本次高质量数据集创新大赛通过"揭榜挂帅"赛事机制，创新性采用"分期发榜、全年办赛"模式，聚焦工 业制造、医疗健康、科技创新、城市治理、交通运输等重点领域发展需求，先找场景、再找数据，以赛 事为载体和抓手，探索建设一批高质量、可复用的数据集，为人工智能模型训练和行业应用提供"燃 料"，率先在全国实现"从0到1"的探索跨越。 启动仪式上，来自能源、生物医药、金融、交通、低空、教育等重点领域的首批高质数据集赛题正 式"发榜"，南方电网、广州（国家）实验室、广东省人医、平安财险、佳都科技、金域医学，广东泰 一、广东职教桥、奥海科技等单位，发出首批高质量数据集需求"发榜单"。 接下来，大赛将通过"1套发榜机制+3个竞赛阶段+N场供需对接会"的"1+3+N"组织体系，构建"数据供给 —技术研发—场景落地—产业升级"的完整闭环。通过以赛促用、以赛促融、以赛促产，在推动成熟数 据应用场景复制推广的同时，更好挖掘低空经济、工业互联网等新兴领域的数智潜力，充分发挥高质量 数据集的赋能作用、有效释放数据要素的宝贵价值，为全国一体化数据市场建设、数据产业生态的繁荣 发展，积极贡献"广东经验"。 高质量数据集是决定大模型质量的关键因素 ...

Core Insights - China aims to cultivate a robust data element industry chain, projecting a data industry scale of 7.5 trillion yuan by 2030 [1][3] - As the first country to incorporate data as a production factor, China has established a comprehensive data industry chain, with a data production total of 41.06 zettabytes in 2024, reflecting a 25% year-on-year growth [3] - The number of data-related enterprises in China exceeds 190,000, with the current data industry scale surpassing 2 trillion yuan [3] Data Sharing and Integration - Public data sharing is a crucial breakthrough for the marketization of data elements, with a 7.5% increase in local public data open platforms and a 7.1% rise in open data volume in 2024 [5] - The number of high-quality data sets has grown by 27.4% year-on-year, indicating a significant push towards integrating public and enterprise data [5] - The National Data Bureau is planning to establish a comprehensive data infrastructure by 2029 [5] Artificial Intelligence and Data Quality - Data has surpassed traditional production factors, becoming a core driver of AI breakthroughs and industrial transformation [6] - High-quality data sets are essential for enhancing AI model performance and reshaping the entire industry chain from R&D to commercial application [6] - The construction of high-quality data sets involves critical processes such as data collection, cleaning, annotation, and quality assessment [8] Data Annotation Industry - China's data annotation industry has surpassed 8 billion yuan in value, entering a new phase of scale and standardization [10] - The number of companies developing or applying AI has increased by 36% year-on-year, with high-quality data sets growing by 27.4% [10] - Companies utilizing large model data technologies and data application enterprises have seen year-on-year growth of 57.21% and 37.14%, respectively [10] Challenges in Data Development - Despite the acceleration in high-quality data set innovation, challenges remain, including low data stock and production, inconsistent data quality, lack of mainstream high-value data, and low data utilization efficiency [12]