Famously, scientific theories are underdetermined by their evidence. This occurs in the factor analytic model, which is often used to connect concrete data to hypothetical notions. After introducing FA, three general topics are addressed. Underdetermination: the precise reasons why FA is underdetermined illuminates various claims about underdetermination, abduction, and theoretical terms. Uncertainties: FA helps distinguish at least four kinds of uncertainties. The prevailing practice, often encoded in statistical software, is to ignore the most difficult kinds, which are essential to FA's (...) underdetermination. What to do: some suggestions for dealing with these hardest types of uncertainty are offered. (shrink)

Research on the human microbiome has generated a staggering amount of sequence data, revealing variation in microbial diversity at the community, species (or phylotype), and genomic levels. In order to make this complexity more manageable and easier to interpret, new units—the metagenome, core microbiome, and enterotype—have been introduced in the scientific literature. Here, I argue that analytical tools and exploratory statistical methods, coupled with a translational imperative, are the primary drivers of this new ontology. By reducing the dimensionality of variation (...) in the human microbiome, these new units render it more tractable and easier to interpret, and hence serve an important heuristic role. Nonetheless, there are several reasons to be cautious about these new categories prematurely ‘‘hardening’’ into natural units: a lack of constraints on what can be sequenced metagenomically, freedom of choice in taxonomic level in defining a ‘‘core microbiome,’’ typological framing of some of the concepts, and possible reification of statistical constructs. Finally, lessons from the Human Genome Project have led to a translational imperative: a drive to derive results from the exploration of microbiome variation that can help to articulate the emerging paradigm of personalized genomic medicine (PGM). There is a tension between the typologizing inherent in much of this research and the personal in PGM. (shrink)

Research on the human microbiome has gen- erated a staggering amount of sequence data, revealing variation in microbial diversity at the community, species (or phylotype), and genomic levels. In order to make this complexity more manageable and easier to interpret, new units—the metagenome, core microbiome, and entero- type—have been introduced in the scientific literature. Here, I argue that analytical tools and exploratory statisti- cal methods, coupled with a translational imperative, are the primary drivers of this new ontology. By reducing the (...) dimensionality of variation in the human microbiome, these new units render it more tractable and easier to interpret, and hence serve an important heuristic role. Nonetheless, there are several reasons to be cautious about these new categories prematurely ‘‘hardening’’ into natural units: a lack of constraints on what can be sequenced metagenomically, freedom of choice in taxonomic level in defining a ‘‘core microbiome,’’ typological framing of some of the concepts, and possible reification of statistical constructs. Finally, lessons from the Human Genome Project have led to a translational imperative: a drive to derive results from the exploration of microbiome variation that can help to articulate the emerging paradigm of per- sonalized genomic medicine (PGM). There is a tension between the typologizing inherent in much of this research and the personal in PGM. (shrink)

We live in a world that increasingly is designed by engineers. So it is worth asking what are engineers doing when they design. There is no simple universal answer to this question, and my strategy for answering it both acknowledges the impossibility of a simple answer, while also identifying and elaborating some important elements to engineering realities. I start with the simple posit that engineering a reality is about controlling aspects of that reality through designed artifice. I then “complexify” this (...) simple idea by examining one company’s multiple contributions to engineering realities. Ometric Corporation was birthed out of the USC NanoCenter in the fall of 2004. The company makes spectrometric equipment that allows for “real time in line” analysis—and control—of materials. Markets that Ometric is focusing on include pharmaceuticals, food and energy. But Ometric lives in an engineer’s reality, while simultaneously working to engineer realities. It must survive financially, initially by selling the ideas behind its innovations to venture capitalists, and then by selling products to markets that may or may not currently exist. Ometric is the product of the University of South Carolina’s efforts to turn its intellectual property into gold for the University. Ometric, thus, is part of a larger effort to re-engineer the University and its relationship to industry. My examination of Ometric, then, identifies some key ways that “control through designed artifice” is a complex, and yet prevalent and powerful force in the construction of our realities. (shrink)