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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


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        <foaf:name>Pau Ferrer-Cid</foaf:name>
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        <foaf:name>Jose M. Barcelo-Ordinas</foaf:name>
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        <foaf:name>Jorge Garcia-Vidal</foaf:name>
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        <foaf:name>Ana Ripoll</foaf:name>
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        <foaf:name>Mar Viana</foaf:name>
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    <dct:title>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"</dct:title>
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    <dcat:keyword>air quality</dcat:keyword>
    <dcat:keyword>sensors</dcat:keyword>
    <dcat:keyword>low-cost</dcat:keyword>
    <dcat:keyword>ozone</dcat:keyword>
    <dcat:keyword>nitrogen dioxide</dcat:keyword>
    <dcat:keyword>metal-oxide</dcat:keyword>
    <dcat:keyword>electro-chemical</dcat:keyword>
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        <foaf:name>European Commission</foaf:name>
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    <dct:description>&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;</dct:description>
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    <dct:title>Collective Awareness Platform for Tropospheric Ozone Pollution</dct:title>
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