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

Pau Ferrer-Cid; Jose M. Barcelo-Ordinas; Jorge Garcia-Vidal; Ana Ripoll; Mar Viana

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).

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Data_description_Zenodo.pdf
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Long-term-concentrations.zip
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