Dataset Open Access

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


MARC21 XML Export

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    <subfield code="a">air quality</subfield>
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    <subfield code="a">low-cost</subfield>
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    <subfield code="a">ozone</subfield>
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    <subfield code="a">nitrogen dioxide</subfield>
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    <subfield code="a">metal-oxide</subfield>
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    <subfield code="a">&lt;p&gt;Data used in paper &amp;quot;A comparative study of calibration methods for low-cost ozone sensors in IoT platforms&amp;quot;, 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).&lt;/p&gt;</subfield>
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