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Paper short abstract

This paper examines how data-intensive biomedicine takes shape as a scientific field. Drawing on laboratory ethnography and 22 interviews, it shows how programming languages become sites where expertise, authority, and disciplinary boundaries are negotiated in the formation of this emerging domain.

Paper long abstract

“All biology is computational biology” has become a recurrent motto in contemporary life sciences. Far from simply describing a methodological shift, this claim signals the consolidation of an emerging field—“data-intensive biomedicine” (diB) —at the intersection of biology, bioinformatics, and computer science.

Public agencies and scientific foundations are investing in computational infrastructures and large-scale data platforms contributing to the institutional consolidation of diB. Yet beneath this infrastructural expansion lies not the flattening of experimental practices into purely computational research environments, but rather a struggle over the epistemic identity of this emerging field.

Actors from different disciplinary backgrounds compete to define the expertise, authority, and intellectual foundations of this expanding domain. Professional jurisdictional claims intersect with epistemic questions about what counts as legitimate knowledge, credible expertise, and proper ways of doing biology in computational environments.

This paper investigates these dynamics through laboratory ethnography and 22 qualitative interviews with bioinformaticians, computational biologists, and computer scientists. It focuses on programming languages as key sites where expertise, authority, and professional identity are negotiated in research practices.

Rather than neutral technical tools, programming languages embody epistemic orientations, ontological assumptions, and practical affordances that shape how biological problems are approached and solved. Commitments to particular coding languages and computational philosophies function both as markers of professional identity and as epistemic resources through which actors claim credibility, distribute authority, and organise divisions of labour.

Broadly, the paper shows how scientific fields stabilise through socio-technical negotiations over tools, practices, and expertise.

Scientific field formation; Boundary work; Biomedicine; Programming languages.