Springer has introduced a new open, peer-reviewed journal focused on Data Science: EPJ Data Science.
What makes this a Data Science journal is novel uses of statistics, data analysis, computer techniques and public data sources to research a topic in another domain, rather than methodological research. Here are a few examples of the papers you’ll find in the journal:
Springer has introduced a new open, peer-reviewed journal focused on Data Science: EPJ Data Science.
What makes this a Data Science journal is novel uses of statistics, data analysis, computer techniques and public data sources to research a topic in another domain, rather than methodological research. Here are a few examples of the papers you’ll find in the journal:
- A confirmation of the “Pollyanna Hypothesis” that we use more positive words than negative words (and so negative sentiments carry more weight than positive ones).
- An analysis of the Love Parade disaster, using photographs, satellite images, and public documents to investigate the causes that led to 21 deaths in a 2010 crowd panic in Germany.
- An analysis of politically-active Twitter users users that reveals that Republicans in 2008 had a more tightly-connected social network that was more effective at broadcasting political material on Twitter.
Unsurprisingly, many of the articles use the R language for the underlying analysis and data visualization. And because this is an open journal, you’re free to read any of the articles at the link below.
EPJ Data Science: Articles (via Drew Conway)
