Visualizing your data

A literature review is much more useful when you can easily see trends in your data. In Silvi, you can quickly create plots to analyze the data you've extracted.

Creating a new plot 📊

Navigate to the Analysis section of your project. Here, you can create a new plot by adding a new analysis tab.

Once your analysis tab is set up, select the type of plot you want to create. You can choose from:

• Line plot

• Bar plot

• Scatter plot

Next, choose what data to display on the X-axis and Y-axis. The available options depend on the tags you've created in your project.

For example, you might create a bar plot showing disease severity across different countries.

What can be plotted? 🏷️

Silvi allows you to plot quantitative data extracted from studies, either manually or through AI extraction. However, you can only use numerical or categorical tags for plotting.

• Bar & Line Plots: The X-axis must be a categorical tag, while the Y-axis must be a numerical tag.

• Scatter Plots: The X-axis can be either categorical or numerical, but the Y-axis must always be numerical.

Aggregation of numerical values 🔢

Imagine you’re reviewing studies on the effectiveness of a medication across different age groups.

• You set age group (e.g., Children, Adults, Elderly) as your X-axis (categorical).

• You set drug dosage (mg/day) as your Y-axis (numerical).

Now, let's say:

• Study 1 reports an average dosage of 50 mg/day for adults.

• Study 2 reports 60 mg/day for the same group.

Since there are multiple values for Adults, you need to decide how to aggregate them:

• Sum → Not ideal here, as it would combine dosages (misleading).

• Average → The preferred method in this case, showing the mean dosage across studies.

If you want to see all reported values individually rather than an aggregated result, a scatter plot would be a better choice.