News / Highlights / Colloquium
EPJ Data Science Highlight - Mining digital crumbs helps predict crowds’ mobility
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- Published on 02 November 2016
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.
EPJ Data Science Highlight - Behavioural studies from mobile crowd-sensing
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- Published on 18 December 2015
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.
EPJ Data Science Highlight - Using patients’ trail of digital crumbs for public health surveillance
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- Published on 20 October 2015
© 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.
EPJ Data Science Highlight - What 15 years of mobile data can say about us
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- Published on 05 August 2015
© 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.
EPJ Data Science Highlight - Big Data reveals classical music creation secrets
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- Published on 29 April 2015
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.
EPJ Data Science Highlight - Towards a scientific process freed from systemic bias
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- Published on 25 January 2015
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.”
EPJ Data Science Highlight - Shadows over data sharing
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- Published on 17 March 2013
Experience gained from data sharing during the human genome sequencing project could apply to the broader research community
In a paper about to be published in EPJ Data Science, Barbara Jasny, deputy editor for commentary at Science magazine in Washington, DC, USA, looks at the history of the debates surrounding data access during and after the human genome “war”. In this context, she outlines current challenges in accessing information affecting research, particularly with regard to the social sciences, personalised medicine and sustainability.
EPJ Data Science Highlight - Tracking gene flow in marine plant evolution
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- Published on 20 December 2012
Physicists and biologists apply Big Data statistical tools to study marine plant evolution
A new method that could give a deeper insight into evolutional biology by tracing directionality in gene migration has just appeared in EPJ Data Science. Paolo Masucci from the Centre for Advanced Spatial Analysis, at University College of London, UK, and colleagues identified the segregation of genes that a marine plant underwent during its evolution. They found that the exchange of genes, or gene flow, between populations of a marine plant went westward from the Mediterranean to the Atlantic. This methodology could also be used to estimate the information flow in complex networks, including other biological or social networks.
EPJ Data Science Highlight - Driven by friendship
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- Published on 11 November 2012
Dynamics of Facebook: the structure of the network drives friends to congregate into many small, highly interconnected communities
For the first time, the dynamics of how Facebook user communities are formed have been identified, revealing surprisingly few large communities and innumerable highly connected small-size communities. These findings are about to be published in EPJ Data Science by Italian scientist Emilio Ferrara, affiliated with both Indiana University in Bloomington, Indiana, USA and his home University of Messina. This work could ultimately help identify the most efficient way to spread information, such as advertising, or ideas over large networks.
EPJ Data Science Highlight - Twitter data crunching: the new crystal ball
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- Published on 21 October 2012
Scientists have devised a means to predict the outcome of election-based processes such as TV talent shows through the big data analysis of tweets.
Fabio Ciulla from Northeastern University, Boston, USA, and his colleagues demonstrated that the elimination of contestants in TV talent shows based on public voting, such as American Idol, can be anticipated. They unveiled the predictive power of microblogging Twitter signals—used as a proxy for the general preference of an audience—in a study recently published in EPJ Data Science.
