1. Determine the data type and target to be displayed. For example, you may want to show the research trends in a certain field, the authors cited in the paper, the number of citations in journals or conferences, and so on.
2. Select the appropriate data visualization tool. There are many tools that can be used for data visualization, such as Tableau, PowerBI, Matplotlib(Python library) and so on. Choose tools that suit your skill level and needs.
3. Organize and clean up the data. Ensure accurate data and eliminate duplicate or irrelevant data. Convert data into a format suitable for visualization, such as CSV, Excel, etc.
4. Create charts and graphs. Select the appropriate chart type according to the target, such as line chart, histogram, pie chart, etc. Add appropriate titles, legends and labels to the chart so that readers can better understand the data.
5. Adjust the color, font and layout. To make the chart more attractive, you can adjust the color, font and layout. Make sure the chart is easy to read and beautiful.
6. Analyze and explain the results. Analyze the data in the chart to find potential trends, patterns or associations. Write a short report explaining your findings and their significance.
7. Optimize and update. Continuously optimize and update your data visualization based on feedback and requirements. This may include adding new charts, adjusting colors and layouts, and so on.
8. Share the show. Share your data visualization results with others, such as colleagues, mentors or the public. You can insert charts into reports, presentations or websites, or use social media and other platforms to show them.