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Computer Science - Information Systems and Applications | EPJ Data Science - a SpringerOpen journal (Editorial Board)

EPJ Data Science

EPJ Data Science

Editor-in-Chief: Markus Strohmaier

ISSN: 2193-1127 (electronic version)

Journal no. 13688

Markus Strohmaier, RWTH Aachen University and GESIS Cologne, Germany

Advisory Editors:
Frank Schweitzer, ETH Zürich, Switzerland
Alessandro Vespignani, Northeastern University, Boston, USA

Luca Maria Aiello, Nokia Bell Labs, UK
Andrea Baronchelli, City, University of London, UK
Alain Barrat, Centre de Physique Théorique, Marseille, France
Stefano Battiston, Zurich University, Switzerland
Dirk Brockmann, Humboldt University Berlin, Germany
John Brownstein, Harvard Medical School, Boston, USA
Ciro Cattuto, ISI Foundation, Torino, Italy
Munmun De Choudhury, Georgia Institute of Technology, USA
Katayoun Farrahi, University of Southampton, UK
Emilio Ferrara, University of Southern California, USA
Santo Fortunato, Aalto University, Finland
David Garcia, Medical University of Vienna and Complexity Science Hub Vienna, Austria
Fosca Giannotti, KDD Lab., CNR, Pisa, Italy
Sandra González-Bailón, University of Pennsylvania, USA
Michael Granitzer, University of Passau, Germany
Denis Helic, Graz University of Technology, Austria
César A. Hidalgo, MIT Media Lab, Cambridge, USA
Andreas Hotho, University of Wuerzburg, Germany
Hawoong Jeong, Korea Advanced Institute of Science and Technology, Taejon, Korea
David Lazer, Harvard University, Cambridge, USA
Kristina Lerman, University of South California, USA
Rosario Nunzio Mantegna, Università di Palermo, Italy
Madhav Marathe, Virginia Bioinformatics Institute, Blacksburg, Virginia, USA
Filippo Menczer, Indiana University, Bloomington, USA
Esteban Moro Egido, Universidad Carlos III de Madrid, Spain
Claudia Müller-Birn, Institute of Computer Science, Freie Universität Berlin, Germany
Jürgen Pfeffer, Technical University of Munich, Germany
Jukka-Pekka Onnela, Harvard T.H. Chan School of Public Health, USA
Daniel Romero, University of Michigan, USA
Marcel Salathé, École Polytechnique Fédérale de Lausanne, Switzerland
Marta Sales-Pardo, Universitat Rovira i Virgili, Spain
Ingo Scholtes, ETH Zürich, Switzerland
Roberta Sinatra, Central European University, Hungary
Ingmar Weber, Qatar Computing Research Institute, QatarRobert West, École Polytechnique Fédérale de Lausanne, Switzerland
Matthew Williams, Cardiff University, UK
Taha Yasseri, University of Oxford, UK

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  • Aims and Scope

    Aims and Scope


    The 21st century is currently witnessing the establishment of data-driven science as a complementary approach to the traditional hypothesis-driven method. This (r)evolution accompanying the paradigm shift from reductionism to complex systems sciences has already largely transformed the natural sciences and is about to bring the same changes to the techno-socio-economic sciences, viewed broadly.

    EPJ Data Science offers a publication platform to address this evolution by bringing together all academic disciplines concerned with the same challenges:

      • how to extract meaningful data from systems with ever increasing complexity
      • how to analyse them in a way that allows new insights
      • how to generate data that is needed but not yet available
      • how to find new empirical laws, or more fundamental theories, concerning how any natural or artificial (complex) systems work

    This is accomplished through experiments and simulations, by data mining or by enriching data in a novel way. The focus of this journal is on conceptually new scientific methods for analyzing and synthesizing massive data sets, and on fresh ideas to link these insights to theory building and corresponding computer simulations. As such, articles mainly applying classical statistics tools to data sets or with a focus on programming and related software issues are outside the scope of this journal.

    EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.

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