cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7809574
title: cucomungo_datasets

description: This dataset is for use with the Colab Notebook “cucomungo-clustering” published on GitHub at https://github.com/jba6/cucomungo-clustering. <br> The citation for this datasets is listed on Zenodo. <br> The citation for the paper describing the data, methods, and results is: Adler, J. B., Rivera-Hernández, F., and Kerner, H. R. (2023). Machine learning techniques aid in Mars analog surface characterization by guiding in-situ exploration to geologic contacts and outliers. Under review, AGU Geophysical Research Letters.
DOI: 10.15468/dl.utxvq6
title: Occurrence Download

description: A dataset containing 2845844 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Syrphidae"
 ]
}

 The dataset includes 2845844 records from 1980 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0093557-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hgpaey
title: Occurrence Download

description: A dataset containing 987 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Capparis erythrocarpos Isert"
 ]
}

 The dataset includes 987 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007684-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15309635
title: REC members perspectives on whether and how to return individual PG results (transcripts)

description: Transcripts of REC members perspectives on whether and how to return individual PG results
DOI: 10.5281/zenodo.15309636
title: REC members perspectives on whether and how to return individual PG results (transcripts)

description: Transcripts of REC members perspectives on whether and how to return individual PG results
DOI: 10.6084/m9.figshare.28902320
title: VBD MASTER SHEET

description: VECTOR BORNE STUDY
DOI: 10.48329/6k32-4x32
title: CPRD Gold May 2025

description: The CPRD Gold database contains longitudinal routinely-collected electronic health records (EHR) from UK primary care practices using Vision® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care.
description: Access to anonymised data from CPRD is subject to a full licence agreement containing detailed terms and conditions of use. Anonymised patient datasets can be extracted for researchers against specific study specifications, following protocol approval via CPRD's Research Data Governance Process. Further information is available at https://www.cprd.com/data-access.
description: Please contact enquiries@cprd.com for further information or if you have any questions.
DOI: 10.48597/drvu-q7eb
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/3tx7-n3hy
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/4wf5-vb8b
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/5rrz-s3uh
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/6u54-jacg
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/bsax-9x5j
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/eehk-skmp
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/j3x8-psfe
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/jv8h-xjhn
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/n8mw-nhq6
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/nut3-296r
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/ppr5-px86
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/q67w-5vbs
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/qzgf-xv7w
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/r5fn-ec5r
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/rq5x-mbkk
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/t8bz-es5a
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/uxek-c9j4
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/wemv-ysw7
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/yazj-7agn
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/yqav-kxee
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.48597/zt9u-4ey5
title: Measurement of Sulphur_dioxide at Aliartos

description: Measurement of Sulphur_dioxide at Aliartos
DOI: 10.5281/zenodo.15276149
title: BIOMAT-NER: A Domain-Specific Corpus for Named Entity Recognition of Chemical Substances and Biomaterials

description: BIOMAT-NER Corpus

BIOMAT-NER is a corpus developed within the scope of the Horizon Europe BIOMATDB project to support the extraction and classification of biomaterials-related concepts from the scientific literature. It focuses on the annotation of chemical substances, compounds, and material types—including trade names—relevant to the field of biomaterials. The corpus was created through a collaborative effort involving domain experts, who were tasked with the establishment of comprehensive and accurate annotation guidelines for the manual annotation of the final gold standard corpus. On this basis, PubMed abstracts were carefully selected based on relevant MeSH (Medical Subject Headings) categories associated with biomaterials and related disciplines to reflect the terminology commonly used in biomaterials research and manually annotated according to the predefined rules in the annotation guidelines.

The BIOMAT-NER corpus is one of four developed within the project and is divided into three subsets: a training set (4,553 documents), a test set (911 documents), and a validation set (607 documents), available in multiple formats, including brat, CSV and CoNLL.

This corpus is part of a broader initiative to support the development of an advanced, searchable biomaterials database with integrated analytical tools and digital advisors. It is also intended for use in training Named Entity Recognition (NER) models, enabling the automatic identification and extraction of biomaterials-related concepts from scientific texts.

Resources



Project Website

Biomaterials Marketplace

Biomaterials Database
DOI: 10.5281/zenodo.15276148
title: BIOMAT-NER: A Domain-Specific Corpus for Named Entity Recognition of Chemical Substances and Biomaterials

description: BIOMAT-NER Corpus

BIOMAT-NER is a corpus developed within the scope of the Horizon Europe BIOMATDB project to support the extraction and classification of biomaterials-related concepts from the scientific literature. It focuses on the annotation of chemical substances, compounds, and material types—including trade names—relevant to the field of biomaterials. The corpus was created through a collaborative effort involving domain experts, who were tasked with the establishment of comprehensive and accurate annotation guidelines for the manual annotation of the final gold standard corpus. On this basis, PubMed abstracts were carefully selected based on relevant MeSH (Medical Subject Headings) categories associated with biomaterials and related disciplines to reflect the terminology commonly used in biomaterials research and manually annotated according to the predefined rules in the annotation guidelines.

The BIOMAT-NER corpus is one of four developed within the project and is divided into three subsets: a training set (4,553 documents), a test set (911 documents), and a validation set (607 documents), available in multiple formats, including brat, CSV and CoNLL.

