@article {49149,
	title = {A community-developed Open-Source computational ecosystem for big neuro data.},
	journal = {Nature Methods},
	volume = {15},
	year = {2018},
	month = {2018 Nov},
	pages = {846-7},
	abstract = {<p>Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visualization, and analysis of these data in the cloud, thusfar spanning 20+ publications and 100+ terabytes including nanoscale ultrastructure, microscale synaptogenetic diversity, and mesoscale whole brain connectivity, making NeuroData the largest and most diverse open repository of brain data.</p>
},
	doi = {10.1038/s41592-018-0181-1},
	author = {Vogelstein, Joshua T and Perlman, Eric and Falk, Benjamin and Baden, Alex and Roncal, William Gray and Chandrashekhar, Vikram and Collman, Forrest and Seshamani, Sharmishtaa and Patsolic, Jesse and Lillaney, Kunal and Kazhdan, Michael and Hider Jr., Robert and Pryor, Derek and Matelsky, Jordan and Gion, Timothy and Manavalan, Priya and Wester, Brock and Chevillet, Mark and Trautman, Eric T and Khairy, Khaled}
}
@article {6904,
	title = {The Open Connectome Project Data Cluster: Scalable analysis and vision for high-throughput neuroscience.},
	journal = {Scientific and Statistical Database Management: International Conference, SSDBM ... : Proceedings. International Conference on Scientific and Statistical Database Management},
	year = {2013},
	month = {2013 Jun 18},
	abstract = {<p>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 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 effec-tiveness of spatial data organization.</p>
},
	doi = {10.1145/2484838.2484870},
	author = {Burns, Randal and Roncal, William Gray and Kleissas, Dean and Lillaney, Kunal and Manavalan, Priya and Perlman, Eric and Berger, Daniel R. and Bock, Davi D and Chung, Kwanghun and Grosenick, Logan and Kasthuri, Narayanan and Weiler, Nicholas C and Deisseroth, Karl and Kazhdan, Michael and Lichtman, Jeff and Reid, R Clay and Smith, Stephen J and Szalay, Alexander S and Vogelstein, Joshua T and Vogelstein, R Jacob}
}
