> top > users > Jin-Dong Kim
Jin-Dong Kim
User info
Collections
Name DescriptionUpdated at
1-10 / 12 show all
bionlp-st-ge-2016The 2016 edition of the Genia event extraction (GE) task organized within BioNLP-ST 20162019-03-11
CORD-19CORD-19 (COVID-19 Open Research Dataset) is a free, open resource for the global research community provided by the Allen Institute for AI: https://pages.semanticscholar.org/coronavirus-research. As of 2020-03-20, it contains over 29,000 full text articles. This CORD-19 collection at PubAnnotation is prepared for the purpose of collecting annotations to the texts, so that they can be easily accessed and utilized. If you want to contribute with your annotation, take the documents in the CORD-19_All_docs project, produce your annotation to the texts using your annotation system, and contribute the annotation back to PubAnnotation (HowTo). 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. Once you have uploaded your annotation, please notify it to admin@pubannotation.org admin@pubannotation.org, so that it can be included in this collection, which will make your annotation much easily findable. Note that as the CORD-19 dataset grows, the documents in this collection also will be updated. IMPORTANT: CORD-19 License agreement requires that the dataset must be used for text and data mining only.2020-04-14
CORD-19-sample-annotation2020-04-21
GlycoBiologyAnnotations made to the titles and abstracts of the journal 'GlycoBiology'2019-03-10
Glycosmos6This collection contains annotation projects which target all the PubMed abstracts (at the time of January 14, 2022) from the 6 glycobiology-related journals: Glycobiology Glycoconjugate journal The Journal of biological chemistry Journal of proteome research Journal of proteomics Carbohydrate research 2023-11-16
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
LitCoin2021-12-14
LitCoin-Test2021-12-23
LitCovid2021-10-18
LitCovid-sampleVarious annotations to a sample set of LitCovid, to demonstrate potential of harmonized various annotations.2021-01-14
Projects
NameTDescription# Ann.Updated atStatus
151-159 / 159 show all
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
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
pmc-enju-pasPredicate-argument structure annotation produced by Enju. This data set is initially produced as a supporting resource for BioNLP-ST 2016 GE task. As so, it currently includes the 34 full paper articles that are in the benchmark data sets of GE 2016 task, reference data set (bionlp-st-ge-2016-reference) and test data set (bionlp-st-ge-2016-test), but will be extended to include more papers from the PubMed Central Open Access subset (PMCOA). 205 K2023-11-28Developing
LitCovid-sample-PD-UBERONPubDictionaries annotation for UBERON terms - updated at 2020-04-30 It is annotation for anatomical entities 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. 3102023-11-28Beta
LitCovid-v1-docsA 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 02023-11-29Released
GlyCosmos6-CLOAutomatic annotation by PC-CLO.1.18 M2023-11-24Developing
LitCovid-sample-docsA 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 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 02023-11-29Uploading
GlyCosmos6-Glycan-Motif-Image87.8 K2023-11-24Developing
mondo_diseaseannotation for diseases and disorders as defined in MONDO. Automatic annotation by PD-MONDO.256 K2023-11-28Developing
Automatic annotators
NameDescription
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.
Editors
NameDescription
1-2 / 2
TextAE-oldTextAE version 4, which was the latest stable version until Apr. 19, 2020.
TextAETextAE version 5, which enables edition of attributes of denotations.