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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 359 すべて表示
NCBIDiseaseCorpusThe NCBI disease corpus is fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community.6.88 KRezarta Islamaj Doğan,Robert Leaman,Zhiyong LuChih-Hsuan Wei2015-08-06公開中
CellFinderCellFinder corpus4.75 KMariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf LeserMariana Neves2015-11-25公開中
bionlp-st-ge-2016-test-proteinsProtein annotations to the benchmark test data set of the BioNLP-ST 2016 GE task. A participant of the GE task may import the documents and annotations of this project to his/her own project, to begin with producing event annotations. For more details, please refer to the benchmark test data set (bionlp-st-ge-2016-test). 4.34 KDBCLSJin-Dong Kim2016-05-04公開中
CyanoBaseCyanobacteria are prokaryotic organisms that have served as important model organisms for studying oxygenic photosynthesis and have played a significant role in the Earthfs history as primary producers of atmospheric oxygen. Publication: http://www.aclweb.org/anthology/W12-24301.1 KKazusa DNA Research Institute and Database Center for Life Science (DBCLS)Yue Wang2016-05-17公開中
bionlp-st-ge-2016-referenceIt is the benchmark reference data set of the BioNLP-ST 2016 GE task. It includes Genia-style event annotations to 20 full paper articles which are about NFκB proteins. The task is to develop an automatic annotation system which can produce annotation similar to the annotation in this data set as much as possible. For evaluation of the performance of a participating system, the system needs to produce annotations to the documents in the benchmark test data set (bionlp-st-ge-2016-test). GE 2016 benchmark data set is provided as multi-layer annotations which include: bionlp-st-ge-2016-reference: benchmark reference data set (this project) bionlp-st-ge-2016-test: benchmark test data set (annotations are blined) 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.14.4 KDBCLSJin-Dong Kim2016-05-23公開中
bionlp-st-ge-2016-corefCoreference annotation to the benchmark data set (reference and test) of BioNLP-ST 2016 GE task. For detailed information, please refer to the benchmark reference data set (bionlp-st-ge-2016-reference) and benchmark test data set (bionlp-st-ge-2016-test).853DBCLSJin-Dong Kim2016-05-23公開中
bionlp-st-ge-2016-spacy-parsedDependency 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. 226 KNico ColicNico Colic2016-05-25公開中
bionlp-st-ge-2016-test-teesNER and event extraction produced by TEES (with the default GE11 model) for the 14 full papers used in the BioNLP 2016 GE task test corpus.9.17 KNico ColicNico Colic2016-05-25公開中
bionlp-st-ge-2016-reference-teesNER and event extraction produced by TEES (with the default GE11 model) for the 20 full papers used in the BioNLP 2016 GE task reference corpus.14.6 KNico Colic Nico Colic2016-05-25公開中
2015-BEL-Sample-2The 295 BEL statements for sample set used for the 2015 BioCreative challenge.11.4 KFabio RinaldiNico Colic2016-05-25公開中
bionlp-st-epi-2011-trainingThe training dataset from the Epigenetics and Post-translational Modifications (EPI) task in the BioNLP Shared Task 2011. The core entities of the task are genes and gene products (RNA and proteins), identified in the data simply as "Protein" annotations. 7.6 KGENIAYue Wang2016-12-06公開中
bionlp-st-cg-2013-trainingThe training dataset from the cancer genetics task in the BioNLP Shared Task 2013. Composed of anatomical and molecular entities.10.9 KNaCTeMYue Wang2016-12-06公開中
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 Wang2017-03-08公開中
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 Wang2017-04-03公開中
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 Wang2017-04-18公開中
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 Wang2017-05-22公開中
Virus300300 abstracts from virology journals annotated with viral proteins and species0http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07公開中
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 Kim2017-08-15公開中
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 Wang2017-08-28公開中
bionlp-st-2016-SeeDev-devEntities and event annotations from the development set of the BioNLP-ST 2016 SeeDev task. SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology. GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events. For more information, please refer to the task website All annotations : Train set Development set Test set (without events) 61EstelleChaix2018-01-13公開中
NameT# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 359 すべて表示
NCBIDiseaseCorpus6.88 KRezarta Islamaj Doğan,Robert Leaman,Zhiyong LuChih-Hsuan Wei2015-08-06公開中
CellFinder4.75 KMariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf LeserMariana Neves2015-11-25公開中
bionlp-st-ge-2016-test-proteins4.34 KDBCLSJin-Dong Kim2016-05-04公開中
CyanoBase1.1 KKazusa DNA Research Institute and Database Center for Life Science (DBCLS)Yue Wang2016-05-17公開中
bionlp-st-ge-2016-reference14.4 KDBCLSJin-Dong Kim2016-05-23公開中
bionlp-st-ge-2016-coref853DBCLSJin-Dong Kim2016-05-23公開中
bionlp-st-ge-2016-spacy-parsed226 KNico ColicNico Colic2016-05-25公開中
bionlp-st-ge-2016-test-tees9.17 KNico ColicNico Colic2016-05-25公開中
bionlp-st-ge-2016-reference-tees14.6 KNico Colic Nico Colic2016-05-25公開中
2015-BEL-Sample-211.4 KFabio RinaldiNico Colic2016-05-25公開中
bionlp-st-epi-2011-training7.6 KGENIAYue Wang2016-12-06公開中
bionlp-st-cg-2013-training10.9 KNaCTeMYue Wang2016-12-06公開中
FSU-PRGE59.5 KCALBC ProjectYue Wang2017-03-08公開中
SCAI-Test1.21 KCALBC ProjectYue Wang2017-04-03公開中
bionlp-st-id-2011-training5.61 KUniversity of Tokyo Tsujii Laboratory, NaCTeM and Biocomplexity Institute of Virginia TechYue Wang2017-04-18公開中
bionlp-st-bb3-2016-training1.29 KINRAYue Wang2017-05-22公開中
Virus3000http://aclweb.org/anthology/W/W17/W17-2311.pdfhelencook2017-08-07公開中
pubmed-sentences-benchmark18.5 KGENIA projectJin-Dong Kim2017-08-15公開中
bionlp-st-pc-2013-training7.86 KNaCTeM and KISTIYue Wang2017-08-28公開中
bionlp-st-2016-SeeDev-dev61EstelleChaix2018-01-13公開中