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Announcing Project ProtoStar: Making Publicly Funded Science AI-Ready

Back to Articles Announcing Project ProtoStar: Making Publicly Funded Science AI-Ready Published October 29, 2026 Update on GitHub Upvote 13 +7 Avijit Ghosh evijit Follow hfpolicy Sayeed Choudhury sayeedmd Follow CMU-OSPO Greg Tananbaum gtananbaum Follow ORCA-Open Emma Scharfmann EmmaScharfmann Follow hugging-science Georgia Channing cgeorgiaw Follow hugging-science Eunice Mercado-Lara EuniceML Follow ORCA-Open Irene Solaiman irenesolaiman Follow hfpolicy 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.

返回文章：宣布 Project ProtoStar：让公共资助的科学研究为人工智能做好准备。发布于 2026 年 10 月 29 日。GitHub 更新。点赞 13 +7。Avijit Ghosh、Sayeed Choudhury、Greg Tananbaum、Emma Scharfmann、Georgia Channing、Eunice Mercado-Lara、Irene Solaiman 等人共同发起。这是一项汇集了大学、研究机构、行业和开放科学社区的倡议，旨在使公共资助的研究更容易被发现、重用和在此基础上进行开发。

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.

今天，我们宣布启动 Project ProtoStar，这是一项旨在释放公共资助科学研究潜力的倡议，通过使数据集、模型和代码更易于访问和重用，特别是针对人工智能驱动的研究。ProtoStar 汇集了研究机构、行业和开放科学社区，支持以人工智能就绪、机器可操作的格式发布科学成果，并通过一系列开放挑战来展示其价值。

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.

首个挑战赛由卡内基梅隆大学主办，将于 11 月 2 日开启。请参阅下方的“如何参与”。该倡议的创始合作伙伴包括卡内基梅隆大学、普渡大学、佐治亚理工学院、亚利桑那州立大学和乔治华盛顿大学（均通过人工智能开放论坛 (OFAI) 作为大学联盟合作伙伴参与）；Astera 研究所；作为基础设施合作伙伴的 Hugging Face；以及作为社区合作伙伴的开放研究社区加速器 (ORCA) 及其“培育创新网络”。

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.

参与组织通过谅解备忘录正式确立其参与关系，支持在开放基础设施上以人工智能就绪格式发布合适的科学数据集、模型和代码。有意加入 Project ProtoStar 的组织可联系 protostar@orcaopen.org。

Making publicly funded science more usable. 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.

让公共资助的科学研究更具可用性。美国联邦政府每年在研发方面投入约 1900 亿美元，联邦政策日益要求将研究成果公开共享。美国国立卫生研究院 (NIH) 和国家科学基金会 (NSF) 等机构已加强了对受资助研究出版物的公共访问要求，并要求研究人员制定共享底层数据的计划。

Hugging Face is among the ten generalist repositories listed by NIH for sharing research data. But 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.

Hugging Face 是 NIH 列出的用于共享研究数据的十个通用存储库之一。但使研究公开并不自动意味着它具有可用性。科学数据集、模型和代码通常分散在不兼容的系统中，以静态或旧格式存储，或者在发布时缺乏理解和重用它们所需的文档、元数据和来源信息。

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.

这些限制使得研究人员更难在现有工作的基础上进行开发，也使得人工智能系统更难跨学科发现、解释和整合科学资源。Project ProtoStar 旨在通过鼓励以人工智能就绪格式系统地共享公共资助的科学成果，并辅以开放基础设施和实用的发布指南，来帮助缩小这一差距。