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DVID: Distributed Versioned Image-Oriented Dataservice

Authors :
William T. Katz
Stephen M. Plaza
Source :
Frontiers in Neural Circuits, Vol 13 (2019), Frontiers in Neural Circuits
Publication Year :
2019
Publisher :
Frontiers Media S.A., 2019.

Abstract

Open-source software development has skyrocketed in part due to community tools like github.com, which allows publication of code as well as the ability to create branches and push accepted modifications back to the original repository. As the number and size of EM-based datasets increases, the connectomics community faces similar issues when we publish snapshot data corresponding to a publication. Ideally, there would be a mechanism where remote collaborators could modify branches of the data and then flexibly reintegrate results via moderated acceptance of changes. The DVID system provides a web-based connectomics API and the first steps toward such a distributed versioning approach to EM-based connectomics datasets. Through its use as the central data resource for Janelia's FlyEM team, we have integrated the concepts of distributed versioning into reconstruction workflows, allowing support for proofreader training and segmentation experiments through branched, versioned data. DVID also supports persistence to a variety of storage systems from high-speed local SSDs to cloud-based object stores, which allows its deployment on laptops as well as large servers. The tailoring of the backend storage to each type of connectomics data leads to efficient storage and fast queries. DVID is freely available as open-source software with an increasing number of supported storage options.

Details

Language :
English
ISSN :
16625110
Volume :
13
Database :
OpenAIRE
Journal :
Frontiers in Neural Circuits
Accession number :
edsair.doi.dedup.....eee3223a10b84a1cf7c1b1f2eef3cdb7
Full Text :
https://doi.org/10.3389/fncir.2019.00005/full