Sabina Leonelli
Sabina Leonelli is a Professor of Philosophy and History of Science and Technology at the Technical University of Munich (TUM), where her work explores the intersection of data science, digitalization, and the life sciences. A leading voice in the epistemology of data-centric biology, her research focuses on how the structure of scientific knowledge is developed and its broader implications for society. To actively bridge the gap between academic research and citizens, she established the Public Science Lab at TUM, an initiative designed to bring diverse parts of society together to participate in the scientific process and address actual social needs. Moving beyond traditional theoretical frameworks, she critically examines the global institutionalization of „Open Science“ and advocates for epistemic diversity to ensure that standardizing data does not marginalize unique research traditions. By combining historical analysis with the philosophy of science, Prof. Leonelli is helping to redefine what constitutes responsible, globally inclusive research practices in the era of Big Data.
Biodata for Discovery: Cultivating Environmental Intelligence
Section titled “Biodata for Discovery: Cultivating Environmental Intelligence”This talk reflects on three challenges of biological data sharing and analysis, and their interrelations. The first challenge is data absence, with massive gaps in collections and surveys despite the enormous efforts invested in data gathering. The second challenge is the integration of AI into data collection, management and analysis, with severe implications for discovery. The third challenge is the development of interoperable and sustainable data ecosystems at a moment of great digital fragility. I argue that these three challenges can be confronted not by attempting to remove human agency and judgement from data analysis, but rather by articulating the role of human agency more clearly and placing it at the centre of AI-supported data management, integration and mining activities. I exemplify this argument through examples from bio- ontologies and other forms of data semantics, data sharing strategies and infrastructures [1, 2], and novel forms of AI-enabled data mining such as data visitations [3]. Historically and conceptually, the study of biodiversity has been closely associated with the attempt to understand and preserve what Darwin aptly characterised as the “endless forms most beautiful and most wonderful”. Contemporary efforts to share phenomic data about organisms across different locations, and between high-resourced and low-resourced research environments, place in sharp relief the complexity and diversity of biological and environmental characteristics as well as of the methods used to generate and process data and the goals, skills and expectations of the stakeholders involved. Building on my research on Environmental Intelligence [4], I show how the attempt to articulate semantic differences among specimen varieties and methods of data collection, sharing and analysis is generating new ways to conceptualise and study organisms.
