Overview

EpiBibR stands for “epidemiology-based bibliography for R.” The R package is under the MIT License and as such is a free resource based on the open science principles (reproducible research, open data, open code). The resource may be used by researchers, whose domain is scientometrics, but also by researchers from other disciplines. For instance, the scientific community in Artificial Intelligence and Data Science may use this package to accelerate new research insights about covid-19. The package follows the methodology put in place by the Allen Institute and its partners to create the CORD-19 dataset with some differences. The latter is accessible through downloads of sub-sets or through a REST API. The data provide important information such as authors, methods, data, and citations to make it easier for researchers to find relevant contributions to their research questions. Our package proposes 22 features for the 112,208 references (on January 22, 2021) and access to the data has been made as easy as possible in order to integrate efficiently in almost any researcher’s pipeline.

Through this package, a researcher can connect the data to her analysis based on the R language. With this workflow in mind, a researcher can save time on collecting data, and can use a very accessible language to perform complex analytical tasks, be it R or Python for instance. Indeed, it is usual that researchers use multiple languages (functional or not) to produce certain outputs. This workflow opens these data to analyses from the largest spectrum of potential options, enhancing multidisciplinary approaches applied to these data (biostatistics, bibliometrics, text mining, etc.).

The goal of this package in this emergency context is to limit the references to the medical domain, hence limiting ourselves to the Pubmed repository, but then to leverage the methodologies used across different disciplines. As we will address this point latter, a further extension could be to add references from other disciplines to not only benefit from the wealth of methodologies but also from their own theories and concepts. For instance, to assess the spread of the disease, the literature - and theories - from researchers in demography would certainly be relevant.

Functionality

The references were collected via PubMed, a free resource that is developed and maintained by the National Center for Biotechnology Information (NCBI), at the U.S. National Library of Medicine (NLM), located at the National Institutes of Health (NIH). PubMed includes over 30 million citations from biomedical literature.

More specifically, to collect our references, we adopted the procedure used by the Allen Institute for AI for their CORD-19 project. We apply a similar query on PubMed: “COVID-19” OR Coronavirus OR “Corona virus” OR “2019-nCoV” OR “SARS-CoV” OR “MERS-CoV” OR “Severe Acute Respiratory Syndrome” OR “Middle East Respiratory Syndrome” to build our own bibliographic data.

To navigate through our dataset, EpiBibR relies on a set of search arguments: author, author’s country of origin, keyword in the title, keyword in the abstract, year and the name of the journal. Each of them can truly help scientists and R users to filter references and find the relevant articles.

In an effort to simplify the workflow between our package and the research methodologies, the format of our dataframe has been designed to integrate with different data pipelines, notably to facilitate the use of the R package Bibliometrix with our data (Aria and Cuccurullo 2017).

Features

Table 1. Features accessible through the package.

Field Tags Descriptions Field Tags Descriptions
AU Authors ISSN Source Code
TI Document Title VOL Volume
AB Abstract ISSUE Issue Number
PY Year LT Language
DT Document Type C1 Author Address
MESH Medical Subject Headings Vocabulary RP Reprint Address
TC Times Cited ID PubMed ID
SO Publication Name (or Source) DE Authors’ Keywords
J9 Source Abbreviation UT Unique Article Identifier
JI ISO Source Abbreviation AU_CO Author’s Country of Origin
DI Digital Object Identifier (DOI) DB Bibliographic Database

Practical usage

Quick start

First, install EpiBibR:

devtools::install_github("warint/EpiBibR")

Next, call EpiBibR to make sure everything is installed correctly.

EpiBibR allows you to search bibligraphic references using several arguments : Author, author’s country of origin, keywords in the title, keywords in the abstract, year and source name. We have also created a new function that synthesizes our previous ones. You can follow the procedure through our Practical Guideand access to reproducible bibliometric material

Bibliometric Analysis

The bibliometrix package allows a thorough bibliometric analysis using R. Our EpiBibR data have been designed to integrate easily with the bibliometrix package. A shinyapp is also available biblioshiny(). This package has been used extensively for various exercises mobilizing massive amounts of data.

Cite ‘epiBibR’

Open access to the article explaining EpibibR
[here](https : //www.jmir.org/2020/8/e19615/)

Please cite as:

Warin T, “Global Research on Coronaviruses: An R Package”, J Med Internet Res 2020;22(8):e19615, DOI: 10.2196/19615, PMID: 32730218, PMCID: 7423387

@Article{info:doi/10.2196/19615,
author="Warin, Thierry",
title="Global Research on Coronaviruses: An R Package",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="11",
volume="22",
number="8",
pages="e19615",
issn="1438-8871",
doi="10.2196/19615",
url="http://www.jmir.org/2020/8/e19615/",
url="https://doi.org/10.2196/19615",
url="http://www.ncbi.nlm.nih.gov/pubmed/32730218"
}

Acknowledgements

The author would like to thank the Center for Interuniversity Research and Analysis of Organizations (CIRANO, Montreal) for its support, as well as Thibault Senegas, Marine Leroi and Martin Paquette. The usual caveats apply.