URL: https://fairdataihub.org/aireadi

AI-READI | FAIR Data Innovations Hub
Projects
Team
Impact
Gallery
Blog
Events
Contact Us
Project
AI-READI
Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights
Generating a flagship AI-ready and ethically-sourced dataset to boost future AI-driven discoveries in type 2 diabetes mellitus (T2DM)
Learn more
Overview
AI-READI is one of the data generation projects funded by the National Institutes of Health (NIH)'s Bridge2AI Program. The AI-READI project is structured into six modules: Data Acquisition, Ethics, Standards, Teaming, Tools, and Skills & Workforce Development. The FAIR Data Innovations Hub is leading the Tools module.
What is the goal of the AI-READI project?
The AI-READI project seeks to create a flagship AI-ready and ethically-sourced dataset that will support future AI-driven research projects to provide critical insights into type 2 diabetes mellitus (T2DM), including salutogenic pathways to return to health.
Learn more about the AI-READI project
What data will be collected?
The project will aim to collect data from 4,000 participants. To ensure the data is population-representative, the participants will be balanced for three factors: disease severity, race/ethnicity, and sex. Various data types will be collected from each participant, including vitals, electrocardiogram, glucose monitoring, physical activity, ophthalmic evaluation, etc.
How will the project data be made AI-ready?
The AI-READI project data will be made FAIR to optimize reuse by humans and machines (i.e., AI/ML program). The data will additionally be shared according to applicable ethical guidelines to enhance AI-readiness.
Learn more about FAIR
What is the FAIR Data Innovations Hub's role in the project?
Our team will lead the development of fairhub.io, a web platform with intuitive user interfaces and automation tools that will help data-collecting researchers from the project (and beyond) with managing, curating, and sharing FAIR, ethically-sourced, and AI-ready datasets.
Impact of AI-READI
Snapshot of key metrics
0
Participants enrolled
0
+
Data types to be collected (vitals, electrocardiogram, etc.)
0
Institutions collaborating on the project
0
+
Team members contributing to the project
Development approach
All software and tools from the AI-READI project, including fairhub.io, are developed under an MIT License from the AI-READI organization on GitHub.
Explore the AI-READI GitHub organization
Funding
The AI-READI project is funded by the National Institutes of Health (NIH)'s Bridge2AI program.
Explore the award on NIH Reporter
Team
Members
Researchers, engineers, and collaborators behind this project.
Bhavesh Patel
Sanjay Soundarajan
Aydan Gasimova
Dorian Portillo
Nada Haboudal
Research partners
The AI-READI project is led by multiple institutions. In addition to the FAIR Data Innovations Hub, other institutions collaborating on the AI-READI project include: University of Washington, Oregon Health & Science University, Johns Hopkins University, University of California at San Diego, Stanford University, Native BioData Consortium, University of Alabama at Birmingham, and Microsoft.
Timeline
Milestone
1
Year 1 development
September 2022 - Aug 2023
The base framework of fairhub.io will be developed and support will be provided uploading data, structuring high-level data and metadata, and sharing data.
1
Impact related to this project
Showing
17
publications
View all our impact
↗
Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective
Open
Copy
2025
Preprints
Caufield, H., Ghosh, S., Kong, S. W., Parker, J., Sheffield, N., Patel, B., Williams, A., Clark, T., & Munoz-Torres, M. C. (2025). Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective. arXiv. https://doi.org/10.48550/arXiv.2508.01141
Dataset Documentation for Responsible AI: Analysis of Suitability and Usage for Health Datasets
Open
Copy
2025
Preprints
Heinke, A., Huang, L., Simpkins, K. U., Kalaw, F. G. P., Karsolia, A., Singh, K., Soundarajan, S., Nebeker, C., Baxter, S. L., Lee, C. S., Lee, A. Y., & Patel, B. (2025). Dataset Documentation for Responsible AI: Analysis of Suitability and Usage for Health Datasets. bioRxiv. https://doi.org/10.1101/2025.11.18.689064
Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence
Open
Copy
2025
Preprints
Hallaj, S., Heinke, A., Kalaw, F. G. P., Gim, N., Blazes, M., Owen, J., Dysinger, E., Benton, E. S., Cordier, B. A., Evans, N. G., Li-Pook-Than, J., Snyder, M. P., Nebeker, C., Zangwill, L. M., Baxter, S. L., McWeeney, S., Lee, C. S., Lee, A. Y., Patel, B., & on behalf of the AI-READI Consortium. (2025). Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence. ArXiv. https://doi.org/10.48550/arXiv.2508.01140
AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond
Open
Copy
2024
Journal Articles
AI-READI Consortium. (2024). AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond. Nature Metabolism. https://doi.org/10.1038/s42255-024-01165-x
AI-readiness for Biomedical Data: Bridge2AI Recommendations
Open
Copy
2024
Preprints
Clark, T., Caufield, H., Mohan, J. A., Al, S. M., Amorim, E., Eddy, J., Gim, N., ... Patel, B., Williams, A., & Munoz-Torres, M. C. (2024). AI-readiness for Biomedical Data: Bridge2AI Recommendations. BioRxiv. https://doi.org/10.1101/2024.10.23.619844
Perspective on Harnessing Large Language Models to Uncover Insights in Diabetes Wearable Data
Open
Copy
2024
Preprints
Alavi, A., Cha, K., Esfarjani, D. P., Patel, B., Than, J. L. P., Lee, A. Y., Nebeker, C., Snyder, M., & Bahmani, A. (2024). Perspective on Harnessing Large Language Models to Uncover Insights in Diabetes Wearable Data. MedRxiv. https://doi.org/10.1101/2024.07.29.24310315
Clinical Dataset Structure: A Universal Standard for Structuring Clinical Research Data and Metadata (Poster)
Open
Copy
2024
Posters
Patel, B., Soundarajan, S., Gasimova, A., Gim, N., Shaffer, J., & Lee, A. (2024). Clinical Dataset Structure: A Universal Standard for Structuring Clinical Research Data and Metadata (Poster) (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.13984769
Introduction to AI-READI, Studying Salutogenesis in T2DM (dkNET Presentation)
Open
Copy
2024
Webinars/Lectures
Lee, C., Patel, B., & Baxter, S. (2024). Introduction to AI-READI, Studying Salutogenesis in T2DM (dkNET Presentation) (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.13984710
Introduction to AI-READI, Studying Salutogenesis in T2DM (Bridge2AI Lecture Series)
Open
Copy
2024
Webinars/Lectures
Lee, C., Patel, B., & Baxter, S. (2024). Introduction to AI-READI, Studying Salutogenesis in T2DM (Bridge2AI Lecture Series) (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.13984755
AI-READI Code of Conduct
Open
Copy
2024
Reports
Lee, A., Owen, J., Patel, B., Nebeker, C., Lee, C., Zangwill, L., Hurst, S., Singer, S., Li-Pook-Than, J., & Matthews, D. (2024). AI-READI Code of Conduct (2.0). Zenodo. https://zenodo.org/records/13328255
License terms for reusing the AI-READI dataset
Open
Copy
2024
Reports
Contreras, J., Evans, B., Hurst, S., Patel, B., Mcweeney, S., Lee, C., & Lee, A. (2024). License terms for reusing the AI-READI dataset (1.0). Zenodo. https://doi.org/10.5281/zenodo.10642459
AI-READI Steering Committee Charter
Open
Copy
2023
Reports
Lee, A., Owen, J., Patel, B., Nebeker, C., Lee, C., Zangwill, L., Hurst, S., & Singer, S. (2023). AI-READI Steering Committee Charter (1.0). Zenodo. https://doi.org/10.5281/zenodo.7641684
FAIRhub Study Management Platform
Open
Copy
2022
Software
FAIRhub study management platform. (started 2022). https://github.com/AI-READI/fairhub-app (Development status: Active)
FAIRhub Data Portal
Open
Copy
2022
Software
FAIRhub data portal. (started 2022). https://github.com/AI-READI/fairhub-portal (Development status: Active)
Pyfairdatatools
Open
Copy
2022
Software
pyfairdatatools. (started 2022). https://github.com/AI-READI/pyfairdatatools (Development status: Active)
Flagship Dataset of Type 2 Diabetes from the AI-READI Project
Open
Copy
2022
Datasets
AI-READI Consortium. (2022). Flagship Dataset of Type 2 Diabetes from the AI-READI Project (1.0.0). FAIRhub. https://doi.org/10.60775/fairhub.1
Software Development Best Practices of the AI-READI Project
Open
Copy
2022
Reports
Patel, B., Soundarajan, S., McWeeney, S., Cordier, B. A., & Benton, E. S. (2022). Software Development Best Practices of the AI-READI Project (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7363102
