cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6510086
title: Bio-ML: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

description:  



This version is used in the Bio-ML track of the OAEI 2024; the only change compared to the OAEI 2023 is the deletion of certain training subsumption mappings.


 

Overview

The purpose of these datasets is to support equivalence and subsumption ontology matching.

There are five ontology pairs extracted from MONDO and UMLS:




Source
Task
Category
#SrcCls
#TgtCls
#Ref (equiv)
#Ref (subs)


Mondo
OMIM-ORDO
Disease
9,648
9,275
3,721
103


Mondo
NCIT-DOID
Disease
15,762
8,465
4,686
3,338 (-1)


UMLS
SNOMED-FMA
Body
34,418
88,955
7,256
5,453 (-53)


UMLS
SNOMED-NCIT
Pharm
29,500
22,136
5,803
4,224 (-1)


UMLS
SNOMED-NCIT
Neoplas
22,971
20,247
3,804
213




The "-" numbers reflect the changes due to lthe deletion of certain training subsumption mappings.

The main track is available at "bio-ml", where each pair is associated with a task folder, containing the source and target ontologies, reference equivalence mappings (in "refs_equiv"), reference subsumption mappings ("refs_subs"). 

The special sub-track is available at "bio-llm", where each pair is associated with a task folder, containing the source and target ontologies, and the test candidate mappings. 

 

Citation

Bio-ML (Main Track)

```@inproceedings{he2022machine,
  title={Machine learning-friendly biomedical datasets for equivalence and subsumption ontology matching},
  author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Jim{\'e}nez-Ruiz, Ernesto and Hadian, Ali and Horrocks, Ian},
  booktitle={International Semantic Web Conference},
  pages={575--591},
  year={2022},
  organization={Springer}
}```

Bio-LLM (Sub-track)

```@article{he2023exploring,
  title={Exploring large language models for ontology alignment},
  author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Horrocks, Ian},
  journal={arXiv preprint arXiv:2309.07172},
  year={2023}
}```

 

Important Links



See detailed documentation at: https://krr-oxford.github.io/DeepOnto/bio-ml.

See the OAEI Bio-ML track at: https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/

See our resource paper for the original Bio-ML at arxiv or springer (accepted at ISWC-2022 and nominated as the best resource paper candidate). See our poster paper for the Bio-LLM sub-track at arxiv (accepted at ISWC-2023 Posters & Demos).


 

Changelog

The only change in this version compared to the OAEI 2023 is the deletion of certain training subsumption mappings that can be directly exploited through deductive reasoning.
DOI: 10.5281/zenodo.6510087
title: Large-scale Biomedical Datasets for Equivalence and Subsumption Ontology Matching

description: The purpose of these datasets is to support <em>equivalence</em> and <em>subsumption</em> ontology matching. There are five ontology pairs extracted from MONDO and UMLS: <strong>Source</strong> <strong>Ontology Pair</strong> <strong>Category</strong> MONDO OMIM-ORDO Disease MONDO NCIT-DOID Disease UMLS SNOMED-FMA Body UMLS SNOMED-NCIT Pharm UMLS SNOMED-NCIT Neoplas Each pair is associated with three folders: "raw_data", "equiv_match", and "subs_match", corresponding to the downloaded source ontologies, the package for equivalence matching, and subsumption matching. See detailed documentation at: https://krr-oxford.github.io/DeepOnto/#/om_resources.
DOI: 10.5281/zenodo.6516125
title: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

description: The purpose of these datasets is to support <em>equivalence</em> and <em>subsumption</em> ontology matching. There are five ontology pairs extracted from MONDO and UMLS: <strong>Source</strong> <strong>Ontology Pair</strong> <strong>Category</strong> MONDO OMIM-ORDO Disease MONDO NCIT-DOID Disease UMLS SNOMED-FMA Body UMLS SNOMED-NCIT Pharm UMLS SNOMED-NCIT Neoplas Each pair is associated with three folders: "raw_data", "equiv_match", and "subs_match", corresponding to the downloaded source ontologies, the package for equivalence matching, and the package for subsumption matching. See detailed documentation at: https://krr-oxford.github.io/DeepOnto/#/om_resources. See the incoming OAEI Bio-ML track at: https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/. See our resource paper at: https://arxiv.org/abs/2205.03447.
DOI: 10.5281/zenodo.6917501
title: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

description: The purpose of these datasets is to support <em>equivalence</em> and <em>subsumption</em> ontology matching. There are five ontology pairs extracted from MONDO and UMLS: <strong>Source</strong> <strong>Ontology Pair</strong> <strong>Category</strong> MONDO OMIM-ORDO Disease MONDO NCIT-DOID Disease UMLS SNOMED-FMA Body UMLS SNOMED-NCIT Pharm UMLS SNOMED-NCIT Neoplas Each pair is associated with three folders: "raw_data", "equiv_match", and "subs_match", corresponding to the downloaded source ontologies, the package for equivalence matching, and the package for subsumption matching. See detailed instructions at: https://krr-oxford.github.io/DeepOnto/#/om_resources. See the incoming OAEI Bio-ML track at: https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/. See our resource paper at: https://arxiv.org/abs/2205.03447. Please use the most recent version as the instructions are updated accordingly.
DOI: 10.5281/zenodo.6946466
title: Bio-ML: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

