Project ProtoStar Aims to Make Publicly Funded Science AI-Ready

Project ProtoStar is a new initiative launched to make publicly funded scientific research more accessible and reusable, particularly for AI-driven research. The project brings together universities, research organizations, industry, and the open science community to release datasets, models, and code in AI-ready, machine-actionable formats. The first challenge, hosted by Carnegie Mellon University, opens on November 2.

What is Project ProtoStar?

Project ProtoStar is an initiative to unlock the potential of publicly funded scientific research by making datasets, models, and code more accessible and reusable. The project involves research institutions, industry, and the open science community to support the release of scientific artifacts in AI-ready formats. The first challenge, hosted by Carnegie Mellon University, opens on November 2. The initiative's founding partners include Carnegie Mellon University, Purdue University, Georgia Institute of Technology, Arizona State University, and The George Washington University, working through the Open Forum for AI (OFAI) as the university coalition partner; the Astera Institute; Hugging Face as the infrastructure partner; and the Open Research Community Accelerator (ORCA), through its Fostering Innovation Network, as the community partner.

Hugging Face, a leading platform for AI models and datasets, is playing a crucial role as the infrastructure partner. The platform hosts more than 17,000 public datasets across core scientific domains, including genomics, protein design, chemistry, weather and climate, Earth observation, and astronomy. This figure approaches 29,000 when medicine and mathematics are included, alongside a comparable number of scientific models. The pace of sharing is also accelerating, with researchers sharing more than three times as many new science datasets on Hugging Face in the first nine months of 2026 as in the same period of 2025.

The Need for AI-Ready Scientific Data

The U.S. federal government invests approximately $190 billion annually in research and development, with federal policy increasingly expecting the results to be shared publicly. Agencies such as the National Institutes of Health (NIH) and National Science Foundation (NSF) have strengthened public-access requirements for funded research publications and require researchers to plan for sharing the underlying data. However, making research publicly available does not automatically make it usable. Scientific datasets, models, and code are often scattered across incompatible systems, stored in static or legacy formats, or released without the documentation, metadata, and provenance needed to understand and reuse them. Project ProtoStar aims to help close this gap by encouraging systematic sharing of publicly funded scientific artifacts in AI-ready formats, supported by open infrastructure and practical publishing guidance.

The initiative builds on efforts such as the National Cancer Institute's AI Data-Readiness Challenge and Hugging Face's guide to preparing public-sector data for AI. The goal is to make it easier for research organizations to meet existing public-access expectations while increasing the scientific value of the work they release. When organizations share research in reusable formats, the scientific community can build on it at scale. The 18 public repositories maintained by NIH's National Center for Biotechnology Information (NCBI) on Hugging Face have collectively received more than 11 million downloads, while NASA IMPACT's 27 repositories have received approximately 770,000 downloads, as of October 26, 2026. Documentation is also an important part of making these resources useful: 90% of dataset downloads on Hugging Face are from fully documented datasets.

Publicly available genomic data has proved important in contributing to research that further advances fields such as biological research. Researchers at Hugging Face have been building on openly available scientific resources, including recent releases from HuggingFaceBio such as Carbon-A and Carbon-X. By releasing the resulting models, datasets, and tools openly, these efforts demonstrate how existing scientific resources can support new research that others can build upon.

How Project ProtoStar Will Demonstrate Value

A central question for ProtoStar is whether publicly funded scientific datasets, models, and code, released in AI-ready formats, can be readily discovered, reused, combined, and extended to accelerate scientific discovery and public benefit. To explore this, ProtoStar will organize a series of open challenges, each hosted by a participating research institution. The first challenge, hosted by Carnegie Mellon University, opens on November 2. These challenges aim to demonstrate what researchers, developers, and the broader scientific community can accomplish when those resources are made available for reuse.

How to Get Involved

Organizations interested in joining Project ProtoStar can reach out to protostar@orcaopen.org. Participating organizations formalize their involvement through a memorandum of understanding supporting the release of suitable scientific datasets, models, and code in AI-ready formats on open infrastructure. As an early example of institutional commitment, CMU's Open Source Programs Office is encouraging its researchers to share suitable datasets, AI models, and other research outputs on Hugging Face, while recognizing the continued importance of domain-specific repositories.