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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

21-40 / 183 show all
LocTextThe manually annotated corpus consists of 100 PubMed abstracts annotated for proteins, subcellular localizations, organisms and relations between them. The focus of the corpus is on annotation of proteins and their subcellular localizations.2.29 KGoldberg et alShrikant Vinchurkar2017-01-20Released
PIR-corpus2The protein tag was used to tag proteins, or protein-associated or -related objects, such as domains, pathways, expression of gene. Annotation guideline: http://pir.georgetown.edu/pirwww/about/doc/manietal.pdf5.52 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2017-03-07Released
FSU-PRGEA 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.pdf59.5 KCALBC ProjectYue Wang2017-03-08Released
BioLarkPubmedHPO228 abstracts manually annotated with Human Phenotype Ontology (HPO) concepts and harmonized by three curators, which can be used as a reference standard for free text annotation of human phenotypes. For more info, please see Groza et al. "Automatic concept recognition using the human phenotype ontology reference and test suite corpora", 2015.7.24 KTudor Grozasimon2017-03-28Released
SCAI-TestA small corpus for the evaluation of dictionaries containing chemical entities. Publication: http://www.scai.fraunhofer.de/fileadmin/images/bio/data_mining/paper/kolarik2008.pdf Original source: https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/corpora-for-chemical-entity-recognition.html1.21 KCALBC ProjectYue Wang2017-04-03Released
jnlpba-st-trainingThe training data used in the task came from the GENIA version 3.02 corpus, This was formed from a controlled search on MEDLINE using the MeSH terms "human", "blood cells" and "transcription factors". From this search, 1,999 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus. For the shared task only the classes protein, DNA, RNA, cell line and cell type were used. The first three incorporate several subclasses from the original taxonomy while the last two are interesting in order to make the task realistic for post-processing by a potential template filling application. The publication year of the training set ranges over 1990~1999.51.3 KGENIAYue Wang2017-04-14Released
bionlp-st-id-2011-trainingThe training dataset from the infectious diseases (ID) task in the BioNLP Shared Task 2011. Entity types: - Genes and gene products: gene, RNA, and protein name mentions. - Two-component systems: mentions of the names of two-component regulatory systems, frequently embedding the names of the two Proteins forming the system.- Chemicals: mentions of chemical compounds such as "NaCL".- Organisms: mentions of organism names or organism specification through specific properties (e.g. "graRS mutant").- Regulons/Operons: mentions of names of specific regulons and operons.5.61 KUniversity of Tokyo Tsujii Laboratory, NaCTeM and Biocomplexity Institute of Virginia TechYue Wang2017-04-18Released
bionlp-st-bb3-2016-trainingEntity (bacteria, habitats and geographical places) annotation to the training dataset of the BioNLP-ST 2016 BB task. For more information, please refer to bionlp-st-bb3-2016-development and bionlp-st-bb3-2016-test. Bacteria Bacteria entities are annotated as contiguous spans of text that contains a full unambiguous prokaryote taxon name, the type label is Bacteria. The Bacteria type is a taxon, at any taxonomic level from phylum (Eubacteria) to strain. The category that the text entities have to be assigned to is the most specific and unique category of the NCBI taxonomy resource. In case a given strain, or a group of strains is not referenced by NCBI, it is assigned with the closest taxid in the taxonomy. Habitat Habitat entities are annotated as spans of text that contains a complete mention of a potential habitat for bacteria, the type label is Habitat. Habitat entities are assigned one or several concepts from the habitat subpart of the OntoBiotope ontology. The assigned concepts are as specific as possible. OntoBiotope defines most relevant microorganism habitats from all areas considered by microbial ecology (hosts, natural environment, anthropized environments, food, medical, etc.). Habitat entities are rarely referential entities, they are usually noun phrases including properties and modifiers. There are rare cases of habitats referred with adjectives or verbs. The spans are generally contiguous but some of them are discontinuous in order to cope with conjunctions. Geographical Geographical entities are geographical and organization places denoted by official names.1.29 KINRAYue Wang2017-05-22Released
Virus300300 abstracts from virology journals annotated with viral proteins and species0http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07Released
pubmed-sentences-benchmarkA benchmark data for text segmentation into sentences. The source of annotation is the GENIA treebank v1.0. Following is the process taken. began with the GENIA treebank v1.0. sentence annotations were extracted and converted to PubAnnotation JSON. uploaded. 12 abstracts met alignment failure. among the 12 failure cases, 4 had a dot('.') character where there should be colon (':'). They were manually fixed then successfully uploaded: 7903907, 8053950, 8508358, 9415639. among the 12 failed abstracts, 8 were "250 word truncation" cases. They were manually fixed and successfully uploaded. During the fixing, manual annotations were added for the missing pieces of text. 30 abstracts had extra text in the end, indicating copyright statement, e.g., "Copyright 1998 Academic Press." They were annotated as a sentence in GTB. However, the text did not exist anymore in PubMed. Therefore, the extra texts were removed, together with the sentence annotation to them. 18.5 KGENIA projectJin-Dong Kim2017-08-15Released
bionlp-st-pc-2013-trainingThe training dataset from the pathway curation (PC) task in the BioNLP Shared Task 2013. The entity types defined in the PC task are simple chemical, gene or gene product, complex and cellular component.7.86 KNaCTeM and KISTIYue Wang2017-08-28Released
craftTest bed for PubAnnotation query development.53 KKevin Bretonnel CohenKevinBretonnelCohen2015-10-13Beta
CRAFT-treebankPenn Treebank markup for each sentence of the Colorado Richly Annotated Full Text Corpus (CRAFT).844 KUColoradoJin-Dong Kim2015-11-19Beta
DisGeNETDisease-Gene association annotation.3.12 MNuria Queralt Jin-Dong Kim2016-01-28Beta
QFMC_MEDLINEQuaero French Medical Corpus: Annotation of MEDLINE titles5.97 KAurélie NévéolPierre Zweigenbaum2016-02-03Beta
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
NameT# Ann.AuthorMaintainerUpdated_atStatus

21-40 / 183 show all
LocText2.29 KGoldberg et alShrikant Vinchurkar2017-01-20Released
PIR-corpus25.52 KUniversity of Delaware and Georgetown University Medical CenterYue Wang2017-03-07Released
FSU-PRGE59.5 KCALBC ProjectYue Wang2017-03-08Released
BioLarkPubmedHPO7.24 KTudor Grozasimon2017-03-28Released
SCAI-Test1.21 KCALBC ProjectYue Wang2017-04-03Released
jnlpba-st-training51.3 KGENIAYue Wang2017-04-14Released
bionlp-st-id-2011-training5.61 KUniversity of Tokyo Tsujii Laboratory, NaCTeM and Biocomplexity Institute of Virginia TechYue Wang2017-04-18Released
bionlp-st-bb3-2016-training1.29 KINRAYue Wang2017-05-22Released
Virus3000http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07Released
pubmed-sentences-benchmark18.5 KGENIA projectJin-Dong Kim2017-08-15Released
bionlp-st-pc-2013-training7.86 KNaCTeM and KISTIYue Wang2017-08-28Released
craft53 KKevin Bretonnel CohenKevinBretonnelCohen2015-10-13Beta
CRAFT-treebank844 KUColoradoJin-Dong Kim2015-11-19Beta
DisGeNET3.12 MNuria Queralt Jin-Dong Kim2016-01-28Beta
QFMC_MEDLINE5.97 KAurélie NévéolPierre Zweigenbaum2016-02-03Beta
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