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EPJ Data Science is delighted to welcome Dr. Yelena Mejova as the new co-Editor-in-Chief, and Dr. Sabrina Gaito, Dr. Anna Monreale, Dr. Barbara Poblete, Dr. Luca Rossi, Dr. Maurizio Tesconi, Dr. Onur Varol and Dr. Shoko Wakamiya on the Board of Associate Editors as of January, 2023.

Articles

2023

Call for papers: Computational Approaches for Cyber Social Threats
Edited by: Francesco Pierri, Matthew R. DeVerna, Kai-Cheng Yang, Jeremy Blackburn, Ugur Kursuncu  
 

2022

The Past, Present, and Future of Computational Social Science
Edited by: Termeh Shafie, Christoph Stadtfeld


2021

Data Science perspectives on Economic Crime
Edited by: Johannes Wachs, Janos Kertesz, Mihaly Fazekas, Elizabeth David-Barrett


2020

Integrating Survey and Non-survey Data to Measure Behavior and Public Opinion 
Edited by:  Antje Kirchner, Trent Buskirk, Ingmar Weber, Nan Zhang


2018

Individual and Collective Human Mobility: Description, Modelling, Prediction
Edited by: Filippo Simini, Gourab Ghoshal, Luca Pappalardo, Michael Szell, Philipp Hövel


2016

Advances in data-driven computational social sciences
Edited by: Fosca Giannotti, Santo Fortunato, Michael Macy


2015

Making Big Data work: Smart, Sustainable, and Safe Cities
Edited by: Fabrizio Antonelli, Bruno Lepri, Alex 'Sandy' Pentland, Fabio Pianesi


2014

Scientific Networks and Success in Science
Edited by: Frank Schweitzer

Collective Behaviors and Networks
Edited by: Giovanni Luca Ciampaglia, Emilio Ferrara, Alessandro Flammini

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Latest article collections

Computational Approaches for Cyber Social Threats
Edited by: Francesco Pierri, Matthew R. DeVerna, Kai-Cheng Yang, Jeremy Blackburn, Ugur Kursuncu  

The Past, Present, and Future of Computational Social Science
Edited by: Termeh Shafie, Christoph Stadtfeld

Integrating Survey and Non-survey Data to Measure Behavior and Public Opinion
Edited by:  Antje Kirchner, Trent Buskirk, Ingmar Weber, Nan Zhang

Individual and Collective Human Mobility: Description, Modelling, Prediction
Edited by: Filippo Simini, Gourab Ghoshal, Luca Pappalardo, Michael Szell, Philipp Hövel

Advances in data-driven computational social sciences
Edited by: Fosca Giannotti, Santo Fortunato, Michael Macy

Making Big Data work: Smart, Sustainable, and Safe Cities
Edited by: Fabrizio Antonelli, Bruno Lepri, Alex 'Sandy' Pentland, Fabio Pianesi

See all article collections here

Aims and scope

The 21st century is 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 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 showcase the latest contributions to the study of techno-socio-economic systems, wherein “digital traces” of human activity are used as first-order objects for the investigation. Specifically, the focus of the journal is on analyzing and synthesizing massive data sets to achieve new insights into societal phenomena and behavior. Application domains include, but are not limited to, 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. Methodologically, EPJ Data Science welcomes approaches from a broad range of disciplines, spanning statistically rigorous analysis of data, social network analysis, complex systems, applied machine learning, and more.

Papers submitted to this journal should not only strive to improve on existing data science methodologies but must provide new insight into human or social behavior or systems, in the areas outlined above. Submissions that focus on purely descriptive statistics or apply standard techniques to mainstream datasets are unlikely to be considered for publication.

Thus, EPJ Data Science offers a publication platform to bring together diverse academic disciplines concerned with challenges around:

  • How to extract signals about techno-socio-economic systems from large, complex data
  • How to interpret these signals in the theoretical context of the relevant disciplines
  • How to find new empirical laws, or fundamental theories, concerning how societies work

About EPJ

EPJ is a rapidly growing series of internationally reputed, peer-reviewed journals that are indexed in all major citation databases. The editorial boards of the EPJ are composed of leading specialists in their respective fields and have made it their mission to uphold the highest standards of scientific quality in the journals. EPJ started in the late 1990s as a merger and co-publication of Zeitschrift für Physik (Springer), Journal de Physique (EDP Sciences) and Il Nuovo Cimento (Società Italiana di Fisica) covering all aspects of the pure and applied physical sciences. Its spectrum has since expanded to encompass many interdisciplinary topics, including complexity and data sciences.

Annual journal metrics

  • 2022 Citation Impact
    3.6 - 2-year Impact Factor
    4.4 - 5-year Impact Factor
    1.625 - SNIP (Source Normalized Impact per Paper)
    1.066 - SJR (SCImago Journal Rank)

    2022 Speed
    21 days submission to first editorial decision for all manuscripts (Median)
    214 days submission to accept (Median)

    2022 Usage 
    471,734 downloads
    10,315 Altmetric mentions 

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Funding your APC

​​​​​​​Open access funding and policy support by SpringerOpen​​

​​​​We offer a free open access support service to make it easier for you to discover and apply for article-processing charge (APC) funding. Learn more here