  Computational microscopy, in which hardware and algorithms of an imaging
system are jointly designed, shows promise for making imaging systems that cost
less, perform more robustly, and collect new types of information. Often, the
performance of computational imaging systems, especially those that incorporate
machine learning, is sample-dependent. Thus, standardized datasets are an
essential tool for comparing the performance of different approaches. Here, we
introduce the Berkeley Single Cell Computational Microscopy (BSCCM) dataset,
which contains over ~12,000,000 images of 400,000 of individual white blood
cells. The dataset contains images captured with multiple illumination patterns
on an LED array microscope and fluorescent measurements of the abundance of
surface proteins that mark different cell types. We hope this dataset will
provide a valuable resource for the development and testing of new algorithms
in computational microscopy and computer vision with practical biomedical
applications.
