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LitCovid Home
Description

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:

  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 this collection:
    1. create your own project at PubAnnotaiton,
    2. upload your annotation to the project (HowTo), and
    3. 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.


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

1-11 / 11
LitCovid-ArguminSci Discourse 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-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 Kim2020-04-08-Released
LitCovid-OGER-BB Using OGER (www.ontogene.com) and Biobert to obtain annotations for 10 different vocabularies.223 KFabio RinaldiNico Colic2020-06-04-Released
LitCovid-PAS-Enju Predicate-argument structure annotation produced by the Enju parser.125 KJin-Dong Kim2020-03-25-Beta
LitCovid-PD-FMA-UBERON 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.4.29 KJin-Dong Kim2020-05-10-Released
LitCovid-PD-HP PubDictionaries annotation for human phenotype terms - updated at 2020-04-20 Disease term annotation based on HP. Version 2020-04-20. 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.3.02 KJin-Dong Kim2020-05-25-Released
LitCovid-PD-MONDO 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 KJin-Dong Kim2020-05-10-Released
LitCovid-PMC-OGER-BB Annotating PMC articles with OGER and BioBert, according to an hand-crafted Covid-specific dictionary and the 10 different CRAFT ontologies (http://bionlp-corpora.sourceforge.net/CRAFT/): Chemical Entities of Biological Interest (CHEBI), Cell Ontology (CL), Entrez Gene (UBERON), Gene Ontology (biological process (GO-BP), cellular component (GO-CC), and molecular function (GO-MF), NCBI Taxonomy (NCBITaxon), Protein Ontology (PR), Sequence Ontology (SO)18.1 KFabio RinaldiNico Colic2020-04-15-Developing
LitCovid-PubTator 22.6 KJin-Dong Kim2020-04-21-Released
LitCovid-PubTatorCentral Named-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
LitCovid-sentences 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 KJin-Dong Kim2020-04-14-Released