作为第五科研范式的AI4S，是科研领域促进新质生产力发展的重要工具，其构成要件之一是数据。AI4S大模型训练以数据为基础，更需要高质量、多类型数据。在商业大模型利用数据日趋受限的当下，及时关注科研大模型的数据要素供给尤为重要，加速将AI4S的新质生产力从代码中释放出来。面向AI4S的数据要素供给是复杂的系统工程，价值取向应是数据利他而不是数据利己，从而实现科学研究的公益目的和服务社会的赋能目标，应根据不同类型数据选择相应路径，即公共数据、企业数据、个人数据、科学数据和作品数据等宜分别选择有条件无偿、成本补偿、自愿同意、互助共享和合理使用的供给路径，同时要注意防控潜在的版权侵害、隐私公开、数据泄露和价值不齐的风险。

As the fifth scientific research paradigm, AI4S is an important tool to promote the development of the new quality productive forces in the field of scientific research, and one of its components is data, which is the basis for the training of AI4S big models, and it needs high-quality and multi-type data. At a time when the utilization of data for commercial big models is becoming more and more limited, it is especially important to pay attention to the supply of data elements for scientific research big models in time, so as to accelerate the release of the new quality productive forces of AI4S from the code. The supply of data elements for AI4S is a complex systematic project, and the value orientation should be data altruism rather than data egoism, so as to realize the public welfare purpose of scientific research and the empowerment goal of serving the society, the corresponding paths should be chosen according to different types of data, i.e., the supply paths of public data, enterprise data, personal data, scientific data, and artwork data, etc. that should be chosen respectively, such as the supply paths of conditional gratuitous, cost-compensated, voluntary agreement, mutual, sharing, and fair use. And at the same time, attention should be paid to preventing and controlling the potential risks of copyright infringement, privacy disclosure, data breaches, data leakage and value alignment.