我国科研大数据生态协同度测度研究

Research on Ecological Synergy Degree Measurement of Scientific Research Big Data in China

[目的/意义]协同发展是提高科研创新效率的有效途径,对科研大数据生态系统协同程度进行测算能够梳理科研大数据发展脉络,为实现科研大数据协同发展提供参考。[方法/过程]在解析科研大数据生态协同内涵的基础上,引入复合系统协同度测度模型,基于2011—2020年《中国统计年鉴》《中国科技统计年鉴》等面板数据,测算科研大数据生态协同度。根据测度结果提出科研大数据发展现存问题的改善策略。[结果/结论]科研大数据协同治理虽然已经初具成效,子系统的有序度呈逐年增长态势,但整体协同度仍处于较低水平,从规制、技术、管理3个维度提出改善策略,为更好地实现科研大数据协同发展提供参考。

[Purpose/significance]Collaborative development is an effective way to improve the efficiency of scientific research innovation.Measuring the collaborative development of the scientific research big data ecosystem can sort out the development context of scientific research big data and provide a reference for realizing the collaborative development of scientific research big data.[Method/process]On the basis of parsing the connotation of ecological synergy of scientific research big data,a composite system synergy measurement model is introduced to measure the ecological synergy of scientific research big data based on the panel data of China Statistical Yearbook and China Science and Technology Statistical Yearbook from 2011 to 2020.According to the measurement results,the improvement strategies for the existing problems in the development of scientific research big data are proposed.[Result/conclusion]The research shows that although the collaborative governance of scientific research big data has been effective and the order degree of the subsystem is increasing year by year,the overall collaborative degree is still not high.The improvement strategies proposed in this paper from the three dimensions of regulation,technology and management can provide a reference for better realizing the collaborative development of scientific research big data.