Accessible to us: platform-agnostic in GitHub repositories (public or private) with basic instructions for data access in README

Accessible to the world: same

• (Meta)data are retrievable by their identifier using a standardised communication protocol

  • The protocol is open, free and universally implementable

  • The protocol allows for an authentication and authorisation where necessary

• Metadata should be accessible even when the data are no longer available

Reusable for us: we can reproduce previous results with documented methods, and use the same data in new analyses

Reusable for the world: has a license for use, includes provenance, meets community standards

• (Meta)data are richly described with a plurality of accurate and relevant attributes

  • (Meta)data are released with a clear and accessible data usage license

  • (Meta)data are associated with detailed provenance

  • (Meta)data meet domain-relevant community standards

Activities:

• Difference between FAIR data and Open Data

• How FAIR is our data? Assessment tool

• Writing good dataset descriptions best practices

• Identifiers like DOIs and licensing; general and US Government

• Dirty data and cleanup--next slide

• Metadata and controlled vocabularies--the slides after

Data documentation enforced in R packages

• distribute data along with documentation
• R data package examples
  • babynames
  • fueleconomy
  • our own ecodata and its landing page

#' Vehicle data
#'
#' Fuel economy data from the EPA, 1985-2015. This dataset contains
#' selected varaibles, and removes vehicles with incomplete data (e.g.
#' no drive train data)
#'
#' @format A data frame with variables:
#' \describe{
#' \item{id}{Unique EPA identifier}
#' \item{make}{Manufacturer}
#' \item{model}{Model name}
#' \item{year}{Model year}
#' \item{class}{EPA vehicle size class,
#'  \url{http://www.fueleconomy.gov/feg/ws/wsData.shtml#VClass}}
#' \item{trans}{Transmission}
#' \item{drive}{Drive train}
#' \item{cyl}{Number of cylinders}
#' \item{displ}{Engine displacement, in litres}
#' \item{fuel}{Fuel type}
#' \item{hwy}{Highway fuel economy, in mpg}
#' \item{cty}{City fuel economy, in mpg}
#' }
#'
#' @source \url{http://www.fueleconomy.gov/feg/download.shtml}
#' @examples
#' if (require("dplyr")) {
#' vehicles
#' vehicles %>% group_by(year) %>% summarise(cty = mean(cty))
#' }
#'
"vehicles"

https://xkcd.com/927/