Approaches and tools for user-driven provenance and data quality information in spatial data infrastructures

Geospatial data are fundamental in most global-change and sustainability-related domains. However, readily accessible information on data quality and provenance is often missing or hardly accessible for users due to technical or perceptual barriers, for example, due to unstructured metadata information or missing references. Within an interdisciplinary process encompassing perspectives of data users, data producers, and software developers, we identified major needs to facilitate effective fitness-for-use assessments by data users and developed approaches to address these. We provided a stylized analysis of large-scale land use data to showcase selected approaches. To support data users, interoperable quality and provenance information need to be meaningfully represented. Data producers need efficient workflows and tools supporting them in creating high-quality, structured and detailed quality and provenance information. Our newly developed approaches to increase the availability of structured metadata synthesize new and existing tools to extract metadata or to generate provenance data during processing. Within our approaches to improve interoperability and accessibility we present novel tools to support (i) the creation of curated and linked registers of data quality indicators and thematic terms, and (ii) linked visualization of data quality and provenance information. Following our approaches increases transparency, facilitates fitness-for-use assessments, and ultimately improves research quality.

Automated summary

Geospatial data are essential in various domains related to global change and sustainability. However, users often face barriers accessing information about data quality and provenance. To address this issue, we conducted an interdisciplinary process involving data users, producers, and software developers, and identified the major needs for effective fitness-for-use assessments. Our approaches include providing structured metadata, developing efficient workflows and tools for data producers, and increasing the availability of quality and provenance information. These approaches increase transparency, facilitate fitness-for-use assessments, and improve research quality.