| LitCovid-PD-GlycoEpitope | | | 999 | | Jin-Dong Kim | 2023-11-29 | Developing | |
| 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 | |
| ICD10 | | Annotation for disease names as defined in ICD10 | 1.6 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Developing | |
| GO-BP | | Annotation for biological processes as defined in the "Biological Process" subset of Gene Ontology | 35.4 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Developing | |
| GO-MF | | Annotation for molecular functions as defined in the "Molecular Function" subtree of Gene Ontology | 19.7 K | DBCLS | Jin-Dong Kim | 2023-12-04 | 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 | |
| Glycosmos6-MAT | | Automatic annotation by PD-MAT. | 263 K | | Jin-Dong Kim | 2023-11-29 | Developing | |
| GlycosmosP-GlycoEpitope | | | 24 | | Jin-Dong Kim | 2023-11-29 | Testing | |
| sentences | | Sentence segmentation annotation.
Automatic annotation by TextSentencer. | 6.96 M | DBCLS | Jin-Dong Kim | 2023-11-24 | Developing | |
| Test-Documents | | | 1 | | Jin-Dong Kim | 2023-11-24 | | |
| GlyCosmosP-Glycan-Motif | | | 8 | | Jin-Dong Kim | 2023-11-24 | Developing | |
| MENA-example2 | | | 3 | | Jin-Dong Kim | 2023-11-24 | Testing | |
| Glycosmos6-GlycoEpitope | | Automatic annotation by PD-GlycoEpitope. | 19.9 K | | Jin-Dong Kim | 2023-11-28 | Developing | |
| GlyCosmos6-UBERON | | | 689 K | | Jin-Dong Kim | 2023-12-15 | Developing | |
| GlyCosmos6-Glycan-Motif-Structure | | Automatic annotation by Covid-19_Glycan-Motif. | 107 K | | Jin-Dong Kim | 2023-11-24 | Developing | |
| NCBITAXON | | annotation for NCBI taxonomy.
Automatic annotation by PD-NCBITaxon. | 502 K | | Jin-Dong Kim | 2023-11-24 | Developing | |
| MyTest | | | 9.81 M | | Jin-Dong Kim | 2023-11-24 | Testing | |
| LitCovid-sentences | | | 5.63 M | | Jin-Dong Kim | 2023-11-24 | Developing | |
| CORD-19_Custom_license_subset | | The Custom license 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. | 5.08 M | | Jin-Dong Kim | 2023-11-24 | Released | |
| CORD-19-PD-UBERON | | PubDictionaries annotation for UBERON terms - updated at 2020-04-30
It is disease term annotation based on Uberon.
The terms in Uberon are uploaded in PubDictionaries
(Uberon), 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. | 1.42 M | | Jin-Dong Kim | 2023-11-24 | Released | |
