How can you use data visualization to track changes in research data?

1 Choose the right type of visualization

Depending on the type and purpose of your research data, you may need different types of visualizations to track changes over time, space, or categories. For example, if you want to show how a variable changes over time, you can use a line chart, a bar chart, or a scatter plot. If you want to show how a variable changes across different locations, you can use a map, a heat map, or a bubble chart. If you want to show how a variable changes across different groups, you can use a pie chart, a stacked bar chart, or a treemap. The key is to choose the type of visualization that best suits your data and your research question.

3 Highlight significant changes

Another way to use data visualization to track changes in research data is to highlight significant changes that are relevant to your research question or hypothesis. Highlighting significant changes can help you draw attention to the most important or interesting findings, as well as identify outliers or anomalies that may require further investigation. You can highlight significant changes by using different colors, shapes, sizes, or labels for the visual elements that represent them. For example, if you want to show how a variable changes over time, you can use a different color or shape for the data points that show a sudden increase or decrease. If you want to show how a variable changes across different locations, you can use a different size or label for the regions that show a high or low value.

4 Add context and annotations

To make your data visualization more informative and understandable, you should also add context and annotations that explain the meaning and significance of the changes you are tracking. Context and annotations can help you provide background information, definitions, sources, or references for your data, as well as clarify the assumptions, limitations, or implications of your analysis. You can add context and annotations by using titles, subtitles, captions, legends, footnotes, or tooltips for your visualizations. For example, if you want to show how a variable changes over time, you can use a title that summarizes the main trend, a subtitle that specifies the time period and the unit of measurement, a caption that describes the data source and the method of calculation, a legend that explains the color or shape codes, a footnote that acknowledges any data gaps or errors, or a tooltip that shows the exact value and date for each data point.

5 Use interactive features

Finally, one of the advantages of data visualization is that it can allow you to use interactive features that enable you and your audience to explore and manipulate your data in different ways. Interactive features can help you track changes in research data more dynamically and flexibly, as well as engage and empower your audience to discover their own insights and perspectives. You can use interactive features by using software or tools that allow you to create and share web-based or mobile-friendly visualizations that have features such as filters, sliders, buttons, menus, or widgets. For example, if you want to show how a variable changes over time, you can use a filter that lets you select a specific time range, a slider that lets you adjust the time interval, a button that lets you play or pause the animation, a menu that lets you choose a different variable, or a widget that lets you zoom in or out of the chart.

6 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?