宣布 Project ProtoStar：让公共资助的科学成果为 AI 做好准备

一项汇聚大学、研究机构、产业界和开放科学社区的倡议，旨在让公众资助的研究更易于发现、重复使用和进一步发展。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.

今天，我们宣布推出 ProtoStar 项目，旨在通过提升数据集、模型和代码的可访问性与可复用性，充分释放公共资金支持的科学研究潜力，尤其是推动人工智能驱动的研究。ProtoStar 汇集了研究机构、业界和开放科学社区，共同支持以适用于人工智能、可供机器处理的格式发布科学成果，并通过一系列开放挑战展示这些成果的价值。首场挑战由卡内基梅隆大学主办，将于 11 月 2 日开放。请参阅下文“如何参与”。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.

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；这些高校通过人工智能开放论坛（OFAI）作为高校联盟合作伙伴参与其中；此外还有 Astera Institute、作为基础设施合作伙伴的 Hugging Face，以及通过其创新培育网络（Fostering Innovation Network）担任社区合作伙伴的开放研究社区加速器（ORCA）。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.

参与组织通过签署谅解备忘录正式确认参与，并支持在开放基础设施上以适用于 AI 的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。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.

让公共资助的科学研究更实用 Making publicly funded science more usable

美国联邦政府每年在研发领域的投资约为1900亿美元，联邦政策也越来越要求公开共享研究成果。美国国立卫生研究院（NIH）和美国国家科学基金会（NSF）等机构加强了对其资助研究成果公开获取的要求，并要求研究人员制定计划，分享相关底层数据。Hugging Face 是 NIH 列出的十个通用型研究数据共享存储库之一。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.

但让研究成果公开并不意味着它们就能直接使用。科学数据集、模型和代码往往散落在彼此不兼容的系统中，以静态或过时的格式存储，或在发布时缺少理解和重用它们所需的文档、元数据和来源信息。这些限制不仅让研究人员更难在现有成果的基础上开展工作，也让 AI 系统更难跨学科发现、解读和整合科学资源。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. 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.

ProtoStar 项目旨在推动以适用于 AI 的格式系统性地共享公共资金资助的科学成果，并通过开放基础设施和实用的发布指南提供支持，从而帮助弥合这一差距。该项目以美国国家癌症研究所的 AI 数据就绪挑战赛、Hugging Face 关于如何为 AI 准备公共部门数据的指南等工作为基础。其目标是帮助研究机构更轻松地满足现有的公共访问要求，同时提升其所发布成果的科学价值。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.

对可复用科学资源的需求已显而易见。Hugging Face 托管着超过 17,000 个涵盖核心科学领域的公开数据集，包括基因组学、蛋白质设计、化学、天气与气候、地球观测和天文学。若将医学和数学领域也包括在内，这一数字将接近 29,000，此外还有数量相近的科学模型。The demand for reusable scientific resources is already evident. Hugging Face hosts more than 17,000 public datasets across core scientific domains, including genomics, protein design, chemistry, weather and climate, Earth observation, and astronomy. That figure approaches 29,000 when medicine and mathematics are included, alongside a comparable number of scientific models.

分享速度也在加快。2026年前九个月，研究人员在 Hugging Face 上分享的新科学数据集数量是2025年同期的三倍多；即使剔除上传量最大的个人用户，增长仍然十分显著。The pace of sharing is also accelerating. Researchers shared 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, with growth remaining substantial even after excluding the largest individual uploader.

图 1. Hugging Face 上每季度新增的公共科学数据集，按领域划分（2023 年第一季度至 2026 年第三季度）。统计仍处于公开状态的数据集，按创建日期计算。来源：Hugging Face Hub 数据，2026 年 10 月。探索数据。Figure 1. New public science datasets on Hugging Face per quarter, by domain (Q1 2023–Q3 2026). Counts datasets still public, by creation date. Source: Hugging Face Hub data, October 2026. Explore the data.

