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NameTDescription# Ann.AuthorMaintainer Updated_atStatus

261-280 / 556 show all
PubMed-2017 abstracts published in 2017.0Jin-Dong Kim2023-11-24Developing
speech-test 6Jin-Dong Kim2023-11-26Testing
CORD-19-SciBite-sentences 11.2 KJin-Dong Kim2023-11-26Testing
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 (FMA and 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 KJin-Dong Kim2023-11-27Released
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 Kim2023-11-27Released
GlyCosmos600-GlycoProteins GlycoProtein annotations were made using the glycoprotein-name dictionary on PubDictionaries: http://pubannotation.org/projects/GlyCosmos600-docs The documents were imported from the GlyCosmos600-docs project: http://pubannotation.org/projects/GlyCosmos600-docs3.68 KJin-Dong Kim2023-11-27Testing
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 Kim2023-11-28Released
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 Kim2023-11-28Released
bionlp-st-ge-2016-uniprot UniProt 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 KDBCLSJin-Dong Kim2023-11-29Beta
LitCovid-docs Updated 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 18Jin-Dong Kim2023-11-28Testing
pmc-enju-pas Predicate-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 KDBCLSJin-Dong Kim2023-11-28Developing
LitCovid-sample-PD-UBERON PubDictionaries 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. 310Jin-Dong Kim2023-11-28Beta
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 Kim2023-11-29Released
GlyCosmos6-CLO Automatic annotation by PC-CLO.1.18 MJin-Dong Kim2023-11-24Developing
LitCovid-sample-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 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 Kim2023-11-29Uploading
GlyCosmos6-Glycan-Motif-Image 87.8 KJin-Dong Kim2023-11-24Developing
mondo_disease annotation for diseases and disorders as defined in MONDO. Automatic annotation by PD-MONDO.256 KJin-Dong Kim2023-11-28Developing
JF-test2 0johanf2020-03-26Testing
JF-test A test corpus for exploring this service9Johan Fridjohanf2023-12-03Testing
causal0001 test0ju-hyuck hanJu-Hyuck Han2024-01-17
NameT# Ann.AuthorMaintainer Updated_atStatus

261-280 / 556 show all
PubMed-2017 0Jin-Dong Kim2023-11-24Developing
speech-test 6Jin-Dong Kim2023-11-26Testing
CORD-19-SciBite-sentences 11.2 KJin-Dong Kim2023-11-26Testing
LitCovid-PD-FMA-UBERON-v1 4.3 KJin-Dong Kim2023-11-27Released
bionlp-st-ge-2016-test-proteins 4.34 KDBCLSJin-Dong Kim2023-11-27Released
GlyCosmos600-GlycoProteins 3.68 KJin-Dong Kim2023-11-27Testing
bionlp-st-ge-2016-coref 853DBCLSJin-Dong Kim2023-11-28Released
pubmed-sentences-benchmark 18.4 KGENIA projectJin-Dong Kim2023-11-28Released
bionlp-st-ge-2016-uniprot 16.2 KDBCLSJin-Dong Kim2023-11-29Beta
LitCovid-docs 18Jin-Dong Kim2023-11-28Testing
pmc-enju-pas 205 KDBCLSJin-Dong Kim2023-11-28Developing
LitCovid-sample-PD-UBERON 310Jin-Dong Kim2023-11-28Beta
LitCovid-v1-docs 0Jin-Dong Kim2023-11-29Released
GlyCosmos6-CLO 1.18 MJin-Dong Kim2023-11-24Developing
LitCovid-sample-docs 0Jin-Dong Kim2023-11-29Uploading
GlyCosmos6-Glycan-Motif-Image 87.8 KJin-Dong Kim2023-11-24Developing
mondo_disease 256 KJin-Dong Kim2023-11-28Developing
JF-test2 0johanf2020-03-26Testing
JF-test 9Johan Fridjohanf2023-12-03Testing
causal0001 0ju-hyuck hanJu-Hyuck Han2024-01-17