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Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

research paradigm, intelligent scientific research, new research informatization infrastructure platform, artificial intelligence

Document Type

Strategy & Policy Decision Research

Abstract

The basic platform of research informatization is an indispensable pedestal for modern scientific research and an important manifestation of national scientific and technological innovation capability. As the research paradigm continues to evolve, the architecture and technologies of research informatization infrastructure platform are bound to evolve as well. The new research paradigm brings unique demands and challenges to the algorithmic computility, network transmission capacity, and data storage and management. Consequently, it is an urgent requirement for research informatization infrastructure platform to make technological breakthroughs in the areas of intelligent computility, large-scale data storage and high throughput read/write, cross-network software and hardware integrated scheduling, domain-specific large artificial intelligence (AI) models, and high-quality data resources oriented to AI, organically integrate AI, high-quality data, intelligent computility, and high-speed networks, and build the new research informatization infrastructure platform for the new research paradigm to drive the development of the new research paradigm and support major scientific and technological breakthroughs.

First page

2048

Last Page

2059

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1 张婧睿, 孙蒙鸽, 韩涛. 科研智能化趋势下科研数据研究. 科学观察, 2023, 18(4): 49-61.

Zhang J R, Sun M G, Han T. Research on scientific research data under the trend of intelligent scientific research. Science Focus, 2023, 18(4): 49-61. (in Chinese)

2 李国杰. 智能化科研（AI4R）: 第五科研范式. 中国科学院院刊, 2024, 39(1): 1-9.

Li G J. AI4R: The fifth scientific research paradigm. Bulletin of Chinese Academy of Sciences, 2024, 39(1): 1-9. (in Chinese)

3 中国科学院. 科技强国建设之路: 中国与世界. 北京: 科学出版社, 2018: 424-455.

Chinese Academy of Sciences. The Road to Building a Powerful Country in Science and Technology: China and the World. Beijing: Science Press, 2018: 424-455. (in Chinese)

4 汪洋, 周园春, 王彦棡, 等. 适度超前推动科研基础平台建设 支撑我国高水平科技自立自强. 中国科学院院刊, 2022, 37(5): 652-660.

Wang Y, Zhou Y C, Wang Y G, et al. Promoting infrastructure construction in advance to support sci-tech self-reliance and self-strengthening at higher level. Bulletin of Chinese Academy of Sciences, 2022, 37(5): 652-660. (in Chinese)

5 叶玉江. 加强科技平台工作 推进科技资源管理. 中国科技资源导刊, 2015, 47(2): 1-6.

Ye Y J. Strengthen construction of science & technology infrastructure to promote management of science & technology resources. China Science & Technology Resources Review, 2015, 47(2): 1-6. (in Chinese)

6 廖方宇, 洪学海, 汪洋, 等. 数据与计算平台是驱动当代科学研究发展的重要基础设施. 数据与计算发展前沿, 2019, 1(5): 2-10.

Liao F Y, Hong X H, Wang Y, et al. The data and computing platform is an important infrastructure which drives modern scientific research development. Frontiers of Data & Computing, 2019, 1(5): 2-10. (in Chinese)

7 温亮明, 李洋, 郭蕾. 国内外开放科学的实践进展与未来探索. 图书情报工作, 2021, 65(24): 109-122.

Wen L M, Li Y, Guo L. The practice progress and future exploration of open science at home and abroad. Library and Information Service, 2021, 65(24): 109-122. (in Chinese)

8 李树深. 数据与计算是科技创新的巨大驱动力. 数据与计算发展前沿, 2019, 1(5): 1.

Li S S. Data and computing are powerful drivers of science and technology innovation. Frontiers of Data & Computing, 2019, 1(5): 1. (in Chinese)

9 Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596: 583-589.

10 陈套. 推动科研范式升级 强化国家战略科技力量. 中国科技奖励, 2020, (8): 67-68.

Chen T. Promote the upgrading of scientific research paradigms and strengthen the national strategic scientific and technological forces. China Awards for Science and Technology, 2020, (8): 67-68. (in Chinese)

11 李亚玲, 魏阙. “未来实验室” 数字平台驱动下的科研范式变革. 科技智囊, 2023, (4): 49-57.

Li Y L, We Q. The transformation of scientific research paradigm driven by the digital platform of “future laboratory”. Think Tank of Science & Technology, 2023, (4): 49-57. (in Chinese)

12 程学旗, 梅宏, 赵伟, 等. 数据科学与计算智能: 内涵、范式与机遇. 中国科学院院刊, 2020, 35(12): 1470-1481.

Cheng X Q, Mei H, Zhao W, et al. Data science and computing intelligence: Concept, paradigm, and opportunities. Bulletin of Chinese Academy of Sciences, 2020, 35(12): 1470-1481. (in Chinese)

13 Zhang Y C, Long M S, Chen K Y, et al. Skilful nowcasting of extreme precipitation with NowcastNet. Nature, 2023, 619: 526-532.

14 Abramson J, Adler J, Dunger J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 2024, 630: 493-500.

15 朱小杰, 王华进, 沈志宏, 等. 端到端的科学数据跨中心工作流分析框架. 数据与计算发展前沿, 2023, 5(1): 15-27.

Zhu X J, Wang H J, Shen Z H, et al. End-to-end workflow framework for cross-center scientific data analysis. Frontiers of Data & Computing, 2023, 5(1): 15-27. (in Chinese)

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