Data on flows of materials and substances through the economy and the environment are collected by many different organizations and play a key role in the science of industrial ecology. In this thesis, a framework is suggested for structuring and organizing such data. First, the investigation focuses on the quantities of primary interest in material flow studies and how they can be stored and organized in a data warehouse. This process is shown to provide easy access to data, well-structured data management, a basis for knowledge discovery, and effective analysis of collected data. Secondly, a theoretical framework is proposed for handling and structuring multidimensional flow data, and for facilitating mathematics-assisted modeling in industrial ecology. In particular, it is shown how mathematical operations can be used to merge and compare flow data originating from different studies. Finally, it is illustrated how bootstrap analysis, Bayesian models and balancing procedures can be employed to systematize the quality and uncertainty assessment of physical flow data. Together, these three different aspects of handling physical flow data constitute a new framework that offers better knowledge, quality, and consistency of the data used in industrial ecology.