Relationship Attributes and Tags

Overview

In SharpCloud, relationships can be enhanced using Attributes and Tags, similar to items. These provide additional context, improve visualisation, and enable more advanced filtering of relationships based on specific criteria. This allows users to better understand how items are connected and to analyse those connections more effectively.

Ways to Create Relationship Attributes and Tags

There are two main ways to create relationship attributes and tags:

1. Importing data into the Data Grid

2. Creating them directly in Story Setup

Creating Relationship Attributes in Story Setup

To create a new relationship attribute:

1. Go to Create mode

2. Open Story Setup

3. Navigate to the Relationships tab

4. Select the attribute type to create (Numeric, Text, List, Date)

5. Optionally create labels for the attribute (excluding Text attributes), including colour and thickness settings

These attributes can then be used to store structured data on relationships, support filtering, and control how relationships appear visually in views.

Creating Relationship Tags in Story Setup

To create relationship tags:

1. Go to Story Setup

2. Open the Relationships tab

3. Navigate to the Tags sub-tab

4. Use the “Add a tag” section to create new tags

Tags are useful for categorising relationships and enabling quick filtering or grouping based on defined categories.

Creating Attributes and Tags via Data Import

When pasting data into the Data Grid, SharpCloud can automatically generate relationship attributes and tags based on the structure of the imported data.

• A Tags column will generate individual relationship tags from each value, without grouping

• Additional columns such as Risk, Impact, or Strength will be created as relationship attributes

• The data paste wizard allows you to define the attribute type during import

This approach is useful for quickly creating multiple attributes and tags from external datasets.

Using Relationship Attributes and Tags

Once created, relationship attributes and tags can be used to filter relationships, enhance visual styling such as colour or thickness, and provide additional context for analysis.

They also support more advanced configurations in views and improve storytelling by making relationships clearer and more meaningful.

Best Practices

• Use attributes for structured or measurable data such as risk or strength, and use tags for flexible categorisation such as scenario or type.
• Apply labels to numeric or list attributes to improve visual clarity, and maintain consistent naming conventions.
• For large datasets, use data import to efficiently create attributes and tags.