2022 Impact factor 1.8
Soft Matter and Biological Physics

EPJ Data Science Highlight - Investigating gender equality in urban cycling

An overview of the gender gap in recreational cycling across cities included in the study according to Strava. Credit: A. Battison et al. (2023)

New research looks at why cycling has a low uptake among women in urban areas

Over recent years not only has cycling proved itself to be an outdoor activity with tremendous health benefits, but it has also presented itself as a useful tool in the quest to find an environmentally friendly method of urban transportation.

Despite the increasing popularity of cycling, many countries still have a negligible uptake in the pursuit and this is even more pronounced when considering how many women engage in cycling. To this day, a mostly unexplained gender gap exists in cycling.

A new paper in EPJ Data Science by the University of Turin Department of Computer Science researcher Alice Battiston and her co-authors attempts to understand the determinants behind the gender gap in cycling on a large scale.


EPJ Data Science appoints Dr Yelena Mejova as co-Editor-in-Chief

Dr Yelena Mejova

EPJ is pleased to announce that Dr Yelena Mejova has been appointed as a co-Editor-in-Chief for EPJ Data Science, effective January 2023. She will be responsible for overseeing the editorial process of the journal, working closely with Dr Ingmar Weber, who continues to serve as co-Editor-in-Chief.

Yelena Mejova is a Senior Research Scientist at the ISI Foundation in Turin, Italy. Specializing in social media analysis and mining, her work concerns the quantification of health and wellbeing signals in social media, as well as tracking of social phenomena, including politics and news consumption. In 2022, she co-chaired International AAAI Conference on Web and Social Media (ICWSM) and the Web & Society track at The Web Conference. As a part of the CRT Foundation's Lagrange Project for Data Science and Social Impact, she is also working with the humanitarian sector including the World Food Program, OCHA, and IMMAP to develop NLP and modeling tools to aid in humanitarian data management and forecasting.

EPJ Data Science Highlight - A data-driven approach for assessing biking safety in cities

A snapshot of an interactive map of results obtained from the authors' model for the city of Pittsburgh, Pennsylvania, USA. Low-risk locations are colored green, while risky locations are colored red.

The bicycle is arguably the most sustainable and eco-friendly mode of transport but biking safety remains a prime concern, especially in cities. In their work recently published in EPJ Data Science Konstantinos Pelechrinis and his co-authors propose a model which provides interpretable findings for practical change.

Continue reading the blog post here.

F. Croccolo, G. Fragneto and H. Stark
To the editorial team (in charge of final copy editing and production): Thank you for your high quality, speedy work.

Jean-Noël Roux, Université Paris-Est, Laboratoire Navier, IFSTARR, ENPC, CNRS, Champs-sur-Marne, France

ISSN (Print Edition): 1292-8941
ISSN (Electronic Edition): 1292-895X

© EDP Sciences, Società Italiana di Fisica and Springer-Verlag