当各组织以可重复使用的格式共享研究成果时，科学界便能大规模地在此基础上开展工作。截至 2026 年 10 月 26 日，美国国立卫生研究院（NIH）国家生物技术信息中心（NCBI）在 Hugging Face 上维护的 18 个公开存储库累计下载量已超过 1,100 万次，而 NASA IMPACT 的 27 个存储库累计下载量约为 77 万次。文档也是让这些资源发挥作用的重要组成部分：Hugging Face 上 90% 的数据集下载量来自文档齐全的数据集。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.

公开可获取的基因组数据已被证明对推动生物学研究等领域的发展发挥了重要作用；Hugging Face 的研究人员一直在利用开放获取的科学资源开展工作，其中包括 HuggingFaceBio 最近发布的 Carbon-A 和 Carbon-X。通过开放发布由此产生的模型、数据集和工具，这些工作展示了现有科学资源如何为新的研究提供支持，供他人进一步拓展。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.

我们正通过参与机构采纳的政策，借助 Project ProtoStar 进一步推动这类活动。作为机构承诺的早期案例，卡内基梅隆大学（CMU）的开源项目办公室正在鼓励研究人员在 Hugging Face 上分享适合公开的 datasets、AI models 及其他研究成果，同时也承认专门领域的存储库仍然十分重要。This is the kind of activity that we are further catalyzing via Project ProtoStar through institutional policies adopted by participating orgs. 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.

开放科学面临的挑战 Open Science Challenges

让科学成果具备 AI 可用性是重要的第一步。我们也希望展示，当这些资源开放出来供他人重复使用时，研究人员、开发者以及更广泛的科学界能够取得怎样的成果。Making scientific artifacts AI-ready is an important first step. We also want to demonstrate what researchers, developers, and the broader scientific community can accomplish when those resources are made available for reuse.

ProtoStar 的一个核心问题是：以适用于 AI 的格式发布的公共资助科学数据集、模型和代码，能否被轻松发现、重复使用、整合和扩展，从而加速科学发现并增进公共利益？A central question for ProtoStar is: Can publicly funded scientific datasets, models, and code, released in AI-ready formats, be readily discovered, reused, combined, and extended to accelerate scientific discovery and public benefit?

为探索这一问题，ProtoStar 将组织一系列开放挑战赛，每项挑战赛均由参与其中的研究机构主办，并围绕该机构开放发布的科学资源展开。这些挑战赛有两个相辅相成的目标：To explore this, ProtoStar will organize a series of open challenges, each hosted by a participating research institution and built around scientific resources that the institution has released openly. The challenges have two complementary goals:

• 展示科学资源的再利用。提供具体案例，说明研究人员和开发者如何基于公共资金资助形成的科学资源开展工作，包括整合不同机构或学科资源的应用。
  Demonstrate scientific reuse. Produce concrete examples of researchers and developers building on publicly funded scientific artifacts, including applications that combine resources across institutions or disciplines.
• 了解哪些因素能够促成复用。识别技术障碍、文档缺口、互操作性挑战和成功实践，为未来的研究基础设施建设、资金决策和公共政策提供参考。
  Learn what makes reuse possible. Identify technical barriers, documentation gaps, interoperability challenges, and successful practices that can inform future research infrastructure, funding decisions, and public policy.

由此产生的项目和经验教训将有助于建立实用的证据基础，使公共资金支持的科学更易于获取、更有价值。The resulting projects and lessons learned will help build a practical evidence base for making publicly funded science more accessible and useful.

第一个挑战：气候风险情报 The first challenge: Climate Risk Intelligence

由卡内基梅隆大学主办 | 2026年11月2日至13日Hosted by Carnegie Mellon University | November 2–13, 2026

首届 ProtoStar 挑战赛邀请参与者测试 AI 是否能够利用美国 210 座城市的历史天气数据，改善对城市气候状况的预测。基于卡内基梅隆大学的研究，各团队将利用截至 2018 年的气候记录，预测 2019 年至 2026 年的实际状况，使其模型能够根据观测到的天气进行评估，而非针对假设的未来情景。The inaugural ProtoStar challenge invites participants to test whether AI can improve predictions of urban climate conditions using historical weather data from 210 U.S. cities. Building on research by Carnegie Mellon University, teams will use climate records through 2018 to predict actual conditions from 2019 to 2026, allowing their models to be evaluated against observed weather rather than hypothetical future scenarios.

