As the Vrije Universiteit Brussel switched from an in-house built CRIS to Pure, a large number of data quality issues were discovered. In order to solve these, a large-scale data quality assessment and improvement program was started. The assessment sought to find data quality issues and prioritize cleaning tasks along different dimensions, such as reusability and complexity, while taking into account compliance and stakeholder happiness. Moreover, in doing these assessments, an attempt was made to isolate relatively easy to clean parts of the data in order to make them more feasible for people with less domain-knowledge. Finally, some of these data quality improvement operations turned out to be straightforward enough to fully automate them. © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Organizing Committee of CRIS2016. Keywords: Data Quality Assessment; Data Cleaning; Data Quality Improvement; Research Data Management The Vrije Universiteit Brussel (VUB henceforth) has been keeping track of research output, projects, activities and their associated metadata since the 1980's. For the most part, this information was stored in a custom-built application, R&D-net, which went to a number of iterations over time. The CRIS at the VUB is not only used to store information on research output and activities, but is also used to pass on financial project information to the financial administration. While R&D-net was used for a long time, numerous complaints were voiced by users, both
In REF , a high number of data quality issues are identified based on the 'testbed' of the Vrije Universiteit, Brussels.
Data Quality Assessment and Improvement: A Vrije Universiteit Brussel Case Study
CRIS
Computer Science