  Neutrino physics is one of the fundamental areas of research into the origins
and properties of the Universe. Many experimental neutrino projects use
sophisticated detectors to observe properties of these particles, and have
turned to deep learning and artificial intelligence techniques to analyze their
data. From this, we have developed \texttt{larcv}, a \texttt{C++} and
\texttt{Python} based framework for efficient IO of sparse data with particle
physics applications in mind. We describe in this paper the \texttt{larcv}
framework and some benchmark IO performance tests. \texttt{larcv} is designed
to enable fast and efficient IO of ragged and irregular data, at scale on
modern HPC systems, and is compatible with the most popular open source data
analysis tools in the Python ecosystem.
