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.
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.
The AI-READI project is funded by the National Institutes of Health (NIH)'s Bridge2AI program.
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.
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.