#' 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"
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