Abstract:

Objective This study aims to improve the management efficiency of zebrafish strains and zebrafish facilities and to transform the traditional management model that relies on manual labor. The goal is to promote a comprehensive transition from traditional manual operation to standardized, digitalized, automated, and refined management, thereby significantly improving management quality and work efficiency. Methods The research group independently developed the "Zebrafish Facility and Zebrafish Strain Automation Management System V1.0" (Software Copyright Registration No. 2021SR0236837), which comprehensively recorded all operational data of the zebrafish facilities and completed data on zebrafish strains. By combining in-depth mining of these data with management experience, management regulations were transformed into executable system instruction sets, and a full-process automated closed-loop management framework of "Monitoring–Analysis–Early Warning–Task Assignment–Execution–Recording" was established. Results This management system has been operating stably for 5 years and has been successfully applied in 3 zebrafish facilities, cumulatively supporting 78 researchers and efficiently managing 1 108 plasmids and 1 123 zebrafish strains, among which 291 strains has been incorporated into zebrafish sperm bank management. A Likert scale survey of 28 core users covering 14 evaluation dimensions across 4 major categories showed average scores of 4.46–4.89, all far above the midpoint (3 points) of the scale, indicating that users gave high evaluations to all dimensions of the system. Meanwhile, the standard deviations of most dimensions were low (s ≤ 0.44), reflecting good consistency in user evaluations. Conclusion This study integrates information technology, big data analysis, and modern management concepts to achieve a virtuous cycle of "data collection–analysis and execution–system optimization" in zebrafish facility management. It significantly improves management efficiency and refinement and promotes the development of management models toward automation.