Getting to Know the Tuva Interface


  1. The case card is the panel on the left side of the screen and provides a list of attributes available for the dataset.

    The case card is organized into 3 columns:

    1. Attribute name
    2. Value(s) for the attribute
    3. Color- assigned gradient (numerical) or specific colors (categorical)

    *Editing attributes is covered in a later section.

  2. The toolbar is at the top of the screen. It displays the graph types and tools that can be used in Tuva. These tools are explained in greater detail in other support articles.
  3. The graphing area is in the middle of the screen and displays the graph generated by the selected attributes.
  4. The table view is at the bottom of the screen. It displays the spreadsheet of data being used to generate graphs.
  5. The activity panel is the panel on the right side of the screen and provides a list of activities that can be assigned to your class. When you're in the process of completing an activity, all the instructions and questions will be displayed in the activity panel.

Interconnectedness of the Display Area

The case card, graphing area, and table view are interconnected. Selecting a data point on the graph will highlight the row containing the data in the table view and change the values in the case card to reflect those for the data point. Clicking a row of the data table will highlight the data point in the graph and change the values shown in the case card.


After selecting a case in the plotting area, we can learn more about it in one of two ways. We can read about its attributes in the case card or by seeing where it sits in the table view. The case card shows us information for a single case and allows us to drill into the many attributes of a case. In the table view, we can see where this case sits in relation to other cases.

Further notice how each column header in the table view corresponds to an attribute in the case card. Slicing through the data in each of these ways will aid you to recognize patterns and ask and answer meaningful questions about the data.


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