cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0761273.10fu
title: SH0761273.10FU

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/sh0761274.10fu
title: SH0761274.10FU

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/sh0761275.10fu
title: SH0761275.10FU

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/sh0761276.10fu
title: SH0761276.10FU

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/sh0761277.10fu
title: SH0761277.10FU

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/sh0761278.10fu
title: SH0761278.10FU

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/sh0761279.10fu
title: SH0761279.10FU

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.c22ydf
title: Occurrence Download

description: A dataset containing 1523798 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cornales"
 ]
}

 The dataset includes 1523798 records from 4347 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062853-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15165882
title: T-REx Bite 1.0

description: T-REx Bite adapts T-REx to the next-token-prediction paradigm by ensuring that, in each text snippet, the subject appears before the object. This alignment mimics real-world scenarios in which a model sees partial information (the subject and some context) and must then predict or "retrieve" the missing object. To keep snippets manageable within the limited context windows of smaller language models (such as GPT-2), each snippet is capped at 512 characters. We further require that the snippet explicitly mentions both subject and object, does not start a new sentence at the object, and is linked to the corresponding subgraph in T-REx Star. 

By applying these constraints, we obtain about 6.4 million short "bites" for training, 0.92 million for testing, and 0.75 million for validation. Each bite is a compact piece of Wikipedia text that retains the original clarity and diversity of T-REx but is tailored to ensure a direct subject--object alignment. This structure lets researchers readily evaluate how well an LLM completes the object token(s) given the preceding subject. The dataset naturally accommodates multi-token objects under modern sub-word tokenization, removing the single-token assumptions of LAMA-like methods.
DOI: 10.5281/zenodo.15165883
title: T-REx Bite 1.0

description: T-REx Bite adapts T-REx to the next-token-prediction paradigm by ensuring that, in each text snippet, the subject appears before the object. This alignment mimics real-world scenarios in which a model sees partial information (the subject and some context) and must then predict or "retrieve" the missing object. To keep snippets manageable within the limited context windows of smaller language models (such as GPT-2), each snippet is capped at 512 characters. We further require that the snippet explicitly mentions both subject and object, does not start a new sentence at the object, and is linked to the corresponding subgraph in T-REx Star. 

By applying these constraints, we obtain about 6.4 million short "bites" for training, 0.92 million for testing, and 0.75 million for validation. Each bite is a compact piece of Wikipedia text that retains the original clarity and diversity of T-REx but is tailored to ensure a direct subject--object alignment. This structure lets researchers readily evaluate how well an LLM completes the object token(s) given the preceding subject. The dataset naturally accommodates multi-token objects under modern sub-word tokenization, removing the single-token assumptions of LAMA-like methods.
DOI: 10.5281/zenodo.15165973
title: T-REx Star 1.0

description: T-REx Star provides star-topology subgraphs from Wikidata for each entity that appears as a subject in T-REx. Each entity’s local subgraph is represented in JSON format, includes up to 100 neighbors ranked by PageRank, and stores both node (Q-ID, English label, PageRank) and edge (P-ID, relation label) metadata. The JSON structure is easily loaded into tools such as NetworkX, enabling further graph-based processing or embedding.

Crucially, T-REx Star aligns with T-REx Bite and Tri-REx by using a consistent partitioning scheme. Every entity that serves as a subject in one of the three datasets appears in exactly one split (train, validation, or test). Entities may nonetheless appear as objects in multiple splits if they are neighbors of different subjects. This consistency is important for fair comparisons of LLM performance across training and evaluation sets.
DOI: 10.5281/zenodo.15165974
title: T-REx Star 1.0

description: T-REx Star provides star-topology subgraphs from Wikidata for each entity that appears as a subject in T-REx. Each entity’s local subgraph is represented in JSON format, includes up to 100 neighbors ranked by PageRank, and stores both node (Q-ID, English label, PageRank) and edge (P-ID, relation label) metadata. The JSON structure is easily loaded into tools such as NetworkX, enabling further graph-based processing or embedding.

Crucially, T-REx Star aligns with T-REx Bite and Tri-REx by using a consistent partitioning scheme. Every entity that serves as a subject in one of the three datasets appears in exactly one split (train, validation, or test). Entities may nonetheless appear as objects in multiple splits if they are neighbors of different subjects. This consistency is important for fair comparisons of LLM performance across training and evaluation sets.
DOI: 10.5281/zenodo.15166162
title: Tri-REx 1.0

description: Instead of extracting text from Wikipedia, Tri-REx synthesizes short subject--predicate--object sentences using Mistral 7B in a few-shot prompting fashion. For example, a triple (Albert Einstein, facial hair, walrus moustache) might generate "Dr. Albert Einstein wore a bushy walrus moustache." Each generated sentence is automatically filtered for coherence, correct mention of both subject and object, and accurate preservation of the S-P-O relationships, resulting in high-quality synthetic data. 

