Researchers at the University of Pittsburgh, combined bike accident data from three different cities collected between 2004 and 2017 with details on the infrastructure supporting and facilitating cycling, such as cycling lanes and protected cycle tracks, and the geography of these cities. The data were used to estimate the likelihood of severe bike accidents, resulting in at least moderate injuries, occurring at different locations in a city. The authors found that higher speed limits, wider streets, closer proximity to the city centre and intersections, curved streets and increased steepness were all associated with higher probabilities of severe accidents across all cities studied. The authors conclude that understanding the relationship between city infrastructure and cycling safety will be important if cities are to rely more on cycling as a sustainable form of transport in the future.

Studying economic crime from a data science perspective offers unique

insights and can inform the design of novel solutions. In this topical

issue, we gather research that highlights novel applications of data

science to the problems and challenges of economic crime. We also

welcome data-critical studies and mixed-methods papers, recognizing that

data-driven methods complement rather than substitute for other

approaches.

Submissions using our TeX template are strongly preferred. Please consider submitting your article in this format.

Authors: Jos茅 de Jes煤s Titla-Tlatelpa, Rosa Mar铆a Ortega-Mendoza, Manuel Montes-y-G贸mez and Luis Villase帽or-Pineda

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6 November 2021

Authors: Elise Jing and Yong-Yeol Ahn

Content type: Regular article

30 October 2021

Authors: Frank Schlosser and Dirk Brockmann

Content type: Regular article

19 October 2021

Authors: Nicolas Pr枚llochs, Dominik B盲r and Stefan Feuerriegel

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18 October 2021

Authors: Stiene Praet, Peter Van Aelst, Patrick van Erkel, Stephan Van der Veeken and David Martens

Content type: Regular article

2 October 2021

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Authors: Andrew G Reece and Christopher M Danforth

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8 August 2017

The Erratum to this article has been published in EPJ Data Science 2017 6:21

Authors: Dirk Helbing and Pratik Mukerji

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25 June 2012

Authors: Valentin Kassarnig, Enys Mones, Andreas Bjerre-Nielsen, Piotr Sapiezynski, David Dreyer聽Lassen and Sune Lehmann

Content type: Regular article

24 April 2018

Authors: Vincent D Blondel, Adeline Decuyper and Gautier Krings

Content type: Review

5 August 2015

Authors: Andrew J Reagan, Lewis Mitchell, Dilan Kiley, Christopher M Danforth and Peter Sheridan Dodds

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4 November 2016

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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 H枚vel

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

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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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 H枚vel

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.

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:

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 鈥渢racks鈥?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.

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 f眉r Physik (Springer), Journal de Physique (EDP Sciences) and Il Nuovo Cimento (Societ脿 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.

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