BossDB – Brain Observatory Storage Service and Database
Type: Optics / Microscopy,
Type: Software,
Keywords: Database, Archive, Neuroscience, Electron Microscopy (EM), X-Ray Microtomography (XRM), Cloud, Volumetric, Connectomics, Scalable
Resource ID: SCR_017273
BossDB is a scalable cloud-based data ecosystem for storing large-scale volumetric neuroscience data from Electron Microscopy and X-ray Microtomography
BossDB (Brain Observatory Storage Service and Database, https://bossdb.org) is a cloud-based data ecosystem for large-scale volumetric 3D and 4D neuroimaging data. BossDB focuses primarily on storing volumetric Electron Microscopy (EM) and X-Ray Microtomography (XRM) datasets generated as a part of the BRAIN Initiative. BossDB stores high resolution, multi-channel image data with registered segmentations, annotations, and meshes, and connects to a number of community resources for data access and data visualization. BossDB also stores connectomics datasets and contains a number of software tools and interfaces for querying and searching connectomes.
* Volumetric neuroscience data ecosystem (multi-channel neuroimaging data, metadata, annotations, connectomes)
* Resilient, multi-tier cloud data storage & data caching for public and private data
Scalable, highly available RESTful interfaces and load balancing through multiple endpoints
* Serverless system
* User authentication / authorization (SSO)
* API with numerous core services, supported clients, integrated software and visualization tools
* Connectomics Research
* Neuroanatomical Research
* Neurally-inspired Artificial Intelligence Research
* Large scale high-resolution neuroimaging data analysis and visualization
Connectomics searching and analysis tools
* Mouse
* Fly (Drosophila Melanogaster)
* Nematode (Caenorhabditis elegans)
* Zebrafinch
* Zebrafish
* Scalability
* High-speed data access
* High-speed data ingest
* An internet connection and web browser are the main requirements to get started, reach out to us at info@bossdb.org with any questions!
Hider, 2022, “The Brain Observatory Storage Service and Database (BossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery,” Frontiers in Neuroinformatics, https://doi.org/10.3389/fninf.2022.828787
Matelsky, 2022, “Scalable graph analysis tools for the connectomics community,” bioRxiv, https://doi.org/10.1101/2022.06.01.494307
Sanchez, 2022, “Annotation Metadata Standardization for Increased Accessibility and Queryability, Frontiers in Neuroinformatics,” https://doi.org/10.3389/fninf.2022.828458
Matelsky, 2021, “An Integrated Toolkit for Extensible and Reproducible Neuroscience,” IEEE EMBC 2021, https://doi.org/10.1109/EMBC46164.2021.9630199
Bishop, 2021, “CONFIRMS: A Toolkit for Scalable, Black Box Connectome Assessment and Investigation,” IEEE EMBC 2021, https://doi.org/10.1109/EMBC46164.2021.9630109
Matelsky, 2021, “DotMotif: An open-source tool for connectome subgraph isomorphism search and graph queries,” Nature Scientific Reports, https://doi.org/10.1038/s41598-021-91025-5
Johnson, 2020, “Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets,” Gigascience, https://doi.org/10.1093/gigascience/giaa147
Drenkow, 2020, “Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity,” MICCAI 2020, https://doi.org/10.1007/978-3-030-59722-1_11″
Brock Wester, PI
Johns Hopkins University Applied Physics Laboratory (JHU/APL), Laurel, MD
TEAM / COLLABORATOR(S)
William Gray-Roncal, Key Personnel, JHU/APL
Sandy Hider, Key Personnel, Software Lead, JHU/APL
Daniel Xenes, Software Developer, JHU/APL
Jordan Matelsky, Software Developer, JHU/APL
Tim Gion, Software Developer, JHU/APL
Erik C Johnson, Software Developer, Standards Lead, JHU/APL
FUNDING SOURCE(S)
* R24MH114785