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 proceedingConference contributionAcademicpeer-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 languageEnglish
Title of host publication2020 39th International Conference of the Chilean Computer Science Society, SCCC 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728183282
DOIs
Publication statusPublished - 9 Dec 2020
Event39th International Conference of the Chilean Computer Science Society, SCCC 2020 - Coquimbo, Chile
Duration: 16 Nov 202020 Nov 2020

Publication series

NameProceedings - International Conference of the Chilean Computer Science Society, SCCC
Volume2020-November
ISSN (Print)1522-4902

Conference

Conference39th International Conference of the Chilean Computer Science Society, SCCC 2020
CountryChile
CityCoquimbo
Period16/11/2020/11/20

Keywords

  • Datasets
  • FAIR principles
  • Stress detection

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