Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"

Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of fiv...

Descripción completa

Detalles Bibliográficos
Autores: Ferrer-Cid, Pau, Barceló-Ordinas, José María, García Vidal, Jorge, Ripoll, Anna, Viana, Mar
Tipo de recurso: conjunto de datos
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/217107
Acceso en línea:http://hdl.handle.net/10261/217107
Access Level:acceso abierto
Palabra clave:Ozone
Calibration
http://aims.fao.org/aos/agrovoc/c_5485
http://aims.fao.org/aos/agrovoc/c_36549
http://aims.fao.org/aos/agrovoc/c_28279
Ozono
Calibración
Sensores
id ES_ff6c4773264fbdd3dc897fcce2abcae6
oai_identifier_str oai:digital.csic.es:10261/217107
network_acronym_str ES
network_name_str España
repository_id_str
spelling Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"Ferrer-Cid, PauBarceló-Ordinas, José MaríaGarcía Vidal, JorgeRipoll, AnnaViana, MarOzoneCalibrationhttp://aims.fao.org/aos/agrovoc/c_5485http://aims.fao.org/aos/agrovoc/c_36549http://aims.fao.org/aos/agrovoc/c_28279OzonoCalibraciónSensoresData used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).Peer reviewedViana, Mar [0000-0002-4073-3802]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202019info:eu-repo/semantics/datasethttp://purl.org/coar/resource_type/c_ddb1http://hdl.handle.net/10261/217107reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2171072026-05-22T06:33:51Z
dc.title.none.fl_str_mv Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
title Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
spellingShingle Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
Ferrer-Cid, Pau
Ozone
Calibration
http://aims.fao.org/aos/agrovoc/c_5485
http://aims.fao.org/aos/agrovoc/c_36549
http://aims.fao.org/aos/agrovoc/c_28279
Ozono
Calibración
Sensores
title_short Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
title_full Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
title_fullStr Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
title_full_unstemmed Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
title_sort Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
dc.creator.none.fl_str_mv Ferrer-Cid, Pau
Barceló-Ordinas, José María
García Vidal, Jorge
Ripoll, Anna
Viana, Mar
author Ferrer-Cid, Pau
author_facet Ferrer-Cid, Pau
Barceló-Ordinas, José María
García Vidal, Jorge
Ripoll, Anna
Viana, Mar
author_role author
author2 Barceló-Ordinas, José María
García Vidal, Jorge
Ripoll, Anna
Viana, Mar
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Viana, Mar [0000-0002-4073-3802]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Ozone
Calibration
http://aims.fao.org/aos/agrovoc/c_5485
http://aims.fao.org/aos/agrovoc/c_36549
http://aims.fao.org/aos/agrovoc/c_28279
Ozono
Calibración
Sensores
topic Ozone
Calibration
http://aims.fao.org/aos/agrovoc/c_5485
http://aims.fao.org/aos/agrovoc/c_36549
http://aims.fao.org/aos/agrovoc/c_28279
Ozono
Calibración
Sensores
description Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
http://purl.org/coar/resource_type/c_ddb1
format dataset
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/217107
url http://hdl.handle.net/10261/217107
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869425773140508672
score 15,811543

Similares en LA Referencia