Dataset Open Access

Benzene Concentration Dataset

Chang Wei Tan; Christoph Bergmeir; Francois Petitjean; Geoffrey I Webb


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/e5309c81-e46f-4c3f-8e7e-41a937b25224/BenzeneConcentration_TEST.ts"
      }, 
      "checksum": "md5:fc4becf25c38c657c3ba25e6a44d3734", 
      "bucket": "e5309c81-e46f-4c3f-8e7e-41a937b25224", 
      "key": "BenzeneConcentration_TEST.ts", 
      "type": "ts", 
      "size": 317763140
    }, 
    {
      "links": {
        "self": "https://zenodo.org/api/files/e5309c81-e46f-4c3f-8e7e-41a937b25224/BenzeneConcentration_TRAIN.ts"
      }, 
      "checksum": "md5:add5e4039e710b7c978338abca5a620f", 
      "bucket": "e5309c81-e46f-4c3f-8e7e-41a937b25224", 
      "key": "BenzeneConcentration_TRAIN.ts", 
      "type": "ts", 
      "size": 207575229
    }
  ], 
  "owners": [
    107790
  ], 
  "doi": "10.5281/zenodo.3902673", 
  "stats": {
    "version_unique_downloads": 110.0, 
    "unique_views": 298.0, 
    "views": 328.0, 
    "version_views": 328.0, 
    "unique_downloads": 110.0, 
    "version_unique_views": 298.0, 
    "volume": 155834000754.0, 
    "version_downloads": 652.0, 
    "downloads": 652.0, 
    "version_volume": 155834000754.0
  }, 
  "links": {
    "doi": "https://doi.org/10.5281/zenodo.3902673", 
    "conceptdoi": "https://doi.org/10.5281/zenodo.3902672", 
    "bucket": "https://zenodo.org/api/files/e5309c81-e46f-4c3f-8e7e-41a937b25224", 
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.3902672.svg", 
    "html": "https://zenodo.org/record/3902673", 
    "latest_html": "https://zenodo.org/record/3902673", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.3902673.svg", 
    "latest": "https://zenodo.org/api/records/3902673"
  }, 
  "conceptdoi": "10.5281/zenodo.3902672", 
  "created": "2020-06-21T13:01:29.423655+00:00", 
  "updated": "2021-03-24T02:33:10.119341+00:00", 
  "conceptrecid": "3902672", 
  "revision": 3, 
  "id": 3902673, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.3902673", 
    "description": "<p>This dataset is part of the Monash, UEA &amp;&nbsp;UCR time series regression repository.&nbsp;<a href=\"http://tseregression.org/\">http://tseregression.org/</a></p>\n\n<p>This goal of this dataset is to predict benzene concentration in an Italian city. This dataset contains 8878 time series obtained from the Air Quality dataset from the UCI repository.&nbsp;The time series has 8 dimensions which consists of hourly averaged responses from an array of 5 metal oxide chemical sensors embedded in an Air Quality Chemical Multisensor Device, as well as temperature, relative humidity and absolute humidity.&nbsp;The Air Quality Chemical Multisensor device was located on the field in a significantly polluted area, at road level, within an Italian city.&nbsp;Data were recorded from March 2004 to February 2005 (one year) representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses.&nbsp;Ground Truth hourly averaged concentrations for CO, Non Metanic Hydrocarbons, Benzene, Total Nitrogen Oxides (NOx) and Nitrogen Dioxide (NO2) and were provided by a co-located reference certified analyzer.<br>\n<br>\nPlease refer to <a href=\"https://archive.ics.uci.edu/ml/datasets/Air+Quality\">https://archive.ics.uci.edu/ml/datasets/Air+Quality</a>&nbsp;for more details.</p>\n\n<p>Relevant papers<br>\nS. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005.<br>\nSaverio De Vito, Marco Piga, Luca Martinotto, Girolamo Di Francia, CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization, Sensors and Actuators B: Chemical, Volume 143, Issue 1, 4 December 2009, Pages 182-191, ISSN 0925-4005.<br>\nS. De Vito, G. Fattoruso, M. Pardo, F. Tortorella and G. Di Francia, Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction, in IEEE Sensors Journal, vol. 12, no. 11, pp. 3215-3224, Nov. 2012. doi: 10.1109/JSEN.2012.2192425</p>\n\n<p><br>\nCitation request<br>\nS. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005</p>", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "title": "Benzene Concentration Dataset", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "3902672"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "3902673"
          }
        }
      ]
    }, 
    "communities": [
      {
        "id": "ts_regression"
      }
    ], 
    "keywords": [
      "time series", 
      "regression"
    ], 
    "publication_date": "2020-06-21", 
    "creators": [
      {
        "orcid": "0000-0001-8377-3241", 
        "affiliation": "Monash University", 
        "name": "Chang Wei Tan"
      }, 
      {
        "orcid": "0000-0002-3665-9021", 
        "affiliation": "Monash University", 
        "name": "Christoph Bergmeir"
      }, 
      {
        "orcid": "0000-0001-5334-3574", 
        "affiliation": "Monash University", 
        "name": "Francois Petitjean"
      }, 
      {
        "orcid": "0000-0001-9963-5169", 
        "affiliation": "Monash University", 
        "name": "Geoffrey I Webb"
      }
    ], 
    "access_right": "open", 
    "resource_type": {
      "type": "dataset", 
      "title": "Dataset"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.3902672", 
        "relation": "isVersionOf"
      }
    ]
  }
}
328
652
views
downloads
All versions This version
Views 328328
Downloads 652652
Data volume 155.8 GB155.8 GB
Unique views 298298
Unique downloads 110110

Share

Cite as