How to identify the development trends in the research field through data analysis?

Identifying research development trends through data analysis employs computational methods to detect emerging topics, shifting interests, and declining areas by analyzing patterns within scholarly data. This approach is feasible using bibliometric and scientometric techniques applied to publication databases. Key principles involve selecting relevant scholarly databases (e.g., Scopus, Web of Science, PubMed), applying analytical techniques like co-word analysis, citation network mapping, bibliographic coupling, or topic modeling. Data quality, coverage, and appropriate time windows are crucial. Visualization tools (e.g., VOSviewer, CitNetExplorer) help interpret complex patterns. Essential considerations include distinguishing genuine trends from noise and understanding the limitations inherent in specific databases or algorithms. Implementation typically follows these steps: (1) Define the specific research field and gather publication data; (2) Clean and preprocess the data (keywords, abstracts); (3) Apply chosen analytical methods (e.g., term frequency analysis over time, co-occurrence network evolution); (4) Visualize and interpret the results (identifying clusters gaining momentum or declining); (5) Validate findings through expert review. This process enables researchers and institutions to spot emerging opportunities, inform strategic research directions, allocate resources effectively, and anticipate future developments in their domain.