#' Short-term Mortality Fluctuations (STMF) data series, restricted to 6 countries (Belgium, France, Italy, Netherlands, Spain, Germany).
#'
#' Weekly death counts provide the most objective and comparable way of assessing the scale of short-term mortality
#' elevations across countries (32 countries) and time. Extraction date: 09/21/2020.
#'
#' @format A data frame with 88146 rows and 19 variables:
#' \describe{
#'   \item{CountryCode}{Mortality database country code}
#'   \item{Year}{Year}
#'   \item{Week}{Week number}
#'   \item{Sex}{Gender ('m': male, 'f': female, 'b': both)}
#'   \item{D0_14}{Age range 0-14}
#'   \item{D15_64}{Age range 15-64}
#'   \item{D65_74}{Age range 65-74}
#'   \item{D75_84}{Age range 75-84}
#'   \item{D85p}{Age range 85-+}
#'   \item{DTotal}{Count of deaths for all ages combined}
#'   \item{R0_14}{Crude death rate for age range 0-14}
#'   \item{R15_64}{Crude death rate for age range 15-64}
#'   \item{R65_74}{Crude death rate for age range 65-74}
#'   \item{R75_84}{Crude death rate for age range 75-84}
#'   \item{R85p}{Crude death rate for age range 85-+}
#'   \item{RTotal}{Crude death rate for all ages combined}
#'   \item{Split}{Indicates if data were split from aggregated age groups (0 if the original data has necessary detailed age scale).
#'   For example, if the original age scale was 0-4, 5-29, 30-65, 65+, then split will be equal to 1}
#'   \item{SplitSex}{Indicates if the original data are available by sex (0) or data are interpolated (1)}
#'   \item{Forecast}{Equals 1 for all years where forecasted population exposures were used to calculate weekly death rates}
#' }
#' @source \url{https://www.mortality.org}
"stmf_small"