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Collaborative Decentralized
Research and Analysis at your fingertips

On all major desktop platforms.


Online Seminar

August 1, 2023

Neuroimaging Workshop: Analysis of Structural and Functional MRI Data

See COINSTAC in action and learn about using our platform for analyses of neuroimaging data and access to curated public datasets.


COINSTAC is software to foster collaborative research, removing large barriers to traditional data-centric collaboration approaches. It enables 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 analyses processes using all contributor data. Decentralized pipelines allow for distributed, iterative, and feature rich analyses to be run, opening new and exciting capabilities for collaborative computation. It also offers data anonymity through differential privacy algorithms, so members do not need to fear PHI traceback.



Collaborative Informatics and Neuroimaging Suite
Toolkit for Anonymous Computation.

The Problem

You want to do research, and you want to include data from around the world. Unfortunately, orchestrating such an event is anything but trivial.


Coordinating data-driven research can be difficult. Who's going to collect all of the files? Who is going to actually "run" all of the data?


Ensuring privacy can be difficult. Can I trust other people or institutions with my research participants' data? Am I even allowed to share it?


Valuable research data may often not be shared due to privacy or IRB constraints.

Large datasets can be expensive to transfer. When file sets are in the GB and TB range, network transfers are not immediately trivial or even practical.


"Smart bullies" have demonstrated ability to extract personal information from various aggregated, anonymized datasets. How can we share data without revealing confidential information?


Bottom line: collaborative group research requires a great deal of coordination. Human and business factors can hamper research from happening at a pace that we are able to handle! Constraints may even forbid group research to occur at all.

The Solution

COINSTAC removes the barriers to collaborative analysis by:


Decentralizing analyses and computation

Each user performs analyses/pipelines/etc all on their own computers. Bits and pieces of each users' output may be sent to a central compute node

A central compute node performs a complimentary component of the group analysis, generally a Machine Learning algorithm. This node may trigger adjusted computations on users' machines, generally in effort to improve a model, which the research is trying to predict!


Not synchronizing full datasets.

Instead, synchronizing only resultant analysis metrics as previously discussed, central compute nodes aggregate these metrics, and attempt to draw conclusions from the contributor swarm.

Because machine learning algorithms can be designed to model outcomes via artifacts of your analysis Pipelines, we keep your data safely and conveniently on your own machine, untouched.


Applying differential privacy strategies to truly anonymize private data, whilst still permitting collaboration.


COINSTAC has been made possible through
the past and present efforts of these fine institutions


The following research groups
are currently using COINSTAC in their research


Of course, none of this would be possible
without support grants from the
National Institutes of Health


Places Using COINSTAC

Get Started

To download and run COINSTAC on your local machine click the button below

To create and run a local development build and contribute to the project start here:

Contribute by creating computations to with our handy documentation guide.

Get In Touch

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