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Jin-Dong Kim
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NameDescriptionUpdated at
1-10 / 12 show all
GlycoBiologyAnnotations made to the titles and abstracts of the journal 'GlycoBiology'2019-03-10
PreeclampsiaPreeclampsia-related annotations for text mining2019-03-10
bionlp-st-ge-2016The 2016 edition of the Genia event extraction (GE) task organized within BioNLP-ST 20162019-03-11
GlyCosmos600A random collection of 600 PubMed abstracts from 6 glycobiology-related journals: Glycobiology, Glycoconjugate journal, The Journal of biological chemistry, Journal of proteome research, Journal of proteomics, and Carbohydrate research. The whole PMIDs were collected on June 11, 2019. From each journal, 100 PMIDs were randomly sampled.2021-10-22
LitCovid-v1This collection includes the result from the Covid-19 Virtual Hackathon. LitCovid is a comprehensive literature resource on the subject of Covid-19 collected by NCBI: https://www.ncbi.nlm.nih.gov/research/coronavirus/ Since the literature dataset was released, several groups are producing annotations to the dataset. To facilitate a venue for aggregating the valuable resources which are highly relevant to each other, and should be much more useful when they can be accessed together, this PubAnnotation collection is set up. It is a part of the Covid19-PubAnnotation project. In this collection, the LitCovid-docs project contains all the documents contained in the LitCovid literature collection, and the other projects are annotation datasets contributed by various groups. It is an open collection, which means anyone who wants to contribute can do so, in the following way: take the documents in the, LitCovid-docs project produce annotation to the texts based on your resource, and contribute the annotation back to this collection: create your own project at PubAnnotaiton, upload your annotation to the project (HowTo), and add the project to this collection. All the contributed annotations will become publicly available. Please note that, during uploading your annotation data, you do not need to be worried about slight changes in the text: PubAnnotation will automatically catch them and adjust the positions appropriately. Should you have any question, please feel free to mail to admin@pubannotation.org. 2020-11-20
LitCovid-sampleVarious annotations to a sample set of LitCovid, to demonstrate potential of harmonized various annotations.2021-01-14
NameTDescription # Ann.Updated atStatus
141-150 / 159 show all
CORD-19-PD-UBERONPubDictionaries annotation for UBERON terms - updated at 2020-04-30 It is disease term annotation based on Uberon. The terms in Uberon are uploaded in PubDictionaries (Uberon), 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.1.42 M2023-11-24Released
LitCovid-PAS-EnjuPredicate-argument structure annotation produced by the Enju parser.125 K2023-11-28Beta
uniprot-mouseProtein annotation based on UniProt11.5 K2023-11-28Developing
LitCovid-docsUpdated at 2021-01-12 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 182023-11-28Testing
CORD-19_bioRxiv_medRxiv_subsetThe bioRxiv/medRxiv subset of the CORD-19 dataset: pre-prints that are not peer reviewed. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT. 02023-11-29Released
CORD-19_Commercial_use_subsetThe Commercial use subset of the CORD-19 dataset. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT.02023-11-29Released
CORD-19_Custom_license_subsetThe Custom license subset of the CORD-19 dataset. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT.5.08 M2023-11-24Released
CORD-19_Non-commercial_use_subsetThe Non commercial use subset of the CORD-19 dataset. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT.02023-11-29Released
bionlp-st-ge-2016-uniprotUniProt protein annotation to the benchmark data set of BioNLP-ST 2016 GE task: reference data set (bionlp-st-ge-2016-reference) and test data set (bionlp-st-ge-2016-test). The annotations are produced based on a dictionary which is semi-automatically compiled for the 34 full paper articles included in the benchmark data set (20 in the reference data set + 14 in the test data set). For detailed information about BioNLP-ST GE 2016 task data sets, please refer to the benchmark reference data set (bionlp-st-ge-2016-reference) and benchmark test data set (bionlp-st-ge-2016-test). 16.2 K2023-11-29Beta
metamap-sampleSample annotation of MetaMep, produced by Aronson, et al. An overview of MetaMap: historical perspective and recent advances, JAMIA 201010.9 K2023-11-27Testing
Automatic annotators
1-10 / 38 show all
PubTator-ChemicalTo pull the pre-computed chemical annotation from PubTator.
PubTator-GeneTo pull the pre-computed gene annotation from PubTator.
PubTator-SpeciesTo pull the pre-computed Species annotation from PubTator.
TextSentencersentence segmentation
PubTator-DiseaseTo pull the pre-computed disease annotation from PubTator.
PubTator-MutationTo pull the pre-computed mutation annotation from PubTator.
discourse-simplifierA discourse analyzer developed by Univ. Manchester.
PD-NGLY1-deficiency-BA batch annotator for NGLY1 deficiency
PD-UBERON-AEIt annotates for anatomical entities, based on the UBERON-AE dictionary on PubDictionaries. Threshold is set to 0.85.
PD-MONDOPubDictionaries annotation with the MONDO dictionary.
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TextAE-oldTextAE version 4, which was the latest stable version until Apr. 19, 2020.
TextAETextAE version 5, which enables edition of attributes of denotations.