What are the best ways to visualize and communicate data analysis results in biotechnology research?

1 Choose the right type of visualization

Depending on the nature, scale, and purpose of your data, you need to choose the most appropriate type of visualization to display it. For example, if you want to show the distribution of a variable, you can use a histogram, a box plot, or a density plot. If you want to compare two or more variables, you can use a scatter plot, a bar chart, or a line chart. If you want to show the relationship between variables and a categorical outcome, you can use a mosaic plot, a stacked bar chart, or a violin plot. You can also use more advanced types of visualization, such as heat maps, network graphs, or principal component analysis (PCA) plots, to show complex patterns and structures in your data. The key is to choose the type of visualization that best suits your data and your research question, and to avoid cluttering, distorting, or misleading your audience with unnecessary or inappropriate elements.

2 Use clear and consistent labels, legends, and colors

Once you have chosen the type of visualization, you need to make sure that it is easy to read and understand by using clear and consistent labels, legends, and colors. Labels are the text that describe the axes, titles, and annotations of your visualization. They should be concise, informative, and accurate, and use appropriate units and formats. Legends are the guides that explain the meaning of the symbols, colors, and shapes used in your visualization. They should be visible, unambiguous, and aligned with your labels. Colors are the visual cues that help you highlight, differentiate, or group your data. They should be chosen carefully, considering the contrast, harmony, and accessibility of your color scheme, and avoiding too many or too similar colors that can confuse or distract your audience.

3 Apply good design principles and tools

Creating an effective visualization requires more than just choosing the right type of visualization and using clear labels, legends, and colors. You also need to apply good design principles and tools to make your visualization more appealing. Balance, emphasis, hierarchy, and simplicity are some of the design principles that can help you improve your visualization. Software such as R, Python, MATLAB, or Excel can be used to generate and customize your visualization with code or graphical user interface (GUI). Libraries such as ggplot2, matplotlib, seaborn, or plotly offer a variety of visualization functions and options with code. Templates from BioRender, Canva, or Piktochart provide predefined and professional visualization designs with GUI.

4 Tailor your communication to your audience and purpose

Finally, you need to tailor your communication to your audience and purpose, which can vary depending on the context and format of your data analysis results. For example, if you are writing a research paper, you need to follow the guidelines and standards of the journal or conference where you are submitting it, and provide detailed and rigorous explanations and interpretations of your data analysis results. If you are presenting a poster or a slide, you need to follow the rules and expectations of the event where you are displaying it, and provide concise and engaging summaries and insights of your data analysis results. If you are sharing a blog post or a tweet, you need to follow the norms and trends of the platform where you are posting it, and provide accessible and appealing stories and messages of your data analysis results. The key is to know your audience and purpose, and to adapt your communication style, tone, and content accordingly.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?