This in-person workshop provides a comprehensive introduction to record linkage for practitioners across diverse scientific disciplines, including medicine, the social sciences, and economics. It addresses the fundamental methodological challenges of record linkage, particularly data quality, classification error, and privacy protection. Participants will gain a practical understanding of these challenges and the conceptual foundations required to address them effectively. Special emphasis will be placed on privacy-preserving record linkage (PPRL), which aims to enable accurate linkage while safeguarding sensitive personal information. The workshop concludes with a hands-on computational demonstration in R that illustrates selected approaches. These include probabilistic record linkage based on the Fellegi-Sunter model and privacy-preserving techniques employing Bloom filters.

Do you ever feel that the data you need for your research is accessible but it’s not in a convenient table, such as company reports or building plans? Perhaps the information you need is spread out across many different documents? If only we could read and extract structured data from thousands of written documents. In this course, we explore how to accomplish this task by combining web scraping, Optical Character Recognition (OCR), and Natural Language Processing (NLP). Over four weeks, we provide online lessons and interactive sessions to learn the fundamentals of these key technologies.
The course includes 4 live Online Meetings, in which you will discuss the week’s contents with the instructor and fellow participants.