Quality Assurance for Spatial Research Data

Abstract

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.

Research data infrastructures in environmental sciences—Challenges and implementation approaches focusing on the integration of software components

“…There are many reasons for this, that is, lacking guidance, usable methods, and proper supporting tools for data producers to track or generate meaningful and structured quality information directly from the analysis workflow (cp. Magagna et al., 2020; Peng et al., 2021; Wagner & Henzen, 2022). Moreover, flexible and easy‐to‐use schemas for quality descriptions that meet data users' needs could be used to foster the provision of user‐friendly user interfaces offering harmonized and structured FAIR quality information.…”

“…We then implemented the developed template and an export plugin to push the metadata from the DMP tool to our data management system, such that they foster automation and facilitate their working smoothly and seamlessly during planning and subsequent phases, for example, updating the DMP during several phases of the data lifecycle (Figure 4). The developed export plugin (Wagner et al., 2022) for the web‐based DMP tool Research Data Management Organizer (RDMO) (https://rdmorganiser.github.io/) uses the CKAN API (https://docs.ckan.org/en/2.9/api/) to create or update metadata entries in our data management system CKAN (Figure 3, indicated by *).…”

“…Dataset description using the GeoDCAT properties in the section “Data description and collection or reuse of existing data” of the Science Europe template implemented as an RDMO questionnaire (Wagner et al., 2022). …”

“…Our quality assurance workflow is implemented along the research data lifecycle and links aspects such as openness, FAIRness, data quality, and data maturity—as the degree of formalization and standardization of data, as used in Höck et al. (2020) (Wagner & Henzen, 2022). We reused and defined quality, openness, and maturity levels to better describe and monitor the current status of a dataset within the phases of the research data lifecycle.…”