|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 K||University of Colorado Anschutz Medical Campus||craft-st||2020-10-02||Released|
|LitCovid-PD-FMA-UBERON-v1 ||PubDictionaries annotation for anatomy terms - updated at 2020-04-20
Disease term annotation based on FMA and Uberon. Version 2020-04-20.
The terms in FMA and Uberon are loaded in PubDictionaries
Uberon), with which the annotations in this project are produced.
The parameter configuration used for this project is
here for FMA and
there for Uberon.
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.||4.3 K||Jin-Dong Kim||2020-11-20||Released|
|LitCovid-PD-HP-v1 ||PubDictionaries annotation for human phenotype terms - updated at 2020-04-20
Disease term annotation based on HP.
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 K||Jin-Dong Kim||2020-11-20||Released|
|LitCovid-PD-MONDO-v1 ||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 K||Jin-Dong Kim||2020-11-20||Released|
|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#200722023||0||2020-12-03||Released|
|LitCovid-v1-docs ||A comprehensive literature resource on the subject of Covid-19 is collected by NCBI:
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:
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,
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
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
|LitCovid-sentences-v1 ||Sentence segmentation of all the texts in the LitCovid literature. The segmentation is automatically obtained using the TextSentencer annotation service developed and maintained by DBCLS.||16.5 K||Jin-Dong Kim||2021-01-17||Released|
|c_corpus ||Documents included in the c_corpus: https://github.com/SMAFIRA/c_corpus/blob/master/SMAFIRAc_0.4_Annotations.csv||107 K||2021-01-27||Released|
|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 K||GENIA||Yue Wang||2021-03-10||Released|
|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 K||NaCTeM||Yue Wang||2021-03-10||Released|
|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 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 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 entities are geographical and organization places denoted by official names.||1.28 K||INRA||Yue Wang||2021-03-10||Released|
|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 K||Tudor Groza||simon||2021-03-10||Released|
|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 K||NaCTeM||Yue Wang||2021-03-10||Released|
|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 K||UPenn Biomedical Information Extraction Project||Yue Wang||2021-03-10||Released|
|PubMed_Structured_Abstracts ||Sections (zones) as retrieved from PubMed.||131 K||zebet||2021-03-10||Released|
|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 K||GENIA project||Jin-Dong Kim||2021-03-10||Released|
|GENIAcorpus || multi_cell (1,782)
||78.9 K||GENIA Project||Yue Wang||2021-03-10||Released|
|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 K||GENIA||Yue Wang||2021-03-10||Released|
|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 K||Rezarta Islamaj Doğan,Robert Leaman,Zhiyong Lu||Chih-Hsuan Wei||2021-03-10||Released|
|PubMed_ArguminSci ||Predictions for PubMed automatically extracted with the ArguminSci tool (https://github.com/anlausch/ArguminSci).||777 K||zebet||2021-03-10||Released|