Abstract:

Objective This study focuses on enhancing zebrafish line and facility management by shifting from a manual, labor-intensive paradigm toward a standardized, digitalized, automated, and refined model. The goal is to achieve a marked improvement in both management quality and operational efficiency. Methods The laboratory independently developed the "Zebrafish Facility and Strain Automation Management System V1.0 (Software Copyright Registration No.2021SR0236837), which comprehensively records all operational data of the zebrafish facility and complete strain data. By deeply analyzing these datasets and integrating practical management expertise, we have translated management protocols into executable system command sets, establishing a fully automated, closed-loop management framework: "Monitoring → Analysis → Early Warning → Task Assignment → Execution → Recording. Results Since its launch in April 2020, the system has been operating stably for five years and has been successfully applied in three zebrafish laboratories, providing support to 78 researchers. It has efficiently managed 1,108 plasmids, 1,123 zebrafish strains, and approximately 5,400 tanks. Among these, 291 strains have been incorporated into the zebrafish sperm bank. A Likert five-point scale survey was conducted among 28 core users, covering 14 evaluation dimensions across four major categories. The results showed high average scores (M = 4.46–4.89), significantly exceeding the midpoint value of 3. This indicates that users provided highly positive evaluations for all aspects of the system. Additionally, user feedback demonstrated high consistency, with most dimensions exhibiting low standard deviations (SD ≤ 0.44). Conclusion This study integrates information technology, big data analytics, and modern management concepts to achieve a virtuous cycle of "data collection – analysis and execution – system optimization" in zebrafish strain and facility management. It significantly enhances the operational efficiency and fine?grained management level of zebrafish strain and facilities and drives the shift of management models toward automation.

Key words: Zebrafish, Information Management System, Automated Management, Big Data Analysis