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Modern science generates large complicated heterogeneous collections of data. In order to effectively exploit these data, researchers must find relevant data, and enough of its associated metadata to understand it and put it in context. This problem exists across a wide range of research domains and is ripe for a general solution. Existing ventures address these issues using ad hoc purpose-built tools. These tools explicitly represent the data relationships by embedding them in their data storage mechanisms and in their applications. While producing useful tools, these approaches tend to be difficult to extend and data relationships are not necessarily traversable symmetrically. We are building a general system for navigational metadata. The relationships between data and between annotations and data are stored as first-class objects in the system. They can be viewed as instances drawn from a small set of graph types. General-purpose programs can be written which allow users explore these graphs and gain insights into their data. This process of data navigation, successive inclusion and filtering of objects provides powerful paradigm for data exploration. 

Rich metadata is required to find and understand the recorded measurements from modern experiments with their immense and complex data stores. Systems to store and manage these metadata have improved over time, but in most cases are ad-hoc collections of data relationships, often represented in domain or site specific application code. We are developing a general set of tools to store, manage, and retrieve datarelationship metadata. These tools will be agnostic to the underlying data storage mechanisms, and to the data stored in them, making the system applicable across a wide range of science domains.