cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.em4k7b
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Meganisoptera)",
 "BasisOfRecord is one of (Human Observation, Observation, Occurrence evidence, Material citation, Living Specimen)",
 "CoordinateUncertaintyInMeters is less than or equal to 10000",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008005-251009101135966/datasets/export for details.
DOI: 10.5281/zenodo.17477038
title: Spectral outgoing longwave radiation derived from the Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) Measurements: Algorithms and Validations

description: Here archived the PREFIRE spectral OLR and the corresponding collocated CERES FM6 SSF broadband OLR, collocated CrIS, AIRS spectral OLR, and collocated in-situ measurements.
DOI: 10.5281/zenodo.17477037
title: Spectral outgoing longwave radiation derived from the Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) Measurements: Algorithms and Validations

description: Here archived the PREFIRE spectral OLR and the corresponding collocated CERES FM6 SSF broadband OLR, collocated CrIS, AIRS spectral OLR, and collocated in-situ measurements.
DOI: 10.15468/dl.h9zxrd
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Brocchinia hechtioides Mez, Brocchinia rupestris (Gleason) B.Holst, Byblis aquatica Lowrie & Conran, Byblis filifolia Planch., Byblis gigantea Lindl., Byblis guehoi Lowrie & Conran, Byblis lamellata Conran & Lowrie, Byblis liniflora Salisb., Byblis pilbarana Lowrie & Conran, Byblis rorida Lowrie & Conran, Catopsis berteroana (Schult. & Schult.f.) Mez, Darlingtonia californica Torr., Dionaea muscipula Ellis, Drosera aberrans (Lowrie & Carlquist) Lowrie & Conran, Drosera acaulis L.f., Drosera adelae F.Muell., Drosera cuneifolia L.f., Drosera affinis Welw. ex Oliv., Drosera afra Debbert, Drosera alba Phill., Drosera aliciae R.Hamet, Drosera allantostigma (N.G.Marchant & Lowrie) Lowrie & Conran, Drosera amazonica Rivadavia, A.Fleischm. & Vicent., Drosera andersoniana W.Fitzg. ex Ewart. & White, Drosera anglica Huds., Drosera arcturi Hook., Drosera arenicola Steyerm., Drosera pauciflora Banks ex DC., Drosera peltata Thunb., Drosera banksii R.Br. ex DC., Drosera barbigera Planch., Drosera bequaertii Taton)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((118 27,123 27,123 31.25,118 31.25,118 27))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1990"
 ]
}

 The dataset includes 20 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014463-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0157633.10fu
title: SH0157633.10FU

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/sh0157634.10fu
title: SH0157634.10FU

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/sh0157635.10fu
title: SH0157635.10FU

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/sh0157636.10fu
title: SH0157636.10FU

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/sh0157637.10fu
title: SH0157637.10FU

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/sh0157638.10fu
title: SH0157638.10FU

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/sh0157639.10fu
title: SH0157639.10FU

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/sh0157640.10fu
title: SH0157640.10FU

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.hvd3k8
title: Occurrence Download

description: A dataset containing 17248 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-79.61388 43.95675,-79.59348 43.96118,-79.59823 43.97204,-79.62757 43.96701,-79.62505 43.95595,-79.61388 43.95675))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 17248 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015118-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18500507
title: Rank–value dataset and analysis code for Tikal architectural economic indices (n=501)

description: This dataset provides the processed rank–value data (n = 501) and analysis codeused in the paper:"Imaizumi, K. (2026a). A comparison of distribution models for ranked architectural sizes at Tikal."
DOI: 10.15468/dl.5nu6tn
title: Occurrence Download

description: A dataset containing 1915 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Galium glaucum, Galium glaucum, Galium glaucum, Asperula glauca, Asperula glauca)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 1915 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015121-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g3haen
title: Occurrence Download

description: A dataset containing 111599 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Herbarium of the University of Coimbra (COI)"
 ]
}

 The dataset includes 111599 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015109-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16935017
title: The EduRABSA Dataset and the ASQE-DPT Annotation Tool

description: This repository contains the full dataset and data annotation tool from our work:  EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks

The full information and instructions are available at  https://github.com/yhua219/edurabsa_dataset_and_annotation_tool

The EduRABSA Dataset

The EduRABSA dataset is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.

Education Review ABSA (EduRABSA) is a manually annotated student review text dataset for multiple Aspect-based Sentiment Analysis tasks, including:



Aspect-(opinion-category)-Sentiment Quadruplet Extraction (ASQE)

Aspect-(opinion)-Sentiment Triplet Extraction (ASTE)

Aspect Sentiment Classification (ASC; a.k.a. Aspect Polarity Classification; APC)

Aspect Category Detection (ACD) / Aspect-opinion Categorisation (AOC)

Aspect-Opinion Pair-Extraction (AOPE)

Aspect Extraction (AE)

Opinion Extraction (OE)


The dataset consists of 6,500 pieces of stratified samples of public tertiary student review text in the English language released in 2020-2023 on courses ("course review", N=3,000), teaching staff ("teacher review", N=3,000), and university ("university review", N=500).

