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@ARTICLE{Betancourt:893368,
      author       = {Betancourt, Clara and Stomberg, Timo and Roscher, Ribana
                      and Schultz, Martin G. and Stadtler, Scarlet},
      title        = {{AQ}-{B}ench: a benchmark dataset for machine learning on
                      global air quality metrics},
      journal      = {Earth system science data},
      volume       = {13},
      number       = {6},
      issn         = {1866-3516},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernics Publications},
      reportid     = {FZJ-2021-02709},
      pages        = {3013 - 3033},
      year         = {2021},
      abstract     = {With the AQ-Bench dataset, we contribute to the recent
                      developments towards shared data usage and machine learning
                      methods in the field of environmental science. The dataset
                      presented here enables researchers to relate global air
                      quality metrics to easy-access metadata and to explore
                      different machine learning methods for obtaining estimates
                      of air quality based on this metadata. AQ-Bench contains a
                      unique collection of aggregated air quality data from the
                      years 2010–2014 and metadata at more than 5500 air quality
                      monitoring stations all over the world, provided by the
                      first Tropospheric Ozone Assessment Report (TOAR). It
                      focuses in particular on metrics of tropospheric ozone,
                      which has a detrimental effect on climate, human morbidity
                      and mortality, as well as crop yields. The purpose of this
                      dataset is to produce estimates of various long-term ozone
                      metrics based on time-independent local site conditions. We
                      combine this task with a suitable evaluation metric.
                      Baseline scores obtained from a linear regression method, a
                      fully connected neural network and random forest are
                      provided for reference and validation. AQ-Bench offers a
                      low-threshold entrance for all machine learners with an
                      interest in environmental science and for atmospheric
                      scientists who are interested in applying machine learning
                      techniques. It enables them to start with a real-world
                      problem relevant to humans and nature. The dataset and
                      introductory machine learning code are available at
                      https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f
                      (Betancourt et al., 2020) and
                      https://gitlab.version.fz-juelich.de/esde/machine-learning/aq-bench
                      (Betancourt et al., 2021). AQ-Bench thus provides a
                      blueprint for environmental benchmark datasets as well as an
                      example for data re-use according to the FAIR principles.},
      cin          = {JSC / NIC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)NIC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / IntelliAQ -
                      Artificial Intelligence for Air Quality (787576) / Deep
                      Learning for Air Quality and Climate Forecasts
                      $(deepacf_20191101)$ / Earth System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      $G:(DE-Juel1)deepacf_20191101$ / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000668053300001},
      doi          = {10.5194/essd-13-3013-2021},
      url          = {https://juser.fz-juelich.de/record/893368},
}