Abstract
Massive amounts of data,dramatically growing computing power,and the development of the digital economy continue to give rise to data science.The rapid development in technologies,such as machine learning,artificial intelligence,and blockchain,has enhanced this trend.Data have become a new factor of production,bringing revolutionary changes to people's lives and production techniques.In this context,scientific research has also begun to shift towards a new data-driven paradigm,using massive amounts of data as a basis to reveal the correlations hidden behind them,thereby adding new dimensions and perspectives to existing conventional research.Recently,data-based technologies,such as artificial intelligence,which are based on Big Data,have been gradually involved in scientific research.These technologies are used for integrating theory,computation,and experimental measurements,injecting new direction and impetus into scientific research.For example,algorithms such as random forests and neural networks have become commonly used data processing and analysis tools in materials science and achieved remarkable results in predicting properties,phase diagrams,and structures.Traditional scientific research methods often rely on specific theoretical assumptions and experimental designs,while data-driven scientific research focuses on obtaining knowledge and insights from data.By mining hidden correlations and patterns from massive amounts of data,researchers can conduct exploratory studies,discover new research directions and questions,expand the boundaries of research fields,and bring novel insights and breakthroughs to their research.Due to the importance of data,higher requirements for data management and utilization are demanded in scientific research.Currently,scientific research data is hindered by diverse formats,fragmented distribution,and inconsistent quality,causing data to remain inside the laboratory;this results in the wastage of scientific research resources and declination in the development of scientific research.To effectively manage data,improve data utilization,and address the issue of"data islands",researchers have developed an Electronic Laboratory for Material Science,a data management platform based on the actual needs of frontline researchers and difficulties associated with the traditional flow of scientific research data.This platform covers the entire lifecycle of data production,collection,storage,analysis,and sharing.Furthermore,it is committed to realizing planned information processing,automated information collection,intelligent data empowerment,and promoting the commencement of a new paradigm in scientific research.This article introduces the development of scientific research paradigms and current advancements in data management at home and abroad,emphasizing the importance of data standards in scientific research and highlighting the successful exploration of the Electronic Laboratory for Material Science for significantly improving the efficiency in developing amorphous alloy materials,enhancing the quality of functional material single-crystal films,intelligently denoising angle-resolved photoemission spectroscopy spectra,and assisting the management of large-scale experimental stations.With this data management platform,researchers can now more effectively organize and manage experimental data,ensuring its integrity,systematicity,and standardization,thereby improving the efficiency and accuracy of their scientific research work and providing feasible paths and exemplary cases for data management in materials science.Based on the successful application of the Electronic Laboratory for Material Science,the research and development team will further improve its function and build an independent data platform for scientific research in China.In the era of Big Data,open sharing data and establishing an information-based ecosystem will establish the foundation for developing a smart laboratory and injecting new vitality into materials science advancements.
基金项目
国家重点研发计划(2022YFA1603903)
国家重点研发计划(2022YFA1403800)
国家重点研发计划(2021YFA0718700)
国家重点研发计划(2022YFA1403900)
国家自然科学基金(11927808)
国家自然科学基金(12225412)
国家自然科学基金(52022106)
国家自然科学基金(11925408)
国家自然科学基金(11921004)
国家自然科学基金(12188101)
国家自然科学基金(12374141)
国家自然科学基金(12274439)
国家自然科学基金优秀青年科学基金(T2222028)
中国科学院战略性先导科技专项(B类)(XDB33000000)
中国科学院战略性先导科技专项(B类)(XDB25000000)
中国科学院网络安全和信息化专项(CAS WX2021SF-0102)
NETL
NSTL
万方数据