EPJ Data Science

EPJ Data Science
Editor-in-Chief:
M. Strohmaier
I. Weber
ISSN 2193-1127
Official EPJ website and eContents
© EDP Sciences, Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature
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2022 Impact Factor: 3.6
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