This paper presents the concepts of metadata assessment and “quantification”, provides a technical outline of data pre-processing, and proposes some visualization techniques that can help us understand metadata characteristics in a given context. Additionally, the closing sections introduce the concept of metadata optimization and explore the use of machine learning techniques to optimize metadata in the context of large-scale metadata aggregators like DPLA. Finally, the article considers the broad potential for machine learning and data science in libraries, academic institutions, and cultural heritage.
Editors’ Choice: Metadata Analytics, Visualization, and Optimization
