Ontologies have established themselves as the major framework for knowledge transfer and sharing. They allow consistent understanding of data to both computers and human modellers. This is done through a standard representation of a domains world view which captures the classes and relationships that exist in a particular domain. The interest in capturing a domains world view has led to the creation of many ontologies as ontological developers create ontologies to model their world views. An ontologies world view is a major contributor to reuse and interoperation. The significant number of ontologies produced has created a wealth a knowledge but particular application or domain specific views creates issue for others. The issue is com- munication and interoperation between ontologies. With so many different designs and terminology it is difficult to make use of existing terms and instances within these ontolo- gies without creating some way to relate or translate terminology. This thesis tackles the problem of data collection among ontologies through answering the question: How does one model the domain of data collection using an ontology while maintaining a level of domain agnosticism such that the ontology can be reused for any domain? We propose that a mid level ontology design can be used to model the domain of data collection while remaining domain generic otherwise. Consequently, we present the Data Collection Ontology (DCO) and evaluate it to show that we enable reusability through its high level class hierarchies that allow domain level terminology to be represented within the DCO. Direct contributions of this work include the Data Collection Ontology (DCO), the DCO Survey Ontology as well the philosophy of Classifiers as a way to introduce reasoning and dynamic ontology support.