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|a 10.1162/dint_a_00130
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041 _ _ |a English
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100 1 _ |0 P:(DE-Juel1)16212
|a Schröder, Sabine
|b 0
|e Corresponding author
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245 _ _ |a Enabling Canonical Analysis Workflows:Documented Data Harmonization on Global Air Quality Data
260 _ _ |a Cambridge, MA
|b MIT Press
|c 2022
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520 _ _ |a Data harmonization and documentation of the data processing are essential prerequisites for enabling Canonical Analysis Workflows. The recently revised Terabyte-scale air quality database system, which the Tropospheric Ozone Assessment Report (TOAR) created, contains one of the world's largest collections of near-surface air quality measurements and considers FAIR data principles as an integral part. A special feature of our data service is the on-demand processing and product generation of several air quality metrics directly from the underlying database. In this paper, we show that the necessary data harmonization for establishing such online analysis services goes much deeper than the obvious issues of common data formats, variable names, and measurement units, and we explore how the generation of FAIR Digital Objects (FDO) in combination with automatically generated documentation may support Canonical Analysis Workflows for air quality and related data.
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536 _ _ |0 G:(EU-Grant)787576
|a IntelliAQ - Artificial Intelligence for Air Quality (787576)
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|a Epp, Eleonora
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|a Mozaffari, Amirpasha
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700 1 _ |0 P:(DE-Juel1)132243
|a Romberg, Mathilde
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700 1 _ |0 P:(DE-Juel1)186637
|a Selke, Niklas
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700 1 _ |0 P:(DE-Juel1)6952
|a Schultz, Martin G.
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770 _ _ |a Canonical Workflow Frameworks for Research
773 _ _ |0 PERI:(DE-600)2973844-1
|a 10.1162/dint_a_00130
|g Vol. 4, no. 2, p. 259 - 270
|n 2
|p 259 - 270
|t Data Intelligence
|v 4
|x 2096-7004
|y 2022
856 4 _ |u https://juser.fz-juelich.de/record/907954/files/SchroederEtAl_Canonical_Workflow_Frameworks_for_Research_2022.pdf
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