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2 results for “crossmodal correspondence”
Data from: Audio-visual crossmodal correspondences in domestic dogs (Canis familiaris)
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Predicting the crossmodal correspondences of odors using an electronic nose
<p>Odour Recordings</p> <p>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).<br> 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).<br> 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.</p> <p>Perceptual Data </p> <p>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.<br> 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).</p> <p>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.<br> NOTE: the pitch ratings only contain data from sixy participants due to it being added to the experiment at a later date.</p> <p>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.</p> <p>The folder raw perceptual data contains the raw unprocessed perceptual data in .csv format.</p> <p>The folder e-nose code contains the code to drive the Arduino circuit and additional libraries required by the sensors.</p> <p>The folder e-nose recorder contains a Unity project and code to receive the UDP packets from the e-nose and log them.</p> <p><br> If you use this data please cite the following papers;</p> <p>Perceptual Data<br> 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</p> <p>Chemical Data<br> 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.<br> 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).</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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