The Translational Section of DCE explores downstream examples of data-centric engineering, the underlying data science principles, techniques and technologies at work in these cases and the lessons that can be learned from their implementations. We aim to promote a model of publishing that appeals to academics, practitioners and industry professionals, catalyzes engagement with end-user organizations, communicates the impact of these examples to a broader audience and gives due recognition to those involved.

Authors from academia, industry, and practitioners are welcome to contribute to the Translational Section of DCE. The defining factor of a translational article is the emphasis on knowledge transfer rather than the affiliation of the author. These articles should be concise (around 5,000 words), with a focus on the environment or setting in which data science has been applied, the challenges encountered during deployment, and the lessons learned. The methods used to enable knowledge transfer should also be discussed.

We encourage submissions from interested authors. See our Call for Translational Contributions and our Guidelines for Authors. A template for Translational Articles can be found here:

• DCE Translational Paper template files

Editor: Eiman Kanjo (Nottingham Trent University)