Bio

Sabina Leonelli is a professor in philosophy and history of science at the University of Exeter, where she co-directs the Centre for the Study of the Life Sciences (Egenis). She gained her PhD at the Vrije Universiteit Amsterdam, following an MSc in history and philosophy of science at the London School of Economics and a BSc (hons) in history, philosophy and social studies of science at University College London.

Her research focuses on the methods and assumptions involved in the use of big data for discovery; the challenges involved in the extraction of knowledge from digital infrastructures, and the implications of choices in data curation for the outputs and uses of science and technology; the role of the open science movement within current landscapes of knowledge production, including concerns around inequality; and the status and history of experimental organisms as scientific models and data sources. She published widely in a variety of disciplines including philosophy, history, social studies of science, data science and biology; and is active in science policy, particularly as adviser on Open Science implementation for the European Commission and the steering boards of various research data infrastructures.

Research interests

A key task for data science is to develop classification systems through which diverse types of data can be aligned to provide common ground for data mining and discovery. Developing such systems constitutes a challenge in the biological, biomedical and environmental sciences, where the methods and vocabulary used to classify data are often unique to the interests of those involved in data generation. 

On the one hand, linking disparate data together requires agreeing on common standards and keywords without losing information that may prove crucial to data interpretation. On the other hand, the choice of data classification systems requires a high degree of domain-specific knowledge and familiarity with the object and processes that data are aimed to document. These systems determine which claims – and about what – data are taken as evidence for; whose knowledge is legitimised or excluded by analytic tools; and whose perspective is incorporated within data-driven knowledge systems. 

What keywords should be used to bring data together or apart, how should these keywords be defined, who should make such decisions, and what are their implications for knowledge production and its impact on global society? This project addresses these questions through philosophical, historical and social scientific methods, aiming to provide a systematic analysis of (1) international initiatives aimed to facilitate data linkage; (2) the historical roots and motivations underpinning their classifications; and (3) the ways in which they map onto contemporary social challenges, such as the pursuit of the Sustainable Development Goals set out by the United Nations.