News / Highlights / Colloquium
EPJ Data Science Highlight - Identifying inequality in the velocity of cryptocurrency
- Details
- Published on 04 April 2025
Analysis of a new framework for tracking cryptocurrency velocity reveals deep inequalities, driven not just by wealth but by economic behaviours of individuals
The ‘velocity of money’ describes the number of times a unit of currency is used to purchase goods or services within a given time period – or in other words, the number of times that money is changing hands. The quantity is a key indicator of the behaviours of economies as a whole – but today, researchers are still uncertain as to how the concept translates to the fast-growing field of cryptocurrency.
Through new analysis published in EPJ Data Science, Francesco Maria De Collibus and colleagues at the University of Zurich investigate a newly developed framework for measuring the velocity of cryptocurrencies – named ‘MicroVelocity’. Their analysis reveals that many of the same inequalities in wealth distribution found in the economy as a whole are also reflected in MicroVelocity.
EPJ Data Science Highlight - Mapping the cross-generational impact of musical sampling
- Details
- Published on 14 February 2025
Advanced network analysis reveals how musical sampling has driven the evolution of contemporary music, and enabled the revival of dormant musical styles to across generations.
Sampling has helped musicians to craft a rich and diverse musical landscape, and has bridged the gap between different generations of creators. By acting as ‘cultural genes’ which are passed down and incorporated into new compositions, samples can fundamentally shape the genre, mood, and identity of contemporary music: posing questions about how sampling has influenced the evolution of popular music over time.
Through new analysis published in EPJ Data Science, Dongju Park and Juyong Park at the Korea Advanced Institute of Science and Technology provide new insights into how sampling has driven the constant development of music, revived styles dormant for generations, and fostered connections between generations of musicians. Their work demonstrates the complex nature of musical evolution, and how it can be understood using the powerful mathematics of network frameworks.
EPJ Data Science appoints Prof. David Garcia as co-Editor-in-Chief
- Details
- Published on 14 January 2025
EPJ is pleased to announce that Prof. David Garcia has been appointed as co-Editor-in-Chief of EPJ Data Science, effective January 2025. He will be responsible for overseeing the editorial process and development of the journal, working closely with Dr Yelena Mejova, who continues to serve as co-Editor-in-Chief.
David Garcia is Professor of Social and Behavioral Data Science at the Center for Data and Methods in the Department of Politics and Public Administration at the University of Konstanz. He is also associate faculty at the Complexity Science Hub Vienna and visiting professor at the Barcelona Supercomputing Center. He works on the analysis of human behavior with digital trace data and computational models. He has specialized in the analysis of collective emotions and polarization with methods from Data Science and responsible Artificial Intelligence.
He has co-authored more than 100 articles in conferences and journals in Computer Science, Physics, Political Science, and Psychology. He served as program co-chair of the 2023 International School and Conference on Network Science and is serving as program co-chair of the 2025 International Conference on Computational Social Science.
EPJ Data Science Highlight - Investigating gender equality in urban cycling
- Details
- 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.
EPJ Data Science appoints Dr Yelena Mejova as co-Editor-in-Chief
- Details
- 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.
EPJ Data Science Highlight - A data-driven approach for assessing biking safety in cities
- Details
- 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.
EPJ Data Science appoints Dr. Ingmar Weber as co-Editor-in-Chief
- Details
- Published on 07 January 2021
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?
- Details
- Published on 02 August 2019
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.
EPJ Data Science Highlight - What ‘Twitch Plays Pokémon’ tells us about crowd behavior
- Details
- Published on 05 July 2019
No one would deny that the behavior of the people we know, and even of our own, can radically change depending on those who surround us. The problem of understanding how being in a group changes the way we behave has been subject of intense research in psychology since the beginning of the past century. The beginning of the XXI century gave rise to a new kind of group: the online crowds. Nowadays, it is no longer necessary to have all individuals in the same place in order to have a ‘crowd’. What is more, it is possible to connect together thousands, even millions, of individuals in a matter of minutes.
In the work recently published in EPJ Data Science, we study one such occasion that gathered millions of users: Twitch Plays Pokémon.
Continue reading the guest post by Alberto Aleta on the SpringerOpen blog.
EPJ Data Science Highlight - What can we learn from billions of food purchases derived from fidelity cards?
- Details
- Published on 23 May 2019
For your health, what you eat is more important than what you earn.
This result comes from our latest project “Poor but Healthy”, which was published in EPJ Data Science, and comes with a @tobi_vierzwo’s stunningly “beautiful map of London” that author Daniele Quercia invites everyone to explore.
By combining 1.6B food item purchases with 1.1B medical prescriptions for the entire city of London for one year, researchers discovered that, to predict health outcomes, socio-economic conditions matter less than what previous research has shown: despite being of lower-income, certain areas are healthy, and that is because of what their residents eat.
Read the full blog post on Medium.
