Curating Scientific Information in Knowledge Infrastructures

Data Science Journal

Martha A. Zaidan¹  Pauli Paasonen¹  Markus Fiebig²  Alex Hardisty³  Markus Stocker⁴ 

[1] Institute for Atmospheric and Earth System Research/Physics, 00014 University of Helsinki;NILU – Norsk Institutt for Luftforskning, Dept. Atmospheric and Climate Research, Instituttveien 18, 2007 Kjeller;School of Computer Science and Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff CF24 3AA;TIB Leibniz Information Centre for Science and Technology, Welfengarten 1 B, 30167 Hannover, MARUM Center for Marine Environmental Sciences, PANGAEA Data Publisher for Earth & Environmental Science, Leobener Strasse 8, 28359 Bremen;

关键词: Data Use;    Data Interpretation;    Linked Data;    Semantic Information;    Environmental Research Infrastructures;    Environmental Knowledge Infrastructures;    Informatics;    Data Science;   

DOI  :  10.5334/dsj-2018-021

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown