Scientific Data Management Based on a Data Life Cycle Perspective: Using the Institutional Repositories Base of 24 Universities in the United States as an Example - Keyi XIAO, Yingying CHEN (Nongye tushu qingbao xuebao)
[Purpose/Significance] The research paradigm is gradually shifting towards a data-intensive model, where research data has become the cornerstone in the realm of academic endeavors. Effective research data management can enhance the research efficiency of scientific researchers, reduce redundant data collection, and reduce costs. As a central repository for the storage of scholarly research outputs, it is essential that university institutional repositories fulfill their role in research data management. [Method/Process] To gain a full understanding of the evolving landscape, we embarked on a meticulous network-based research investigation. We specifically selected the institutional repositories of 24 prestigious American universities as our research subjects, with the aim of exploring the diverse range of services they provide at different stages of the research lifecycle. Our research was firmly grounded in the data lifecycle framework, which enabled us to systematically examine a wide range of research data management (RDM) services. This included critical aspects such as developing comprehensive research data management plans, establishing robust data organization services and standardized protocols, providing reliable long-term data storage solutions to ensure continued accessibility, enhancing data sharing policies to foster collaboration, strengthening research data quality control measures to maintain integrity, and developing comprehensive research data management training programs to empower researchers. Furthermore, we conducted an in-depth analysis to summarize the characteristics and valuable experiences of American universities in building and maintaining the basic infrastructure of their institutional repositories. [Results/Conclusions] Given the unique circumstances of China's modernization process, this paper distills effective insights and strategies from the institutional repositories of domestic university libraries in the field of research data management services. Our findings highlight the importance of building a localized research data management platform tailored to the specific needs and contexts of Chinese academia. Enhancing the quality of research data management is critical to building a trusted institutional knowledge base and fostering an environment of credibility and reliability. By applying the FAIR (Findable, Accessible, Interoperable, Reusable) and TRUST (Transparent, Responsible, Usable, Sustainable, and Trustworthy) principles, we can facilitate the open and seamless sharing of research data, breaking down barriers to collaboration and innovation. Finally, building a professional scientific research data management team is essential to provide the human capital necessary to navigate the complexities of data management and to promote the development and adoption of best practices in scientific research data sharing. Taken together, these findings help to improve the abiity of the scientific community to harness the full potential of research data to drive the creation and dissemination of knowledge.