Modelling Inhomogeneous Geodata Quality in a Dataset’s Metadata

Abstract

Abstract. Extensive data quality descriptions as a vital part of a dataset’s metadata are widely accepted, albeit their provision in a formalized manner is often lacking. This is due to a number of problems that are frequently encountered by geodata producing scientists. As one of these problems, we identified missing, unknown or unused options to model inhomogeneity of data quality across space, time, and theme in a dataset’s metadata. Detailed information of inhomogeneous geodata quality beyond dataset-wide statistical measures (variance, min, max, etc.) is often only described in dataset accompanying papers or quality reports. These text-based approaches prevent precise querying and hinder the development of advanced data discovery tools that could make valuable use of inhomogeneous data quality information. We propose a profile for the data quality vocabulary (DQV) that allows to model inhomogeneous geodata quality. Considering established vocabularies typically used to describe geographic metadata, as well as ensuring compatibility with the default version of DQV, enhances the usability and thus, minimizes the effort for data producers to provide formalized descriptions of inhomogeneous data quality.

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

“…With properties (4)–(6), we enable data providers to describe an inhomogeneous distribution of a dataset's data quality by applying the same metric multiple times with different spatial, temporal, and/or thematic constraints (Rümmler et al., 2022).…”