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


Citation Style Language JSON Export

{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3233516", 
  "title": "Data used in paper \"A comparative study of calibration methods for low-cost ozone sensors in IoT platforms\"", 
  "issued": {
    "date-parts": [
      [
        2019, 
        5, 
        28
      ]
    ]
  }, 
  "abstract": "<p>Data used in paper &quot;A comparative study of calibration methods for low-cost ozone sensors in IoT platforms&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).</p>", 
  "author": [
    {
      "family": "Pau Ferrer-Cid"
    }, 
    {
      "family": "Jose M. Barcelo-Ordinas"
    }, 
    {
      "family": "Jorge Garcia-Vidal"
    }, 
    {
      "family": "Ana Ripoll"
    }, 
    {
      "family": "Mar Viana"
    }
  ], 
  "version": "v1", 
  "type": "dataset", 
  "id": "3233516"
}
43
11
views
downloads
All versions This version
Views 4343
Downloads 1111
Data volume 4.4 MB4.4 MB
Unique views 3838
Unique downloads 66

Share

Cite as