This corpus is part of a broader initiative to support the development of an advanced, searchable biomaterials database with integrated analytical tools and digital advisors. It is also intended for use in training Named Entity Recognition (NER) models, enabling the automatic identification and extraction of biomaterials-related concepts from scientific texts.

Resources



Project Website

Biomaterials Marketplace

Biomaterials Database
DOI: 10.15468/dl.54ysdb
title: Occurrence Download

description: A dataset containing 1140 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "DatasetKey is iNaturalist Research-grade Observations",
 "OccurrenceStatus is Present",
 "TaxonKey is Pteropus giganteus (Brünnich, 1782)"
 ]
}

 The dataset includes 1140 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007686-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15309651
title: Researchers perspectives on whether and how to return individual PG results (transcripts)
DOI: 10.5281/zenodo.15309652
title: Researchers perspectives on whether and how to return individual PG results (transcripts)
DOI: 10.5281/zenodo.15309649
title: PAHTEI-Proceedings of Azerbaijan High Technical Educational Institutions

description: PAHTEI-Proceedings of Azerbaijan High Technical Educational Institutions
DOI: 10.5281/zenodo.15309650
title: PAHTEI-Proceedings of Azerbaijan High Technical Educational Institutions

description: PAHTEI-Proceedings of Azerbaijan High Technical Educational Institutions
DOI: 10.15468/dl.9a29vx
title: Occurrence Download

description: A dataset containing 413 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Leucaena leucocephala (Lam.) de Wit"
 ]
}

 The dataset includes 413 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007688-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15309582
title: BIOMAT-NER Corpus: Train and Validation Sets

description: BIOMAT-NER Train and Validation Sets

BIOMAT-NER is a corpus developed within the scope of the Horizon Europe BIOMATDB project to support the extraction and classification of biomaterials-related concepts from the scientific literature. It focuses on the annotation of chemical substances, compounds, and material types—including trade names—relevant to the field of biomaterials. The corpus was created through a collaborative effort involving domain experts, who were tasked with the establishment of comprehensive and accurate annotation guidelines for the manual annotation of the final gold standard corpus. On this basis, PubMed abstracts were carefully selected based on relevant MeSH (Medical Subject Headings) categories associated with biomaterials and related disciplines to reflect the terminology commonly used in biomaterials research and manually annotated according to the predefined rules in the annotation guidelines.

This repository contains the train (4,553 documents) and validation (607 documents) sets of the BIOMAT-NER Corpus, which are made available under open access for public use. These sets have been released to support the development of Named Entity Recognition (NER) models for biomaterials-related concept extraction from scientific literature, particularly those mentions referring to chemical substances and compounds, and material types.

The test set is not included in this repository, as it is reserved for a future shared task planned within the scope of the project. For this reason, access to the full corpus remains restricted, but will be made publicly available upon completion of the shared task.

Resources



Project Website

Biomaterials Marketplace

Biomaterials Database

BIOMAT-NER full corpus

Annotation Guidelines
DOI: 10.5281/zenodo.15309583
title: BIOMAT-NER Corpus: Train and Validation Sets

description: BIOMAT-NER Train and Validation Sets

BIOMAT-NER is a corpus developed within the scope of the Horizon Europe BIOMATDB project to support the extraction and classification of biomaterials-related concepts from the scientific literature. It focuses on the annotation of chemical substances, compounds, and material types—including trade names—relevant to the field of biomaterials. The corpus was created through a collaborative effort involving domain experts, who were tasked with the establishment of comprehensive and accurate annotation guidelines for the manual annotation of the final gold standard corpus. On this basis, PubMed abstracts were carefully selected based on relevant MeSH (Medical Subject Headings) categories associated with biomaterials and related disciplines to reflect the terminology commonly used in biomaterials research and manually annotated according to the predefined rules in the annotation guidelines.

This repository contains the train (4,553 documents) and validation (607 documents) sets of the BIOMAT-NER Corpus, which are made available under open access for public use. These sets have been released to support the development of Named Entity Recognition (NER) models for biomaterials-related concept extraction from scientific literature, particularly those mentions referring to chemical substances and compounds, and material types.

The test set is not included in this repository, as it is reserved for a future shared task planned within the scope of the project. For this reason, access to the full corpus remains restricted, but will be made publicly available upon completion of the shared task.

Resources



Project Website

Biomaterials Marketplace

Biomaterials Database

BIOMAT-NER full corpus

Annotation Guidelines
DOI: 10.5281/zenodo.14646282
title: Data for: Influencing the Draping Behaviour of Solid Epoxy Prepregs by applying 3D-printed Resin Patterns
DOI: 10.15468/dl.s87tpm
title: Occurrence Download

description: A dataset containing 966 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Lepus saxatilis F.Cuvier, 1823"
 ]
}

 The dataset includes 966 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007689-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1376398.10fu
title: SH1376398.10FU

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/sh1376400.10fu
title: SH1376400.10FU

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/sh1376401.10fu
title: SH1376401.10FU

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/sh1376402.10fu
title: SH1376402.10FU

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/sh1376404.10fu
title: SH1376404.10FU

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/sh1376405.10fu
title: SH1376405.10FU

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/sh1376406.10fu
title: SH1376406.10FU

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.