The chapters in this volume emanate from the presentations and enriching discussions that characterized the Minerva School 2022 “An Interdisciplinary Exploration of Future Data Pedagogies and Digital Tools to Nurture Citizens Reasoning with Models and Modeling in the Big Data Era” held in Paderborn, Germany. This Minerva School brought together a range of expertise from different subject domains, theoretical perspectives, and methodological approaches. Contents:Preface (by Susanne Podworny)The multidimensional pedagogical potential of data modeling (by Michal Dvir, Susanne Podworny, Dani Ben-Zvi, and Daniel Frischemeier)Young learners perspectives on the concept of data as a model: what are data and what are they used for? (by Susanne Podworny and Daniel Frischemeier)Modeling situations with two binary events and different visualizations (by Karin Binder)Supporting students modeling and data practices by engaging with digital tools (by Tom Bielik)Mathematical hands-on experimentation as a possibility to engage students in authentic modeling with real data (by Ramona Hagenkötter, Valentina Nachtigall, Katrin Rolka, and Nikol Rummel)Design principles for developing statistical literacy by integrating data, models, and context in a digital learning environment (by Christian Büscher)From representation to transformation: rethinking modeling in computer science education (by Carsten Schulte)Reimagining data education: Bridging between classical statistics and data science (by Ronit Gafny and Dani Ben-Zvi)Traditional statistical models in a sea of data: teaching introductory data science (by Robert Gould)What do citizens need to know about real-world statistical models and the teaching of data modeling (by Iddo Gal)Some reflections on the role of data and models in a changing information ecosystem (by Joachim Engel)Afterword: what we mean when we say “modeling” (by Tim Erickson)