In this paper, a detailed workflow for analyzing and improving the quality of metadata of scientific publications is presented and tested.
The workflow was developed based on approaches from the literature. Frequently occurring types of errors from the literature were compiled and mapped to the data-quality dimensions most relevant for publication data – completeness, correctness, and consistency – and made measurable. Based on the identified data errors, a process for improving data quality was developed. This process includes parsing hidden data, correcting incorrectly formatted attribute values, enriching with external data, carrying out deduplication, and filtering erroneous records.
The effectiveness of the workflow was confirmed in an exemplary application to publication data from Open Researcher and Contributor ID (ORCID), with 56\% of the identified data errors corrected. The workflow will be applied to publication data from other source systems in the future to further increase its performance
