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LitCovid
Description

Thanks to NCBI, a comprehensive literature resource on the subject of Covid-19 is being collected:
https://www.ncbi.nlm.nih.gov/research/coronavirus/

This collection is to collect annotations to the documents of the dataset. If you want to contribute,

  1. take the documents in the LitCovid-docs project
  2. produce annotation to the texts based on your resource, and
  3. 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.


Maintainer Jin-Dong Kim
Projects
Name TDescription# Ann.MaintainerUpdated_atRDFized_atStatus

1-7 / 7
LitCovid-ArguminSciDiscourse elements for the documents in the LitCovid dataset. Annotations were automatically predicted by the ArguminSci tool (https://github.com/anlausch/ArguminSci)4.9 Kzebet2020-03-25-Released
LitCovid-CellosaurusAnnotation for cell lines for the documents in the LitCovid dataset. Annotations were automatically predicted by the Cellosaurus annotator (http://pubannotation.org/annotators/Cellosaurus_v33), based on Cellosaurus (https://web.expasy.org/cgi-bin/cellosaurus/search).2.55 Kzebet2020-04-01-Developing
LitCovid-docsThanks to NCBI, a comprehensive literature resource on the subject of Covid-19 is being collected: 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 4 entries of the following PMIDs could not be included because they were not available from PubMed:32161394, 32104909, 32090470, 32076224. 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 Kim2020-03-18-Released
LitCovid-OGERUsing OGER (http://www.ontogene.org/resources/oger) to detect entities from 10 different vocabularies9.31 KNico Colic2020-04-02-Released
LitCovid-OGER-BioBertUsing OGER (http://www.ontogene.org/resources/oger) in conjunction with BioBert as described here (https://arxiv.org/pdf/2003.07424.pdf)4.03 KNico Colic2020-04-02-Released
LitCovid-PAS-EnjuPredicate-argument structure annotation produced by the Enju parser.125 KJin-Dong Kim2020-03-25-Beta
LitCovid-PubTatorCentralNamed-entities for the documents in the LitCovid dataset. Annotations were automatically predicted by the PubTatorCentral tool (https://www.ncbi.nlm.nih.gov/research/pubtator/)4.64 Kzebet2020-04-01-Released