geneset_names | | | 0 | | alo33 | 2022-04-26 | Released | |
Inflammaging | | Inflammation axis | 23.4 M | | alo33 | 2023-11-24 | Released | |
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 | |
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-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 | |
PubMed_ArguminSci | | Predictions for PubMed automatically extracted with the ArguminSci tool (https://github.com/anlausch/ArguminSci). | 777 K | | zebet | 2023-11-24 | Released | |
DisGeNET5_variant_disease | | The file contains variant-disease associations obtained by text mining MEDLINE abstracts using the BeFree system, including the variant and disease off sets. | 144 K | IBI Group | Yue Wang | 2023-11-24 | Released | |
FSU-PRGE | | A new broad-coverage corpus composed of 3,306 MEDLINE abstracts dealing with gene and protein mentions.
The annotation process was semi-automatic.
Publication: http://aclweb.org/anthology/W/W10/W10-1838.pdf | 59.5 K | CALBC Project | Yue Wang | 2023-11-26 | Released | |
CORD-19-PD-MONDO | | PubDictionaries annotation for MONDO terms - updated at 2020-04-30
It is disease term annotation 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. | 6.32 M | | Jin-Dong Kim | 2023-11-27 | Released | |
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 | |
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 | |
craft-sa-dev | | Development data for CRAFT SA shared task. This project contains the development (training) annotations for the Structural Annotation task of the CRAFT Shared Task 2019. This particular set contains token and sentence annotations with tokens linked via dependency relations. These dependency relations were automatically generated using the manually curated CRAFT constituency treebank files as input. | 490 K | University of Colorado Anschutz Medical Campus | craft-st | 2023-11-27 | Released | |
Zoonoses_partialAnnotation | | This is a part of Zoonoses project used by PanZoora. But Zoonoses project provides whole manual annotated data but this is partial ones. | 266 | | AikoHIRAKI | 2023-11-27 | Released | |
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 | |
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 | |
spacy-test | | Random set of articles used for testing in the development of the RESTful spaCy parsing web service. Since development is now finished, they are released for the community to use. | 131 K | Nico Colic | Nico Colic | 2023-11-29 | Released | |
CORD-19-PD-HP | | PubDictionaries annotation for HP terms - updated at 2020-04-30
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. | 1.15 M | | Jin-Dong Kim | 2023-11-29 | Released | |
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 | |
TEST-DiseaseOrPhenotypicFeature | | Annotated by Mesh_All_FN | 795 | | Eisuke Dohi | 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 | |