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Intellectual Property Protection of Scientific Data in the Algorithm Era: Factors Influencing Service Quality and Optimization Strategies
XU Yue, XI Zijie, PAN Chao
Journal of library and information science in agriculture
2023, 35 (11 ):
23-39.
DOI: 10.13998/j.cnki.issn1002-1248.23-0483
[Purpose/Significance] With the advent of the algorithmic era, libraries' information delivery channels have shifted from offline physical entities to online digital platforms. This transformation has brought about significant changes in the way users access and utilize scientific data. The frequency of use of scientific data has increased exponentially, as more and more users rely on data to support their research, education, and innovation activities. The huge demand and application of use of data poses challenges to the development of libraries, among which the service guarantee of intellectual property rights (IPR) for scientific data is becoming a key factor affecting the development of digital libraries. IPR is a legal concept that protects the ownership and control of data creators and providers over their data. It also regulates the rights and obligations of data users and re-users. Therefore, this study aims to explore the influencing factors and optimization strategies of libraries' IPR service quality for scientific data. [Method/Process] To achieve this goal, this study used a questionnaire analysis method to collect data from a sample of 252 individuals belonging to a highly knowledgeable group, such as researchers, academics, and students. These individuals are the main users and producers of scientific data, and their perceptions and expectations of the quality of IPR services by libraries are crucial for improving the service. The questionnaire consists of four parts: demographic information, IPR awareness, IPR satisfaction, and IPR improvement suggestions. The reliability of the questionnaire factors is between 0.724 and 0.913, indicating a high level of internal consistency. The validity of the questionnaire is verified by confirmatory factor analysis, which shows a good fit between the data and the model. Based on the data, this study conducts a path analysis to test the hypotheses and obtain the results. [Results/Conclusions] The results show that the following factors have a significant positive impact on the quality of libraries' IPR services for scientific data: the implementation efficiency of policies and regulations (beta=0.326, p <0.001), talent team building (beta=0.274, p <0.001), the data management technology (beta=0.211, p <0.001), the diversification of service models (beta=0.358, p <0.001), and the number of data IPR sharing agreements (beta=0.329, p <0.001). These factors reflect the importance of improving the legal, human, technical, and organizational aspects of libraries' scientific data IPR services. Based on the findings, this study proposes five optimization strategies for libraries in scientific data IPR service: strengthening the implementation of policies and regulations, improving the training and motivation of talent teams, upgrading the data management technology, innovating the service model, and increasing the number of data IPR sharing agreements. These strategies can help libraries to improve the quality of their scientific data IPR services and meet the needs of the users in the algorithmic era.
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