description: <strong>NOTE</strong>: This version is used in the Bio-ML track of the <strong>OAEI 2022</strong>; a new version for the OAEI 2023 will be available soon. <strong>About</strong> The purpose of these datasets is to support <em>equivalence</em> and <em>subsumption</em> ontology matching. There are five ontology pairs extracted from MONDO and UMLS: <strong>Source</strong> <strong>Task</strong> <strong>Category</strong> <strong>#SrcCls</strong> <strong>#TgtCls</strong> <strong>#TgtCls (subs)</strong> <strong>#Ref (equiv)</strong> <strong>#Ref (subs)</strong> Mondo OMIM-ORDO Disease 9,642 8,838 8,735 3,721 103 Mondo NCIT-DOID Disease 6,835 8,448 5,113 4,686 3,339 UMLS SNOMED-FMA Body 24,182 64,726 59,567 7,256 5,506 UMLS SNOMED-NCIT Pharm 16,045 15,250 12,462 5,803 4,225 UMLS SNOMED-NCIT Neoplas 11,271 13,956 13,790 3,804 213 Each pair is associated with three folders: "raw_data", "equiv_match", and "subs_match", corresponding to the downloaded source ontologies, the package for equivalence matching, and the package for subsumption matching. <strong>Citation</strong> <pre><code>@inproceedings{he2022machine, title={Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching}, author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Jim{\'e}nez-Ruiz, Ernesto and Hadian, Ali and Horrocks, Ian}, booktitle={The Semantic Web--ISWC 2022: 21st International Semantic Web Conference, Virtual Event, October 23--27, 2022, Proceedings}, pages={575--591}, year={2022}, organization={Springer} }</code></pre> <strong>Links</strong> See detailed instructions at: https://krr-oxford.github.io/DeepOnto/bio-ml. See the OAEI Bio-ML track at: https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/ See our resource paper at arxiv or springer (accepted by <em>ISWC-2022</em> and nominated as the <em>best resource paper candidate</em>). <strong>Changelog</strong> Update against the previous versions: Class entities in the mapping files are now represented using their full IRIs (against v2). Files for candidate mappings for local ranking evaluation are simplified to just one <strong>.tsv</strong> file.
DOI: 10.5281/zenodo.8193375
title: Bio-ML: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

description:  



This version is used in the Bio-ML track of the OAEI 2023; a few signifcant changes have been made as compared to the OAEI 2022 version.


 

Overview

The purpose of these datasets is to support equivalence and subsumption ontology matching.

There are five ontology pairs extracted from MONDO and UMLS:




Source
Task
Category
#SrcCls
#TgtCls
#Ref (equiv)
#Ref (subs)


Mondo
OMIM-ORDO
Disease
9,648 (+6)
9,275 (+437)
3,721
103


Mondo
NCIT-DOID
Disease
15,762 (+8,927)
8,465 (+17)
4,686
3,339


UMLS
SNOMED-FMA
Body
34,418 (+10,236)
88,955 (+24,229)
7,256
5,506


UMLS
SNOMED-NCIT
Pharm
29,500 (+13,455)
22,136 (+6,886)
5,803
4,225


UMLS
SNOMED-NCIT
Neoplas
22,971 (+11,700)
20,247 (+6291)
3,804
213




The "+" numbers reflect the changes due to locality module enrichment.

The main track is available at "bio-ml", where each pair is associated with a task folder, containing the source and target ontologies, reference equivalence mappings (in "refs_equiv"), reference subsumption mappings ("refs_subs"). 

The special sub-track is available at "bio-llm", where each pair is associated with a task folder, containing the source and target ontologies, and the test candidate mappings. 

 

Citation

Bio-ML

```@inproceedings{he2022machine,
  title={Machine learning-friendly biomedical datasets for equivalence and subsumption ontology matching},
  author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Jim{\'e}nez-Ruiz, Ernesto and Hadian, Ali and Horrocks, Ian},
  booktitle={International Semantic Web Conference},
  pages={575--591},
  year={2022},
  organization={Springer}
}```

Bio-LLM

```@article{he2023exploring,
  title={Exploring large language models for ontology alignment},
  author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Horrocks, Ian},
  journal={arXiv preprint arXiv:2309.07172},
  year={2023}
}```

 

Important Links



See detailed documentation at: https://krr-oxford.github.io/DeepOnto/bio-ml.

