2017 Impact factor 5.172
Particles and Fields

EPJ Data Science Highlight - Using deep learning to “see” inside homes across the world

Copyright: Pixabay License

How much does someone's living room tell about how they live? Peeking into another person's life might be just part of natural human curiosity, but the answer to this question may provide insights in a wide range of aspects of human behavior. A new study published in EPJ Data Science uses the power of machine learning to explore patterns of home decors—and what they could tell about their owners—in popular accommodation website Airbnb.

See guest post by Clio Andris and Xi Liu originally published in the SpringerOpen blog

EPJ Data Science Highlight - Twitter’s tampered samples: Limitations of big data sampling in social media

Photo by Con Karampelas on Unsplash

Social networks are widely used as sources of data in computational social science studies, and so it is of particular importance to determine whether these datasets are bias-free. In EPJ Data Science, Jürgen Pfeffer, Katja Mayer and Fred Morstatter demonstrate how Twitter’s sampling mechanism is prone to manipulation that could influence how researchers, journalists, marketeers and policy analysts interpret their data.

(Guest post by Jürgen Pfeffer, Katja Mayer and Fred Morstatter, originally published in the SpringerOpen blog)

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EPJ Data Science Highlight - Listening to the changes in the urban rhythm

Photo: Pixabay, CC0 License

Cities evolve and undergo constant re-organisation as their population grow. This evolving process makes cities resilient and adaptive but also poses a challenge to analyse urban phenomena. For a long time, there has been evidence that suggests temporal and spatial regularities in crime, but so far studies about this have been based on the assumption that cities are static. A new study published in EPJ Data Science takes these factors into consideration and analyses spatio-temporal variation in criminal occurrences.

(Guest post by Marcos Oliveira & Ronaldo Menezes, originally published on the SpringerOpen blog)

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Editors-in-Chief
L. Baudis, G. Dissertori, K. Skenderis and D. Zeppenfeld
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Tim Scanlon

ISSN: 1434-6044 (Print Edition)
ISSN: 1434-6052 (Electronic Edition)

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