coordinates, sampling depth, date of collection, research area, depth zones, cruise details, research vessel, and sampling tools and models.

Supplementary Data Table 2 presents the abundance of microplastics in marine sediments, both in terms of number and mass, including the abundance of 21 microplastic categorys.

Supplementary Data Table 3 presents the basic morphological characteristics of the monitored microplastics, including abundance data for 19 color types, 7 shape categories, 87 detailed polymer types, and 6 size ranges.

Supplementary Data Table 4 provides the average carbon concentration and carbon ratio in microplastics from each sediment sample.

Supplementary Data Table 5 provides environmental parameters for each sediment sampling site, including physical parameters describing surface waves, Stokes drift rate of particles, TOM and TOC content in sediments, and the corresponding particle size of microplastics.

Supplementary Data Table 6 provides global ocean 2D latitude-longitude grid data at a resolution of 0.5° and the corresponding SRTM15+V2.6 seabed depth data at a resolution of 1 arc minute.

Supplementary Data Table 7, Table MP Extraction Process presents key information extracted from 95 published microplastic studies, including basic sediment sampling information, the microplastic extraction process, and abundance data for microplastics. Hence, table MP Correction Process presents microplastic abundance data corrected to full size based on the selected extraction solutions, digestion solutions, and identification tools from the literature.

Supplementary Data Table 8 provides physical parameters for surface waves and seabed currents at all sampling points, valid literature points, and global ocean 2D latitude-longitude grid points.

Supplementary Data Table 9, Table Three Prediction Methods presents the performance of three machine learning models applied to microplastic abundance at sampling points and literature points, with data split into training and testing sets. Table Keras for Predicting Abundancd presents the predicted abundance of microplastics in marine sediments at global ocean 2D latitude-longitude grid points, based on the BP neural network model implemented in the Keras framework.
DOI: 10.15468/dl.893qgf
title: Occurrence Download

description: A dataset containing 27006423 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Suliformes"
 ]
}

 The dataset includes 27006423 records from 4811 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006216-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1181659.10fu
title: SH1181659.10FU

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/sh1181664.10fu
title: SH1181664.10FU

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/sh1181667.10fu
title: SH1181667.10FU

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/sh1181669.10fu
title: SH1181669.10FU

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/sh1181671.10fu
title: SH1181671.10FU

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/sh1181674.10fu
title: SH1181674.10FU

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/sh1181685.10fu
title: SH1181685.10FU

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: 10.15468/dl.dv969d
title: Occurrence Download

description: A dataset containing 297 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Metzgeria fruticulosa",
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2020"
 ]
}

 The dataset includes 297 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002759-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1181688.10fu
title: SH1181688.10FU

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/sh1181695.10fu
title: SH1181695.10FU

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/sh1181700.10fu
title: SH1181700.10FU

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.