| testtesttest | | | 17.4 K | | Jin-Dong Kim | 2024-09-16 | Developing | |
| LitCovid-sentences-v1 | | Sentence segmentation of all the texts in the LitCovid literature. The segmentation is automatically obtained using the TextSentencer annotation service developed and maintained by DBCLS. | 16.5 K | | Jin-Dong Kim | 2023-11-27 | Released | |
| bionlp-st-ge-2016-uniprot | | UniProt protein annotation to the benchmark data set of BioNLP-ST 2016 GE task: reference data set (bionlp-st-ge-2016-reference) and test data set (bionlp-st-ge-2016-test).
The annotations are produced based on a dictionary which is semi-automatically compiled for the 34 full paper articles included in the benchmark data set (20 in the reference data set + 14 in the test data set).
For detailed information about BioNLP-ST GE 2016 task data sets, please refer to the benchmark reference data set (bionlp-st-ge-2016-reference) and benchmark test data set (bionlp-st-ge-2016-test).
| 16.2 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Beta | |
| LitCoin-GeneOrGeneProduct-v0 | | https://pubdictionaries.org/text_annotation.json?dictionary=NCBIGene-NER&threshold=0.85&abbreviation=true | 15.8 K | | Jin-Dong Kim | 2023-11-29 | | |
| LitCoin-training-merged | | | 14.8 K | | Jin-Dong Kim | 2023-11-24 | | |
| 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 | |
| events-check-again | | | 14.4 K | | | 2023-11-30 | Testing | |
| 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-test-ihmc | | | 14.4 K | Lucian Galescu | | 2023-11-29 | Testing | |
| LitCoin-PubTator-for-Tuning | | A set of randomly selected PubMed articles with PubTator annotation.
The labels of PubTator annotations are converted to corresponding labels for LitCoin as follows:
'Gene' -> 'GeneOrGeneProduct',
'Disease' -> 'DiseaseOrPhenotypicFeature',
'Chemical' -> 'ChemicalEntity'
'Species' -> 'OrganismTaxon'
'Mutation' -> 'SequenceVariant'
'CellLine' -> 'CellLine' | 14.2 K | | Jin-Dong Kim | 2023-11-29 | | |
| GlyCosmos600-FMA | | | 13.8 K | | Jin-Dong Kim | 2024-09-28 | | |
| LitCoin-entities | | | 13.6 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| LitCovid-PD-MONDO-v1 | | PubDictionaries annotation for disease terms - updated at 2020-04-20
It is based on MONDO Version 2020-04-20.
The terms in MONDO 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. | 13.4 K | | Jin-Dong Kim | 2023-11-29 | Released | |
| LitCovid-sample-Enju | | | 13.1 K | | Jin-Dong Kim | 2023-11-29 | Developing | |
| EDAM-DFO | | annotation for EDAM terms for data, formats, and operations | 12.5 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| test2 | | | 12.3 K | | ykyao | 2023-11-29 | | |
| EDAM-topics | | annotation for EDAM topics | 11.6 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| uniprot-mouse | | Protein annotation based on UniProt | 11.5 K | | Jin-Dong Kim | 2023-11-28 | Developing | |
| 2015-BEL-Sample-2 | | The 295 BEL statements for sample set used for the 2015 BioCreative challenge. | 11.4 K | Fabio Rinaldi | Nico Colic | 2023-11-28 | Released | |
| CORD-19-SciBite-sentences | | | 11.2 K | | Jin-Dong Kim | 2023-11-26 | Testing | |
