News organizations increasingly tailor their news offering to the reader through personalized recommendation algorithms. However, automated recommendation algorithms reflect a commercial logic based on calculated relevance to the user, rather than aiming at a well-informed citizenry. In this paper, we introduce the EventDNA corpus, a dataset of 1773 Dutch-language news articles annotated with information on entities, news events and IPTC Media Topic codes, with the ultimate goal to outline a recommendation algorithm that uses news event diversity rather than previous reading behaviour as a key driver for personalized news recommendation. We describe the EventDNA annotation guidelines, which are inspired by the well-known ERE framework and conclude that it is not practical to apply a fixed event typology such as used in ERE to an unrestricted data context. The corpus and related source code is made available at haps://github.com/NewsDNA-LT3/.github.