| LitCoin_Mondo | | | 1.96 K | | Yasunori Yamamoto | 2023-11-28 | Testing | |
| LitCovid_AGAC | | | 904 | | xiajingbo | 2023-11-29 | | |
| LitCovid-sample-PD-GO-BP-0 | | | 708 | | Jin-Dong Kim | 2023-11-29 | Beta | |
| testing_230112 | | | 2 | | eatfish | 2023-11-29 | Testing | |
| glytoucan-iupac | | retrying glytoucan-iupac annotation as of march 9, 2018 | 0 | | kiyoko | 2023-11-29 | Testing | |
| LitCovid-GlycoBiology | | Articles from GlycoBiology, received by the keyword "Covid-19" | 0 | | Jin-Dong Kim | 2023-11-29 | Testing | |
| 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 | |
| CORD-19-sample-UBERON | | | 54 | | Jin-Dong Kim | 2023-11-26 | Developing | |
| LitCovid-sample-PD-NCBITaxon | | | 1.35 K | | Jin-Dong Kim | 2023-11-29 | Beta | |
| LitCovid-OGER-BB | | Using OGER (www.ontogene.com) and Biobert to obtain annotations for 10 different vocabularies. | 308 K | Fabio Rinaldi | Nico Colic | 2023-11-28 | Released | |
| Test-merged-2 | | | 3.51 K | | admin | 2023-11-29 | | |
| Test-GeneOrGeneProduct | | | 1.17 K | | Jin-Dong Kim | 2023-11-29 | | |
| CORD-19-sample-FMA-UBERON | | | 61 | | Jin-Dong Kim | 2023-11-29 | Developing | |
| LitCovid-sample-Enju | | | 13.1 K | | Jin-Dong Kim | 2023-11-29 | Developing | |
| LitCovid-sample-UniProt | | | 1.25 K | | Jin-Dong Kim | 2023-11-30 | Testing | |
| performance-test | | a project for performance test | 480 K | | Jin-Dong Kim | 2023-11-27 | Testing | |
| NEUROSES | | This corpus is composed of PubMed articles containing cognitive enhancers and anti-depressants drug mentions. The selected sentences are automatically annotated using the NCBO Annotator with the Chemical Entities of Biological Interest (CHEBI) and Phenotypic Quality Ontology (PATO) ontologies, we also produced annotations using PhenoMiner ontology via a dictionary-based tagger. | 2.14 M | | nestoralvaro | 2023-11-24 | Beta | |
| DisGeNET5_gene_disease | | The file contains gene-disease associations obtained by text mining MEDLINE abstracts using the BeFree system including the gene and disease off sets. | 2.04 M | IBI Group | Yue Wang | 2023-11-24 | Released | |
| CORD-PICO | | Automatic annotation of the CORD-19 dataset with PICO categories. The corpus was automatically labeled with an LSTM-CRF model trained on human-annotated PubMed abstracts from https://github.com/bepnye/EBM-NLP. Currently, titles and abstracts only are annotated using Population, Intervention and Outcome labels, as well as more fine-grained labels such as Age, Drug, Mortality and others. | 69.6 K | Simon Suster | ssuster | 2023-11-27 | Developing | |
| OryzaGP_2021 | | Updating OryzaGP | 1.08 M | Pierre Larmande | larmande | 2023-11-28 | Developing | |
