The AI Commons Consortium (AICC) is a consortium managed by the 501(c)(3) Center for Computational Science (CCSR) with the goal of helping to develop and support open-source AI Commons and AI Meshes based upon the open-source Gen3 data platform.

One of the main projects for the AICC is to develop the Genomic AI Commons (GAC), which is a Gen3 AI Commons for cancer genomics that leverages data from the Genomic Data Commons (GDC), which is the world’s largest collection of harmonized cancer genomics datasets in the world and is used by over 175,000 users each month. 

The Gen3 AI Commons are based upon the technology developed by the Meshes of Midscale Models Initiative (M3) led by the Center for Translational Data Science at the University of Chicago.  The M3 architecture is based upon a core set of AI services that are supported by Gen3 AI Commons, which includes a model repository, agentic services, and services for embeddings, inferencing, and training small and midscale AI models.  With these AI services, data does not have to leave a Gen3 Commons and you can use AI models to search, discover and explore your data. Of course, you can also interoperate with third party models, but M3 studies have shown that with high quality data, you can build your own small and midscale models which in many cases are as powerful or more powerful than Frontier models.  

The M3 Initiative is also integrating services into Gen Commons and Gen3 Meshes so two or more Gen3 AI Commons can support federated learning and build more powerful AI models without any data leaving a Gen3 AI Commons in the AI Mesh.  The technology is structured so that a Gen3 AI Commons can easily join multiple AI Meshes using what are called node and mesh cards.

The AICC is structured like the BLOODPAC consortium, with memberships based upon the size of your organization and all work done through AICC Working Groups.  Benefits of AICC membership include:

  • Early access to Gen3 AI Commons and Gen3 AI Meshes technology.

  • The ability to shape the priorities of the AICC.

  • Providing support to accelerate the development of the Genomic AI Commons and help improve the outcomes of cancer patients. 

  • Gain practical experience with small and midscale AI models and associated agentic services and learn how they can complement and interoperate with third party models, including frontier models.

Please reach out to us at info@occ-data.org to learn more about the AICC.

AI Commons Consortium (AICC)


The BLOODPAC Data Commons supports and manages the BLOODPAC member work focused on accelerating the development and validation of liquid biopsy assays to improve the outcomes of patients with cancer. BPDC combines liquid biopsy data from academic, government, and industry partners and aims to accelerate discovery and development of therapies, diagnostic tests, and other technologies for the treatment and prevention of cancer.

To learn more about the BLOODPAC Data Commons visit data.bloodpac.org

BLOODPAC Data Commons

Veterans Affairs data Commons

The VA Data Commons supports researchers and scientists working with US military Veteran medical and genomic data, the data commons enables researchers to explore genetic variations and their potential links to various health conditions or traits. The goal of the VA Data Commons is to accelerate scientific discoveries and advancements in therapies, diagnostic tests, and other technologies that can enhance the well-being of Veterans and potentially benefit the broader population as well. The data commons features GWAS analyses on harmonized data.

To learn more about VA Data Commons visit: https://va.data-commons.org/

canine DATA COMMONS

To analyze and share genomic architecture of modern dog breeds and run analysis for canine cancer to create clean, easy to navigate visualizations for data-driven discovery for canine cancer.

To learn more about the Canine Data Commons visit caninedc.org

pandemic response Commons

The Pandemic Response Commons represents a collaborative data ecosystem powering research to improve health outcomes, epidemiological models, and back-to-work models. It is run by a private-public partnership to develop and operate an open, standards-based data ecosystem to support researchers working on COVID-19 diagnostics and therapeutics, and researchers, including epidemiologists, public health officials, and others trying to identify emerging hot spots of potential COVID-19 activity.

To learn more about the Pandemic Response Commons visit pandemicresponsecommons.org

veterans precision oncology data commons

The Veterans Precision Oncology Data Commons supports the management, analysis and sharing of veteran oncologic data for the research community and aims to accelerate discovery and development of therapies, diagnostic tests, and other technologies for precision oncology.

To learn more about the Veterans Precision Oncology Data Commons visit vpodc.org

environmental Data commons (Retired)

The Environmental Data Commons (EDC) was a collaborative effort managed by the Environmental Data Commons Working Group to support the open redistribution of environmental datasets from stakeholders such as NASA and NOAA.

During its active phase, the Working Group focused on:

  • Developing and operating a petabyte-scale data commons for environmental data;

  • Collaborating with the community to identify and prioritize key datasets and middleware services;

  • Establishing connectivity and data exchange protocols with data providers;

  • Promoting interoperability through metadata standards and ID services;

  • Supporting sustainability efforts for long-term data access and reuse.

The EDC is no longer actively maintained. However, the information and resources remain available at https://edc.occ-data.org.

If there is renewed interest or need from the community, the Environmental Data Commons can be reinstated to support future environmental data efforts.