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COINSTAC (Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) promotes collaborative research by removing large barriers to traditional data-centric approaches. It allows groups of users to run common analyses on their own machines over their own datasets with ease. The results of these analyses are synchronized to the cloud and undergo aggregate analysis processes using all contributor data. Federated (decentralized) pipelines enable distributed, iterative, and feature-rich analyses, opening up new possibilities for collaborative computation. It also offers data anonymity through differentially private algorithms, so members do not need to fear protected health information (PHI) traceback.
The goal of this discussion is to briefly introduce COINSTAC and its new features/algorithms to perform statistical analysis on various datasets. New features include:
Doing statistical analysis using Singularity containers
Command line pre-processing tools
New algorithms including but not limited to Decentralized Source Based Morphometry
New COINSTAC federated analysis architecture powered by NVIDIA FLARE
We would like to hear feedback about our software such as how to improve the experience for researchers. We welcome anyone who wants to contribute to this open source and open data project with their datasets, algorithms, and code. We would also like to work with other organizations to pursue grants together, including small business grants. Collaborating with other organizations is the best way for us to answer interesting neuroscience-related questions that would not have been possible without COINSTAC and COINSTAC Vaults.
Hi there, this sounds like an interesting session. Thanks for submitting. We have allocated emergent session 4, Tuesday 25th 08:00-09:00, to this topic. If you send me an email I can provide you with more information: [email protected]. Looking forward to meet you in Montreal. Best, Selma
By Sandeep Panta, Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University
Recording and streaming volunteer
Emergent session #3, #1
Short description and the goals for the session
COINSTAC (Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) promotes collaborative research by removing large barriers to traditional data-centric approaches. It allows groups of users to run common analyses on their own machines over their own datasets with ease. The results of these analyses are synchronized to the cloud and undergo aggregate analysis processes using all contributor data. Federated (decentralized) pipelines enable distributed, iterative, and feature-rich analyses, opening up new possibilities for collaborative computation. It also offers data anonymity through differentially private algorithms, so members do not need to fear protected health information (PHI) traceback.
The goal of this discussion is to briefly introduce COINSTAC and its new features/algorithms to perform statistical analysis on various datasets. New features include:
Doing statistical analysis using Singularity containers
Command line pre-processing tools
New algorithms including but not limited to Decentralized Source Based Morphometry
New COINSTAC federated analysis architecture powered by NVIDIA FLARE
We would like to hear feedback about our software such as how to improve the experience for researchers. We welcome anyone who wants to contribute to this open source and open data project with their datasets, algorithms, and code. We would also like to work with other organizations to pursue grants together, including small business grants. Collaborating with other organizations is the best way for us to answer interesting neuroscience-related questions that would not have been possible without COINSTAC and COINSTAC Vaults.
Useful Links
COINSTAC Github
COINSTAC Vaults intro webpage
Vaults demo video on Freesurfer data
COINSTAC educational videos
https://trendscenter.org/
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