Dataset.

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

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217107
Digital.CSIC. Repositorio Institucional del CSIC
  • Ferrer-Cid, Pau
  • Barceló-Ordinas, José María
  • García Vidal, Jorge
  • Ripoll, Anna
  • Viana, Mar
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)., 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 reviewed
 
DOI: http://hdl.handle.net/10261/217107
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217107

HANDLE: http://hdl.handle.net/10261/217107
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217107
 
Ver en: http://hdl.handle.net/10261/217107
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217107

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/217107
Dataset. 2019

DATA USED IN PAPER "A COMPARATIVE STUDY OF CALIBRATION METHODS FOR LOW-COST OZONE SENSORS IN IOT PLATFORMS"

Digital.CSIC. Repositorio Institucional del CSIC
  • Ferrer-Cid, Pau
  • Barceló-Ordinas, José María
  • García Vidal, Jorge
  • Ripoll, Anna
  • Viana, Mar
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)., 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 reviewed




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