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Instructions for authors

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

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.

EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), 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 up to and including policy advice.

Each article type published by EPJ Data Science follows a specific format, as detailed in the corresponding instructions for authors; please choose an article type from the list on the left to view the instructions for authors.

The instructions for authors includes information about preparing a manuscript for submission to EPJ Data Science, criteria for publication and the online submission process. Other relevant information about the journal's policies, the refereeing process and so on can be found in 'About this journal'.

EPJ Data Science publishes the following article types:

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This journal is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE).

Authors and referees are asked to declare any competing interests.

Copyright rests with the authors. For more information on copyright of articles, see our publisher's copyright and license policy.