About
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 AI-READI project?
- The AI-READI project seeks to create a flagship AI-ready and ethically-sourced dataset that will support future AI-drive research projects to provide critical insights into type 2 diabetes mellitus (T2DM), including salutogenic pathways to return to health.
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.
What is the fairdataihub'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.
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.
Funding
The AI-READI project is funded by the National Institutes of Health (NIH)'s Bridge2AI program.
Research Partners
The AI-READI project is lead 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
September 2022 - Aug 2023 - Year 1 development
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.
Impact
AI-READI: rethinking AI data collection, preparation and sharing in diabetes research and beyond
Citation
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
Citation
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
Citation
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
FAIRhub Study Management Platform
Citation
FAIRhub study management platform. (started 2022). https://github.com/AI-READI/fairhub-app (Development status: Active)
FAIRhub Data Portal
Citation
FAIRhub data portal. (started 2022). https://github.com/AI-READI/fairhub-portal (Development status: Active)
Pyfairdatatools
Citation
pyfairdatatools. (started 2022). https://github.com/AI-READI/pyfairdatatools (Development status: Active)
Flagship Dataset of Type 2 Diabetes from the AI-READI Project
Citation
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
Clinical Dataset Structure: A Universal Standard for Structuring Clinical Research Data and Metadata (Poster)
Citation
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)
Citation
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)
Citation
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
Citation
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
Citation
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
Citation
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
Software Development Best Practices of the AI-READI Project
Citation
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