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图3.1DABSTEP基准测试榜单（截至2026年2月

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所属报告：

上海交通大学：2026“人工智能+”行业发展蓝皮书（200页）.pdf

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3.3数据生态：开源创新与数据治理当前，AI数据生态呈现出“开源与商业并重，流通与安全共存"的复杂格局。技术构成了骨架，而开放协同的生态则为其注入血肉，共同推动智能应用从中心化垄断走向普惠与多样化。开源协同正经历从“代码共享"到“数据共建"的范式扩展。中国信息通讯研究院研究指出，开源社区推动高质量数据集像开源软件一样，由开发者社区共同完成清洗、标注与版本维护，为基础的人工智能模型任务提供数据支持。与此同时，MinerU等聚焦于文档智能解析的开源工具链，显著降低了企业处理私有数据的门槛，促进了Al能力在更广泛产业场景中的落地。作为"中国方案"的MinerU等智能文档解析产品，已经走在世界前沿，多次登顶GitHubTrending-Python等权威榜单第一名，受到OpenClaw 等全球开发者的欢迎。在数据要素流通层面，制度设计与技术进步结合互补。顶层设计中，中国"十五五"规划明确指出要据资源开发利用”，为数据流通提供清晰的政策引导。而在具体的技术端，数据流通利用技术发展日趋成熟，隐私计算与新型架构正成为打破“数据孤岛"的关键。联邦学习、可信执行环境、数据沙箱等技术基本满足各行业基本需求，金融、医疗等领域的敏感数据得以在“可用不可见"的前提下安全融合，释放价值。企业内部则基于数据研发运营一体化（DataOps）理念，将敏捷、精益等理念融入数据开发过程通过对数据相关人员、工具和流程的重新组织，构建智能化的数据编织网络，打破协作壁垒，实现对跨云、跨系统数据资产的自动发现与集成，为AI应用提供统一的、可治理的数据服务入口。

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