A FAIR evaluation of public datasets for stress detection systems

Alvaro Cuno; Nelly Condori-Fernandez; Alexis Mendoza; Wilber Ramos Lovon

doi:10.1109/SCCC51225.2020.9281274

A FAIR evaluation of public datasets for stress detection systems

Alvaro Cuno, Nelly Condori-Fernandez, Alexis Mendoza, Wilber Ramos Lovon

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	2020 39th International Conference of the Chilean Computer Science Society, SCCC 2020
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728183282
DOIs	https://doi.org/10.1109/SCCC51225.2020.9281274
State	Published - 16 Nov 2020
Externally published	Yes
Event	39th International Conference of the Chilean Computer Science Society, SCCC 2020 - Coquimbo, Chile Duration: 16 Nov 2020 → 20 Nov 2020

Publication series

Name	Proceedings - International Conference of the Chilean Computer Science Society, SCCC
Volume	2020-November
ISSN (Print)	1522-4902

Conference

Conference	39th International Conference of the Chilean Computer Science Society, SCCC 2020
Country/Territory	Chile
City	Coquimbo
Period	16/11/20 → 20/11/20

Bibliographical note

Funding 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.

Publisher Copyright:
© 2020 IEEE.

Keywords

Datasets
FAIR principles
Stress detection

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10.1109/SCCC51225.2020.9281274

Cite this

Cuno, A., Condori-Fernandez, N., Mendoza, A., & Lovon, W. R. (2020). A FAIR evaluation of public datasets for stress detection systems. In 2020 39th International Conference of the Chilean Computer Science Society, SCCC 2020 [9281274] (Proceedings - International Conference of the Chilean Computer Science Society, SCCC; Vol. 2020-November). IEEE Computer Society. https://doi.org/10.1109/SCCC51225.2020.9281274

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Cuno, A, Condori-Fernandez, N, Mendoza, A & Lovon, WR 2020, A FAIR evaluation of public datasets for stress detection systems. in 2020 39th International Conference of the Chilean Computer Science Society, SCCC 2020., 9281274, Proceedings - International Conference of the Chilean Computer Science Society, SCCC, vol. 2020-November, IEEE Computer Society, 39th International Conference of the Chilean Computer Science Society, SCCC 2020, Coquimbo, Chile, 16/11/20. https://doi.org/10.1109/SCCC51225.2020.9281274

A FAIR evaluation of public datasets for stress detection systems. / Cuno, Alvaro; Condori-Fernandez, Nelly; Mendoza, Alexis et al.

2020 39th International Conference of the Chilean Computer Science Society, SCCC 2020. IEEE Computer Society, 2020. 9281274 (Proceedings - International Conference of the Chilean Computer Science Society, SCCC; Vol. 2020-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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A FAIR evaluation of public datasets for stress detection systems

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