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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 381 show all
craft-sa-dev Development 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.490 KUniversity of Colorado Anschutz Medical Campuscraft-st2020-09-21Released
craft-ca-core-ex-dev Development 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.90.2 KUniversity of Colorado Anschutz Medical Campuscraft-st2020-09-21Released
craft-ca-core-dev Development data for CRAFT CA shared task, core concepts only. 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" set. See the task description for details, but this set contains only annotations to concepts that appear in the original 10 Open Biomedical Ontologies used for annotation. (That is to say, it does not contain any annotations to extension classes).59.8 KUniversity of Colorado Anschutz Medical Campuscraft-st2020-09-21Released
bionlp-st-ge-2016-test-proteins Protein 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 Kim2020-09-18Released
bionlp-st-ge-2016-spacy-parsed Dependency 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. 225 KNico ColicNico Colic2020-09-18Released
bionlp-st-ge-2016-test-tees NER 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 Colic2020-09-18Released
bionlp-st-ge-2016-test It 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 Kim2020-09-18Released
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 Kim2020-09-18Released
bionlp-st-id-2011-training The 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 Wang2020-09-17Released
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) 61EstelleChaix2020-09-15Released
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) 184EstelleChaix2020-09-15Released
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) 35EstelleChaix2020-09-15Released
CellFinder CellFinder corpus4.75 KMariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf LeserMariana Neves2020-09-15Released
bionlp-st-ge-2016-reference-tees NER 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 Colic2020-09-13Released
bionlp-st-ge-2016-reference It 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 Kim2020-09-13Released
SMAFIRA_Feedback_Research_Goal 15zebet2020-09-09Released
LitCovid-OGER-BB Using OGER (www.ontogene.com) and Biobert to obtain annotations for 10 different vocabularies.308 KFabio RinaldiNico Colic2020-06-04Released
LitCovid-PD-HP PubDictionaries annotation for human phenotype terms - updated at 2020-04-20 Disease term annotation based on HP. Version 2020-04-20. The terms in HP are loaded in PubDictionaries, 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.3.03 KJin-Dong Kim2020-05-25Released
CORD-19-PD-HP PubDictionaries annotation for HP terms - updated at 2020-04-30 Disease term annotation based on HP. Version 2020-04-20. The terms in HP are loaded in PubDictionaries, 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.1.15 MJin-Dong Kim2020-05-12Released
LitCovid-PD-MONDO PubDictionaries annotation for disease terms - updated at 2020-04-20 It is based on MONDO Version 2020-04-20. The terms in MONDO are loaded in PubDictionaries, 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.13.4 KJin-Dong Kim2020-05-10Released
NameT# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 381 show all
craft-sa-dev 490 KUniversity of Colorado Anschutz Medical Campuscraft-st2020-09-21Released
craft-ca-core-ex-dev 90.2 KUniversity of Colorado Anschutz Medical Campuscraft-st2020-09-21Released
craft-ca-core-dev 59.8 KUniversity of Colorado Anschutz Medical Campuscraft-st2020-09-21Released
bionlp-st-ge-2016-test-proteins 4.34 KDBCLSJin-Dong Kim2020-09-18Released
bionlp-st-ge-2016-spacy-parsed 225 KNico ColicNico Colic2020-09-18Released
bionlp-st-ge-2016-test-tees 9.17 KNico ColicNico Colic2020-09-18Released
bionlp-st-ge-2016-test 7.99 KDBCLSJin-Dong Kim2020-09-18Released
bionlp-st-ge-2016-coref 853DBCLSJin-Dong Kim2020-09-18Released
bionlp-st-id-2011-training 5.61 KUniversity of Tokyo Tsujii Laboratory, NaCTeM and Biocomplexity Institute of Virginia TechYue Wang2020-09-17Released
bionlp-st-2016-SeeDev-dev 61EstelleChaix2020-09-15Released
bionlp-st-2016-SeeDev-test 184EstelleChaix2020-09-15Released
bionlp-st-2016-SeeDev-training 35EstelleChaix2020-09-15Released
CellFinder 4.75 KMariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf LeserMariana Neves2020-09-15Released
bionlp-st-ge-2016-reference-tees 14.6 KNico Colic Nico Colic2020-09-13Released
bionlp-st-ge-2016-reference 14.4 KDBCLSJin-Dong Kim2020-09-13Released
SMAFIRA_Feedback_Research_Goal 15zebet2020-09-09Released
LitCovid-OGER-BB 308 KFabio RinaldiNico Colic2020-06-04Released
LitCovid-PD-HP 3.03 KJin-Dong Kim2020-05-25Released
CORD-19-PD-HP 1.15 MJin-Dong Kim2020-05-12Released
LitCovid-PD-MONDO 13.4 KJin-Dong Kim2020-05-10Released