| LitCovid-sample-PD-NCBITaxon | | | 1.35 K | | Jin-Dong Kim | 2023-11-29 | Beta | |
| LitCovid-sample-PD-GO-BP-0 | | | 708 | | Jin-Dong Kim | 2023-11-29 | Beta | |
| QFMC_MEDLINE | | Quaero French Medical Corpus:
Annotation of MEDLINE titles | 5.9 K | Aurélie Névéol | Pierre Zweigenbaum | 2023-11-29 | Beta | |
| TEST-ChemicalEntity | | ChemicalEntity : Annotated by PD-MeSH2022_CHEBI_tuned-B | 827 | | yucca | 2023-11-29 | Beta | |
| blah6_medical_device | | BLAH6 hackathon project to annotate medical device indications in premarket approval statement summaries. The documents in this project serve as a corpus of premarket approval (PMA) statements that have undergone quality control. In particular, we have (1) removed non-ascii characters, (2) fixed some text segmentation errors, and (3) fixed some capitalization errors. | 0 | Stefano Rensi | therightstef | 2023-11-29 | Beta | |
| bionlp-st-ge-2016-uniprot | | UniProt protein annotation to the benchmark data set of BioNLP-ST 2016 GE task: reference data set (bionlp-st-ge-2016-reference) and test data set (bionlp-st-ge-2016-test).
The annotations are produced based on a dictionary which is semi-automatically compiled for the 34 full paper articles included in the benchmark data set (20 in the reference data set + 14 in the test data set).
For detailed information about BioNLP-ST GE 2016 task data sets, please refer to the benchmark reference data set (bionlp-st-ge-2016-reference) and benchmark test data set (bionlp-st-ge-2016-test).
| 16.2 K | DBCLS | Jin-Dong Kim | 2023-11-29 | Beta | |
| sonoma2 | | sonoma2 | 9.09 K | Standigm | chanung | 2023-11-29 | Beta | |
| LitCovid-sample-PD-UBERON | | PubDictionaries annotation for UBERON terms - updated at 2020-04-30
It is annotation for anatomical entities 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.
| 310 | | Jin-Dong Kim | 2023-11-28 | Beta | |
| LitCovid-sample-PD-FMA | | | 1.93 K | | Jin-Dong Kim | 2023-11-28 | Beta | |
| LitCovid-PAS-Enju | | Predicate-argument structure annotation produced by the Enju parser. | 125 K | | Jin-Dong Kim | 2023-11-28 | Beta | |
| LitCovid-sample-PD-IDO | | | 1.27 K | | Jin-Dong Kim | 2023-11-28 | Beta | |
| consensus_PMA_Age_Indications | | | 1.7 K | | laurenc | 2023-11-28 | Beta | |
| LitCovid-PD-HP | | | 922 K | | Jin-Dong Kim | 2023-11-28 | Beta | |
| PubCasesORDO | | ORDO annotation in PubCases | 865 K | | Toyofumi Fujiwara | 2023-11-24 | Beta | |
| 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 | |
| DisGeNET | | Disease-Gene association annotation. | 3.12 M | Nuria Queralt | Jin-Dong Kim | 2023-11-24 | Beta | |
| PubCasesHPO | | HPO annotation in PubCases | 3.18 M | | Toyofumi Fujiwara | 2023-11-24 | Beta | |
| LitCovid-PubTator | | | 5.88 M | | Jin-Dong Kim | 2023-11-24 | Beta | |
| PubmedHPO | | Human phenotype annotation to PubMed abstracts, based on the HPO ontology | 12.4 M | Tudor Groza | tudor | 2023-11-24 | Beta | |
| PT_NER_NEL_pruas | | | 334 | Pedro Ruas | pruas_18 | 2023-11-30 | Uploading | |
