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In this paper three algorithms are developed for the streaming compression of scientific data. The algorithms presented are reliant on the theory of vector-valued reproducing kernel Hilbert spaces and operator valued kernels. The scientific data is modeled as a snapshot of time dependent vector-field F(x,t) over a manifold M and the recovery of the data is framed as a learning problem. These processes are then appropriately modified and analyzed for the streaming scenario in which data is generated without the ability to revisit past entries.