As the main body of China's national scientific and technological innovation institutions, universities play a vital role in the national innovation system. China’s university should be based on the forefront of scientific research and adapt to the new paradigm of scientific research characterized by data intensive computing; effectively manage scientific data from the perspective of data assets, ensuring that scientific data meets current and future research needs, and maximizing the value of institutional data assets. The United Kingdom is one of the earliest countries in the world to put forward the concept of "data as assets". As a problem-oriented scientific data management tool, the UK’s university Data Audit Framework has important reference value for China's university scientific data management.

This paper introduces the background of the Data Audit Framework (DAF) project in UK universities, and analyzes the scope, content, procedures and methods of the implementation of the framework, as well as the main practices in Warwick University, Bath University, and Emory University. On this basis, this paper summarizes the applicability characteristics of DAF. The first is overall planning, which set up a data management committee composed of university managers and senior professors, formulate a unified data audit plan, rationally define the scope of audit, and coordinate the audit schedule. The second is classification and grading, according to the value and importance of data, important, secondary and unimportant data assets are divided to provide a basis for the adoption of targeted resource allocation and management strategies. The third is problem-orientation; the purpose of DAF is to assist institutions to assess the risk areas faced by the current environment, and to raise the awareness of managers and researchers on the best practices of scientific data management. The fourth is continuous improving. DAF designs two main lines of identifying problems and solving problems. As a closed loop, its ultimate goal is to solve problems and serve the self-improvement process of scientific data management.

This paper also discusses the enlightenment of DAF to the management of data assets in China. First, establish the awareness of data assets and optimize the audit environment. Universities should strengthen top-level design, regard data asset audit as an important scientific data management tool, and promote the excellent management of data assets while carrying out excellent research. Second, make sound management systems and improve audit basis. Universities should clearly define data assets management objectives, management methods and codes of conduct to ensure that data assets audit has good grounds. Third, promote standardized management and conduct management audit. The audit of data assets in universities should be positioned as management audit, focusing on three aspects. (1) The status of establishment and maintenance of data assets catalogue, understanding the scientific research resource base of the university. (2) The status of classification management of data assets, evaluating the value of scientific research resources of the university. (3) The status of utilization of data assets, grasping the contribution of scientific research resources of the university. Fourth, strengthen professional training and consolidate audit conditions. Universities should strengthen the training of researchers and auditors, popularize and improve data literacy to adapt to the arrival of data intensive research paradigms.

scientific data management  /  data assets  /  data audit framework  /  university  /  The United Kingdom