cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0194086.10fu
title: SH0194086.10FU

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/sh0194087.10fu
title: SH0194087.10FU

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/sh0194088.10fu
title: SH0194088.10FU

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/sh0194089.10fu
title: SH0194089.10FU

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/sh0194090.10fu
title: SH0194090.10FU

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/sh0194091.10fu
title: SH0194091.10FU

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/sh0194092.10fu
title: SH0194092.10FU

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/sh0194093.10fu
title: SH0194093.10FU

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/sh0194094.10fu
title: SH0194094.10FU

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.684f5k
title: Occurrence Download

description: A dataset containing 1189 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Peru",
 "TaxonKey is Phyllotis xanthopygus (Waterhouse, 1837)"
 ]
}

 The dataset includes 1189 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020472-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17613/19xz8-69944
title: BBC Desert Island Discs Dataset v 1.0

description: This is the first version of a larger project that I've been working on in my spare time to create a dataset of the guests, songs, books, and luxuries on the long-running BBC radio program Desert Island Discs (https://www.bbc.co.uk/programmes/b006qnmr, https://en.wikipedia.org/wiki/Desert_Island_Discs). Originally, I began with data gathered by webscraping with Python for the Guardian's Datablog in November 2011 (https://www.theguardian.com/tv-and-radio/datablog/2010/nov/11/desert-island-discs-radio4).

However, I knew that that version of the dataset was incomplete and had errors. In addition to wanting to develop a more complete version of the dataset (encompassing as much info as possible from the eight decades that Desert Island Discs has been on the air), I wanted to add information that would make it possible to ask other sorts of questions, for example, about the gender balance of the program, and the different roles of people who have been invited onto the show. 

To that end, I have modified the original Guardian spreadsheet by adding columns for gender, role, and for composer, as well as for performer. To create the information within these columns, I drew on info from the BBC Desert Island Discs website. In particular, for "role", I was drawing on the short description used in the archives of the program by date (https://www.bbc.co.uk/programmes/b006qnmr/broadcasts/2001/12) -- chiefly because the description ("Sue Lawley's castaway is TV chef Jamie Oliver") is what I have heard used on BBC 4 to announce and promote the program; so that label is an indication of what the BBC has seen as a legible and meaningful identity for each guest. It should be said that I see this as a dataset about the BBC program, and the choices its creators have made. It is certainly a better representation of the BBC and its choices, more than it is a complete representation of any of the guests themselves.
DOI: 10.17613/m6rj48t8x
title: BBC Desert Island Discs Dataset v 1.0

description: This is the first version of a larger project that I've been working on in my spare time to create a dataset of the guests, songs, books, and luxuries on the long-running BBC radio program Desert Island Discs (https://www.bbc.co.uk/programmes/b006qnmr, https://en.wikipedia.org/wiki/Desert_Island_Discs). Originally, I began with data gathered by webscraping with Python for the Guardian's Datablog in November 2011 (https://www.theguardian.com/tv-and-radio/datablog/2010/nov/11/desert-island-discs-radio4).

However, I knew that that version of the dataset was incomplete and had errors. In addition to wanting to develop a more complete version of the dataset (encompassing as much info as possible from the eight decades that Desert Island Discs has been on the air), I wanted to add information that would make it possible to ask other sorts of questions, for example, about the gender balance of the program, and the different roles of people who have been invited onto the show. 

To that end, I have modified the original Guardian spreadsheet by adding columns for gender, role, and for composer, as well as for performer. To create the information within these columns, I drew on info from the BBC Desert Island Discs website. In particular, for "role", I was drawing on the short description used in the archives of the program by date (https://www.bbc.co.uk/programmes/b006qnmr/broadcasts/2001/12) -- chiefly because the description ("Sue Lawley's castaway is TV chef Jamie Oliver") is what I have heard used on BBC 4 to announce and promote the program; so that label is an indication of what the BBC has seen as a legible and meaningful identity for each guest. It should be said that I see this as a dataset about the BBC program, and the choices its creators have made. It is certainly a better representation of the BBC and its choices, more than it is a complete representation of any of the guests themselves.
DOI: 10.15468/dl.46d6j4
title: Occurrence Download

description: A dataset containing 8172362 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-78.27998 0.28501,-75.99809 -8.06833,-65.03687 -16.66616,-54.40164 -11.04294,-52.50291 -1.09157,-50.34456 1.27573,-51.40688 3.62271,-53.23506 5.57442,-56.42202 5.74736,-57.85492 6.48851,-59.86187 8.14938,-61.19011 8.31669,-63.30306 8.57476,-67.42156 6.23007,-71.32838 4.74146,-74.55064 3.18404,-78.27998 0.28501))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Mammalia, Amphibia, Squamata, Testudines, Insecta, Arachnida, Malacostraca, Clitellata, Mollusca, Passeriformes, Psittaciformes, Piciformes, Apodiformes, Accipitriformes)",
 "Year 1990-2024"
 ]
}

