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Many collective human activities have been shown to exhibit universalpatterns. However, the possibility of universal patterns across timing eventsof researcher migration has barely been explored at global scale. Here, we showthat timing events of migration within different countries exhibit remarkablesimilarities. Specifically, we look at the distribution governing the data ofresearcher migration inferred from the web. Compiling the data in itselfrepresents a significant advance in the field of quantitative analysis ofmigration patterns. Official and commercial records are often accessrestricted, incompatible between countries, and especially not registeredacross researchers. Instead, we introduce GeoDBLP where we propagategeographical seed locations retrieved from the web across the DBLP database of1,080,958 authors and 1,894,758 papers. But perhaps more important is that weare able to find statistical patterns and create models that explain themigration of researchers. For instance, we show that the science job market canbe treated as a Poisson process with individual propensities to migratefollowing a log-normal distribution over the researcher's career stage. Thatis, although jobs enter the market constantly, researchers are generally not"memoryless" but have to care greatly about their next move. The propensity tomake k>1 migrations, however, follows a gamma distribution suggesting thatmigration at later career stages is "memoryless". This aligns well but actuallygoes beyond scientometric models typically postulated based on small casestudies. On a very large, transnational scale, we establish the first generalregularities that should have major implications on strategies for educationand research worldwide.

GeoDBLP: Geo-Tagging DBLP for Mining the Sociology of Computer Science