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.
EPJ Data Science
is a new, Open Access, journal copublished by Societa Italiana di Fisica, EDP Sciences and Springer.
The journal offers a publication platform to address this evolution by bringing together all academic disciplines concerned with the same challenges:
- Frank Schweitzer, ETH Zurich
- Alessandro Vespignani, Northeastern University
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.
- 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
The recent articles include
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