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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

41-60 / 256 show all
craft-ca-core-ex-devDevelopment data for CRAFT CA shared task, core concepts + EXTENSIONS. This project contains the development (training) annotations for the Concept Annotation task of the CRAFT Shared Task 2019. This particular set of concept annotations is the "core+extensions" set. See the task description for details, but this set contains annotations to concepts that appear in the original 10 Open Biomedical Ontologies used for annotation PLUS annotations to extension classes created using the core concepts.94.3 KUniversity of Colorado Anschutz Medical Campuscraft-st2019-03-25Released
craft-sa-devDevelopment 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.512 KUniversity of Colorado Anschutz Medical Campuscraft-st2019-03-25Released
123123123123123123150yaoxinzhi2019-04-12Released
bionlp-st-ge-2016-testIt is the benchmark test data set of the BioNLP-ST 2016 GE task. It includes Genia-style event annotations to 14 full paper articles which are about NFκB proteins. For testing purpose, however, annotations are all blinded, which means users cannot see the annotations in this project. Instead, annotations in any other project can be compared to the hidden annotations in this project, then the annotations in the project will be automatically evaluated based on the comparison. A participant of GE task can get the evaluation of his/her result of automatic annotation, through following process: Create a new project. Import documents from the project, bionlp-st-2016-test-proteins to your project. Import annotations from the project, bionlp-st-2016-test-proteins to your project. At this point, you may want to compare you project to this project, the benchmark data set. It will show that protein annotations in your project is 100% correct, but other annotations, e.g., events, are 0%. Produce event annotations, using your system, upon the protein annotations. Upload your event annotations to your project. Compare your project to this project, to get evaluation. GE 2016 benchmark data set is provided as multi-layer annotations which include: bionlp-st-ge-2016-reference: benchmark reference data set bionlp-st-ge-2016-test: benchmark test data set (this project) bionlp-st-ge-2016-test-proteins: protein annotation to the benchmark test data set Following is supporting resources: bionlp-st-ge-2016-coref: coreference annotation bionlp-st-ge-2016-uniprot: Protein annotation with UniProt IDs. pmc-enju-pas: dependency parsing result produced by Enju UBERON-AE: annotation for anatomical entities as defined in UBERON ICD10: annotation for disease names as defined in ICD10 GO-BP: annotation for biological process names as defined in GO GO-CC: annotation for cellular component names as defined in GO A SPARQL-driven search interface is provided at http://bionlp.dbcls.jp/sparql.7.99 KDBCLSJin-Dong Kim2019-04-30Released
GlyCosmos600-docsA 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.0Jin-Dong Kim2019-06-11Released
AGAC_sample874xiajingbo2019-06-30Released
AGAC_test0xiajingbo2019-07-12Released
AGAC_training3.33 Kxiajingbo2019-07-12Released
DisGeNETDisease-Gene association annotation.3.12 MNuria Queralt Jin-Dong Kim2016-01-28Beta
NEUROSESThis 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.2.15 Mnestoralvaro2016-02-24Beta
bionlp-st-ge-2016-uniprotUniProt 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 KDBCLSJin-Dong Kim2016-05-22Beta
Ab3P-abbreviationsThis corpus was developed during the creation of the Ab3P abbreviation definition identification tool. It includes 1250 manually annotated MEDLINE records. This gold standard includes 1221 abbreviation-definition pairs. Abbreviation definition identification based on automatic precision estimates Sunghwan Sohn, Donald C Comeau, Won Kim and W John Wilbur BMC Bioinformatics20089:402 DOI: 10.1186/1471-2105-9-4022.34 KSunghwan Sohn, Donald C Comeau, Won Kim and W John Wilburcomeau2016-07-29Beta
PubCasesHPOHPO annotation in PubCases3.2 MToyofumi Fujiwara2017-09-06Beta
PubCasesORDOORDO annotation in PubCases869 KToyofumi Fujiwara2017-09-14Beta
PubmedHPOHuman phenotype annotation to PubMed abstracts, based on the HPO ontology12.4 MTudor Grozatudor2017-10-11Beta
QFMC_MEDLINEQuaero French Medical Corpus: Annotation of MEDLINE titles5.97 KAurélie NévéolPierre Zweigenbaum2018-01-24Beta
Genomics_InformaticsGenomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Text corpus for this journal annotated with various levels of linguistic information would be a valuable resource as the process of information extraction requires syntactic, semantic, and higher levels of natural language processing. In this study, we publish our new corpus called GNI Corpus version 1.0, extracted and annotated from full texts of Genomics & Informatics, with NLTK (Natural Language ToolKit)-based text mining script. The preliminary version of the corpus could be used as a training and testing set of a system that serves a variety of functions for future biomedical text mining.35.3 KHyun-Seok Parkewha-bio2018-11-27Beta
IMDB-NLPAnnotations for chunking and semantic role labeling based on in-memory databases.02016-05-06Uploading
AnEM_full-texts250 documents selected randomly from full-text papers Entity types: organism subdivision, anatomical system, organ, multi-tissue structure, tissue, cell, developing anatomical structure, cellular component, organism substance, immaterial anatomical entity and pathological formation Together with AnEM_abstracts, it is probably the largest manually annotated corpus on anatomical entities.689NaCTeMYue Wang2016-07-27Uploading
CoGe_Citation_AnnotationsAnnotated PMC abstracts+full articles, that cite the "CoGe" papers (PMID: 18952863, 18269575). Total Num Citations: 165 Total Num Unique Citations: 141 Total Num Abstracts: 165 Total Num Whole Articles: 165 0Heather Lenthclent2016-10-11Uploading
NameT# Ann.AuthorMaintainerUpdated_atStatus

41-60 / 256 show all
craft-ca-core-ex-dev94.3 KUniversity of Colorado Anschutz Medical Campuscraft-st2019-03-25Released
craft-sa-dev512 KUniversity of Colorado Anschutz Medical Campuscraft-st2019-03-25Released
123123123150yaoxinzhi2019-04-12Released
bionlp-st-ge-2016-test7.99 KDBCLSJin-Dong Kim2019-04-30Released
GlyCosmos600-docs0Jin-Dong Kim2019-06-11Released
AGAC_sample874xiajingbo2019-06-30Released
AGAC_test0xiajingbo2019-07-12Released
AGAC_training3.33 Kxiajingbo2019-07-12Released
DisGeNET3.12 MNuria Queralt Jin-Dong Kim2016-01-28Beta
NEUROSES2.15 Mnestoralvaro2016-02-24Beta
bionlp-st-ge-2016-uniprot16.2 KDBCLSJin-Dong Kim2016-05-22Beta
Ab3P-abbreviations2.34 KSunghwan Sohn, Donald C Comeau, Won Kim and W John Wilburcomeau2016-07-29Beta
PubCasesHPO3.2 MToyofumi Fujiwara2017-09-06Beta
PubCasesORDO869 KToyofumi Fujiwara2017-09-14Beta
PubmedHPO12.4 MTudor Grozatudor2017-10-11Beta
QFMC_MEDLINE5.97 KAurélie NévéolPierre Zweigenbaum2018-01-24Beta
Genomics_Informatics35.3 KHyun-Seok Parkewha-bio2018-11-27Beta
IMDB-NLP02016-05-06Uploading
AnEM_full-texts689NaCTeMYue Wang2016-07-27Uploading
CoGe_Citation_Annotations0Heather Lenthclent2016-10-11Uploading