Research
My current research falls into two broad areas. Both are really about helping scientists to think clearly and communicate effectively.
Scientific Explanation
One of the goals of science is to explain. Scientists explain to other scientists how a particular gene regulates a phenotype, how patterns in the luminance of a particular star indicate the presence of planets, how the sizes of the populations of two particular linked species vary with environmental changes. Scientists explain to their students how genes are transcribed and code for proteins, how planets orbit and occlude stars, how the sizes of populations of organisms change and correlate. And scientists explain to the public and to policy makers how genes affect health and disease, the place of our planet and sun among the stars, and the effects of ecology on our lives and livelihoods. Science explains insofar as the body of scientific work provides the resources – the facts, the formalisms, and the arguments – to support these explanations that scientists offer.
I approach scientific explanation from the perspective of philosophy of science. Philosophers want to know what a scientific explanation is, and what makes a good scientific explanation. There are many different philosophical accounts of explanation in science, but none of them works across all the sciences, or applies to all the different practices of scientific explanation.
In my dissertation research I take a new look at the evidence we have about scientific explanation. I collect one year of articles from the journal Science and apply text-mining techniques to find patterns in the usage of words such as “explain” and “explanation”. Then I use representative random samples from the data set to develop and test a general account of explanation in science.
Building on my dissertation, I’m interested in formal models for scientific explanation. I’ve developed my account in part through an engagement with scientific informatics, and I have an interest in developing software tools that help scientists build and share better explanations. Among the key components for that task are ontologies.
Ontologies
The rise of science informatics demands attention from philosophers. “Big science” projects such as the Human Genome Project and the Large Hadron Collider devote large portions of their budgets to information technology, and wouldn’t be possible without it. Granting agencies are starting to require smaller research projects to share their data using systems such as the National Cancer Institute’s cancer Biomedical Informatics Grid (caBIG). But computer systems, like social institutions, shape our practices and our thinking by making some things easy, some things difficult, and some things impossible. We usually start with software designed around a simplification of our concepts and practices, and we end up redesigning our concepts and practices around the strengths and limitations of the software.
The Open Biomedical Ontologies Consortium (OBO) brings together dozens of domain ontology projects, from amphibian gross anatomy to vaccines, under a set of shared best practices. Each domain ontology provides a network of terminology within a scientific domain, where each term is carefully defined and linked to other terms using well-defined relations. While each domain ontology is narrowly focused, they are designed to interoperate and form a larger network of biomedical terminology. And all OBO ontologies share a common Basic Formal Ontology which makes fundamental ontological classifications familiar to philosophers.
Recently I’ve become involved with the development of the Ontology for Biomedical Investigations (OBI). I also collaborate with Dr. Cesare Romagnoli on applying ontologies to radiology. I’m using OBI and other ontologies to develop a structured reporting system for radiology.