2020 Impact factor 3.184

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.

EPJ Data Science appoints Dr. Ingmar Weber as co-Editor-in-Chief

Dr. Ingmar Weber

EPJ is pleased to announce that Dr Ingmar Weber of Qatar Computing Research Institute (QCRI) has been appointed as a co-Editor-in-Chief for EPJ Data Science, effective January 2021. He will be responsible for overseeing the editorial process of the journal, working closely with Prof Markus Strohmaier, who continues to serve as co-Editor-in-Chief. Ingmar Weber is the Research Director for Social Computing at QCRI where his research focuses on using social media and other non-traditional data to study phenomena such as international migration, digital gender gaps, and wealth inequalities. Of particular interest is how such data can be used to provide better statistics on global development and how, in turn, such statistics can be used for better interventions and policy decisions. This work is done in close collaboration with different United Nations entities and NGOs. Ingmar Weber currently serves as an ACM Distinguished Speaker and is a Senior Member of AAAI, IEEE, and ACM. He has been a member of the Editorial Board for EPJ Data Science since 2018.

EPJ Data Science Highlight - Women’s disadvantage: because of who they are, or what they do?

Photo by Christina Morillo from Pexels

Women often find themselves strongly disadvantaged in the field of software development, in particular when it comes to open source. In a study recently published in EPJ Data Science, Orsolya Vasarhelyi and Balazs argue that this disadvantage stems from gendered behavior rather than categorical discrimination: women are at a disadvantage because of what they do, rather than because of who they are.

Continue reading the guest post by Orsolya Vasarhelyi and Balazs Vedres on the SpringerOpen blog.

Editors-in-Chief
M. Strohmaier and I. Weber