The AI:CULT project addresses the gap between AI and our digital cultural heritage. Cultural heritage data is rarely objective data. The very reasons for certain heritage data to be preserved, its interpretation throughout time, and the way heritage data is accessed after digitalisation is all subject to strong biases. The inherent richness, subjectivity and polyvocal … Read more
Computers have become indispensable for the storage, dissemination, and representation of literary and historical sources. What is more, computers can be used not only to assist our research practice, but also as research instruments in and by themselves. This means we have to represent texts in a way that is computationally processable. This entails thinking … Read more
Many large digital text collections and computational tools that are available online today, allow humanities scholars to address a wide array of research questions. This often involves many data transformations: gathering and selecting documents, extracting and modelling the relevant data in them, cleaning and normalising this data, and linking dispersed information both within the resource … Read more
Collation, an important step in scholarly editing, involves comparing two or more versions of a work. The aim of this project is on the one hand to reflect on the collation process, where we deal with questions such as “how do we define ‘textual variation’?” and “what are the methodological ramifications of automating this important … Read more
