EPJ Data Science

Publisher: EPD Science/Springer

Website: http://www.epjdatascience.com/

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



    Frank Schweitzer ETH Zürich, Switzerland
    Alessandro Vespignani Northeastern University, USA

Editorial Board

    Stefano Battiston, University of Zurich, Switzerland
    Vincent D Blondel Universite catholique de Louvain, Belgium
    John Brownstein Harvard Medical School, USA
    Ciro Cattuto ISI Foundation, Italy
    Santo Fortunato Aalto University, Finland
    Fosca Giannotti KDD Lab, Italy
    Jennifer Golbeck University of Maryland, USA
    César A. Hidalgo MIT Media Lab, USA
    Janusz Hołyst Warsaw University of Technology, Poland
    Hawoong Jeong Korea Advanced Institute of Science and Technology, South Korea
    David Lazer Northeastern University, USA
    Rosario Nunzio Mantegna Università di Palermo, Italy
    Madhav Marathe Virginia Bioinformatics Institute, USA
    Filippo Menczer Indiana University, USA
    Jukka-Pekka Onnela Harvard University, USA
    Marcel Salathé The Pennsylvania State University, USA
    Maxi San Miguel Universitat de les Illes Balears, Spain