Cultural Analytics

The Cultural Analytics theme’s research projects focus around data science’s application to cultural heritage and use in the arts & humanities. For example, machine learning has been growing in importance over the past years, mainly due to advances in its capacity to automate increasingly complex tasks and to the availability of ever larger amounts of data. The cultural heritage sector has witnessed a parallel increase in the application of AI techniques to growing digital and digitized collections.  

This CREATE theme focuses on several areas of application including, but not limited to: data extraction and augmentation (e.g., Handwritten/Optical Character Recognition), structured information extraction (e.g., event extraction), enrichment and creation of data/metadata (e.g., visual link retrieval), search and retrieval systems, engagement and creativity (e.g., generative AI art), fundamental methodological research questions (e.g., model interpretability, active and transfer learning, multimodality).  

The Cultural Analytics theme associated with AI for Cultural Heritage is organized into the following tracks: 

• Fundamental research relevant to cultural data and collections, in close collaboration with the Informatics Institute at the Faculty of Science and other computer science experts.  
• Data-driven arts & humanities research, by collaborating with scholars in applying data-driven approaches and digital methods to arts & humanities research. 
• A.I. R&D in partnership with heritage institutions. A.I. can significantly help heritage institutions deliver on their missions. After several years of intense digitization campaigns, all heritage institutions seek to make their big data available to a broader public. Challenges include the automation of data curation end extraction, the improvement of means for information retrieval and user engagement. CREATE seeks to collaborate with heritage institutions in Amsterdam and beyond, and support them in developing innovative AI applications.