International Journal of Data Science and Analytics (2018) 6:163–165 https://doi.org/10.1007/s41060-018-0153-7 EDITORIAL Introduction to the special issue on Data Science in Europe Peter Flach 1 · Myra Spiliopoulou 2 · Serge Allegrezza 3 · Matthias Böhmer 4 · Burkhard Hess 5 · Berthold Lausen 6 Published online: 6 October 2018 © Springer Nature Switzerland AG 2018 It is our great pleasure to welcome the reader to this col- lection of papers devoted to current issues in Data Science viewed from a European perspective. This special issue of the Journal of Data Science and Analytics has its origin in the European Data Science Conference (EDSC), an invitation- only event organised by Professor Sabine Krolak-Schwerdt and her team in November 2016 in Luxembourg as the inaugural conference of the European Association for Data Science (EuADS). We thank the JDSA Editor-in-Chief, Pro- fessor Longbing Cao, for the opportunity to guest-edit this collection. The conference programme of EDSC consisted of selected plenary talks, symposia, workshops and panel discussions on the following topics: • The question of trust, transparency and provenance of data including where data come from and by which mech- anisms trust in data might be achieved. • Legal aspects of Data Science such as data protection, data privacy and data access, among others. • The question how data scientists might navigate the com- plex chain from raw data to actionable outputs and how they might be supported by appropriate tools. • The role of Data Science in medicine and health care with a specific focus on Open medical data and personalized Medicine. • The question what makes Data Science different from other fields such as statistics and how to define the methodological substance of the field. B Peter Flach Peter.Flach@bristol.ac.uk 1 University of Bristol, Bristol, UK 2 University of Magdeburg, Magdeburg, Germany 3 Luxembourg Institute for Statistics and Economic Studies, Luxembourg, Luxembourg 4 University of Luxembourg, Luxembourg, Luxembourg 5 Max Planck Institute Luxembourg, Luxembourg, Luxembourg 6 University of Essex, Colchester, UK Prior to the conference, participants were invited to con- tribute position statements for presentation and discussion at the conference. They then were given the opportunity to submit revised and extended versions to this special issue. The submissions received went through rigorous peer review, resulting in twelve papers that are briefly described below. They can be broadly grouped into three main categories. Perspectives on Data Science research and training In 1962 John Tukey published an article entitled “The future of data analysis” in The Annals of Mathematical Statistics. In What makes Data Science different? A discussion involving Statistics2.0 and Computational Sciences, Christophe Ley and Stéphane Bordas take as their starting point the question what has really changed since then. Their paper can be seen as a dialogue between statistics and computational sciences, and their main conclusion is that ‘Data Science enhances the traditional and more conservative world of Statistics with advanced algorithms’ which is necessary to make sense of the large volumes of data we encounter today. In his position paper Data Science as a language: chal- lenges for computer science, Arno Siebes develops the viewpoint that Data Science is a way to conduct enquiries in ‘datafied sciences’ using computer science as a language, in much the same way as mathematics is the language of physics. He then goes on to consider the challenges this poses for computer science, and the future research directions these challenges entail. Claus Weihs and Katja Ickstadt, in their paper Data Sci- ence: The impact of statistics, emphasise the importance of statistics in many steps of the Data Science pipeline, in partic- ular data acquisition and enrichment, data exploration, data analysis and modelling, and validation and reporting. They argue that this importance is not always recognised and rec- ommend statisticians to ‘more offensively play their role in this modern and well accepted field of Data Science’. 123