New directions of digitally driven S&T evaluation

Abstract

Science and Technology (S&T) evaluation plays a baton role in developing science and technology innovation. However, traditional S&T evaluation indicators and methods are difficult to apply effectively in S&T evaluation practice. This paper analyzes the transformation of the S&T evaluation paradigm in the digital environment. Theories, methods, and tools of S&T evaluation research are continuously innovated and optimized; big data becomes the driving force of S&T evaluation development; the role played by S&T evaluation is shifting from a provider of statistical data and information to a participant in S&T decision-making activities.S&T evaluation research should focus on improving data retrieval and organization, knowledge mining and knowledge discovery, and intelligent evaluation models. Moreover, we suggest that scientists carry out S&T evaluation in agreement with the needs of S&T development: 1) monitoring and sensing the development of science and technology in real-time with the help of emerging digital technologies; 2) exploring solutions to major concerns such as technical project management mechanisms, utilizing advanced data science and digital technologies to identify important scientific frontiers, and leveraging big data in science of science to reveal patterns and characteristics of scientific structures and activities; 3) carrying out problem-oriented evaluation research practice focused on four aspects, including intelligent project evaluation, evaluation of the critical technology competitiveness, talent assessment, and diagnostic evaluation of the research entity competitiveness.