Data collection is essential in contemporary science, yet publishing datasets for reuse remains challenging as important context can be lost in the process. Lise Stork, assistant professor in the Intelligent Data Engineering Lab at UvA’s Informatics Institute, focuses on creating tools that assist researchers in overcoming these difficulties. Stork, appointed as a MacGillavry Fellow in 2024, works across several research domains to address these issues.

Her work includes FAIR data principles, which emphasize findability, accessibility, interoperability and reusability of data, alongside efforts in hybrid intelligence and e-Science. These combined approaches help researchers interpret varied data types, such as images, text and tables, and derive meaningful insights.

In practice, a PhD candidate in Stork’s group is developing techniques to detect outliers in biodiversity data and explain their significance, aiding scientists in deciding whether anomalies represent errors or new information. AI-enhanced workflows under development aim to support researchers in documenting dataset context, facilitating reuse and interdisciplinary communication. Stork highlights the role of AI as a mediator across different scientific fields.

Stork plans to build a focused research line at the intersection of hybrid intelligence, FAIR data and e-Science to advance scientific data practices at UvA.