See the OAEI Bio-ML track at: https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/

See our resource paper for the original Bio-ML at arxiv or springer (accepted at ISWC-2022 and nominated as the best resource paper candidate). See our poster paper for the new Bio-LLM at arxiv (accepted at ISWC-2023 Posters & Demos).


 

Changelog

Several signifcant changes have been made and they are well-documented in the Bio-ML documentation.
DOI: 10.15468/dl.c78p2j
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Senna hirsuta var. puberula H.S.Irwin & Barneby)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040380-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10875301
title: A machine framework for extracting biologial pathway information from the literature

description: Supplementary datasets for the paper "A machine framework for extracting biologial pathway information from the literature".

The datasets include Supplementary Data 1 (Training dataset images for training EBPI arrow detection model), Supplementary Data 2 (Test dataset images for validating EBPI arrow detection model), Supplementary Data 3 (Labeling dataset excel file for training and test dataset), Supplementary Data 4 (EBPI text classification model training dataset), Supplementary Data 5 (Collecting and processing of images illustrating biological pathways across 466 target chemicals from the bio-based chemicals map), Supplementary Data 6 (Information on reactions obtained through EBPI from images of biological pathways, focusing on target chemicals from maps of bio-based chemicals), Supplementary Data 7 (Target chemicals satisfying criteria for biosynthetic reactions not covered by biological pathway databases).
DOI: 10.5281/zenodo.11075692
title: A machine learning framework for extracting information from biological pathway images in the literature

description: Training and validation datasets_arrow detection.zip:Training and validation datasets for arrow detection using Faster R-CNN model. A total of 6,471 images have been prepared, including 2,332 images from five different sources and 4,139 augmented images.

Test dataset_arrow detection.zip:Test dataset for arrow detection using Faster R-CNN model. A total of 100 images have been prepared from 89 papers searched through PubMed Central (PMC).

EBPI outputs.txt:Reaction information extracted using EBPI from 49,846 biological pathway images across 466 target chemicals.
DOI: 10.5281/zenodo.10875300
title: A machine learning framework for extracting information from biological pathway images in the literature

description: 466 target chemicals_selected chemicals:

Target chemicals satisfying the criteria for biochemical reactions not covered by MetaNetX and KEGG.

 

466 target chemicals_statistics:Numbers of MetaCyc reactions, papers, and pathway images collected for 466 target chemicals from the bio-based chemicals map (Jang et al., Trends in Biotechnology, 2023).

 

arrow detection_bounding box labels:Bounding box labels for 6,471 images in the training and validation datasets and 100 images in the test dataset. The corresponding images are available in "arrow detection_training and validation datasets.zip".

 

arrow detection_test dataset:

Test dataset for arrow detection using Faster R-CNN model. A total of 100 images have been prepared from 89 papers searched through PubMed Central (PMC).

 

arrow detection_training and validation datasets:

Training and validation datasets for arrow detection using Faster R-CNN model. A total of 6,471 images have been prepared, including 2,332 images from five different sources and 4,139 augmented images.

 

EBPI outputs:

Reaction information extracted using EBPI from 49,846 biological pathway images across 466 target chemicals.

 

text classification_training, validation and test datasets:

Dataset for text classification using BioBERT. A total of 59,370 terms have been prepared, including 15,101 “gene” terms, 21,417 “protein” terms, and 22,852 “others” terms by combining the data from MetaCyc and the PaddleOCR results from the papers.
DOI: 10.5281/zenodo.12730236
title: A machine learning framework for extracting information from biological pathway images in the literature

description: Training and validation datasets_arrow detection.zip:

Training and validation datasets for arrow detection using Faster R-CNN model. A total of 6,471 images have been prepared, including 2,332 images from five different sources and 4,139 augmented images.

 

Test dataset_arrow detection.zip:Test dataset for arrow detection using Faster R-CNN model. A total of 100 images have been prepared from 89 papers searched through PubMed Central (PMC).

 

EBPI outputs.txt:Reaction information extracted using EBPI from 49,846 biological pathway images across 466 target chemicals.

 

Supplementary Data 1:

Bounding box labels for 6,471 images in the training and validation datasets and 100 images in the test dataset. 

 

Supplementary Data 2:

Dataset for text classification using BioBERT. A total of 59,370 terms have been prepared, including 15,101 “gene” terms, 21,417 “protein” terms, and 22,852 “others” terms by combining the data from MetaCyc and the PaddleOCR results from the papers.

 

Supplementary Data 3:

Collection and processing of pathway images illustrating biological pathways for 466 target chemicals from the bio-based chemicals map.

 

Supplementary Data 4:

Target chemicals satisfying criteria for biochemical reactions not covered by MetaNetX and KEGG.
DOI: 10.5281/zenodo.13118448
title: A machine learning framework for extracting information from biological pathway images in the literature

description: 466 target chemicals_selected chemicals:

Target chemicals satisfying the criteria for biochemical reactions not covered by MetaNetX and KEGG.

