Journal of Agricultural Big Data >
Governance of Scientific Data Sharing within International Organizations and International Science and Technology Project
Received date: 2024-02-02
Accepted date: 2024-04-25
Online published: 2024-07-03
The increasing interconnectedness and reusability of scientific data have brought forth unparalleled challenges in the realm of international science and technology (S&T) cooperation. Against the backdrop of a dynamic and intricate international landscape, the rapid evolution of information technology has accentuated the salience of security concerns surrounding scientific data, thereby imposing significant challenges upon extant security frameworks. This paper, following a meticulous elucidation of the concept of scientific data safety, systematically consolidates governance policies and practices pertaining to scientific data sharing gleaned from prominent international S&T organizations and major international S&T initiatives. Four discerning recommendations are delineated: (1) Advocating the principle of "openness as the norm, with exceptions," by discerningly deploying "exceptions" and "exemption lists" to strike a harmonious balance between obligations and rights; (2) Continuously enhancing the domestic governance policy ecosystem for scientific data security, thereby fostering transparency and compliance with scientific data policies within the ambit of international S&T collaborations; (3) Adopting a comprehensive approach that duly considers factors such as scientific data resources and risk control technologies, to facilitate genuine peer-to-peer scientific data exchanges; (4) Instituting a proficient data safety team adept in international policies, regulations, and best practices, and fortified with a robust array of technical competencies.
LI YiZhan, DONG Lu, WANG DongYao, ZHANG Hong, WANG ZhiQiang, WEI Ren, LI ZeXia . Governance of Scientific Data Sharing within International Organizations and International Science and Technology Project[J]. Journal of Agricultural Big Data, 2024 , 6(2) : 161 -169 . DOI: 10.19788/j.issn.2096-6369.000031
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