Hugging Face and US Universities Launch ProtoStar to Make Public Science Data AI-Ready

Hugging Face announced the launch of Project ProtoStar on October 29, 2026, an initiative bringing together research institutions, industry, and the open science community to make publicly funded scientific datasets, models, and code more accessible and reusable, particularly for AI-driven research. The 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); the Astera Institute; Hugging Face as the infrastructure partner; and the Open Research Community Accelerator (ORCA).

The initiative addresses a critical gap in the approximately $190 billion annually invested by the U.S. federal government in research and development. While agencies like the National Institutes of Health (NIH) and National Science Foundation (NSF) have strengthened public-access requirements, many scientific resources remain scattered across incompatible systems or stored in static formats lacking the metadata and documentation needed for AI systems to interpret them. ProtoStar aims to encourage systematic sharing of these artifacts in "AI-ready," machine-actionable formats. Hugging Face notes that over 17,000 public datasets across core scientific domains are already hosted on its platform, with the number of new science datasets shared in the first nine months of 2026 being three times higher than in the same period of 2025.

To demonstrate the value of this approach, ProtoStar will organize a series of open challenges. The inaugural challenge, "Climate Risk Intelligence," is hosted by Carnegie Mellon University and runs from November 2 to 13, 2026. Participants will use historical weather data from 210 U.S. cities up to 2018 to predict actual conditions from 2019 to 2026, including average annual temperature, precipitation, and cooling degree days. This setup allows models to be evaluated against observed weather rather than hypothetical future scenarios, testing whether AI can effectively combine and extend public scientific resources.