Aline Potiron

Measuring diversity in microbial ecology and microbiome studies is fraught with challenges, rendering the assessment of its “real-world” value nearly impossible. The instability of taxonomic classification, difficulty in isolating individuals, and reliance on DNA-based methods and statistical tools all contribute to the complexity of measuring diversity reliably. This manuscript explores the underlying philosophical issues, relating them to the measurement problem in philosophy. I argue that traditional philosophical accounts of measurement, including representational, operationalist, and realist approaches, are insufficient to address these issues. Instead, I examine these challenges through the lens of a model-based perspective on measurement, which can remain agnostic about entities and property ontologies, clarify the role of assumptions in diversity measurement, and provide solutions for justifying measurement procedures. This work emphasizes the importance of calibration and clearly defining measurement purposes, providing avenues for scientists to improve their measurement procedures. Ultimately, I contribute to a deeper understanding of the challenges and opportunities in measuring microbial diversity by bridging the gap between philosophy and scientific practice.

This paper proposes an original definition of samples as a kind of data within the relational framework of data. The distinction between scientific objects (e.g., samples, data, models) often needs to be clarified in the philosophy of science to understand their role in the scientific inquiry. The relational framework places data at the forefront of knowledge construction. Their epistemic status depends on their evaluation as potential evidence in a research situation and their ability to circulate among researchers. While samples are significant in data-generating science, their role has been underexplored in the philosophy of data literature. I draw on a case study from data-centric microbiology, viz. amplicon sequencing, to introduce specifications of the relational framework. These specifications capture the distinctive epistemic role of samples, allowing the discussion of their significance in the inquiry process. I argue that samples are necessarily transformed to be considered as evidence, portable in the limits of a situation, and they act as world anchors for claims about a phenomenon. I compare these specifications with other data and evidence frameworks and suggest they are compatible. The paper concludes by considering the extension of these criteria in the context of biobanking. The specifications proposed here help analyze other life sciences cases and deepen our understanding of samples and their epistemological role in scientific research.