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@INPROCEEDINGS{Kaffashzadeh:875345,
      author       = {Kaffashzadeh, Najmeh and Kleinert, Felix and Schultz,
                      Martin},
      title        = {{A} {N}ew {T}ool for {A}utomated {Q}uality {C}ontrol of
                      {E}nvironmental {T}ime {S}eries ({A}uto{QC}4{E}nv) in {O}pen
                      {W}eb {S}ervices},
      volume       = {373},
      publisher    = {Springer},
      reportid     = {FZJ-2020-01968},
      pages        = {513-518},
      year         = {2019},
      comment      = {Business Information Systems Workshops},
      booktitle     = {Business Information Systems
                       Workshops},
      abstract     = {We report on the development of a new software tool
                      (AutoQC4Env) for automated quality control (QC) of
                      environmental time series data. Novel features of this tool
                      include a flexible Python software architecture, which makes
                      it easy for users to configure the sequence of tests as well
                      as their statistical parameters, and a statistical concept
                      to assign each value a probability of being a valid data
                      point. There are many occasions when it is necessary to
                      inspect the quality of environmental data sets, from first
                      quality checks during real-time sampling and data
                      transmission to assessing the quality and consistency of
                      long-term monitoring data from measurement stations.
                      Erroneous data can have a substantial impact on the
                      statistical data analysis and, for example, lead to wrong
                      estimates of trends. Existing QC workflows largely rely on
                      individual investigator knowledge and have been constructed
                      from practical considerations and with a least theoretical
                      foundation. The statistical framework that is being
                      developed in AutoQC4Env aims to complement traditional data
                      quality assessments and provide environmental researchers
                      with a tool that is easy to use but also based on current
                      statistical knowledge.},
      month         = {Jun},
      date          = {2019-06-26},
      organization  = {22nd International Conference on
                       Business Information Systems Workshops,
                       Sevilla (Spain), 26 Jun 2019 - 28 Jun
                       2019},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / IntelliAQ - Artificial Intelligence for Air
                      Quality (787576) / Earth System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF3-512 / G:(EU-Grant)787576 /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:000611408800043},
      doi          = {10.1007/978-3-030-36691-9_43},
      url          = {https://juser.fz-juelich.de/record/875345},
}