cord19: COVID-19 Open Research Dataset

#' Metadata for papers in the CORD-19 dataset
#'
#' Metadata such as titles, authors, journal, and publication IDs for each
#' paper in the CORD-19 dataset. This comes from the
#' \code{all_sources_metadata_DATE.csv} file in the decompressed dataset.
#' Note that the papers have been deduplicated based on paper_id, doi, or
#' title, and papers without a paper_id or title have been removed.
#'
#' @format A tibble with one observation for each paper, and the following columns:
#' \describe{
#'   \item{paper_id}{Unique identifier that can link to full text and citations.
#'   SHA of the paper PDF.}
#'   \item{source}{Source (e.g. pubmed, CZI...)}
#'   \item{title}{Title}
#'   \item{doi}{Digital Object Identifier}
#'   \item{pmcid}{pmcid}
#'   \item{pubmed_id}{PubMed ID}
#'   \item{license}{License}
#'   \item{abstract}{Abstract}
#'   \item{publish_time}{Publication year}
#'   \item{authors}{Authors}
#'   \item{journal}{Journal}
#'   \item{microsoft_academic_paper_id}{Microsoft Academic Paper ID}
#'   \item{who}{CovidenceWHO}
#'   \item{has_full_text}{Does it have full text}
#' }
#'
#' @examples
#'
#' library(dplyr)
#'
#' # What are the most common journals?
#' cord19_papers %>%
#'   count(journal, sort = TRUE)
#'
#' # What are the most common words in titles (or abstracts)?
#' library(tidytext)
#'
#' cord19_papers %>%
#'   unnest_tokens(word, title) %>%
#'   count(word, sort = TRUE) %>%
#'   anti_join(stop_words, by = "word")
#'
#' @source \url{https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge},
#' specifically the \code{all_sources_metadata_DATE.csv} file.
"cord19_papers"