 The dataset includes 8172362 records from 1123 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020458-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t7wc44
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-23.60408 11.45837,96.60408 11.45837,96.60408 59.54163,-23.60408 59.54163,-23.60408 11.45837))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Puccinia pulvinata Rabenh."
 ]
}

 The dataset includes 21 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020473-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.stzwhd
title: Occurrence Download

description: A dataset containing 544 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is TGS Seismic Survey Visual Sightings in Chukchi Sea 2013"
 ]
}

 The dataset includes 544 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020474-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tnrkdz
title: Occurrence Download

description: A dataset containing 177 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Peru",
 "OccurrenceStatus is Present",
 "TaxonKey is Pteronura brasiliensis Zimmermann, 1780"
 ]
}

 The dataset includes 177 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020475-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4tapdh
title: Occurrence Download

description: A dataset containing 107813 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((-79.31416 43.64942,-79.27851 43.66972,-79.28777 43.68979,-79.29269 43.68872,-79.29427 43.69239,-79.30888 43.68929,-79.30814 43.68756,-79.31358 43.6863,-79.31485 43.68919,-79.34804 43.6824,-79.34876 43.68409,-79.36197 43.68189,-79.3617 43.67971,-79.36677 43.67852,-79.36763 43.68913,-79.38008 43.69504,-79.37174 43.69676,-79.37749 43.7131,-79.38571 43.71136,-79.39127 43.73245,-79.39999 43.73527,-79.40456 43.73427,-79.40494 43.73602,-79.41698 43.73335,-79.4135 43.71877,-79.42222 43.71704,-79.41933 43.70996,-79.43969 43.70562,-79.43884 43.70205,-79.44069 43.70162,-79.43925 43.69807,-79.42555 43.70101,-79.41942 43.68589,-79.41317 43.68808,-79.4119 43.68495,-79.44245 43.67877,-79.44495 43.6849,-79.476 43.67888,-79.4735 43.67521,-79.47837 43.67409,-79.48078 43.67252,-79.48008 43.67078,-79.48405 43.6699,-79.48025 43.66042,-79.48872 43.65827,-79.48491 43.64909,-79.49282 43.64674,-79.4898 43.64146,-79.48522 43.64204,-79.48484 43.63848,-79.4775 43.63774,-79.4755 43.63412,-79.47071 43.63127,-79.4669 43.63033,-79.46663 43.63272,-79.45435 43.63545,-79.42129 43.62565,-79.40697 43.626,-79.38981 43.61107,-79.34763 43.61038,-79.32055 43.61699,-79.31709 43.64844,-79.31416 43.64942))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (March, April, May, June)",
 "OccurrenceStatus is Present",
 "Year 2019-2019"
 ]
}

 The dataset includes 107813 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020460-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14182980
title: Clinical Metaproteomics-discovery-input-files-iwc
DOI: 10.5281/zenodo.14182981
title: Clinical Metaproteomics-discovery-input-files-iwc
DOI: 10.15468/dl.8c9qen
title: Occurrence Download

description: A dataset containing 465 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Lagidium peruanum Meyen, 1833"
 ]
}

 The dataset includes 465 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020477-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z3f7ej
title: Occurrence Download

description: A dataset containing 485 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Peru",
 "TaxonKey is Abrothrix jelskii (Thomas, 1894)"
 ]
}

 The dataset includes 485 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020479-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4y5tka
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Peru",
 "TaxonKey is Tremarctos ornatus (F.G.Cuvier, 1825)"
 ]
}

 The dataset includes 33 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020478-241107131044228/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.27170980
title: Network Embedding-based Directed Community Detection with Unknown Community Number

description: Community detection of network analysis plays an important role in numerous application areas, in which estimating the number of communities is a fundamental issue. However, many existing methods focus on undirected networks ignoring the directionality of edges or unrealistically assume that the number of communities is known a priori. In this article, we develop a data-dependent community detection method for the directed network to determine the number of communities and recover community structures simultaneously, which absorbs the ideas of network embedding and penalized fusion by embedding the out- and in-nodes into low-dimensional vector space and forcing the embedding vectors toward its center. The asymptotic consistency properties of the proposed method are established in terms of network embedding, directed community detection, and estimation of the number of communities. The proposed method is applied on synthetic networks and real brain functional networks, which demonstrate the superior performance of the proposed method against a number of competitors. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27170980.v2
title: Network Embedding-based Directed Community Detection with Unknown Community Number

description: Community detection of network analysis plays an important role in numerous application areas, in which estimating the number of communities is a fundamental issue. However, many existing methods focus on undirected networks ignoring the directionality of edges or unrealistically assume that the number of communities is known a priori. In this article, we develop a data-dependent community detection method for the directed network to determine the number of communities and recover community structures simultaneously, which absorbs the ideas of network embedding and penalized fusion by embedding the out- and in-nodes into low-dimensional vector space and forcing the embedding vectors toward its center. The asymptotic consistency properties of the proposed method are established in terms of network embedding, directed community detection, and estimation of the number of communities. The proposed method is applied on synthetic networks and real brain functional networks, which demonstrate the superior performance of the proposed method against a number of competitors. Supplementary materials for this article are available online.