News Item | 22. May 2023

Workshop Series "Improving Sustainability in Four Steps. Data Quality Strategies for Museums and Collections"

By Angela Kailus, M. A.

Keywords
News Item Standards & Curation Qualification & Training Data Curation Data Quality Data Life Cycle
Engraving with a loom for carpet weaving, cutting with scissors

Textiles: a loom for carpet weaving, cutting with scissors

"Engraving with a loom for carpet weaving, cutting with scissors" Public Domain Mark Creator: Robert Benard

Collection management systems and the holdings they describe are the core and starting point for a whole range of digital scenarios which enable scientific memory institutions such as museums, archives and similar collections to offer new, broader and more ambitious uses of their holdings in the context of the digital transformation. At the same time, digitality entails a dissolution of the boundaries of traditional collection concepts in all GLAM institutions.

Therefore, it is all the more important to increase the sustainable usability of the permanently curated data holdings by improving data quality. This often involves a change in the focus of documentation and in the corresponding work processes, places new demands on the data literacy of the staff and, last but not least, on the performance spectrum of the software used. How can criteria for data sustainability and quality be specified? How can collections actively shape the process towards better data?

The NFDI4Culture task areas "Standards, Data Quality, Curation" and "Cultural Research Data Academy" are holding a four-part workshop series on this topic in association with the Documentation Section of the German Museums Association. It is aimed primarily at digital managers or digital curators in cultural heritage collections and related projects, as well as at persons involved in process management, the adaptation of digital collection strategies, data publication and consultancy in these areas. The course will provide quality management strategies for all stages of the data life cycle and practical measures for their implementation.