The concept of significant properties, properties that must be identified and preserved in any successful digital object preservation, is now common in data curation. Although this notion has clearly demonstrated its usefulness in cultural heritage domains its application to the preservation of scientific datasets is not as well developed. One obstacle to this application is that the familiar preservation models are not sufficiently explicit to identify the relevant entities, properties, and relationships involved in dataset preservation. We present a logic-based formal framework of dataset concepts that provides the levels of abstraction necessary to identify and correctly assign significant properties to their appropriate entities. A unique feature of this model is that it recognizes that a typed symbol structure is a unique requirement for datasets, but not for other information objects.