As part of ongoing research projects, three Jagiellonian University units -- the Jagiellonian University Museum, the Jagiellonian University Archives, and the Jagiellonian Library -- are collaborating to digitize cultural heritage documents, describe them in detail, and then integrate these descriptions into a linked data cloud. Achieving this goal requires, as a first step, the development of a metadata model that, on the one hand, complies with existing standards, on the other hand, allows interoperability with other systems, and on the third, captures all the elements of description established by the curators of the collections. In this paper, we present a report on the current status of the work, in which we outline the most important requirements for the data model under development and then make a detailed comparison with the two standards that are the most relevant from the point of view of collections: Europeana Data Model used in Europeana and Encoded Archival Description used in Kalliope.

Enrichment plays a fundamental role in Europeana's activities. In our context, enrichment can be defined as generating metadata from the data provided by our partners, adding extra value to the data we receive. We use the combination of original and enriched metadata for indexing our records, and this lets us build functionalities that allow people to search and browse our collections, and receive recommendations. Achieving automatic enrichment using machine learning algorithms is one of the objectives of the Europeana Strategy 2020-2025, triggering projects such as Saint George on a Bike. Europeana's R&D team is exploring how computer vision techniques (systems which can make sense of visual data) can improve the enrichment Europeana conducts.

AI terminology can be complex, so let's clear up some definitions. While reading our posts you might see terms like'machine learning', 'deep learning', 'models' or'training'. Machine learning vs deep learning is a common area of confusion for those not familiar with AI techniques. Machine learning consists of a set of algorithms which automatically learn from data. Deep learning is a type of machine learning that excels in solving problems with high dimensionality (where the number of features is much greater than the number of observations). Deep learning uses a family of models inspired by the structure and functioning of the brain (artificial neural networks) that effectively learn to extract relevant features from the data.