 

466 target chemicals_statistics:Numbers of MetaCyc reactions, papers, and pathway images collected for 466 target chemicals from the bio-based chemicals map (Jang et al., Trends in Biotechnology, 2023).

 

arrow detection_bounding box labels:Bounding box labels for 6,471 images in the training and validation datasets and 100 images in the test dataset. The corresponding images are available in "arrow detection_training and validation datasets.zip".

 

arrow detection_test dataset:

Test dataset for arrow detection using Faster R-CNN model. A total of 100 images have been prepared from 89 papers searched through PubMed Central (PMC).

 

arrow detection_training and validation datasets:

Training and validation datasets for arrow detection using Faster R-CNN model. A total of 6,471 images have been prepared, including 2,332 images from five different sources and 4,139 augmented images.

 

EBPI outputs:

Reaction information extracted using EBPI from 49,846 biological pathway images across 466 target chemicals.

 

text classification_training, validation and test datasets:

Dataset for text classification using BioBERT. A total of 59,370 terms have been prepared, including 15,101 “gene” terms, 21,417 “protein” terms, and 22,852 “others” terms by combining the data from MetaCyc and the PaddleOCR results from the papers.
DOI: 10.15468/dl.ee79ty
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Senna italica subsp. arachoides (Burch.) Lock)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 45 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040381-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3chwhn
title: Occurrence Download

description: A dataset containing 9243118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Peru",
 "GbifRegion is Latin America and the Caribbean",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 9243118 records from 2423 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040367-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xr56qw
title: Occurrence Download

description: A dataset containing 1635 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pteronia incana (Burm.fil.) DC."
 ]
}

 The dataset includes 1635 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004756-250123221155621/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yrwmwm
title: Occurrence Download

description: A dataset containing 937 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cassia leiandra Benth."
 ]
}

 The dataset includes 937 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004758-250123221155621/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14744143
title: A new dataset of province- and prefecture-level human development index in China

description: Summary

Here we introduce the Chinese Human Development Index (CHDI) dataset for the period 2010–2020, which extends the HDI framework to a more granular spatial scale. It encompasses the CHDI values, the three underlying dimension indices (health, education, and income), and the four indicators required to construct them: life expectancy at birth, mean years of schooling, expected years of schooling, and gross national income per capita. These indicators were compiled from population censuses, official development plans, and other authoritative statistical sources. The dataset’s fine-grained resolution and methodological rigor ensure both temporal and spatial comparability, providing a robust empirical foundation for analyzing evolving patterns, policy mechanisms, and regional divergences in China’s human development.

With detailed provincial and prefectural division codes, the dataset can be merged with other data sources for comprehensive analyses.

 

Contact Person

Pu Gong (gongpu@tsinghua.edu.cn)
DOI: 10.5281/zenodo.14747677
title: Evaluation of Plaque Removal by Triple-Headed Manual Toothbrush: A Randomized Controlled Clinical Trial
DOI: 10.5281/zenodo.14747678
title: Evaluation of Plaque Removal by Triple-Headed Manual Toothbrush: A Randomized Controlled Clinical Trial
DOI: 10.5281/zenodo.14747713
title: Protests Armenia 2018: Video Database – Velvet Revolution 2018

description: The collection "Protests Armenia 2018: Video Database" is part of a larger and ongoing collection of videos on protest events in the post-Soviet region. It contains a database describing 219 videos (mp4) of protests in Armenia in April 2018 (mainly Yerevan area) also known as the Velvet Revolution of 2018. We compiled the material in February to April 2019. All data is processed in an MS Excel database with metadata.
For this kind of collections on protest events we take into account all videos that 1) are event related AND show actions of this event, 2) are fully accessible, 3) we can find with our search words during a particular period. We strictly aim at a systematic and unbiased selection and organized precessing of protest-related videos. We identify particular event-related search words or phrases after intense research on the event. According to the snowball principle, we then start the search of videos and try to describe as much relevant content as possible. However, we cannot guarantee the completeness of protest videos on the particular event. We search the videos and include them into the collection until a particular degree of saturation has been reached.
The videos have been posted mainly by the participants of the events. Therefore, the material is only an extract and biased by the perspective of the single creator.
Due to data privacy regulations and copyright restrictions, we are only allowed to give access to the database of the collected video files including the hyperlinks with its metadata and not to the videos themselves. YouTube's user guidelines do not allow us to store copies of the video collection described above.
description: Published on Discuss Data, https://discuss-data.net/dataset/1d69b4ef-b2f3-457c-8a03-b9887513872b/
description: Method(s) of data collection: Keyword Search, Snow Ball SearchMethod(s) of data analysis: Raw Data, Raw Data For Analysis