The U.S. Army Research Laboratory (ARL) conducted an initial study on the performance of XML and HDF5 in three
popular computational software environments, MATLAB, Octave, and Python, all of which use high-level scripting
languages and computational software tools designed for computational processing. Although usable for sharing and
exchanging data, the initial results of the study indicated XML has clear limitations in a computational environment.
Popular computational tools are unable to handle very large XML formatted files, thus limiting processing of large XML
archived data files. We show the breakdown points of XML formatted files for various popular computational tools and
explore the performance dependencies of XML and HDF5 formatted files in popular computational environments on the
hardware, operating system, and mathematical function. This study also explores the inverse file size relationship
between HDF5 and XML data files. Several organizations, including ARL, use both XML and HDF5 for archiving and
exchanging data. XML is best suited for storing "light" data (such as metadata) and HDF5 is best suited for storing
"heavy" scientific data. Integrating and using both XML and HDF5 for data archiving offers the best solution for data
providers and consumers to share information for computational and scientific purposes.