Tri-REx comprises 21.5 million training sentences, 0.9 million test sentences, and 1.7 million validation sentences, each of which is typically under 30 tokens. This collection stands out because it is intentionally free of pretraining overlap: models cannot simply rely on memorized Wikipedia text. Instead, they must learn or leverage newly provided knowledge sources to recover the correct object tokens during next-token prediction. Researchers can thus verify whether a RAG technique or knowledge-injection approach genuinely conveys facts to a model, rather than merely triggering recall of memorized text.
DOI: 10.5281/zenodo.15166163
title: Tri-REx 1.0

description: Instead of extracting text from Wikipedia, Tri-REx synthesizes short subject--predicate--object sentences using Mistral 7B in a few-shot prompting fashion. For example, a triple (Albert Einstein, facial hair, walrus moustache) might generate "Dr. Albert Einstein wore a bushy walrus moustache." Each generated sentence is automatically filtered for coherence, correct mention of both subject and object, and accurate preservation of the S-P-O relationships, resulting in high-quality synthetic data. 

Tri-REx comprises 21.5 million training sentences, 0.9 million test sentences, and 1.7 million validation sentences, each of which is typically under 30 tokens. This collection stands out because it is intentionally free of pretraining overlap: models cannot simply rely on memorized Wikipedia text. Instead, they must learn or leverage newly provided knowledge sources to recover the correct object tokens during next-token prediction. Researchers can thus verify whether a RAG technique or knowledge-injection approach genuinely conveys facts to a model, rather than merely triggering recall of memorized text.
DOI: 10.15468/dl.t79dq2
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acanthoscelis ruficornis (Fabricius, 1801))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 45 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008033-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fxzt67
title: Occurrence Download

description: A dataset containing 1886 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mullus barbatus Linnaeus, 1758",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen, Material sample)"
 ]
 }
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 10000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 1886 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004491-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t3ztja
title: Occurrence Download

description: A dataset containing 18482 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lupinus angustifolius L.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 18482 records from 363 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015129-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.11044.81287
title: Black Unicorn
DOI: 10.13140/rg.2.2.18394.67521
title: Data Tesis_Doctoral_Eveligh Prado-Carpio
DOI: 10.13140/rg.2.2.22641.40809
title: XyloDensMap Les principales caractéristiques des bois des forêts françaises-Le hêtre
DOI: 10.13140/rg.2.2.34647.60321
title: Se puede hablar emprendimiento sin haber creado empresa
DOI: 10.15468/dl.ut85zy
title: Occurrence Download

description: A dataset containing 9440 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Amphibia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((7.5 0,15 0,15 7.5,7.5 7.5,7.5 0))"
 ]
}

 The dataset includes 9440 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041627-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.26075.49445
title: Sustainable Oceans and Coasts Tasmania Perspectives 2020
DOI: 10.13140/rg.2.2.29850.36805
title: Urban Systems Transformation - Greater Western Sydney 2018-2019
DOI: 10.15468/dl.9f9upv
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sepia plana Lu & A.Reid, 1997"
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154623-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fnd0zm
title: Occurrence Download

description: A dataset containing 1329 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Phaethon aethereus"
 ]
}

 The dataset includes 1329 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005746-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nfx4a8
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Synthetonychia oliveae Forster, 1954"
 ]
}

 The dataset includes 6 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154613-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.lrioob
title: Occurrence Download

description: A dataset containing 403 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sciurus nayaritensis J.A.Allen, 1890"
 ]
}

 The dataset includes 403 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005747-150615163101818/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.84qy45
title: Occurrence Download

description: A dataset containing 508 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cuphea nitidula Kunth"
 ]
}

 The dataset includes 508 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154615-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j3ft41
title: Occurrence Download

description: A dataset containing 337 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Xenospiza baileyi Bangs, 1931"
 ]
}

 The dataset includes 337 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005747-160311141623029/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.anj5sm
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cuphea luteola S.A.Graham & T.B.Cavalc."
 ]
}

 The dataset includes 16 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154605-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c7smu1
title: Occurrence Download

description: A dataset containing 631 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cestrum diurnum L."
 ]
}

 The dataset includes 631 records from 51 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005747-160526112335914/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gnwm6p
title: Occurrence Download

description: A dataset containing 166 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cuphea spruceana Koehne"
 ]
}

 The dataset includes 166 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154607-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xmoc0m
title: Occurrence Download

description: A dataset containing 117632 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Seychelles"
 ]
}

 The dataset includes 117632 records from 316 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005747-160623103540712/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kbxvsj
title: Occurrence Download

description: A dataset containing 135 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cuphea mimuloides Cham. & Schltdl."
 ]
}

 The dataset includes 135 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154612-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.