Frontiers 在 AI in Science 峰会承诺提供价值 160 万欧元的 Horizon Europe 数据 AI 化支持

Frontiers 的“AI in Science Enablers”承诺今日启动。该承诺包括为 1,000 个 Horizon Europe 数据集提供 AI 驱动的数据整理，以及为早期职业研究人员提供 100 项 FAIR² 数据出版资助，帮助将研究数据转化为经过同行评审、可引用的成果，使其更好地为复用和 AI 应用做好准备。 

Frontiers’ AI in Science Enablers pledge opens today. The pledge includes AI-powered data curation for 1,000 Horizon Europe datasets and 100 FAIR² Data Publication Grants for early-career researchers, helping turn research data into peer-reviewed, citable outputs that are better prepared for reuse and AI applications. 

Frontiers 在爱尔兰都柏林举行的 2026 年 AI in Science Summit（AIS26）上宣布了其“AI in Science Enablers”承诺。在未来 12 个月内，Frontiers 将为 1,000 个 Horizon Europe 数据集提供 AI 驱动的数据整理，向早期职业研究人员颁发 100 项 FAIR² 数据出版资助，并开放 Frontiers Data 的早期访问——这是其 FAIR² 数据管理服务的扩展，研究人员可在其中借助 AI 探索数据并为其出版做准备。数据整理和资助均免费提供给研究人员，等值服务价值超过 160 万欧元。

Frontiers announced its AI in Science Enablers pledge at the 2026 AI in Science Summit (AIS26) in Dublin, Ireland. Over the next 12 months, Frontiers will provide AI-powered data curation for 1,000 Horizon Europe datasets, award 100 FAIR² Data Publication Grants to early-career researchers, and open early access to Frontiers Data, an extension to its FAIR² Data Management services, where researchers can explore data with AI and prepare it for publication. Provided at no cost to researchers, the curation and grants represent more than €1.6 million in equivalent services.

该承诺应对 AI 赋能科学面临的一项关键挑战：让欧洲公共资助的研究数据更易于发现、理解和复用。欧盟委员会 2018 年的一项研究估计，管理不善且难以复用的研究数据每年给欧洲造成至少 102 亿欧元的损失。通过帮助研究人员为 AI 应用准备数据集，并将其作为经过同行评审、可引用的研究成果发表，使其他研究人员和 AI 工具能够在此基础上继续发展，Frontiers 旨在延伸研究投资的价值，同时让研究人员因共享数据而获得认可。

The pledge addresses a critical challenge for AI-enabled science: making Europe's publicly funded research data easier to discover, understand, and reuse. A 2018 European Commission study estimated that poorly managed and difficult-to-reuse research data costs Europe at least €10.2 billion a year. By helping researchers prepare datasets for AI applications and publish them as peer-reviewed, citable research outputs that other researchers and AI tools can build on, Frontiers aims to extend the value of research investment while giving researchers recognition for sharing their data.

Ekaterina Zaharieva，欧盟初创企业、研究与创新事务专员表示：

Ekaterina Zaharieva, European Commissioner for Startups, Research and Innovation, said:

“只有汇聚研究界、产业界和其他合作伙伴的优势，欧洲在科学中应用 AI 的雄心才能成为现实。通过‘AI in Science Enablers’活动，我们正将这一雄心转化为具体承诺，让研究人员获得创新所需的工具、专业知识和资源。”

“Europe’s ambition in AI in Science can only become reality if we bring together the strengths of our research community, industry and other partners. Through the AI in Science Enablers campaign, we are turning this ambition into concrete commitments that give researchers access to the tools, expertise and resources they need to innovate.”

Kamila Markram 博士，Frontiers 首席执行官兼联合创始人补充道：

Dr Kamila Markram, CEO and Co-founder of Frontiers, added:

“AI 正在创造连接、分析和基于科学知识构建的新机遇，而高质量的研究数据是这一进展的基础。欧洲在开放科学方面拥有坚实基础，现在有机会在此基础上更进一步，让研究数据更易于发现、理解和复用。通过这一承诺，Frontiers 正为这一雄心提供切实支持，并助力构建一个更开放、更互联、更 AI 就绪的研究生态系统。”

“AI is creating new opportunities to connect, analyze, and build on scientific knowledge, and high-quality research data is fundamental to that progress. Europe has a strong foundation in open science, and now has an opportunity to build on it by making research data easier to discover, understand, and reuse. Through this pledge, Frontiers is putting practical support behind that ambition and contributing to a more open, connected, and AI-ready research ecosystem.”

AIS26 是推动欧洲科学中 AI 战略实施的欧盟旗舰会议，由都柏林大学学院和Taighde Éireann-Research Ireland协调，并与爱尔兰欧盟轮值主席国计划合作举办，是欧洲 AI 创新月的一部分。在今年“在研究与创新中达到新雄心水平”的主题下，Frontiers 的承诺支持使研究数据更易发现、可复用、可验证并可为 AI 应用做好准备的努力。

AIS26, the flagship EU conference driving implementation of the European Strategy on AI in Science, is coordinated by University College Dublin and Taighde Éireann-Research Ireland in association with Ireland’s EU Presidency Programme, as part of European AI Innovation Month. Under this year's theme, “Reaching a New Level of Ambition in Research and Innovation,” Frontiers' pledge supports efforts to make research data more discoverable, reusable, verifiable, and ready for AI applications.

