We present the DAE Platform in the specic context of reproducible research. DAE was developed at Lehigh University targeted at the Document Image Analysis research community for distributing document images and associated document analysis algorithms, as well as an unlimited range of annotations and ground truth for benchmark-ing and evaluation of new contributions to the state-of-the-art. DAE was conceived from the beginning with the idea of reproducibility and data provenance in mind. In this paper we more specically analyze how this approach answers a number of challenges raised by the need of providing fully reproducible experimental research. Furthermore, since DAE has been up and running without interruption since 2010, we are in a position of providing a qualitative analysis of the technological choices made at the time, and suggest some new perspectives in light of more recent technologies and practices.