Dataset Information

Please visit  https://github.com/yhua219/edurabsa_dataset_and_annotation_tool

Unannotated dataset Source




Review Type
Dataset Name
Publish Year
Licence
Total Entries
Sampled (N=6,500)




Course review
Course Reviews University of Waterloo [1]
October 2022
CC0: Public Domain
14,810
3,000


Teacher review
Big Data Set from RateMyProfessor.com for Professors' Teaching Evaluation [2]
March 2020
CC BY 4.0
19,145
3,000


University review
University of Exeter Reviews [3]
June 2023
CC0: Public Domain
557
500




[1]: Waterloo Course Reviews. Course Reviews University of Waterloo. October 2022.[2]: RateMyProfessor Dataset. Big Data Set from RateMyProfessor.com for Professors' Teaching Evaluation. March 2020.[3]: Exeter Reviews. University of Exeter Reviews. June 2023.

 

The ASQE-DPT Annotation Tool



⭐  [NEW in v2 upload, Feb 2026] ASQE-DPT is now upgraded to V1.1 with auto-save (to localStorage) and leave-page alert features. 


The ASQE-DPT data annotation tool is licensed under a MIT License.

ASQE-DPT is a manual ABSA annotation tool that we extended based on the ABSA Dataset Prepare Tool (DPT) (source) for more comprehensive and challenging ABSA tasks.

ASQE_DPT is a no-code, no-installation small HTML file that can be used locally and offline to protect data security/privacy.

The .zip file contains the annotation tool and real unannotated and annotated data samples. 

For usage instructions, please visit  https://github.com/yhua219/edurabsa_dataset_and_annotation_tool
DOI: 10.15468/dl.aegbey
title: Occurrence Download

description: A dataset containing 1544523 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, scientificname, decimallatitude, decimallongitude, eventdate, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= -37.42857426825602
 AND decimallatitude &lt;= -33.06103686093294
 AND decimallongitude &gt;= 133.58679704081885
 AND decimallongitude &lt;= 143.91030448513666
 AND GBIF_Within('POLYGON ((143.910304 -37.428574, 143.910304 -33.061037, 133.586797 -33.061037, 133.586797 -37.428574, 143.910304 -37.428574))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 1544523 records were derived from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015106-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18500517
title: The EduRABSA Dataset and the ASQE-DPT Annotation Tool

description: This repository contains the full dataset and data annotation tool from our work:  EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks

The full information and instructions are available at  https://github.com/yhua219/edurabsa_dataset_and_annotation_tool

The EduRABSA Dataset

The EduRABSA dataset is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.

Education Review ABSA (EduRABSA) is a manually annotated student review text dataset for multiple Aspect-based Sentiment Analysis tasks, including:



Aspect-(opinion-category)-Sentiment Quadruplet Extraction (ASQE)

Aspect-(opinion)-Sentiment Triplet Extraction (ASTE)

Aspect Sentiment Classification (ASC; a.k.a. Aspect Polarity Classification; APC)

Aspect Category Detection (ACD) / Aspect-opinion Categorisation (AOC)

Aspect-Opinion Pair-Extraction (AOPE)

Aspect Extraction (AE)

Opinion Extraction (OE)


The dataset consists of 6,500 pieces of stratified samples of public tertiary student review text in the English language released in 2020-2023 on courses ("course review", N=3,000), teaching staff ("teacher review", N=3,000), and university ("university review", N=500).

Dataset Information

Please visit  https://github.com/yhua219/edurabsa_dataset_and_annotation_tool

Unannotated dataset Source




Review Type
Dataset Name
Publish Year
Licence
Total Entries
Sampled (N=6,500)




Course review
Course Reviews University of Waterloo [1]
October 2022
CC0: Public Domain
14,810
3,000


Teacher review
Big Data Set from RateMyProfessor.com for Professors' Teaching Evaluation [2]
March 2020
CC BY 4.0
19,145
3,000


University review
University of Exeter Reviews [3]
June 2023
CC0: Public Domain
557
500




[1]: Waterloo Course Reviews. Course Reviews University of Waterloo. October 2022.[2]: RateMyProfessor Dataset. Big Data Set from RateMyProfessor.com for Professors' Teaching Evaluation. March 2020.[3]: Exeter Reviews. University of Exeter Reviews. June 2023.

 

The ASQE-DPT Annotation Tool



⭐  [NEW in v2 upload, Feb 2026] ASQE-DPT is now upgraded to V1.1 with auto-save (to localStorage) and leave-page alert features. 


The ASQE-DPT data annotation tool is licensed under a MIT License.

ASQE-DPT is a manual ABSA annotation tool that we extended based on the ABSA Dataset Prepare Tool (DPT) (source) for more comprehensive and challenging ABSA tasks.

ASQE_DPT is a no-code, no-installation small HTML file that can be used locally and offline to protect data security/privacy.

The .zip file contains the annotation tool and real unannotated and annotated data samples. 

For usage instructions, please visit  https://github.com/yhua219/edurabsa_dataset_and_annotation_tool
DOI: 10.15468/dl.bxdwpc
title: Occurrence Download

description: A dataset containing 4800 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Galium mollugo)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 4800 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015127-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t4juk9
title: Occurrence Download

description: A dataset containing 1391 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((153.45458 -28.09122,153.46055 -28.09122,153.46055 -28.08716,153.45458 -28.08716,153.45458 -28.09122))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Gastropoda",
 "Year 2023-2025"
 ]
}

 The dataset includes 1391 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015124-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14747280
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.egab4b
title: Occurrence Download

description: A dataset containing 120 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is WNMU Mammal Collection (Arctos)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 120 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015128-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.