Scientific Data Sharing Virtual Organization Patterns Based on Supply Chain

Abstract

Since 1990s, as technique and social progress more quick, the world economy has experienced significant change. People ask for the varieties and specifications of products according to their own needs of work and life. So enterprises should become highly flexible and response rapidly to fit with constantly changing conditions. Therefore, a new organization mode appeared. Under this mode, the modern businesses make knowledge, technique, capital, materials, market and management resources in everywhere together using information technology. This organizational structure and management pattern was known as Virtual Organization (VO). Sharing of scientific data is an important contemporary scientific and technical infrastructure construction activity. This activity focuses on how scientific data resources, especially those supported by public research funding to be obtained, are organized, largescale produced, processed and preserved within the maximum extent. The goal of this activity is to provide useful data services for researchers conveniently, rapidly and efficiently. Many countries and regions, such as USA, English, Germany, China, and so on, are mobilizing the relevant institutions and resources to build the scientific data infrastructure for the whole society. One of the most important organization types is virtual organization, which is a way of structuring and managing goal-oriented activities. This thesis introduces the concepts, development, applications, research status of virtual organizations, analysed the Scientific Data Sharing Virtual Organization (SDSVO) operation problems. Then the work prompted that supply chain management should be led into SDSVO to solve above problems. In the aspects of champion selection, members choose and IT system design, it would be best for SDSVO managers to supply chain integration. Three theoretical basis includes resources dependences, transaction cost minimization and Gametheory must be considered while supply chain integration. The paper analysed the scientific data supply chain includes data creators, data centres, data service providers and data users based on three theory, then defined analytical frameworks, then chose two cases, which are German National Science Data Infrastructure (GNSDI) and China National Scientific Data Sharing Program (CNSDSP), to discuss operation mechanism construction, such as champion selection, members composition, IT system development,