This PDF file includes:

• Fig. S1. No indications of hyperbolic geometry in shuffled odor data sets.
• Fig. S2. Alternative ways of evaluating differences between Betti curves also support hyperbolic geometry of natural odor spaces.
• Fig. S3. Error bar plots of Betti curves statistics for the hyperbolic model of different dimensions.
• Fig. S4. Test of the nonmetric multidimensional scaling algorithm in the hyperbolic space on synthetic data.
• Fig. S5. Odors within the identified space do not cluster by functional group.
• Fig. S6. Comparison between embedded geometric distances and reported perceptual distances.
• Fig. S7. Analysis of sensitivity of integrated Betti value to noise in the input distances.
• Table S1. Statistical tests (P values) for consistency with hyperbolic models based on integrated Betti values.
• Table S2. Statistical tests (P values) for consistency with hyperbolic models based on L1 distances between Betti curves.
• Table S3. Statistical tests (P values) for evaluating consistency of experimental Betti curves with respect to 3D hyperbolic model or optimal optimal Euclidean model.
• Table S4. Statistical tests (P values) for evaluating consistency of Betti curves computed based on logarithm of odor concentrations with respect to hyperbolic model.
• Table S5. P values of hyperbolic and Euclidean model using integrated Betti values for perceptual data set.

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