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Name TDescription# Ann.AuthorMaintainerUpdated_atStatus

41-60 / 556 show all
bionlp-ost-19-BB-norm-ner-test 125ldeleger2023-11-27Developing
bionlp-ost-19-BB-norm-ner-train 2.45 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-norm-test 2.05 Kldeleger2023-11-28Developing
bionlp-ost-19-BB-norm-train 2.45 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-rel-dev 1.97 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-rel-ner-dev 1.98 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-rel-ner-test 125ldeleger2023-11-24Developing
bionlp-ost-19-BB-rel-ner-train 3.62 Kldeleger2023-11-28Developing
bionlp-ost-19-BB-rel-test 2.07 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-rel-train 3.52 Kldeleger2023-11-28Developing
bionlp-ost-19-SeeDev-bin-dev 2.58 Kldeleger2023-11-28Developing
bionlp-ost-19-SeeDev-bin-test 2.32 Kldeleger2023-11-28Developing
bionlp-ost-19-SeeDev-bin-train 5.08 Kldeleger2023-11-29Developing
bionlp-st-2016-SeeDev-dev Entities 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) 61EstelleChaix2023-11-29Released
bionlp-st-2016-SeeDev-test Entities annotations from the test 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) 184EstelleChaix2023-11-29Released
bionlp-st-2016-SeeDev-training Entities and event annotations from the training 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) 35EstelleChaix2023-11-28Released
bionlp-st-bb3-2016-training Entity (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.1.28 KINRAYue Wang2023-11-29Released
bionlp-st-cg-2013-training The training dataset from the cancer genetics task in the BioNLP Shared Task 2013. Composed of anatomical and molecular entities.10.9 KNaCTeMYue Wang2023-11-28Released
bionlp-st-epi-2011-training The 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. 7.59 KGENIAYue Wang2023-11-29Released
bionlp-st-ge-2016-coref Coreference 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 Kim2023-11-28Released
Name T# Ann.AuthorMaintainerUpdated_atStatus

41-60 / 556 show all
bionlp-ost-19-BB-norm-ner-test 125ldeleger2023-11-27Developing
bionlp-ost-19-BB-norm-ner-train 2.45 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-norm-test 2.05 Kldeleger2023-11-28Developing
bionlp-ost-19-BB-norm-train 2.45 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-rel-dev 1.97 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-rel-ner-dev 1.98 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-rel-ner-test 125ldeleger2023-11-24Developing
bionlp-ost-19-BB-rel-ner-train 3.62 Kldeleger2023-11-28Developing
bionlp-ost-19-BB-rel-test 2.07 Kldeleger2023-11-29Developing
bionlp-ost-19-BB-rel-train 3.52 Kldeleger2023-11-28Developing
bionlp-ost-19-SeeDev-bin-dev 2.58 Kldeleger2023-11-28Developing
bionlp-ost-19-SeeDev-bin-test 2.32 Kldeleger2023-11-28Developing
bionlp-ost-19-SeeDev-bin-train 5.08 Kldeleger2023-11-29Developing
bionlp-st-2016-SeeDev-dev 61EstelleChaix2023-11-29Released
bionlp-st-2016-SeeDev-test 184EstelleChaix2023-11-29Released
bionlp-st-2016-SeeDev-training 35EstelleChaix2023-11-28Released
bionlp-st-bb3-2016-training 1.28 KINRAYue Wang2023-11-29Released
bionlp-st-cg-2013-training 10.9 KNaCTeMYue Wang2023-11-28Released
bionlp-st-epi-2011-training 7.59 KGENIAYue Wang2023-11-29Released
bionlp-st-ge-2016-coref 853DBCLSJin-Dong Kim2023-11-28Released