Abstract:

In the paradigm of data-intensive scientific research, the use of scientific data has become a valuable academic resource in society and an important strategic resource for technological innovation and competition. Scientific data management has attracted the attention of international organizations, governments, research-funding institutions, the publishing industry, scientific research institutions, and other subjects at different levels. In particular, scientific research institutions, which are at the forefront of scientific research, are not only the production subject of scientific data, but also a direct-interest subject of scientific data. These institutions are obliged to undertake the management, sharing, preservation, and reuse of their own scientific data, and play a role in the management of this precious scientific and technological resource for all humankind. At present, the scientific data generated by scientific research institutions are still scattered in the hands of various project groups, research groups, and individual researchers, and there is no effective management and sharing mechanism. The scientific data management capacity at the institutional level is immature and must be strengthened further. This paper focuses on scientific research institutions’ data management by introducing progress in scientific data management in scientific research institutions at home and abroad from two aspects of data management policy and management practice. The scientific data management framework of scientific research institutions is also analyzed, focusing on the Chinese Academy of Agricultural Sciences’ development process in their scientific data management, including an agricultural science database, scientific data center (platform), and data-management regulations. Finally, based on the previous analysis, this paper proposes scientific data management for scientific research institutions in China from the aspects of combining scientific data management and scientific research informatization, cooperation in institutional data management, scientific research discovery based on scientific big data, data management and analysis of personnel training to provide references for scientific data management in other Chinese scientific research institutions.

Key words: scientific research institutions, scientific data, data management, policy, scientific data management, data management methods