  We describe a scalable database cluster for the spatial analysis and
annotation of high-throughput brain imaging data, initially for 3-d electron
microscopy image stacks, but for time-series and multi-channel data as well.
The system was designed primarily for workloads that build connectomes---neural
connectivity maps of the brain---using the parallel execution of computer
vision algorithms on high-performance compute clusters. These services and
open-science data sets are publicly available at http://openconnecto.me.
  The system design inherits much from NoSQL scale-out and data-intensive
computing architectures. We distribute data to cluster nodes by partitioning a
spatial index. We direct I/O to different systems---reads to parallel disk
arrays and writes to solid-state storage---to avoid I/O interference and
maximize throughput. All programming interfaces are RESTful Web services, which
are simple and stateless, improving scalability and usability. We include a
performance evaluation of the production system, highlighting the effectiveness
of spatial data organization.
