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| Título: | Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms" |
Autor: | Ferrer-Cid, Pau; Barceló-Ordinas, José María; García Vidal, Jorge; Ripoll, Anna CSIC; Viana, Mar CSIC ORCID | Palabras clave: | Ozone Calibration |
Tesauro AGROVOC: | Ozono Calibración Sensores |
Fecha de publicación: | may-2019 | Resumen: | 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). | Descripción: | 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). | URI: | http://hdl.handle.net/10261/217107 | DOI: | 10.20350/digitalCSIC/12564 |
| Aparece en las colecciones: | (IDAEA) Conjuntos de datos |
Ficheros en este ítem:
| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| Data.zip | Dataset | 560,39 kB | Unknown | Visualizar/Abrir |
| Long-term-concentrations.zip | Dataset | 702,37 kB | Unknown | Visualizar/Abrir |
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