EPJ Data Science is a peer-reviewed open access journal published under the SpringerOpen brand.

Data-driven science is rapidly emerging as a complementary approach to the traditional hypothesis-driven method. This revolution 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.

The journal EPJ Data Science addresses the challenges of the data revolution across academic disciplines:

· how to extract meaningful data from systems with ever-increasing complexity

· how to analyze data in ways that inspire new insights

· how to generate data that is needed but not yet available

· how to develop new empirical laws, or more fundamental theories, concerning the function of complex natural or artificial systems

EPJ Data Science spans a broad range of research areas and applications with a focus on social systems, where it comprises those research lines that regard digital traces of human behavior as first-order objects for scientific investigation. This includes human and animal social behavior and interaction, economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting.

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