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Raw data sizes are growing and proliferating in scientific research, drivenby the success of data-hungry computational methods, such as machine learning.The preponderance of proprietary and shoehorned data formats make computationsslower and make it harder to reproduce research and to port methods to newplatforms. Here we present the RawArray format: a simple, fast, and extensibleformat for archival storage of multidimensional numeric arrays on disk.  The RawArray file format is a simple concatenation of a header array and adata array. The header comprises seven or more 64-bit unsigned integers. Thearray data can be anything. Arbitrary user metadata can be appended to anRawArray file if desired, for example to store measurement details, colorpalettes, or geolocation data.  We present benchmarks showing a factor of 2--3$\times$ speedup over HDF5 fora range of array sizes and a speedup of up to 20$\times$ in reading the commondeep learning datasets MNIST and CIFAR10.

RawArray: A Simple, Fast, and Extensible Archival Format for Numeric Data