Abstract
Technological advances in imaging and data acquisition are leading to the development of petabyte-scale neuroscience image datasets. These large-scale volumetric datasets pose unique challenges since analyses often span the entire volume, requiring a unified platform to access it. In this paper, we describe the Brain Observatory Storage Service and Database (BossDB), a cloud-based solution for storing and accessing petascale image datasets. BossDB provides support for data ingest, storage, visualization, and sharing through a RESTful Application Programming Interface (API). A key feature is the scalable indexing of spatial data and automatic and manual annotations to facilitate data discovery. Our project is open source and can be easily and cost effectively used for a variety of modalities and applications, and has effectively worked with datasets over a petabyte in size.
Highlights
Mapping the brain to better understand cognitive processes and the biological basis for disease is a fundamental challenge of the BRAIN Initiative
Many of our design requirements for the Brain Observatory Storage Service and Database (BossDB) ecosystem were motivated by the activities planned for the Intelligent Advanced Research Projects Activity (IARPA) Machine Intelligent from Cortical Networks (MICrONS) Program4
This effort seeks to enable the rapid advancement of artificial intelligence capabilities by creating novel machine learning algorithms that use neurally-inspired architectures and mathematical abstractions of the representations, transformations, and learning rules employed by the brain4
Summary
Technological advances in neuroimaging have grown rapidly over the last ten years, making it almost routine to image high-resolution (sub-micron) brain volumes in many laboratories around the world using Electron Microscopy (EM) and X-Ray Microtomography (XRM), among other imaging modalities (Bock et al, 2011; Helmstaedter et al, 2013; Kasthuri et al, 2015; Lee et al, 2016; Dupre and Yuste, 2017; Witvliet et al, 2021). These datasets, which provide the means to resolve individual neurons and the individual connections (synapses) between them, are highly valuable for providing key insights into neural connectivity and neuroanatomical features. While research groups are beginning to embrace data archives, most treat the system as a place to
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