参与者将预测四项与城市规划相关的指标：年平均气温、年总降水量、全年最热日的气温，以及制冷度日数（衡量空调需求的指标）。研究将把他们的预测与简单的统计基准进行比较，包括历史平均值和线性趋势，以评估人工智能方法能否显著提高准确性。Participants will forecast four indicators relevant to urban planning: average annual temperature, total annual precipitation, the hottest day of the year, and cooling degree days, a measure of demand for air conditioning. Their predictions will be compared against simple statistical baselines, including historical averages and linear trends, to assess whether AI approaches offer meaningful improvements in accuracy.

除了进行预测外，参与者还将根据从数据中获得的认识，提出一项简短的气候适应政策构想。这一环节鼓励团队将科学分析与实际公共利益相结合，例如帮助城市应对极端高温、提高能源效率，或管理降水模式的变化。Beyond forecasting, participants will also propose a short climate adaptation policy idea informed by what they learned from the data. This component encourages teams to connect scientific analysis with practical public benefits, such as preparing cities for extreme heat, improving energy efficiency, or managing changing precipitation patterns.

各团队将提交一个包含这些输出的 Hugging Face 数据集。预测表现将通过公开排行榜进行评估，该排行榜会将预测误差与成熟基线进行比较；政策构想则会单独考量。待 2026 年完整天气记录公布后，最终预测结果将于 2027 年初发布。Teams will submit a Hugging Face dataset with these outputs. Forecasting performance will be assessed through a public leaderboard measuring prediction errors against established baselines, while policy ideas will receive separate consideration. Final forecasting results will be published in early 2027, once the complete 2026 weather records are available.

探索 CMU 气候风险情报挑战赛Explore the CMU Climate Risk Intelligence Challenge

CMU 挑战是本系列的首场挑战。其他参与机构将依次举办各自的挑战活动，每场都会重点介绍不同的公共资助科学资源及其再利用机会。后续挑战信息将在 Hugging Face 上的 Project ProtoStar 页面发布。The CMU challenge is the first in this series. Other participating institutions will host their own challenges sequentially, each highlighting different publicly funded scientific resources and opportunities for reuse. Future challenges will be announced through the Project ProtoStar page on Hugging Face.

如何加入 Project ProtoStar How to Join Project ProtoStar

无论你是学术研究人员、学生、独立开发者，还是基于开放科学基础设施开展工作的公司，ProtoStar 都能为你提供机会，让公共资金支持的研究成果发挥作用，并帮助我们了解如何让面向 AI 的开放科学惠及更多人。Whether you're an academic researcher, student, independent developer, or company building on open scientific infrastructure, ProtoStar offers opportunities to put publicly funded research to work and help us learn what is needed to make AI-ready open science more widely useful.

我们也欢迎有意让其科学资源更易获取、复用的大学、联邦研究机构、国家实验室、非营利组织及其他研究机构。若想了解如何参与或合作，请联系 protostar@orcaopen.org。We also welcome universities, federal research agencies, national laboratories, nonprofits, and other research organizations interested in making their scientific resources more accessible and reusable. To explore participation or collaboration, reach out to protostar@orcaopen.org.

我们希望 ProtoStar 能让公共资金支持的科学研究更容易触达能够在其基础上继续开展工作的人和工具，同时为大规模支持开放科学所需的基础设施和实践提供切实的证据。Our hope is that ProtoStar will make it easier for publicly funded scientific work to reach the people and tools that can build on it, while generating practical evidence about the infrastructure and practices needed to support open science at scale.