  In High Energy Physics (HEP), experimentalists generate large volumes of data
that, when analyzed, helps us better understand the fundamental particles and
their interactions. This data is often captured in many files of small size,
creating a data management challenge for scientists. In order to better
facilitate data management, transfer, and analysis on large scale platforms, it
is advantageous to aggregate data further into a smaller number of larger
files. However, this translation process can consume significant time and
resources, and if performed incorrectly the resulting aggregated files can be
inefficient for highly parallel access during analysis on large scale
platforms. In this paper, we present our case study on parallel I/O strategies
and HDF5 features for reducing data aggregation time, making effective use of
compression, and ensuring efficient access to the resulting data during
analysis at scale. We focus on NOvA detector data in this case study, a
large-scale HEP experiment generating many terabytes of data. The lessons
learned from our case study inform the handling of similar datasets, thus
expanding community knowledge related to this common data management task.
