This dissertation presents the results of three efforts to build practical systems for formalizing concepts in science:neuron types, brain regions, and experimental protocols. Neuron types and brain regions are foundational concepts in neuroscience, and protocols are foundational for all scientific results and the concepts we build from them.

Chapter One presents the Neuron Phenotype Ontology and supporting tools and their application to model common types generallyknown in the field and to experimental types defined by the exact techniques employed in a single lab. The goal: to provide a common method to name and communicate about neuron types by composing types as collections of phenotypes with each phenotype being a pair of a term for the value of the phenotype drawn from existing shared terminology and a relationship that captures the data modality of and methodology used to determine that value.

Chapter Two presents the AtOM ontology model for anatomical atlases and the results of applying it to model a wide rangeof extant atlases. The goal: to establish standard ways of identifying atlases and their versions and enabling their use in digital infrastructure to facilitate a wide variety of use cases. Examples include clear communication about the exact spatial and semantic brain regions from which experimental data are collected and linking those regions to the methodologically defined criteria used to delineate their boundaries.

Chapter Three presents protc/ur, a domain specific language for specifying protocols, and presents the results of applying it toextract structured data from experimental protocols. The goals: to validate the protc/ur domain model and curation workflows, show that protc/ur enables queries over complex relationships to find quantitative data extracted from natural language, and, ultimately, demonstrate that protc/ur and the system as a whole are an effective way to formalize protocols and make the details of methodology visible in information systems.

Taken together these chapters show the effectiveness of using experimental methodology as an organizing principle in scientificinformation systems and the potential that it has as a guiding principle for building practical tools for working scientists.