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.
Highlights
Environmental research infrastructures in atmospheric, marine, solid earth, and biodiversity domains are maturing their support for the acquisition, curation, publishing, processing, and use of data
With increasing automation of observational data acquisition and standardization of curated observational data, we argue that the current frontier for environmental research infrastructures must address the challenge of curating machine-readable meaning of data that result from data interpretation
We argue that the concept of knowledge infrastructure (Edwards et al, 2013; Borgman et al, 2015; Karasti et al, 2016) can help to identify and organize some of the challenges faced by research infrastructures, e-Infrastructures, university research data management infrastructures, digital libraries, etc. as elements of networks that transform data into knowledge
Summary
Environmental research infrastructures in atmospheric, marine, solid earth, and biodiversity domains are maturing their support for the acquisition, curation, publishing, processing, and use of data. Research infrastructures that build on sensor network based operational observation systems are approaching full automation in observational data acquisition (Hellström et al, 2016). Contrasting the standardization of curated observational data, scientists have so far had little research infrastructure support to curate information resulting from interpreting observational data. With increasing automation of observational data acquisition and standardization of curated observational data, we argue that the current frontier for environmental research infrastructures must address the challenge of curating machine-readable meaning of data that result from data interpretation. This implies prior acquisition of meaning in machine readable format
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