Embedding objects by semantic characteristics in multi-dimensional space is well understood. However, in spite of a long history of contesting the primacy of semantics in the experience of art, no consistent way of embedding objects in haptic or tactile space has emerged. This task is especially important in cross-cultural analyses of art, in which the semantic categories are not easily translated. The Data Analytics in Student Hands (DASH) team in the University of Houston Honors College is offering a new approach to high resolution 3-D scans, where a vocabulary appropriate to the haptic experience is allowed to emerge from interactions with the physicality of the object. By capturing and encoding the audience interactions with the objects, and by treating those interactions consistently without presupposing the semantic categories, essential steps in constructing better art historical datasets can be made, and the inclusion of 3-D scans can produce qualitatively different insights than yet another set of images would have done.