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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 321 show all
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 Kim2021-07-28Released
2015-BEL-Sample-2 The 295 BEL statements for sample set used for the 2015 BioCreative challenge.11.4 KFabio RinaldiNico Colic2021-03-11Released
NCBIDiseaseCorpus The 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.6.85 KRezarta Islamaj Doğan,Robert Leaman,Zhiyong LuChih-Hsuan Wei2021-03-10Released
GENIAcorpus multi_cell (1,782) mono_cell (222) virus (2,136) protein_family_or_group (8,002) protein_complex (2,394) protein_molecule (21,290) protein_subunit (942) protein_substructure (129) protein_domain_or_region (1,044) protein_other (97) peptide (521) amino_acid_monomer (784) DNA_family_or_group (332) DNA_molecule (664) DNA_substructure (2) DNA_domain_or_region (39) DNA_other (16) RNA_family_or_group (1,545) RNA_molecule (554) RNA_substructure (106) RNA_domain_or_region (8,237) RNA_other (48) polynucleotide (259) nucleotide (243) lipid (2,375) carbohydrate (99) other_organic_compound (4,113) body_part (461) tissue (706) cell_type (7,473) cell_component (679) cell_line (4,129) other_artificial_source (211) inorganic (258) atom (342) other (21,056) 78.9 KGENIA ProjectYue Wang2021-03-10Released
pubmed-sentences-benchmark A 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. 18.4 KGENIA projectJin-Dong Kim2021-03-10Released
jnlpba-st-training The 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 1990~1999.51.1 KGENIAYue Wang2021-03-10Released
PubMed_Structured_Abstracts Sections (zones) as retrieved from PubMed.131 Kzebet2021-03-10Released
PennBioIE The PennBioIE corpus (0.9) covers two domains of biomedical knowledge. One is the inhibition of the cytochrome P450 family of enzymes (CYP450 or CYP for short) , and the other domain is the molecular genetics of dance (oncology or onco for short).23.8 KUPenn Biomedical Information Extraction ProjectYue Wang2021-03-10Released
AnEM_abstracts 250 documents selected randomly from citation abstracts 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_full-texts, it is probably the largest manually annotated corpus on anatomical entities.1.91 KNaCTeMYue Wang2021-03-10Released
BioLarkPubmedHPO 228 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", 2015.7.16 KTudor Grozasimon2021-03-10Released
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 Wang2021-03-10Released
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 Wang2021-03-10Released
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 Wang2021-03-10Released
c_corpus Documents included in the c_corpus: https://github.com/SMAFIRA/c_corpus/blob/master/SMAFIRAc_0.4_Annotations.csv107 K2021-01-27Released
LitCovid-v1-docs A comprehensive literature resource on the subject of Covid-19 is collected by NCBI: https://www.ncbi.nlm.nih.gov/research/coronavirus/ The LitCovid project@PubAnnotation is a collection of the titles and abstracts of the LitCovid dataset, for the people who want to perform text mining analysis. Please note that if you produce some annotation to the documents in this project, and contribute the annotation back to PubAnnotation, it will become publicly available together with contribution from other people. If you want to contribute your annotation to PubAnnotation, please refer to the documentation page: http://www.pubannotation.org/docs/submit-annotation/ The list of the PMID is sourced from here The 6 entries of the following PMIDs could not be included because they were not available from PubMed:32161394, 32104909, 32090470, 32076224, 32161394 32188956, 32238946. Below is a notice from the original LitCovid dataset: PUBLIC DOMAIN NOTICE National Center for Biotechnology Information This software/database is a "United States Government Work" under the terms of the United States Copyright Act. It was written as part of the author's official duties as a United States Government employee and thus cannot be copyrighted. This software/database is freely available to the public for use. The National Library of Medicine and the U.S. Government have not placed any restriction on its use or reproduction. Although all reasonable efforts have been taken to ensure the accuracy and reliability of the software and data, the NLM and the U.S. Government do not and cannot warrant the performance or results that may be obtained by using this software or data. The NLM and the U.S. Government disclaim all warranties, express or implied, including warranties of performance, merchantability or fitness for any particular purpose. Please cite the authors in any work or product based on this material : Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 0Jin-Dong Kim2020-12-22Released
RELISH-DB Abstracts contained in the data of the RELISH-DB (https://relishdb.ict.griffith.edu.au) made available for download here. Data was downloaded from here: https://figshare.com/projects/RELISH-DB/60095 Related publication: https://academic.oup.com/database/article/doi/10.1093/database/baz085/5608006#20072202302020-12-03Released
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-st2020-10-02Released
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-st2020-10-02Released
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 Kim2020-10-02Released
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 Kim2020-10-02Released
NameT# Ann.AuthorMaintainerUpdated_atStatus

1-20 / 321 show all
bionlp-st-ge-2016-reference 14.4 KDBCLSJin-Dong Kim2021-07-28Released
2015-BEL-Sample-2 11.4 KFabio RinaldiNico Colic2021-03-11Released
NCBIDiseaseCorpus 6.85 KRezarta Islamaj Doğan,Robert Leaman,Zhiyong LuChih-Hsuan Wei2021-03-10Released
GENIAcorpus 78.9 KGENIA ProjectYue Wang2021-03-10Released
pubmed-sentences-benchmark 18.4 KGENIA projectJin-Dong Kim2021-03-10Released
jnlpba-st-training 51.1 KGENIAYue Wang2021-03-10Released
PubMed_Structured_Abstracts 131 Kzebet2021-03-10Released
PennBioIE 23.8 KUPenn Biomedical Information Extraction ProjectYue Wang2021-03-10Released
AnEM_abstracts 1.91 KNaCTeMYue Wang2021-03-10Released
BioLarkPubmedHPO 7.16 KTudor Grozasimon2021-03-10Released
bionlp-st-bb3-2016-training 1.28 KINRAYue Wang2021-03-10Released
bionlp-st-cg-2013-training 10.9 KNaCTeMYue Wang2021-03-10Released
bionlp-st-epi-2011-training 7.59 KGENIAYue Wang2021-03-10Released
c_corpus 107 K2021-01-27Released
LitCovid-v1-docs 0Jin-Dong Kim2020-12-22Released
RELISH-DB 02020-12-03Released
craft-ca-core-ex-dev 90.2 KUniversity of Colorado Anschutz Medical Campuscraft-st2020-10-02Released
craft-ca-core-dev 59.8 KUniversity of Colorado Anschutz Medical Campuscraft-st2020-10-02Released
bionlp-st-ge-2016-test 7.99 KDBCLSJin-Dong Kim2020-10-02Released
bionlp-st-ge-2016-test-proteins 4.34 KDBCLSJin-Dong Kim2020-10-02Released