| LitCoin-sentences | | | 4.94 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| LitCovid-ArguminSci | | Discourse elements for the documents in the LitCovid dataset.
Annotations were automatically predicted by the ArguminSci tool (https://github.com/anlausch/ArguminSci) | 4.9 K | | zebet | 2023-11-27 | Released | |
| KAIST_NLP_Annotation11 | | | 4.88 K | | kaist_nlp | 2023-11-26 | Developing | |
| FirstAuthor10 | | FirstAuthor10 | 4.78 K | | AikoHIRAKI | 2023-11-29 | Developing | |
| KAIST_NLP_Annotation10 | | | 4.76 K | | kaist_nlp | 2023-11-29 | Developing | |
| CellFinder | | CellFinder corpus | 4.75 K | Mariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf Leser | Mariana Neves | 2023-11-27 | Released | |
| KAIST_NLP_Annotation3 | | | 4.73 K | | kaist_nlp | 2023-11-29 | Developing | |
| LitCoin-GeneOrGeneProduct-v3 | | GeneOrGeneProduct
after false positive control | 4.67 K | | Jin-Dong Kim | 2023-11-24 | | |
| KAIST_NLP_Annotation7 | | | 4.64 K | | kaist_nlp | 2023-11-29 | Developing | |
| LitCovid-PubTatorCentral | | Named-entities for the documents in the LitCovid dataset. Annotations were automatically predicted by the PubTatorCentral tool (https://www.ncbi.nlm.nih.gov/research/pubtator/) | 4.64 K | | zebet | 2023-11-27 | Released | |
| Covid19_manual_annotation_v2 | | | 4.58 K | | AikoHIRAKI | 2023-11-24 | Developing | |
| DLUT931 | | DLUT NLP Lab.Test our event extration result for 16 GE task. | 4.57 K | | DLUT931 | 2023-11-30 | Testing | |
| KAIST_NLP_Annotation6 | | | 4.47 K | | kaist_nlp | 2023-11-29 | Developing | |
| PIR-corpus1 | | The Protein Information Resource (PIR) is not biased towards any particular biomedical domain, and is expected to provide more diverse protein names in a given sample size.
Annotation category: protein, compound-protein, acronym. | 4.44 K | University of Delaware and Georgetown University Medical Center | Yue Wang | 2023-11-27 | Released | |
| bionlp-st-ge-2016-test-proteins | | Protein annotations to the benchmark test data set of the BioNLP-ST 2016 GE task.
A participant of the GE task may import the documents and annotations of this project to his/her own project, to begin with producing event annotations.
For more details, please refer to the benchmark test data set (bionlp-st-ge-2016-test).
| 4.34 K | DBCLS | Jin-Dong Kim | 2023-11-27 | Released | |
| simple1 | | | 4.34 K | | hxr-2016_dlut | 2023-11-29 | | |
| FirstAuthor_s_Plants | | For only Plants | 4.3 K | | AikoHIRAKI | 2023-11-29 | Testing | |
| LitCovid-PD-FMA-UBERON-v1 | | PubDictionaries annotation for anatomy terms - updated at 2020-04-20
Disease term annotation based on FMA and Uberon. Version 2020-04-20.
The terms in FMA and Uberon are loaded in PubDictionaries
(FMA and
Uberon), with which the annotations in this project are produced.
The parameter configuration used for this project is
here for FMA and
there for Uberon.
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. | 4.3 K | | Jin-Dong Kim | 2023-11-27 | Released | |
| GlycoBiology-Motifs | | | 4.15 K | | Jin-Dong Kim | 2023-11-29 | | |
| LitCoin-MeSH-Disease-2 | | Flase Negative全部入れてみた | 4.08 K | | yucca | 2023-11-29 | | |
