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
Editors-in-Chief
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
|