# The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience

arXiv: https://arxiv.org/abs/1306.3543 · HTML: https://mlsanity.com/paper/1306.3543/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2013.

## Built on by (most cited first, 1 of 1)

- [Cascade Detector Analysis and Application to Biomedical Microscopy](https://mlsanity.com/paper/2504.21598/) (2025)

## Abstract (arXiv)

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.