About EPJ

The European Physical Journal (EPJ) is a series of peer-reviewed journals covering the whole spectrum of physics and related interdisciplinary subjects. EPJ is committed to high scientific quality in publishing and is indexed in all main citation databases.

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Latest news

EPJ Data Science Highlight - Predicting future sports rankings from evolving performance

 Rank diversity of chess.
Rank diversity of chess.

Competitive sport ranking evolution over time is used to predict the future evolution of rankings

Competitive sports and games are all about the performance of players and teams, which results in performance-based hierarchies. Because such performance is measurable and is the result of varied rules, sports and games are considered a suitable model to help understand unrelated social or economic systems characterised by similar rules-based complexity. Now, a team of Mexican scientists have used the performance of national teams in tennis, chess, golf, poker and football as a test-bed for identifying universal features in the creation of hierarchies—such as the stratified structure found in the global hierarchical distribution of wealth. José Morales from the National Autonomous University of Mexico and his colleagues found they could, in principle, predict changes in rank occupancy over the course of a contender's lifetime, regardless of the particularities of the sports or activity. These findings, published in EPJ Data Science, enhance our ability to forecast how stratification occurs in competitive activities.

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EPJ Data Science Highlight - Fiction-book narratives: only six emotional storylines

Annotated emotional arc of Harry Potter and the Deathly Hallows, by JK Rowling.
Annotated emotional arc of Harry Potter and the Deathly Hallows, by JK Rowling.

How scientists are using big data analysis to deconstruct the art of storytelling

Our most beloved works of fiction hide well-trodden narratives. And most fictions is based on far fewer storylines than you might have imagined. To come to this conclusion, big data scientists have worked with colleagues from natural language processing to analyse the narrative in more than a thousand works of fiction. By deconstructing some of the magic of narrative in fiction books, they have also confirmed that there are six different, common ways of telling a story that can be found time and time again in popular stories. They were inspired by the work of US fiction author Kurt Vonnegut, who originally proposed the similarity of emotional story lines in a Masters’s thesis rejected by the University of Chicago. These findings have just been published in EPJ Data Science by Andrew Reagan from the University of Vermont, USA, and colleagues.

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EPJ Data Science Highlight - Mining digital crumbs helps predict crowds’ mobility

Daily number of commuters arriving in New York City from the different counties of New York State.

Analysing the traces of human behaviour from geolocalisation data gives clues for more accurate urban planning

Getting urban planning right is no mean feat. It requires understanding how and when people travel between different places. This knowledge, in turn, helps in dimensioning roads and motorways and in scaling the capacity of utilities, such as power grids or mobile phone towers. Now, physicists at the Institute for Scientific Interchange Foundation in Turin, Italy, have exploited the geolocalisation data from millions of users of the photo sharing site Flickr to show how it is possible to predict crowd movements. Mariano Beiró and colleagues have combined this data with existing theoretical models explaining the movement of people. In a study published in EPJ Data Science, they show that their approach can help improve predictions concerning the nature of travel of large crowds of people between two places.

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Conference announcements

FUSION17

Hobart, Tasmania, 20–24 February 2017