This paper provides an overview of the design and motivation for creating the Social Cognition Parallax Interview Corpus (SCOPIC), an open-ended, accessible corpus that balances the need for language-specific annotation with typologically-calibrated markup. SCOPIC provides richly annotated data, focusing on functional categories relevant to social cognition, the social and psychological facts that place people and others within an interconnected social context and allow people to interact with one another. By "parallax corpus" we mean "broadly comparable formulations resulting from a comparable task", to avoid the implications of "parallel corpus" that there will be exact semantic equivalence across languages. We describe the data structure of the corpus and the language functions being annotated, and provide an example of a typological analysis using recursive partitioning, a modern statistical technique. The current paper should be seen as the introductory chapter of an open-ended special issue of LDC whose goal is to make available both the original corpus, the evolving annotated versions, and analyses coming from them, so that any investigator can examine the corpus with their own questions in mind. A range of new papers, linked to the evolving corpus, will be added to this special issue over time.