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.
|Title of host publication||2020 39th International Conference of the Chilean Computer Science Society, SCCC 2020|
|Publisher||IEEE Computer Society|
|State||Published - 16 Nov 2020|
|Event||39th International Conference of the Chilean Computer Science Society, SCCC 2020 - Coquimbo, Chile|
Duration: 16 Nov 2020 → 20 Nov 2020
|Name||Proceedings - International Conference of the Chilean Computer Science Society, SCCC|
|Conference||39th International Conference of the Chilean Computer Science Society, SCCC 2020|
|Period||16/11/20 → 20/11/20|
Bibliographical noteFunding Information:
A. Cuno, N. Condori-Fernandez, A. Mendoza, and W. Ramos acknowledge financial support from the “Proyecto Concytec - Banco Mundial, Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia Tecnología e Innovación Tecnológica” 8682-PE, through its executing unit FONDECYT [Contract Nº 014-2019-FONDECYT-BM-INC.INV]. Also, this work has been partially supported by Datos 4.0 (TIN2016-78011-C4-1-R) funded by MINECO-AEI/FEDER-UE.
© 2020 IEEE.
- FAIR principles
- Stress detection