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Curating scientific information in knowledge infrastructures

Stocker, Markus, Paasonen, Pauli, Fiebig, Markus, Zaidan, Martha A and Hardisty, Alex 2018. Curating scientific information in knowledge infrastructures. Data Science Journal 17 , 21. 10.5334/dsj-2018-021

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Abstract

Interpreting 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Uncontrolled Keywords: Data Use, Data Interpretation, Linked Data, Semantic Information, Environmental Research Infrastructures, Environmental Knowledge Infrastructures, Informatics, Data Science
Publisher: Ubiquity Press
ISSN: 1683-1470
Funders: European Union Horizon 2020, Academy of Finland
Date of First Compliant Deposit: 4 September 2018
Date of Acceptance: 31 August 2018
Last Modified: 21 Sep 2018 12:30
URI: http://orca-mwe.cf.ac.uk/id/eprint/114548

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