Data Quality Assurance at Research Data Repositories

Authors

MAXI KINDLING, Freie Universität Berlin
DOROTHEA STRECKER, Humboldt-Universität zu Berlin

Document Type

Article

Date of this Version

11-22-2022

Citation

Kindling, M and Strecker, D. 2022. Data Quality Assurance at Research Data Repositories. Data Science Journal, 21: 18, pp. 1–17. DOI: https://doi. org/10.5334/dsj-2022-018

Comments

Open access.

Abstract

This paper presents findings from a survey on the status quo of data quality assurance practices at research data repositories.

The personalised online survey was conducted among repositories indexed in re3data in 2021. It covered the scope of the repository, types of data quality assessment, quality criteria, responsibilities, details of the review process, and data quality information and yielded 332 complete responses.

The results demonstrate that most repositories perform data quality assurance measures, and overall, research data repositories significantly contribute to data quality. Quality assurance at research data repositories is multifaceted and nonlinear, and although there are some common patterns, individual approaches to ensuring data quality are diverse. The survey showed that data quality assurance sets high expectations for repositories and requires a lot of resources. Several challenges were discovered: for example, the adequate recognition of the contribution of data reviewers and repositories, the path dependence of data review on review processes for text publications, and the lack of data quality information. The study could not confirm that the certification status of a repository is a clear indicator of whether a repository conducts in-depth quality assurance.