Themes Manager

The themes manager is where you build the tagging system you'll use for an in-depth analysis of all your qualitative data.

Why should you use Themes Manager?

The themes manager is designed for in-depth analysis of your qualitative data. It helps researchers, program teams, and decision-makers make sense of large volumes of unstructured data by offering a structured tagging system for further analysis.

Let's understand with an example:

Your water sanitation and hygiene (WASH) research organization is conducting a cross-country comparative study (India & Kenya) on household sanitation practices. You will likely collect qualitative data: in-depth interviews, focus-group discussions, open-ended survey responses, community observation notes, possibly transcripts from meetings or workshops across different communities.

Once the data is uploaded on the platform and ready for analysis, you will need to assign labels to tag recurring patterns or emerging findings in the dataset. That is when you make use of the tagging structure. By applying tags (codes) in a structured, hierarchical way, you can label excerpts systematically, making your qualitative analysis much more organized and easier to work with.

How the structure works - the two tabs

The Themes Manager is built around two tabs: a Thematics tab and an Attributes tab. The simplest way to hold them in mind:

• Thematics tell you what an excerpt is about — the topic, area, or component of the work. It has two levels: Themes > Tags (described below).

• Attributes tell you what kind of annotation it is — a data cue about the nature of the excerpt, such as whether it points to a barrier, an enabler, or an outcome. This also has two levels: Attribute categories > Tags (described below).

So Thematics are the subject of an excerpt, and Attributes are the lens you read it through. You switch between the two tabs as you build and view each one.

Using our WASH example, your structure might look like this:

With this structure in place, an excerpt like:

“We share one toilet with five families, so the women wait until night to use it” ...would be tagged under Thematics as Shared Community Access and under Attributes as Barrier (Signal) and Negative (Sentiment Polarity).

Build your structure

Building works the same way in both tabs. Open the Thematic tab to create the themes and tags that describe what your data is about, then switch to the Attribute tab to do the same for your attribute categories and their tags.

1

Create your theme or attribute

Type a title in the 'Type to add a new Theme' input box and add it to the canvas using the + button. In the Thematic tab these are your themes, the main analytical areas of your data (for example, Sanitation Access & Infrastructure or Hygiene Practices). In the Attribute tab these are your attribute categories (for example, Signal).

2

Create your tags

You can create a tag in two ways. Either type it in the 'Type to add a new Tags' and add it with the + button, the same way you added a theme; or create it directly under the theme or attribute category you want it in. To do that, hover over the theme (or category), click the + icon that appears, and type the tag name in the box that pops up. Creating a tag this way assigns it to that theme or category straight away. Tags are the specific labels you’ll apply to excerpts (for example, Shared Community Access).

3

Assign or rearrange tags

Any tag you created in the tag window can be dragged and dropped into the theme or attribute category it belongs to. You can also drag tags from one theme to another to reorganise them, or leave a tag unassigned by keeping it in the tag section until you decide where it fits. Tags you created directly under a theme or category are already assigned, so you can skip this step for them.

4

Add a description

Click the pencil icon on any theme, attribute category, or tag to type a description, and click save. Descriptions give the platform context to apply your tags more accurately. See Writing good tag descriptions at the end of this page for how to write them well.

The difference between the two tabs

The mechanics are identical; only the meaning changes. Thematic tags capture what an excerpt is about. Attribute tags are the data cues you want applied across your data: for example, a Signal category with tags like Barrier, Enabler, and Outcome.

Manage and refine your structure

As your analysis develops, these three tools will help you keep your structure tidy and under control:

• Lock / unlock themes — Locking a theme prevents AI from adding new (inductive) suggested tags to it. Your existing tags are still applied during annotation; locking just stops the structure from changing. Useful once a theme is finalised and you don't want it to change. Unlock it at any point to allow suggestions again.

• Shift categories between thematics and attributes — If something you set up as a topic in the Thematics tab turns out to be a data cue (for example, if you created Barrier as a thematic tag but it belongs in Attributes), you can move it across without rebuilding it.

• Show / hide tag descriptions — Toggle all tag descriptions on or off to suit your needs. Turn it on to review the context behind each tag, or off for a cleaner, more compact view.

Writing good tag descriptions

Descriptions help AI apply your tags accurately - for both the Thematic and Attribute tabs, so they are worth writing well. Keep these principles in mind:

• Keep it short and focused. Aim for roughly 20–40 words. A tight, specific description works better for AI suggestions than a long, general one.

• Start with a consistent opener. Begin each description with a phrase like “Apply this tag when the segment contains evidence of…”, “Use this tag when there is evidence that…”, or “Tag segments that indicate…”.

• Describe the evidence, not the concept. Spell out what the excerpt actually says or shows rather than naming an abstract idea. Instead of “social isolation” write “respondent describes being alone, excluded, or having no one to rely on.”

• Describe the pattern, not just the setting. A description like “migration” will tag every mention of migration. Capture the specific situation instead: “respondent describes moving to another place for work, education, marriage, or displacement”.

• Make clear why this tag and not another. Each description should set the tag apart from other similar tags so AI doesn’t confuse them. Note briefly what does not belong under the tag where it helps avoid confusion.

• Use action verbs. Words like prevented, restricted, supported, received, participated, experienced, and changed help AI match real content — models match actions better than abstract nouns.