Influencing Factors of Scientific Data Value Increment Based on System Dynamics

[Purpose/Significance] To explore the influencing factors of the added value of scientific data and reveal the inherent development laws of the added value of scientific data. [Method/Process] First, based on the interview data of 18 experts and the research results related to the value appreciation of scientific data in existing literature, the grounded theory method was adopted. Through open coding, main-axis coding, and selective coding, 19 categories, 6 main categories, and 3 core categories were finally obtained. From this, a theoretical model of the factors influencing the value appreciation of scientific data was obtained. On this basis, the Vensim PLE tool was used to establish a dynamic model of the value appreciation system of scientific data, and the process of value appreciation of scientific data was dynamically simulated and analyzed to reveal the relationship between various influencing factors and the value appreciation of scientific data. [Results/Conclusions] In the process of increasing the value of scientific data, the quality factor of raw data is a prerequisite, and high-quality raw scientific data are conducive to the integration and secondary development of subsequent scientific data. The data literacy of data producers represented by researchers has the most significant impact on the quality of scientific data. The level of data storage and payment has a significant impact on the added value of scientific data. When the perceived effort and perceived risk of data storage and payment by researchers decrease, accompanied by the pressure of scientific data sharing policies, researchers become more likely to increase their willingness to save and pay, thereby significantly improving the scale effect of data storage and payment. The organization and integration of scientific data is the key to the formation of value-added scientific data. Overall, the metadata quality has the most significant impact on the level of scientific data organization and integration. The quality of metadata is the foundation of scientific data processing, classification, and integration. The higher the quality of metadata, the more it helps to improve the level of scientific data organization and integration. The sharing and development of scientific data is the key to realizing the value-added of scientific data. It is the final step in realizing the value-added of scientific data. In this process, the scale of scientific data openness, the quality of scientific data sharing platforms, and development capabilities all have a positive promoting effect on the level of scientific data sharing and development.