I was very impressed by the friendship map made by Facebook intern, Paul Buffer and I realized that I had access to a similar dataset. Instead of a database of friendship data, I had access to a database of scientific collaboration.
My employer, Science-Metrix, is bibliometric consulting firm. In other words, we engineer ways to measure the impact and growth of scientific discovery (and publications) in the world. To accomplish this, we license data from scientific journal aggregators like Elsevier’s Scopus and Thomson Reuter’s Web of Science. The data we have is bibliographic in nature. We don’t have the full text of the articles but rather citation networks, authors and their affiliations, abstracts, etc.
From this data, I extracted and aggregated scientific collaboration between cities all over the world. For example, if a UCLA researcher published a paper with a colleague at the University of Tokyo, this would create an instance of collaboration between Los Angeles and Tokyo. The result of this process is a very long list of city pairs, like Los Angeles-Tokyo, and the number of instances of scientific collaboration between them. Following that, I used the geoname.org database to convert the cities’ names to geographical coordinates.
The next steps were then similar to those of the Facebook friendship map. I used a Mercator projection to project the geographical coordinates onto the map and used the Great Circle algorithm to trace the lines of collaboration between cities. The brightness of the lines is a function of the logarithm of the number of collaborations betweena pair of cities and the logarithm of the distance between those same two cities.
A zoomable very high resolution map can be consulted there: http://collabo.olihb.com/