If you have a modern phone, you can capture a visual scene as a photograph, alter it, send it to a relative in another country in an instant, and store it so you can look at it for years to come. None of this is currently possible in olfaction. In vision and audition, we know how to map physical properties to perception: wavelength translates into color and frequency translates into pitch. By contrast, the mapping from chemical structure to olfactory percept is poorly understood, limiting our ability to describe and control odors. This, in turn, limits our ability to understand how the olfactory system encodes perception. Olfaction has a higher dimensionality than the other senses, but recent models have shown that with enough data, machine learning techniques can predict human perception from molecular structure. We hypothesized that the rate-limiting step for building a model that predicts human perception from molecular structure is the collection of high-quality psychophysical data. Here I will discuss how these datasets have enabled us to create an odor map, analogous to a color wheel where nearby stimuli are perceptually similar, that individuals can explore using an olfactometer that generates an odor corresponding to each position on the map. We demonstrate that individuals can navigate this organized odor map better than a shuffled map. Additionally, navigation was quicker with subsequent trials, demonstrating a learning effect. Our ability to organize odor space and nonverbally assess odor quality paves the way toward digitizing olfaction.