Predicting the crossmodal correspondences of odors using an electronic nose

Odour Recordings

There are 100 recordings in total of 10 different essential oils; five were from Mystic Moments™ (caramel, cherry, coffee, freshly cut grass, and pine) and five from Miaroma™ (black pepper, lavender, lemon, orange, and peppermint).
Each recording is 10 minutes in duration (600 seconds). Columns in each of the .csv files are in the following order: time, air quality, pollution level, temperature, pressure, humidity, gas, MQ3, MQ5, MQ9, and HCHO. The file's name denotes the odour being recorded and the record number (1 - 10).
For more information, please view the publication - R.J. Ward, S. Rahman, S.M. Wuerger, A. Marshall, Predicting the crossmodal correspondences of odors using an electronic nose, Heliyon.

Perceptual Data 

The underlying perceptual data used from R.J. Ward, S.M. Wuerger, A. Marshall, Smelling Sensations: Olfactory Crossmodal Correspondences, J. Percept. Imaging. 4 (2021) 1–12. https://doi.org/10.2352/j.percept.imaging.2021.4.2.020402.
The data used from the later paper is the (angularity of shapes, smoothness of texture, perceived pleasantness, pitch, and the colour ratings in L*a*b* space).

Each file contains the raw perceptual ratings for the ten different odours (columns) from sixty-eight different participants (rows) in the following order: black pepper, caramel, cherry, coffee, freshly cut grass, lavender, lemon, orange, peppermint, and pine.
NOTE: the pitch ratings only contain data from sixy participants due to it being added to the experiment at a later date.

The folder regression models contains the required MATLAB code to train and test the regression models for predicting the crossmodal correspondences of odors using physicochemical data.

The folder raw perceptual data contains the raw unprocessed perceptual data in .csv format.

The folder e-nose code contains the code to drive the Arduino circuit and additional libraries required by the sensors.

The folder e-nose recorder contains a Unity project and code to receive the UDP packets from the e-nose and log them.

If you use this data please cite the following papers;

Perceptual Data
R.J. Ward, S.M. Wuerger, A. Marshall, Smelling Sensations: Olfactory Crossmodal Correspondences, J. Percept. Imaging. 4 (2021) 1–12. https://doi.org/10.2352/j.percept.imaging.2021.4.2.020402

Chemical Data
R. Ward, S. Rahman, S. Wuerger, A. Marshall, Predicting the colour associated with odours using an electronic nose, in: 1st Work. Multisensory Exp. - SensoryX’21, 2021: pp. 1–6. https://doi.org/10.5753/sensoryx.2021.15683.
R.J. Ward, S. Rahman, S.M. Wuerger, A. Marshall, Predicting the crossmodal correspondences of odors using an electronic nose, Heliyon, (under review as of file upload).