研究人员如何参与

How researchers can take part

• AI 驱动的数据管理：Horizon Europe 的研究人员可注册抢先体验 Frontiers Data，以利用 AI 探索和管理数据。

• 数据发表资助：面向 Horizon Europe 项目早期职业研究人员的公开征集将于 2026 年 10 月 19 日开放。

• 申请或了解更多信息请点击此处。

• AI-powered data curation: Horizon Europe researchers can can register for early access to Frontiers Data to explore and curate data with AI.

• Data Publication Grants: An open call for early-career researchers on Horizon Europe projects opens 19 October 2026.

• Apply or request more information here.

Frontiers 科学领域 AI 赋能者承诺

Frontiers AI in Science Enablers pledge

Frontiers 将：

Frontiers will:

• 为 1,000 个 Horizon Europe 数据集提供 AI 驱动的数据管理。FAIR² AI 数据管家 Clara 将识别文档、元数据和来源记录中的缺口，并为研究人员提供切实可行的路线图，以满足资助方和机构的数据要求（包括数据管理计划义务），并为数据的复用和 AI 应用做好准备。

• 向 Horizon Europe 早期职业研究人员颁发 100 项 FAIR² 数据发表资助。每项资助将覆盖一篇经过完整同行评审、可引用且被索引的 FAIR² 数据文章，为研究人员的数据提供正式发表渠道。

• 以抢先体验形式推出 Frontiers Data。这一全新的开放获取入口通往 FAIR² 数据管理，将允许研究人员导入开放数据集或上传自己的数据，通过提问和交互式可视化与 Clara 一起探索数据，查找相关数据集，并为他们的数据准备一篇经同行评审的 FAIR² 数据文章。

• 为 Horizon Europe 科学基础模型团队提供 FAIR² 数据服务。在材料科学、农业、环境科学和气候领域工作的团队将能够管理和发布其训练数据集，以满足训练数据遵循 FAIR 原则和通用标准的要求。

• 承诺将 FAIR² 开放规范作为开放欧洲研究基础设施。该规范源自欧洲、由社区治理、由 FAIR² 联盟管理，并基于包括 Croissant、schema.org 和 PROV-O 在内的开放标准构建，将可供研究机构、存储库和基础设施采用。

• Provide AI-powered data curation for 1,000 Horizon Europe datasets. Clara, the FAIR² AI data steward, will identify gaps in documentation, metadata, and provenance, and give researchers a practical roadmap to meet funder and institutional data requirements, including Data Management Plan obligations, and prepare data for reuse and AI applications.

• Award 100 FAIR² Data Publication Grants to early-career Horizon Europe researchers. Each grant will cover a fully peer-reviewed, citable, and indexed FAIR² Data Article, giving researchers a formal publication for their data.

• Launch Frontiers Data in early access. The new open-access entry point to FAIR² Data Management will allow researchers to import open datasets or upload their own, explore them with Clara through questions and interactive visualizations, find related datasets, and prepare their data for a peer-reviewed FAIR² Data Article.

• Offer FAIR² data services to the Horizon Europe foundation models in science teams. Teams working in materials science, agriculture, environmental science, and climate, will be able to curate and publish their training datasets to meet the requirement that training data follow the FAIR principles and common standards.

• Commit the FAIR² open specification as open European research infrastructure. European in origin, community-governed, stewarded by the FAIR² Alliance, and built on open standards including Croissant, schema.org, and PROV-O, the specification will be available for research institutions, repositories, and infrastructures to adopt.

关于 Frontiers

About Frontiers

Frontiers 是一家领先的研究出版机构，其使命是通过让科学开放来加速合作与发现——使研究人员能够找到我们所有人所需的解决方案，在健康的地球上实现健康生活。借助定制技术、人工智能以及高效严谨的同行评审，Frontiers 社区期刊为 1,700 多个学术领域的专家提供开放获取平台，以发表高质量、高影响力的研究。通过与商业机构、政策制定者和教育工作者建立牢固伙伴关系的外联工作，Frontiers 引领向开放科学的转型。

Frontiers is a leading research publisher on a mission to accelerate collaboration and discovery by making science open – enabling researchers to find the solutions we all need for healthy lives on a healthy planet. Powered by custom-built technology, artificial intelligence, and efficient and rigorous peer review, Frontiers community journals give experts in more than 1,700 academic fields an open access platform to publish high quality, high impact research. Through outreach work to build strong partnerships with businesses, policymakers, and educators, Frontiers leads the transition to open science.

关于 Senscience

About Senscience

Senscience 是 Frontiers 的 AI 合资企业，构建 FAIR² 数据管理，将研究数据转化为经同行评审、AI 就绪的数据出版物，并打造其开放入口 Frontiers Data。

Senscience, the AI venture of Frontiers, builds FAIR² Data Management, which turns research data into peer-reviewed, AI-ready data publications, and its open entry point, Frontiers Data.

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