| Training_Data_English_pt_en | | | 0 | | wmtbio | 2023-11-29 | Developing | |
| SNPPhenoExt | | | 3 | behrouz bokharaeian | bokharaeian | 2023-11-29 | Developing | |
| korean_corpus | | validation korean | 90 | | donghwan kim | 2023-11-29 | | |
| cancer_precision | | for gene mutaiton and cancer therapy | 8 | | serenity | 2023-11-29 | Testing | |
| Trait curation | | Project for trait curation in PGDBj | 479 | Sachiko Shirasawa | Sachiko Shirasawa | 2023-11-24 | Testing | |
| namedentityrecognition | | | 0 | | white | 2016-05-13 | Testing | |
| Annotation-Euglena-Enzymes | | | 0 | | Shuichi Kawashima | 2016-06-13 | Developing | |
| NAKLEE | |
| 0 | | Nakyolee | 2017-07-13 | | |
| bionlp-st-pc-2013-training | | The training dataset from the pathway curation (PC) task in the BioNLP Shared Task 2013.
The entity types defined in the PC task are simple chemical, gene or gene product, complex and cellular component. | 7.86 K | NaCTeM and KISTI | Yue Wang | 2023-11-27 | Released | |
| test-integbio | | | 0 | | yucca | 2016-08-03 | | |
| kaiyin_test | | | 3.33 K | | zhoukaiyin | 2023-11-26 | | |
| AIMed | | The AIMed corpus is one of the most widely used corpora for protein-protein interaction extraction. The protein annotations are either parts of the protein interaction annotations, or are uninvolved in any protein interaction annotation.
Publication: http://www.cs.utexas.edu/~ml/papers/bionlp-aimed-04.pdf | 4.04 K | The University of Texas at Austin | Yue Wang | 2023-11-27 | Testing | |
| Training_Data_French_fr_en | | | 0 | | wmtbio | 2023-11-27 | Developing | |
| Nalee | | trial version | 1 | | Nakyolee | 2023-11-28 | | |
| bionlp-ost-19-SeeDev-bin-test | | | 2.32 K | | ldeleger | 2023-11-28 | Developing | |
| Training_Data_English_ja_en | | | 0 | | wmtbio | 2023-11-26 | Developing | |
| KAIST_NLP_Annotation9 | | | 6.32 K | | kaist_nlp | 2023-11-28 | Developing | |
| Gene_Chemical | | EMU abstract annotation | 0 | | zhoukaiyin | 2023-11-29 | Developing | |
| RDoCTask2SampleData | | Each annotation file contains an annotated abstract with the most relevant sentence. The relevant sentence is annotated with the RDoC category name. The annotation data are formatted as json files. Please refer to the following page for a more detailed description of the json format http://www.pubannotation.org/docs/annotation-format/.
| 10 | | mmanani1s | 2023-11-29 | Released | |
| ngly1-sample9 | | | 7 | | Nuria | 2023-11-28 | | |
