Throughout the years, political scientists have devised a multitude of techniques to position political parties on various ideological and policy/issue dimensions. So far, however, none of these techniques was able to evolve into a "gold standard" in party positioning. Against this background, one could recently witness the appearance of a new methodology for party positioning tightly connected to the spread of Voting Advice Applications (VAAs), i.e. an iterative method that aims at improving existing techniques using a combination of party self-placement and expert judgement. Such a method, as pioneered by the Dutch Kieskompas, was first systematically employed on a large cross-national scale by the EU Profiler VAA in the context of the 2009 European Parliamentary elections. This article introduces the party placement datasets generated by euandi (reads: EU and I), a transnational VAA for the 2014 EP elections. The scientific relevance of the euandi endeavour lies primarily in its choice to stick to the iterative method of party positioning employed by the EU Profiler in 2009 as well as in the choice to keep as many as 17 policy statements in the 2014 questionnaire in order to allow for cross-national, longitudinal research on party competition in Europe across a five-year period. This article provides a brief review of traditional methods of party positioning and contrasts them to the iterative method employed by the euandi team. It then introduces the specifics of the project, facts and figures of the data collection procedure, and the details of the resulting dataset encompassing 242 parties from the whole EU28.