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Predicting the crossmodal correspondences of odors using an electronic nose

Authors: Ryan J. Ward; Shammi Rahman; Sophie Wuerger; Alan Marshall;

Predicting the crossmodal correspondences of odors using an electronic nose

Abstract

Underlying perceptual and chemical data for the article "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. 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. 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, (under review as of file upload).

Related Organizations
Keywords

E-nose recordings, Perceptual data, machine learning, regression, crossmodal correspondences

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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