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Project ProtoStar Launched to Make Publicly Funded Science AI-Ready
Key points
- Project ProtoStar makes publicly funded research AI-ready.
- Partners include universities, Astera Institute, Hugging Face, and ORCA.
- First challenge on climate risk intelligence opens November 2.
- Addresses issues of scattered, incompatible, or poorly documented research data.
Initiative Overview
Project ProtoStar is a new initiative focused on transforming publicly funded scientific research into AI-ready formats. The goal is to make scientific datasets, models, and code more discoverable, reusable, and buildable, especially for AI-driven research. This involves releasing scientific artifacts in machine-actionable formats and demonstrating their utility through open challenges.
Founding Partners and Structure
The initiative's founding partners include Carnegie Mellon University, Purdue University, Georgia Institute of Technology, Arizona State University, and The George Washington University, collaborating through the Open Forum for AI (OFAI). Additional partners are the Astera Institute, Hugging Face as the infrastructure provider, and the Open Research Community Accelerator (ORCA) as the community partner. Participating organizations formalize their commitment via a memorandum of understanding to release scientific data in AI-ready formats on open infrastructure.
First Open Challenge
The first open challenge under Project ProtoStar, focusing on climate risk intelligence, will be hosted by Carnegie Mellon University and is scheduled to open on November 2. These challenges are designed to demonstrate the value of making scientific artifacts more accessible and reusable.
Addressing Research Accessibility Gaps
The U.S. federal government invests approximately $190 billion annually in research and development, with increasing policy expectations for public sharing of results. Agencies like NIH and NSF require public access to publications and data. However, publicly available research often remains difficult to use due to scattered data across incompatible systems, legacy formats, or a lack of necessary documentation, metadata, and provenance. Project ProtoStar aims to bridge this gap, making research truly usable for AI applications.
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Reporting from
Project ProtoStar has been announced to improve the accessibility and reusability of publicly funded scientific research for AI applications. The initiative aims to standardize datasets, models, and code into AI-ready formats, supported by a coalition of universities, industry partners, and open science communities. This effort addresses the challenge of making publicly available research truly usable by AI, despite significant government investment and public access mandates.