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...
| Autores: | , , , , |
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| 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 |
| Sumario: | 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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