  We investigate a prototype application for machine-readable literature. The
program is called "pyDataRecognition" and serves as an example of a data-driven
literature search, where the literature search query is an experimental
data-set provided by the user. The user uploads a powder pattern together with
the radiation wavelength. The program compares the user data to a database of
existing powder patterns associated with published papers and produces a rank
ordered according to their similarity score. The program returns the digital
object identifier (doi) and full reference of top ranked papers together with a
stack plot of the user data alongside the top five database entries. The paper
describes the approach and explores successes and challenges.
