New, Open Access, Data Science Journal

EPJ Data Science is a new, Open Access, journal, addressing key challenges including extract meaningful data from complex systems; facilitating new insights; finding new empirical laws describing natural or artificial systems.

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EPJ Data Science is a new, Open Access, journal copublished by Societa Italiana di Fisica, EDP Sciences and Springer.

Editors-in-Chief:

• Frank Schweitzer, ETH Zurich
• Alessandro Vespignani, Northeastern University

The journal 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.

The recent articles include

• Crowd Disasters as Systemic Failures: Analysis of the Love Parade Disaster,
  Helbing D and Mukerji P
• Partisan Asymmetries in Online Political Activity,
  Conover MD, Goncalves B, Flammini A and Menczer F
• Social Dynamics of Digg,
  Hogg T and Lerman K EPJ
• Positive words carry less information than negative words, (highly accessed)
  Garcia D, Garas A and Schweitzer F

For more information, visit www.epjdatascience.com/