Abstract
Abstract. Metadata management is core to support discovery and reuse of data products, and to allow for reproducibility of the research data in Earth System Sciences (ESS). Thus, ensuring acquisition and provision of meaningful and quality assured metadata should become an integral part of data-driven ESS projects.We propose an open-source tool for the automated metadata and data quality extraction to foster the provision of FAIR data (Findable, Accessible, Interoperable Reusable). By enabling researchers to automatically extract and reuse structured and standardized ESS-specific metadata, in particular quality information, in several components of a research data infrastructure, we support researchers along the research data life cycle.
Highlights
The automated acquisition of meaningful and quality assured metadata should become an essential part of research data management (RDM)
To cover Earth System Sciences (ESS)-specific meta information, like linked spatial, temporal, and thematic information, ISO 19115-1:2014 provides a schema for the description of geographic information, which can be implemented as XML (International Organization for Standardization, 2014 and 2016)
We propose an open-source Java tool, called metadataFromGeodata 6, for the automated metadata extraction and quality assurance to foster the provision of FAIR geospatial data
Summary
These principles strengthen the importance of the availability of meaningful and quality assured metadata for the research data. While the FAIR principles include domain-independent guidelines, several communities strongly encourage connecting the FAIR principles to domain-specific standards for data quality In ESS, quality information plays a major role to evaluate research data. We provide an open-source RDM component for the automated extraction of ESS-specific metadata, in particular quality metadata, to foster the provision of FAIR data in ESS. Metadata provide descriptions for these data and are core to support discovery, evaluation and reuse of the created data products. The automated acquisition of meaningful and quality assured metadata should become an essential part of research data management (RDM). The FAIR principles provide guidelines to improve the findability, accessibility, interoperability, and reuse of digital objects, in particular data
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