000875345 001__ 875345
000875345 005__ 20230127125337.0
000875345 0247_ $$2doi$$a10.1007/978-3-030-36691-9_43
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000875345 0247_ $$2WOS$$aWOS:000611408800043
000875345 037__ $$aFZJ-2020-01968
000875345 041__ $$aEnglish
000875345 1001_ $$0P:(DE-Juel1)165903$$aKaffashzadeh, Najmeh$$b0$$eCorresponding author
000875345 1112_ $$a22nd International Conference on Business Information Systems Workshops$$cSevilla$$d2019-06-26 - 2019-06-28$$gBIS2019$$wSpain
000875345 245__ $$aA New Tool for Automated Quality Control of Environmental Time Series (AutoQC4Env) in Open Web Services
000875345 260__ $$bSpringer$$c2019
000875345 29510 $$aBusiness Information Systems Workshops
000875345 300__ $$a513-518
000875345 3367_ $$2ORCID$$aCONFERENCE_PAPER
000875345 3367_ $$033$$2EndNote$$aConference Paper
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000875345 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
000875345 4900_ $$v373
000875345 520__ $$aWe report on the development of a new software tool (AutoQC4Env) for automated quality control (QC) of environmental time series data. Novel features of this tool include a flexible Python software architecture, which makes it easy for users to configure the sequence of tests as well as their statistical parameters, and a statistical concept to assign each value a probability of being a valid data point. There are many occasions when it is necessary to inspect the quality of environmental data sets, from first quality checks during real-time sampling and data transmission to assessing the quality and consistency of long-term monitoring data from measurement stations. Erroneous data can have a substantial impact on the statistical data analysis and, for example, lead to wrong estimates of trends. Existing QC workflows largely rely on individual investigator knowledge and have been constructed from practical considerations and with a least theoretical foundation. The statistical framework that is being developed in AutoQC4Env aims to complement traditional data quality assessments and provide environmental researchers with a tool that is easy to use but also based on current statistical knowledge.
000875345 536__ $$0G:(DE-HGF)POF3-512$$a512 - Data-Intensive Science and Federated Computing (POF3-512)$$cPOF3-512$$fPOF III$$x0
000875345 536__ $$0G:(EU-Grant)787576$$aIntelliAQ - Artificial Intelligence for Air Quality (787576)$$c787576$$fERC-2017-ADG$$x1
000875345 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x2
000875345 7001_ $$0P:(DE-Juel1)176602$$aKleinert, Felix$$b1
000875345 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin$$b2
000875345 770__ $$aquality of open data (QOD)$$z2078-2489
000875345 773__ $$a10.1007/978-3-030-36691-9_43
000875345 8564_ $$uhttps://link.springer.com/chapter/10.1007/978-3-030-36691-9_43
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000875345 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176602$$aForschungszentrum Jülich$$b1$$kFZJ
000875345 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)6952$$aForschungszentrum Jülich$$b2$$kFZJ
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000875345 9141_ $$y2020
000875345 920__ $$lyes
000875345 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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