SmellSpace: An Odor-Based Social Network as a Platform for Collecting Olfactory Perceptual Data.

摘要: A common goal in olfaction research is modeling the link between odorant structure and odor perception. Such efforts require large data sets on olfactory perception, yet only a few of these are publicly freely available. Given that individual perception may be informative personal makeup interpersonal relationships, we hypothesized people would gladly provide perceptual estimates context an odor-based social network. We developed web-based infrastructure for such network called SmellSpace distributed 10 000 scratch-and-sniff registration booklets each containing subset 12 out 35 microencapsulated odorants. Within ~100 days, obtained from ~1000 participants who rated odorants along 13 verbal descriptors. To verify comparable to lab-collected tested 26 controlled lab setting using same observed remarkably high overall group correlations data, implying this method provides credible group-representations further estimated usability by applying it two previously published models used alone predict either pleasantness or pairwise similarity. statistically significant predictions both cases, thus current helpful toward future structure. conclude potentially useful instrument collecting extensive here post complete raw set first participants.