A FAIR evaluation of public datasets for stress detection systems

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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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Detalles Bibliográficos
Autores: Cuno A., Condori-Fernandez N., Mendoza A., Lovon W.R.
Formato: artículo
Fecha de Publicación:2020
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/2462
Enlace del recurso:https://hdl.handle.net/20.500.12390/2462
https://doi.org/10.1109/SCCC51225.2020.9281274
Nivel de acceso:acceso abierto
Materia:Stress detection
Datasets
FAIR principles
http://purl.org/pe-repo/ocde/ford#3.02.25
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network_name_str CONCYTEC-Institucional
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spelling Publicationrp06242600rp02079600rp06241600rp06240600Cuno A.Condori-Fernandez N.Mendoza A.Lovon W.R.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2020https://hdl.handle.net/20.500.12390/2462https://doi.org/10.1109/SCCC51225.2020.92812742-s2.0-85098624680Nowadays, 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. © 2020 IEEE.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengIEEE Computer SocietyProceedings - International Conference of the Chilean Computer Science Society, SCCCinfo:eu-repo/semantics/openAccessStress detectionDatasets-1FAIR principles-1http://purl.org/pe-repo/ocde/ford#3.02.25-1A FAIR evaluation of public datasets for stress detection systemsinfo:eu-repo/semantics/articlereponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/2462oai:repositorio.concytec.gob.pe:20.500.12390/24622024-05-30 16:08:26.115http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="bd3f5bf5-ca8f-40ce-92b3-8cecb9517bcb"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A FAIR evaluation of public datasets for stress detection systems</Title> <PublishedIn> <Publication> <Title>Proceedings - International Conference of the Chilean Computer Science Society, SCCC</Title> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <DOI>https://doi.org/10.1109/SCCC51225.2020.9281274</DOI> <SCP-Number>2-s2.0-85098624680</SCP-Number> <Authors> <Author> <DisplayName>Cuno A.</DisplayName> <Person id="rp06242" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Condori-Fernandez N.</DisplayName> <Person id="rp02079" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Mendoza A.</DisplayName> <Person id="rp06241" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Lovon W.R.</DisplayName> <Person id="rp06240" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>IEEE Computer Society</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Stress detection</Keyword> <Keyword>Datasets</Keyword> <Keyword>FAIR principles</Keyword> <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. © 2020 IEEE.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
dc.title.none.fl_str_mv A FAIR evaluation of public datasets for stress detection systems
title A FAIR evaluation of public datasets for stress detection systems
spellingShingle A FAIR evaluation of public datasets for stress detection systems
Cuno A.
Stress detection
Datasets
FAIR principles
http://purl.org/pe-repo/ocde/ford#3.02.25
title_short A FAIR evaluation of public datasets for stress detection systems
title_full A FAIR evaluation of public datasets for stress detection systems
title_fullStr A FAIR evaluation of public datasets for stress detection systems
title_full_unstemmed A FAIR evaluation of public datasets for stress detection systems
title_sort A FAIR evaluation of public datasets for stress detection systems
author Cuno A.
author_facet Cuno A.
Condori-Fernandez N.
Mendoza A.
Lovon W.R.
author_role author
author2 Condori-Fernandez N.
Mendoza A.
Lovon W.R.
author2_role author
author
author
dc.contributor.author.fl_str_mv Cuno A.
Condori-Fernandez N.
Mendoza A.
Lovon W.R.
dc.subject.none.fl_str_mv Stress detection
topic Stress detection
Datasets
FAIR principles
http://purl.org/pe-repo/ocde/ford#3.02.25
dc.subject.es_PE.fl_str_mv Datasets
FAIR principles
dc.subject.ocde.none.fl_str_mv http://purl.org/pe-repo/ocde/ford#3.02.25
description 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. © 2020 IEEE.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/2462
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/SCCC51225.2020.9281274
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85098624680
url https://hdl.handle.net/20.500.12390/2462
https://doi.org/10.1109/SCCC51225.2020.9281274
identifier_str_mv 2-s2.0-85098624680
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Proceedings - International Conference of the Chilean Computer Science Society, SCCC
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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score 13.274781
A FAIR evaluation of public datasets for stress detection systems
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

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