| KAIST_NLP_Annotation13 | | | 6.27 K | | kaist_nlp | 2023-11-29 | Developing | |
| KAIST_NLP_Annotation9 | | | 6.32 K | | kaist_nlp | 2023-11-28 | Developing | |
| BioNLP16_Messiy | | | 6.5 K | | Messiy | 2023-11-29 | Testing | |
| BioNLP16_DUT | | | 6.5 K | | Messiy | 2023-11-29 | Testing | |
| LitCovid_Glycan-Motif-Structure | | PubDictionaries annotation for glycan-Motif terms. | 6.51 K | | ISSAKU YAMADA | 2023-11-29 | Beta | |
| proj_h_1 | | | 6.7 K | | | 2023-11-24 | | |
| testone | | | 6.76 K | | ykyao | 2023-11-29 | | |
| 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 | |
| LitCoin-Disease-Tuning-1 | | Annotator=PD-MeSH2022_C_F03_plus_allFN-B | 6.98 K | | yucca | 2023-11-29 | | |
| UseCases_ArguminSci_Discourse | | Predictions from ArguminSci(http://lelystad.informatik.uni-mannheim.de/) for the seven datasets and for discourse categories | 7.12 K | | zebet | 2023-11-29 | Developing | |
| 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 | |
| Staphylococcus | | | 7.46 K | haruo | haruo | 2023-11-29 | Testing | |
| bionlp-st-epi-2011-training | | The training dataset from the Epigenetics and Post-translational Modifications (EPI) task in the BioNLP Shared Task 2011.
The core entities of the task are genes and gene products (RNA and proteins), identified in the data simply as "Protein" annotations. | 7.59 K | GENIA | Yue Wang | 2023-11-29 | Released | |
| bionlp-st-pc-2013-training | | The training dataset from the pathway curation (PC) task in the BioNLP Shared Task 2013.
The entity types defined in the PC task are simple chemical, gene or gene product, complex and cellular component. | 7.86 K | NaCTeM and KISTI | Yue Wang | 2023-11-27 | 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-gro-2013-training | | The training data set of the BioNLP-ST 2013 GRO task, including 150 MEDLINE abstracts that are annotated with concepts and relations of the Gene Regulation Ontology (GRO; http://www.ebi.ac.uk/Rebholz-srv/GRO/GRO.html) | 8.02 K | Jung-jae Kim | Jung-jae Kim | 2023-11-29 | Testing | |
| Grays_part2_test | | | 8.57 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| Grays_part2 | | Osteology w/o 21 | 8.63 K | | okubo | 2023-11-29 | Testing | |
| LitCoin-GeneOrGeneProduct-v2 | | threshold = 0.93 | 8.98 K | | Jin-Dong Kim | 2023-11-29 | | |
| sonoma2 | | sonoma2 | 9.09 K | Standigm | chanung | 2023-11-29 | Beta | |
