- Published on 08 May 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.
- Published on 09 January 2023
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.
- Published on 25 March 2021
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.