| ichiharatest_150830_1 | | test | 99 | | Hisako Ichihara | 2023-11-29 | Testing | |
| korean_corpus_sample | | | 53 | | donghwan kim | 2023-11-29 | Testing | |
| pqqtest_sentence | | | 565 K | | yaoxinzhi | 2023-11-29 | Testing | |
| LitCovid-sample-HP | | | 0 | | Jin-Dong Kim | 2023-11-29 | Testing | |
| GlycoBiology-Motifs | | | 4.15 K | | Jin-Dong Kim | 2023-11-29 | | |
| CORD-19-PD-HP | | PubDictionaries annotation for HP terms - updated at 2020-04-30
Disease term annotation based on HP.
Version 2020-04-20.
The terms in HP are loaded in PubDictionaries, with which the annotations in this project are produced. The parameter configuration used for this project is here.
Note that it is an automatically generated dictionary-based annotation. It will be updated periodically, as the documents are increased, and the dictionary is improved. | 1.15 M | | Jin-Dong Kim | 2023-11-29 | Released | |
| ggdb-test | | | 2.4 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| LitCovid_AGAC | | | 904 | | xiajingbo | 2023-11-29 | | |
| 0mytest | | | 144 | | Yue Wang | 2023-11-29 | | |
| bionlp-st-ge-2016-spacy-parsed | | Dependency parses produced by spaCy parser, and part-of-speech tags produced by Stanford tagger (with the wsj-0-18-left3words-nodistsim model). The exact procedure is described here. Data set contains the 34 full paper articles used in the BioNLP 2016 GE task.
| 225 K | Nico Colic | Nico Colic | 2023-11-29 | Released | |
| EDAM-topics | | annotation for EDAM topics | 11.6 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| 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 | |
| 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-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-ge-2016-reference | | It is the benchmark reference data set of the BioNLP-ST 2016 GE task.
It includes Genia-style event annotations to 20 full paper articles which are about NFκB proteins.
The task is to develop an automatic annotation system which can produce annotation similar to the annotation in this data set as much as possible.
For evaluation of the performance of a participating system, the system needs to produce annotations to the documents in the benchmark test data set (bionlp-st-ge-2016-test).
GE 2016 benchmark data set is provided as multi-layer annotations which include:
bionlp-st-ge-2016-reference: benchmark reference data set (this project)
bionlp-st-ge-2016-test: benchmark test data set (annotations are blined)
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. | 14.4 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Released | |
| bionlp-st-ge-2016-reference-eval | | | 426 | | Jin-Dong Kim | 2023-11-29 | Testing | |
| Gene_Chemical | | EMU abstract annotation | 0 | | zhoukaiyin | 2023-11-29 | Developing | |
| Systems_Biology | | | 0 | | Sophie Nam | 2023-11-29 | | |
| TEST-CellLine | | Annotation: AnnotationByCellosaurus | 76 | | Yasunori Yamamoto | 2023-11-29 | Testing | |
| TEST-ChemicalEntity | | ChemicalEntity : Annotated by PD-MeSH2022_CHEBI_tuned-B | 827 | | yucca | 2023-11-29 | Beta | |
