This guide explains how to use the timeline feature to explore information based on different "reality types." Reality types represent various ways of experiencing data associated with specific dates.
Understanding the Timeline:
The timeline displays events or data points as dots along a horizontal line.
Hovering over a dot reveals a pop-up displaying:
Date: The date associated with the dot.
Colored Rings: These represent the available reality types for that date. (Similar to the Date Selector explained earlier)
Solid Ring: One reality type available.
Half Rings: Two reality types available.
Multiple Arcs: More than two reality types available (equal segments for each type).
Legend: A miniature legend appears alongside the date, explaining the meaning of each color and its corresponding reality type. (This legend disappears when you move your mouse away from the dot.)
Selecting a Reality:
Hover over the desired dot on the timeline.
The pop-up with the date, rings, and legend will appear.
Click directly on the date within the pop-up. This will (ideally) filter the view to show only the information related to the available reality type(s) for that specific date.
Legend:
A separate, permanent legend might be available on the screen to explain the meaning of each colour and its corresponding reality type. This can be helpful for quick reference without needing to hover over every dot.
This feature allows you to quickly identify dates with specific reality types of interest.
By clicking on the date within the pop-up, you can efficiently access the information related to that reality type.
Tips:
Use the timeline to scan for dates with the reality types you're looking for.
Hover over dots to reveal details and use the legend to understand the color codes.
Click on the date within the pop-up to explore the specific reality type for that date (functionality under development).
This enhanced timeline empowers you to explore information chronologically while considering different reality types, leading to a more comprehensive understanding of the data.
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