Mapping science – an astonishing figure of change through citation-mapping the past ten years of research
While scientists have developed tools for understanding the complexity of biological systems, mapping how these systems change over time has proven a much more difficult task. Specifically, without identifying the statistical noise in a data set, real trends can get lost and false trends can be fabricated.
Now, Martin Rosvall of Umeå University in Sweden and Carl Bergstrom of the University of Washington present a new mathematical technique to tackle this problem. Rather than applying it to a biological system, though, they investigate a more meta-problem. Running more than 35 million citations of articles from over 7000 scientific journals through their model, they create a map of how science has changed over the last 10 years.
“This network of citations represents the flow of information between researchers in the world and the results show that significant changes have occurred in the life sciences,” Rosvall said in a press release. Specifically, “neuroscience has gone from being an interdisciplinary research area to being a scientific discipline in its own right.”
The figure is available at: http://www.eurekalert.org/multimedia/pub/media/19806.JPG
Original article: http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0008694
Mapping Change in Large Networks
Martin Rosvall & Carl T. Bergstrom
Change is a fundamental ingredient of interaction patterns in biology, technology, the economy, and science itself: Interactions within and between organisms change; transportation patterns by air, land, and sea all change; the global financial flow changes; and the frontiers of scientific research change. Networks and clustering methods have become important tools to comprehend instances of these large-scale structures, but without methods to distinguish between real trends and noisy data, these approaches are not useful for studying how networks change. Only if we can assign significance to the partitioning of single networks can we distinguish meaningful structural changes from random fluctuations. Here we show that bootstrap resampling accompanied by significance clustering provides a solution to this problem. To connect changing structures with the changing function of networks, we highlight and summarize the significant structural changes with alluvial diagrams and realize de Solla Price’s vision of mapping change in science: studying the citation pattern between about 7000 scientific journals over the past decade, we find that neuroscience has transformed from an interdisciplinary specialty to a mature and stand-alone discipline.