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

An Efficient, Scalable IO Framework for Sparse Data: larcv3