A FAIR evaluation of public datasets for stress detection systems

Nowadays, datasets are an essential asset used to train, validate, and test stress detection systems based on machine learning. In this paper, we used two sets of FAIR metrics for evaluating five public datasets for stress detection. Results indicate that all these datasets comply to some extent wit...

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Bibliographic Details
Published in2020 39th International Conference of the Chilean Computer Science Society (SCCC) pp. 1 - 8
Main Authors Cuno, Alvaro, Condori-Fernandez, Nelly, Mendoza, Alexis, Lovon, Wilber Ramos
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.11.2020
Subjects
Datasets
FAIR principles
Stress detection
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DOI10.1109/SCCC51225.2020.9281274

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Summary:Nowadays, datasets are an essential asset used to train, validate, and test stress detection systems based on machine learning. In this paper, we used two sets of FAIR metrics for evaluating five public datasets for stress detection. Results indicate that all these datasets comply to some extent with the (F)indable, (A)ccessible, and (R)eusable principles, but none with the (I)nteroperable principle. These findings contribute to raising awareness on (i) the need for the FAIRness development and improvement of stress datasets, and (ii) the importance of promoting open science in the affective computing community.
DOI:10.1109/SCCC51225.2020.9281274