The goal of meta-analysis is to synthesize results from a collection of studies in order to identify patterns that have broader applicability. In many of the global change sciences, these synthesis studies attempt to bring together results of local case studies to make claims about global patterns. In order to substantiate claims of generality, it is crucial to establish that the collected case studies are representative of the regions they claim to characterize. Said differently, a

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... must demonstrate that their choice of studies was not biased in a way that would undermine her claims. The GLOBE project aims to shorten the gap between local and global researchers by, among other things, providing analytics that help assess the representativeness of a collection of study sites and assist in correcting any bias found. In this paper we present the methods used by GLOBE to formalize the concept of representativeness, to analyze and visualize it, to address sampling bias, and present a use case in the domain of land change science.