Digital libraries support researchers by providing public access to a vast collection of state-of-the-art literature. The considerable variety of statements, claims, observations and insights that form the narrations of these documents can be used as a valuable groundwork for further research. However, when confronted with these narrations, concerns regarding their reproducibility might arise. Tackling these concerns usually requires a careful analysis of the underlying data sets and a search for similar repositories that support the questioned claims. In short, it is necessary to find repositories whose data narrations match those of the publication. Unfortunately, data analysis and mining are far too often reduced to basic statistical analyses that usually fail to be helpful. In this paper, we propose a novel idea to use structured narratives as a template to discover supporting data narrations, hence reducing the problem of assessing the reproducibility of a publication to a simple matching task between a document and data set. To realize this idea, we outline a novel two-step matching strategy by describing the individual steps along the lines of a pharmaceutical use case. We thereby identify the main open research tasks and discuss problems that need to be solved to develop a full-fledged matching algorithm.