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dc.contributor.authorStocker, Markus
dc.contributor.authorPaasonen, Pauli
dc.contributor.authorFiebig, Markus
dc.contributor.authorZaidan, Martha A
dc.contributor.authorHardisty, Alex
dc.date.accessioned2018-12-10T08:50:58Z
dc.date.available2018-12-10T08:50:58Z
dc.date.created2018-12-04T18:20:53Z
dc.date.issued2018
dc.identifier.citationData Science Journal. 2018, 17, 21.nb_NO
dc.identifier.issn1683-1470
dc.identifier.urihttp://hdl.handle.net/11250/2576762
dc.description.abstractInterpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information—meaningful secondary or derived data—about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation—as machine readable data and their machine readable meaning—is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.nb_NO
dc.language.isoengnb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectMachine learning
dc.titleCurating scientific information in knowledge infrastructuresnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder© 2018 The Author(s).nb_NO
dc.source.volume17nb_NO
dc.source.journalData Science Journalnb_NO
dc.identifier.doi10.5334/dsj-2018-021
dc.identifier.cristin1639144
dc.relation.projectEC/H2020/654182nb_NO
dc.relation.projectEC/H2020/654003nb_NO
dc.relation.projectEC/H2020/654109nb_NO
dc.relation.projectEC: 742206nb_NO
dc.relation.projectNILU: 115054nb_NO
cristin.unitcode7460,57,0,0
cristin.unitnameAtmosfære og klima
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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