摘要
作为人类的新型战略资源,大数据已成为知识经济时代的战略高地。其少量依赖因果关系、主要依靠数据相关性发现知识的新模式,使得其成为继经验、理论和计算模式之后的数据密集型科学范式的典型代表,带来了科研方法论的变革,正成为科学发现的新引擎。科学大数据作为大数据的重要分支,具有不可重复性、高度不确定性、高维性及计算分析高度复杂性的内部特征,以及在数据内容、数据体量、数据获取、数据分析等方面的外部特征,这给科学大数据的处理技术与方法提出了新的挑战。在以上分析基础上,文章提出了科学认知科学大数据,建设科学大数据基础设施,建立科学数据研究中心,以及构建科学大数据学术平台等建议。
Big data occupies the strategic high ground in the era of knowledge economies and also constitutes a new national and global strategic resource. It is a new pattern for scientific discovery with less dependence on causality and heavy dependence on data correlation. It has become a data-intensive scientific paradigm, following previous paradigms of empirical, theoretical and computational science. The paradigm has shifted the methodology of scientific research from theories and models based on causal analysis to comprehensive mechanistic scientific discovery including correlation analysis. As a branch of big data, scientific big data includes internal characteristics such as non-repeatability, high uncertainty, high dimensionality, and computational complexity. External characteristics include data type, data volume, data acquisition, and data analysis. All these characteristics bring new challenges for the techniques and methods of processing scientific big data. On the basis of the above analysis, we raise four recommendations： scientific cognition of scientific big data, construction of scientific big data infrastructure, establishment of a scientific data research center, and the structuring of a scientific big data academic platform.
作者
郭华东
GUO Huadong(Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100094,China)
出处
《中国科学院院刊》
CSCD
北大核心
2018年第8期768-773,共6页
Bulletin of Chinese Academy of Sciences
基金
中国科学院战略性先导科技专项(A类)(XDA19030000)
