Data mapping framework in a digital library with computational epidemiology datasets

Computational epidemiology employs computer models and informatics tools to reason about the spatio-temporal spread of diseases. The diversity of models, data sources, data representations, and modalities that are collected, used, and modified motivate the development of a digital library (DL) framework to support computational epidemiology. The heterogeneous content includes metadata, text, tables, spreadsheets, experimental descriptions, and large result files. There is no accepted framework that allows unified access to such content. We propose a framework for a digital library system tailored to such datasets to support computational network epidemiology.