| PT_NER_NEL_CONSENSUS | | | 354 | | dpavot | 2023-11-27 | | |
| PT_NER_NEL_Diana | | | 318 | | dpavot | 2023-11-24 | Developing | |
| PGR-FAL | | Identification of False Relations | 128 | Diana Sousa | dpavot | 2023-11-29 | Developing | |
| ENG_RE | | Entities and relations annotations from the following ontologies: Disease Ontology ('DO'), Gene Ontology ('GO'), Human Phenotype Ontology ('HPO'), and ChEBI ontology ('CHEBI'). | 224 | Diana Sousa | dpavot | 2023-11-29 | Developing | |
| PGR-UNK | | Identification of Unknown Relations
| 91 | Diana Sousa | dpavot | 2023-11-29 | Developing | |
| PGR-NEG | | Identification of Negative Relations
| 23 | Diana Sousa | dpavot | 2023-11-28 | Developing | |
| ENG_RE_CONSENSUS | | | 250 | | dpavot | 2023-11-28 | | |
| ENG_RE_Diana | | | 213 | | dpavot | 2023-11-29 | Developing | |
| testing_230112 | | | 2 | | eatfish | 2023-11-29 | Testing | |
| AxD_symptoms | | Symptoms of AxD from available case report and case series | 401 | | Eisuke Dohi | 2023-11-29 | Developing | |
| LitEisuke | | | 6.12 K | | Eisuke Dohi | 2023-11-29 | Developing | |
| TEST-DiseaseOrPhenotypicFeature | | Annotated by Mesh_All_FN | 795 | | Eisuke Dohi | 2023-11-29 | Released | |
| LitCoin-MONDO_bioort2019 | | DiseaeseOrPhenotypicFeature | 3.72 K | | Eisuke Dohi | 2023-11-29 | Testing | |
| test01 | | | 0 | | Erika Asamizu | 2015-09-11 | Testing | |
| Erin_test | | @ Yonsei University | 0 | Erin | ErinHJ_Kim | 2023-11-29 | Testing | |
| bionlp-st-2016-SeeDev-test | | Entities annotations from the test set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 184 | | EstelleChaix | 2023-11-29 | Released | |
| bionlp-st-2016-SeeDev-training | | Entities and event annotations from the training set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 35 | | EstelleChaix | 2023-11-28 | Released | |
| bionlp-st-2016-SeeDev-dev | | Entities and event annotations from the development set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 61 | | EstelleChaix | 2023-11-29 | Released | |
| disease_ontology_term_microbe | | | 5 | | evangelos | 2023-11-29 | Developing | |
| SPECIES800_autotagged | | This project comprises the SPECIES800 corpus documents automatically annotated by the Jensenlab tagger.
Annotated entity types are:
Genes/proteins from the mentioned organisms (and any human ones)
PubChem Compound identifiers
NCBI Taxonomy entries
Gene Ontology cellular component terms
BRENDA Tissue Ontology terms
Disease Ontology terms
Environment Ontology terms
The SPECIES 800 (S800) comprises 800 PubMed abstracts. In its original form species mentions were manually identified and mapped to the corresponding NCBI Taxonomy identifiers.
Described in:
The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text.
Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, et al. (2013). PLoS ONE, 2013, 8(6): e65390. doi:10.1371/journal.pone.0065390.
The manually annotated corpus is also available as a PubAnnotation project (see here).
| 0 | Evangelos Pafilis, Sampo Pyysalo, Lars Juhl Jensen | evangelos | 2015-11-20 | Testing | |
