| BioLarkPubmedHPO | | 228 abstracts manually annotated with Human Phenotype Ontology (HPO) concepts and harmonized by three curators, which can be used as a reference standard for free text annotation of human phenotypes. For more info, please see Groza et al. "Automatic concept recognition using the human phenotype ontology reference and test suite corpora", 2015. | 7.16 K | Tudor Groza | simon | 2023-11-29 | Released | |
| CORD-19_All_docs | | All the documents in the whole CORD-19 dataset.
The documents in this project will be updated as the CORD-19 dataset grows.
See the COVID DATASET LICENSE AGREEMENT. | 0 | | Jin-Dong Kim | 2023-11-29 | Released | |
| CORD-19_Commercial_use_subset | | The Commercial use subset of the CORD-19 dataset.
The documents in this project will be updated as the CORD-19 dataset grows.
See the COVID DATASET LICENSE AGREEMENT. | 0 | | Jin-Dong Kim | 2023-11-29 | Released | |
| CORD-19_Non-commercial_use_subset | | The Non commercial use subset of the CORD-19 dataset.
The documents in this project will be updated as the CORD-19 dataset grows.
See the COVID DATASET LICENSE AGREEMENT. | 0 | | Jin-Dong Kim | 2023-11-29 | Released | |
| CORD-19_bioRxiv_medRxiv_subset | | The bioRxiv/medRxiv subset of the CORD-19 dataset: pre-prints that are not peer reviewed.
The documents in this project will be updated as the CORD-19 dataset grows.
See the COVID DATASET LICENSE AGREEMENT.
| 0 | | Jin-Dong Kim | 2023-11-29 | Released | |
| bionlp-st-ge-2016-reference-tees | | NER and event extraction produced by TEES (with the default GE11 model) for the 20 full papers used in the BioNLP 2016 GE task reference corpus. | 14.6 K | Nico Colic | Nico Colic | 2023-11-29 | Released | |
| bionlp-st-ge-2016-test | | It is the benchmark test data set of the BioNLP-ST 2016 GE task. It includes Genia-style event annotations to 14 full paper articles which are about NFκB proteins. For testing purpose, however, annotations are all blinded, which means users cannot see the annotations in this project. Instead, annotations in any other project can be compared to the hidden annotations in this project, then the annotations in the project will be automatically evaluated based on the comparison.
A participant of GE task can get the evaluation of his/her result of automatic annotation, through following process:
Create a new project.
Import documents from the project, bionlp-st-2016-test-proteins to your project.
Import annotations from the project, bionlp-st-2016-test-proteins to your project.
At this point, you may want to compare you project to this project, the benchmark data set. It will show that protein annotations in your project is 100% correct, but other annotations, e.g., events, are 0%.
Produce event annotations, using your system, upon the protein annotations.
Upload your event annotations to your project.
Compare your project to this project, to get evaluation.
GE 2016 benchmark data set is provided as multi-layer annotations which include:
bionlp-st-ge-2016-reference: benchmark reference data set
bionlp-st-ge-2016-test: benchmark test data set (this project)
bionlp-st-ge-2016-test-proteins: protein annotation to the benchmark test data set
Following is supporting resources:
bionlp-st-ge-2016-coref: coreference annotation
bionlp-st-ge-2016-uniprot: Protein annotation with UniProt IDs.
pmc-enju-pas: dependency parsing result produced by Enju
UBERON-AE: annotation for anatomical entities as defined in UBERON
ICD10: annotation for disease names as defined in ICD10
GO-BP: annotation for biological process names as defined in GO
GO-CC: annotation for cellular component names as defined in GO
A SPARQL-driven search interface is provided at http://bionlp.dbcls.jp/sparql. | 7.99 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Released | |
| bionlp-st-ge-2016-test-tees | | NER and event extraction produced by TEES (with the default GE11 model) for the 14 full papers used in the BioNLP 2016 GE task test corpus. | 9.17 K | Nico Colic | Nico Colic | 2023-11-29 | Released | |
| craft-ca-core-dev | | Development data for CRAFT CA shared task, core concepts only. This project contains the development (training) annotations for the Concept Annotation task of the CRAFT Shared Task 2019. This particular set of concept annotations is the "core" set. See the task description for details, but this set contains only annotations to concepts that appear in the original 10 Open Biomedical Ontologies used for annotation. (That is to say, it does not contain any annotations to extension classes). | 59.8 K | University of Colorado Anschutz Medical Campus | craft-st | 2023-11-29 | Released | |
| RELISH-DB | | Abstracts contained in the data of the RELISH-DB (https://relishdb.ict.griffith.edu.au) made available for download here.
Data was downloaded from here: https://figshare.com/projects/RELISH-DB/60095
Related publication: https://academic.oup.com/database/article/doi/10.1093/database/baz085/5608006#200722023 | 0 | | | 2023-11-29 | Released | |
| LitCovid-OGER | | Using OGER (http://www.ontogene.org/resources/oger) to detect entities from 10 different vocabularies | 9.31 K | Fabio Rinaldi | Nico Colic | 2023-11-29 | Released | |
| NCBIDiseaseCorpus | | The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. | 6.85 K | Rezarta Islamaj Doğan,Robert Leaman,Zhiyong Lu | Chih-Hsuan Wei | 2023-11-29 | Released | |
| PubmedHPO | | Human phenotype annotation to PubMed abstracts, based on the HPO ontology | 12.4 M | Tudor Groza | tudor | 2023-11-24 | Beta | |
| LitCovid-PubTator | | | 5.88 M | | Jin-Dong Kim | 2023-11-24 | Beta | |
| PubCasesHPO | | HPO annotation in PubCases | 3.18 M | | Toyofumi Fujiwara | 2023-11-24 | Beta | |
| DisGeNET | | Disease-Gene association annotation. | 3.12 M | Nuria Queralt | Jin-Dong Kim | 2023-11-24 | Beta | |
| NEUROSES | | This corpus is composed of PubMed articles containing cognitive enhancers and anti-depressants drug mentions. The selected sentences are automatically annotated using the NCBO Annotator with the Chemical Entities of Biological Interest (CHEBI) and Phenotypic Quality Ontology (PATO) ontologies, we also produced annotations using PhenoMiner ontology via a dictionary-based tagger. | 2.14 M | | nestoralvaro | 2023-11-24 | Beta | |
| PubCasesORDO | | ORDO annotation in PubCases | 865 K | | Toyofumi Fujiwara | 2023-11-24 | Beta | |
| LitCovid-PD-HP | | | 922 K | | Jin-Dong Kim | 2023-11-28 | Beta | |
| consensus_PMA_Age_Indications | | | 1.7 K | | laurenc | 2023-11-28 | Beta | |
