Project ProtoStar Launches to Enhance AI Accessibility in Publicly-Fin

Published October 29, 2026

An initiative bringing together universities, research organizations, industry, and the open science community to make publicly funded research easier to discover, reuse, and build upon.

Today, we're announcing Project ProtoStar, an initiative to unlock the potential of publicly funded scientific research by making datasets, models, and code more accessible and reusable, particularly for AI-driven research. ProtoStar brings together research institutions, industry, and the open science community to support the release of scientific artifacts in AI-ready, machine-actionable formats, and to demonstrate their value through a series of open challenges. The first challenge, hosted by Carnegie Mellon University, opens on November 2. See How to Participate below.

The initiative's founding partners include Carnegie Mellon University, Purdue University, Georgia Institute of Technology, Arizona State University, and The George Washington University, all 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. 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. Organizations interested in joining Project ProtoStar can reach out to protostar@orcaopen.org.

The U.S. federal government invests approximately $190 billion annually in research and development, and federal policy increasingly expects 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. Hugging Face is among the ten generalist repositories listed by NIH for sharing research 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. These limitations make it harder for researchers to build on existing work, and harder still for AI systems to discover, interpret, and combine scientific resources across disciplines.

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. It 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.

Project ProtoStar was announced on October 29, 2026, with the goal of making publicly funded scientific research more accessible and reusable for AI-driven applications. This initiative is spearheaded by Carnegie Mellon University, along with other prominent institutions such as Purdue University, Georgia Institute of Technology, and Arizona State University. The project is set against a backdrop of $190 billion in annual U.S. federal investment in research and development. The first challenge, hosted by Carnegie Mellon University, will open on November 2, 2026, marking a significant step toward aligning scientific outputs with AI-readiness. This initiative is unique as it leverages collaboration between universities, industry, and the open science community to ensure datasets, models, and code are available in machine-actionable formats.

Federal policy increasingly mandates that research results from publicly funded initiatives be shared, highlighting a shift toward transparency and accessibility. Such policies are supported by agencies like the National Institutes of Health (NIH) and the National Science Foundation (NSF), which have reinforced public-access requirements for funded research.

The launch of Project ProtoStar represents a strategic move towards enhancing the interoperability and usability of scientific data. By focusing on AI-readiness, the initiative directly supports the U.S. goal of maintaining leadership in AI technology. Entities like Hugging Face, involved as an infrastructure partner, stand to gain as they facilitate the dissemination and standardization of research data.

This initiative also signals a power shift towards organizations that can adapt to new data-sharing norms. Institutions that embrace these changes will likely gain competitive advantages in securing future funding and collaborations. Conversely, those unable to transition to AI-ready formats may find themselves marginalized.

In the coming months, we can expect increased participation from other universities and research organizations, drawn by the potential to enhance their research visibility and impact. By the end of Q1 2027, at least five more universities are predicted to join the initiative, driven by the growing demand for open-access research.

Regulatory adjustments may follow, with federal agencies potentially introducing stricter guidelines for data sharing and AI-readiness. These changes could be formalized by mid-2027, reinforcing the importance of Project ProtoStar’s objectives.

The increased focus on AI-ready data will likely influence adjacent sectors, such as software development and cloud computing, as demand for compatible tools and platforms rises. Companies offering data transformation and integration services may see a surge in business, particularly those aligned with open science principles.

Furthermore, this initiative could prompt similar efforts internationally, as other countries recognize the strategic advantages of AI-ready scientific infrastructure. This could lead to global collaborations or competitive dynamics in establishing AI research standards.

Project ProtoStar exemplifies the U.S.'s commitment to leveraging its vast research capabilities for AI advancement. By prioritizing AI-readiness, the initiative not only enhances scientific research but also strengthens national AI sovereignty. Experts predict this approach will set a precedent, encouraging other nations to develop comparable frameworks to support AI-driven innovation.

The initiative’s focus on accessibility and reuse aligns with broader trends in open science, which emphasize transparency and collaboration. As such, Project ProtoStar is likely to be a catalyst for broader policy changes, shaping the future of research data management globally.