Data Visualization Best Practices
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Whether it's running a regression or analyzing an ANOVA, as a scientist you spend valuable time and energy visualizing research data. So when it comes time to tell the story of your research, don't miss out on these best practices.
If you've ever wondered:
- What makes a good graph?
- How do I pick the right type of chart, graph, or table?
- How do I tell a cohesive story with my data?
- How can I make my graphs more informative?
- What could I be doing better with text, color, labeling, etc.?
...then you're at the right place.
We've broken down data visualization best practices into 8 easy chapters, geared for scientists.
When you're ready to get started, BioRender Graph makes data visualization easy. Learn more today.
Chapter 1
Intro to Data Visualization
What is data visualization? Learn more about this concept and its inherent advantages and disadvantages. Explore why it's important to help communicate your research.
Chapter 2
How to Choose the Right Visual Representation
Choosing the right visual to represent your data is crucial for clarity and comprehension. In this post, we'll break down the main types of data visualizations and how to choose the right graph to tell your story.
Chapter 3
How to Turn Data into Visual Stories
Telling clear, cohesive stories with your data is no small feat. In this post, we'll explore ways to transform complex research data into easy-to-digest visuals.
Chapter 4
Why Reducing Visual Clutter Maximizes Data Impact
When visualizing data, less is often more. In this post, we'll explore reasons why paring down information can help you make your point in a more powerful way.
Chapter 5
Why Layout and Scale Matter
When it comes to graphs and charts, the legend, axis, and labels are often (and wrongly) an afterthought. In this post, you'll learn why they're so important and best practices around layout and scale that'll help your audience understand your graphs.
Chapter 6
Text Considerations in Graphs
Text may be secondary to research data, but it serves a critical purpose in graphs. In this post, we'll explore how text size and formatting can enhance (or detract) from clarity and comprehension.
Chapter 7
Color Considerations in Graphs
In this post, we'll learn how to use color strategically to complement and emphasize your data story, as well as tips on leveraging color to display quantitative data.
Chapter 8
How to Ensure Accurate Data Representation
To ensure accurate data representation, it’s important to maintain the integrity of the graph. Let's explore some examples of challenges you’ll want to take into consideration.
