Abstract In recent years, cultural heritage institutions have been exploring the benefits of applying Linked Open Data to their catalogs and digital materials. Innovative and creative methods have emerged to publish and reuse digital contents to promote computational access, such as the concepts of Labs and Collections as Data. Data quality has become a requirement for researchers and training methods based on artificial intelligence and machine learning. This article explores how the quality of Linked Open Data made available by cultural heritage institutions can be automatically assessed. The results obtained can be useful for other institutions who wish to publish and assess their collections.

Abstract GLAM organizations have been digitizing their collections and making them available for the public for several decades. Recent methods for publishing digital collections such as “GLAM Labs” and “Collections as Data” provide guidelines for the application of computational methods to reuse the contents of cultural heritage institutions in innovative and creative ways. Jupyter Notebooks have become a powerful tool to foster use of these collections by digital humanities researchers. Based on previous approaches for quality assessment, which have been adapted for cultural heritage collections, this paper proposes a methodology for assessing the quality of projects based on Jupyter Notebooks published by relevant GLAM institutions. A list of projects based on Jupyter Notebooks using cultural heritage data has been evaluated. Common features and best practices have been identified. A detailed analysis, that can be useful for organizations interested in creating their own Jupyter Notebooks projects, has been provided. Open issues requiring further work and additional avenues for exploration are outlined.

Abstract For some decades now, galleries, libraries, archives, and museums (GLAM) institutions have provided access to information resources in digital format. Although some datasets are openly available, they are often not used to their full potential. Recently, approaches such as the so‐called Labs within GLAM institutions promote the reuse of digital collections in innovative and inspiring ways. In this article, we explore a straightforward computational procedure to identify emerging topics in periodical materials such as newspapers, bibliographies, and journals. The method is illustrated in three use cases based on public digital collections. This type of tools are expected to promote further usage by researchers of the digital collections.