Quality Assurance for Spatial Research Data

In Earth System Sciences (ESS), spatial data are increasingly used for impact research and decision-making. To support the stakeholders' decision, the quality of the spatial data and its assurance play a major role. We present concepts and a workflow to assure the quality of ESS data. Our concepts and workflow are designed along the research data life cycle and include criteria for openness, FAIRness of data (findable, accessible, interoperable, reusable), data maturity, and data quality. Existing data maturity concepts describe (community-specific) maturity matrices, e.g., for meteorological data. These concepts assign a variety of maturity metrics to discrete levels to facilitate evaluation of the data. Moreover, the use of easy-to-understand level numbers enables quick recognition of highly mature data, and hence fosters easier reusability. Here, we propose a revised maturity matrix for ESS data including a comprehensive list of FAIR criteria. To foster the compatibility with the developed maturity matrix approach, we developed a spatial data quality matrix that relates the data maturity levels to quality metrics. The maturity and quality levels are then assigned to the phases of the data life cycle. With implementing openness criteria and matrices for data maturity and quality, we build a quality assurance (QA) workflow that comprises various activities and roles. To support researchers in applying this workflow, we implement an interactive questionnaire in the tool RDMO (research data management organizer) to collaboratively manage and monitor all QA activities. This can serve as a blueprint for use-case-specific QA for other datasets. As a proof of concept, we successfully applied our criteria for openness, data maturity, and data quality to the publicly available SPAM2010 (crop distribution) dataset series.

Automated summary

In this paper, the concepts and workflow for assuring the quality of spatial data in Earth System Sciences (ESS) are presented. They focus on openness, FAIRness of data, data maturity, and data quality. A revised maturity matrix and a spatial data quality matrix are proposed, and the maturity and quality levels are assigned to the phases of the data life cycle. Furthermore, a quality assurance (QA) workflow is built, including various activities and roles, and an interactive questionnaire in the tool RDMO is implemented to support researchers in applying this workflow.