| LitCoin-Disease-MeSH | | MeSH C and F03, plus false negatives that appear in two or more documents in LitCoin-entities | 3.56 K | | yucca | 2023-11-29 | Testing | |
| GlyCosmos600-docs | | A random collection of 600 PubMed abstracts from 6 glycobiology-related journals: Glycobiology, Glycoconjugate journal, The Journal of biological chemistry, Journal of proteome research, Journal of proteomics, and Carbohydrate research. The whole PMIDs were collected on June 11, 2019. From each journal, 100 PMIDs were randomly sampled. | 0 | | Jin-Dong Kim | 2023-11-29 | Released | |
| JournalClub | | | 170 | | AikoHIRAKI | 2023-11-29 | Developing | |
| LitEisuke | | | 6.12 K | | Eisuke Dohi | 2023-11-29 | Developing | |
| LitCovid-sample-sentences | | | 2.3 K | | Jin-Dong Kim | 2023-11-29 | Beta | |
| LitCovid-sample-PD-NCBITaxon | | | 1.35 K | | Jin-Dong Kim | 2023-11-29 | Beta | |
| LitCovid-sample-PD-MAT | | | 251 | | Jin-Dong Kim | 2023-11-29 | Developing | |
| LitCovid-sample-PD-GO-BP-0 | | | 708 | | Jin-Dong Kim | 2023-11-29 | Beta | |
| LitCovid-sample-Glycan | | | 3.21 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| LitCovid-docs-s | | | 0 | | Jin-Dong Kim | 2023-11-29 | Released | |
| 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-PD-HP-v1 | | PubDictionaries annotation for human phenotype terms - updated at 2020-04-20
Disease term annotation based on HP.
Version 2020-04-20.
The terms in HP 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. | 3.03 K | | Jin-Dong Kim | 2023-11-29 | Released | |
| Test-merged-2 | | | 3.51 K | | admin | 2023-11-29 | | |
| Test-merged | | | 3.21 K | | Jin-Dong Kim | 2023-11-29 | | |
| TEST-DiseaseOrPhenotypicFeature | | Annotated by Mesh_All_FN | 795 | | Eisuke Dohi | 2023-11-29 | Released | |
| TEST-ChemicalEntity | | ChemicalEntity : Annotated by PD-MeSH2022_CHEBI_tuned-B | 827 | | yucca | 2023-11-29 | Beta | |
| TEST-CellLine | | Annotation: AnnotationByCellosaurus | 76 | | Yasunori Yamamoto | 2023-11-29 | Testing | |
| bionlp-st-ge-2016-reference-eval | | | 426 | | Jin-Dong Kim | 2023-11-29 | Testing | |
| EDAM-topics | | annotation for EDAM topics | 11.6 K | | Jin-Dong Kim | 2023-11-29 | Testing | |
| bionlp-st-ge-2016-spacy-parsed | | Dependency parses produced by spaCy parser, and part-of-speech tags produced by Stanford tagger (with the wsj-0-18-left3words-nodistsim model). The exact procedure is described here. Data set contains the 34 full paper articles used in the BioNLP 2016 GE task.
| 225 K | Nico Colic | Nico Colic | 2023-11-29 | Released | |
