2016 Impact factor 2.787

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EPJ Data Science Highlight - Using social media for large-scale studies of gender differences

Photo from Pixabay, CC0 public domain.
Photo from Pixabay, CC0 public domain.

Social networks capture data about most aspects of the daily lives of millions of people around the world. The analysis of this rich and ready-available source of information can help us better understand the complex dynamics of society.

In a recent article published in EPJ Data Science the authors propose the use of location-based social networks to study the activity patterns of different gender groups, which they summarise in a guest post on the SpringerOpen blog.

Gender differences have a subjective nature and may vary greatly across cultures, making them challenging to explain. Indeed, over the past decades, this topic has received a lot of attention by researchers, but there is still a long way to reach a consensus on the subject.

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EPJ Data Science - View featured video: How teams and players stack up and why

“Winning is not a sometime thing; it’s an all the time thing. Winning is a habit,” said legendary American football coach Vince Lombardi.

Human sports and games, with their rules of competition and measures of performance, serve as an ideal test-bed to look for universal features of hierarchy formation. In a recent article published in EPJ Data Science, José A. Morales and colleagues study the behaviour of performance rankings over time of players and teams for several sports and games, and find statistical regularities in the dynamics of ranks. This finding dispels the commonly held notion that rank changes are due to the intrinsic strengths or qualities of teams and players. The same phenomenon may apply to more complex competition settings with further examinations.

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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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EPJ Data Science Highlight - Behavioural studies from mobile crowd-sensing

Impact of exercise and socialisation on stress levels.

Smart phone monitoring has become a boon for scientists studying human behaviour and factors influencing stress

Using mobile phones for research is not new. However, interpreting the data collected from volunteers’ own smart phones--which has the potential to emulate randomised trials--can advance research into human behaviour. In a new study published in EPJ Data Science, scientists have just demonstrated the potential of using smart phones for conducting large-scale behavioural studies.The results stem from the work of Fani Tsapeli from the University of Birmingham, UK, and her colleague and Mirco Musolesi from University College London, UK. In their study, they evaluate the cause of increased stress levels of participants using user-generated data, harvested from their phones.

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EPJ Data Science Highlight - Using patients’ trail of digital crumbs for public health surveillance

Patients' digital crumbs could be used to complement existing disease surveillance mechanisms.
© bakhtiarzein / Fotolia

Public health agencies could capitalise on streams of data related to patients on the internet but only once interpretation methods have been validated.

Data is ubiquitous. In the area of heath, there are growing data streams directly initiated by patients through their activities on the internet and on social networks and other related ones such as electronic medical records and pharmacy sales data. These so-called Novel Data Streams (NDS) are very appealing to public health surveillance officials due to their ease of collection. A new paper published in EPJ Data Science evaluates the currently available NDS surveillance papers before outlining a conceptual framework for integrating such data into current public health surveillance systems. The authors, who hail from public health agencies, academia, and the private sector, highlight the need for future rigorous evaluation and validation of standards before NDS can effectively reinforce existing public health surveillance systems.

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EPJ Data Science Highlight - What 15 years of mobile data can say about us

Sample of a mobile phone network, obtained with a snowball sampling
© Blondel et al.

Mobile communication has not shrunk the world as expected, according to an overview of big data analysis revealing the nature of our social interactions with greater accuracy than ever before.

Large-scale anonymised datasets from mobile phones can give a better picture of society than ever before available. Mobile phone use helps us understand social networks, mobility and human behaviour. A review article recently published in EPJ Data Science highlights the main contributions in the field of mobile phone datasets analysis in the past 15 years. Vincent Blondel from the Université Catholique de Louvain, in Belgium, and colleagues conclude, among other things, that predictions that the world would shrink into a small village have not completely materialised as distance still plays a role. Meanwhile, individuals appear to have highly predictable movements as populations evolve in a remarkably synchronised way.

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EPJ Data Science Highlight - Big Data reveals classical music creation secrets

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The backbone network of Western classical composers, built from comprehensive recordings data

Study uncovers how classical music composers collaborate, mix, and influence one another. Results show how culture evolves and predict the future of the recording market

A team of scientists has shed light on the dynamics of the creation, collaboration and dissemination processes involved in classical music works and styles. Their study focuses on analysing networks of composers contemporary to CD publications, using modern data analysis and data modelling techniques. These findings have just been published in EPJ Data Science by Doheum Park from the Graduate School of Culture Technology at Korea Advanced Institute of Science and Technology in Daejeon and colleagues. This work explores the nature of culture in novel ways, as part of a broader movement of applying quantitative methods to music, the visual arts and literature.

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EPJ Data Science Highlight - Towards a scientific process freed from systemic bias

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Frank Schweitzer. © Frank Schweitzer

Large-scale analysis of bibliographic data can help us better understand the complex social processes in science and provide more accurate evaluation methods

Research on how science works—the science of science—can benefit from studying the digital traces generated during the research process, such as peer-reviewed publications. This type of research is crucial for the future of science and that of scientists, according to Frank Schweitzer, Chair of Systems Design at ETH Zurich, in Switzerland. Indeed, quantitative measures of scientific output and success in science already impact the evaluation of researchers and the funding of proposals. He shares his views in an Editorial spearheading a thematic series of articles entitled “Scientific networks and success in science”, published in EPJ Data Science. There, Schweitzer notes, “it is appropriate to ask whether such quantitative measures convey the right information and what insights might be missing.”

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Editors-in-Chief
Frank Schweitzer and Alessandro Vespignani

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