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We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and unconfounded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentiallydoi:10.1145/2993901.2993907 dblp:conf/beliv/SchulzNEFHHKNSB16 fatcat:axyikya6efejtpeqvdxa4wshja