政策计量视角下我国科学数据开放共享政策演进与体系构建

Features and current situation of the evolution of China’s scientific data open sharing policy from the perspective of policy bibliometric

大数据时代,科学研究迈入数据密集型的“第四范式”,科学数据成为国家创新发展的基础性战略资源,在国家科技创新中占据越来越重要的战略地位[1,2].开放共享是科学数据作为人类知识财富的应有之义,也是实现科学数据自身价值与意义的根本途径.欧洲联盟委员会副主席Neelie Kroes曾指出:“唯有开放和共享,我们方能在科学上进步”[3].

In the era of big data,data has emerged as a critical component of productivity,and a key driver in the restructuring of production relationships.As scientific research advances into the data-intensive era,often referred to as the fourth paradigm,scientific data has become a fundamental strategic resource for national innovation.It holds an increasingly significant position in the domain of national science and technology innovation.In the face of the escalating competition between China and the United States,China is confronted with the risk of being hindered in the realm of scientific data due to restricted access imposed by Western countries.Simultaneously,China must enhance the sharing and utilization of scientific data to overcome the technological blockade imposed by the West,and achieve a high level of self-sufficiency in science and technology.However,China’s scientific data largely remain isolated in silos and have not been effectively exchanged and utilized.Therefore,a rigorous policy analysis is imperative to promote the sharing and exploitation of scientific data,while also providing support for the enhancement of policies governing the opening and sharing of scientific data.It is of paramount importance to analyze policies aimed at promoting scientific data sharing and exploitation and to bolster the improvement of policies related to the open sharing of scientific data.In this paper,we focus on examining the national-level scientific open sharing policies pertaining to scientific data,including those issued by various ministries and commissions.Through the application of content analysis and policy bibliometrics,we analyze a total of 132 scientific data open sharing policies in China.The primary objective is to gain insights into the characteristics of the existing scientific data open sharing policies in China,and identify the challenges encountered in their implementation.Based on the historical development of scientific data open sharing policies,we categorize them into three distinct stages:the pilot exploration stage(2001-2011),the rapid development stage(2012-2017),and the stage of perfection and improvement(2018-2022).Notably,the policy contents across these different stages exhibit noticeable differences,reflecting the evolving nature of the policies over time.After conducting an in-depth analysis of the current scientific data open sharing policies in China,several significant issues have been identified.Firstly,although these policies emphasize scientific data management,the allocation of data rights is not clearly defined,which could potentially lead to infringements on the rights of data subjects.Secondly,while these policies address data security and privacy protection,the level of enforceability during policy implementation is relatively weak.Consequently,there are certain challenges in effectively implementing and enforcing these policies.Thirdly,scientific data open sharing policies involve multiple stakeholders;however,the absence of an efficient organizational synergy mechanism may hinder the effective implementation of these policies.Lastly,scientific data open sharing policies predominantly rely on supply-based tools,with limited emphasis on demand-based policy tools.This imbalance may result in suboptimal outcomes in terms of policy implementation.Overall,addressing these problems is crucial for the improvement and enhancement of scientific data open sharing policies in China.Based on the characteristics and challenges identified in the current scientific data open sharing policies in China,we propose the following recommendations.(1)Accelerate the establishment of clear data rights to provide a solid institutional basis for promoting scientific data open sharing.(2)Improve the data classification and grading management system to enhance the operability and implementation of policies.(3)Strengthen the linkages between technology and management to enhance data services.(4)Optimize policy tools and strengthen demand-based policy design to achieve better policy implementation results.(5)Increase investment in the construction of public service platforms for scientific data to promote effective data sharing and utilization.