Observational data (i.e., data that records observations and measurements) plays a key role in many scientific disciplines. Observational data, however, are typically structured and described in ad hoc ways, making its discovery and integration difficult. The wide range of data collected, the variety of ways the data are used, and the needs of existing analysis applications make it impractical to define "one-size-fits-all" schemas for most observational data sets. Instead, new approaches are needed to flexibly describe observational data for effective discovery and integration. In this paper, we present a generic conceptual-modeling framework for capturing the semantics of observational data. The framework extends standard conceptual modeling approaches with new constructs for describing observations and measurements. Key to the framework is the ability to describe observation context, including complex, nested context relationships. We describe our proposed modeling framework, focusing on context and its use in expressing observational data semantics.