Use these links to quickly navigate to the following sections:
Tags are a powerful way to "code" or classify data in order to:
organize data by groups, such as personas or sources
classify or enrich data to create new patterns
evolve raw data into higher meaning
Your list of tags, either in a research project or across the repository with global tags, is often referred to as a taxonomy.
Creating Tags & a Tag Taxonomy
Taxonomy is the science of naming, describing and classifying. With Notably, you can create a naming system for your tags. To create & manage your tags, click on the "Tags" menu within a project. There are 5 parts of a tag in Notably, including:
Tag Group: By grouping your tags, you can bring an extra layer of hierarchy and organization to your tags. This is ideal for grouping similar tags together. For example, Persona Tags or Research Type.
Tag Name: This is what you'll see in your tags dropdown as you highlight and tag data.
Description: Here is a simple space to give tags a definition. This small, best practice with documentation built-in helps researchers collaborate with tags and build shared understanding.
Keywords: These are words or phrases separated by commas that teach researchers and AI alike on how to properly and consistently tag data. Keywords are one of the ways our algorithms help suggest tags to make highlighting faster and more effective. We'll cover more about suggested tags later this article.
Instances: This represents the total number of times a tag has been used in Analysis. Later on you can filter by instances to surface your most commonly used tags.
Create a new tag using the "New tag" button at the top right or the "+ tag" link at the bottom of the tags list.
View tagged data and manage a tag by clicking on a tag to open a more detailed view. See the screenshot below.
Deleting, Merging, & Editing Tags
In the Tags menu, you can also delete, edit, and merge existing tags.
To delete, select the tag(s) you would like to remove, and click "Delete".
To merge, select the tags you would like to merge together, and click "Merge."
These actions cannot be undone, so tread carefully. See the example below:
To edit, begin typing in inline on the tag name you want to change.
You can think of the Tags menu as a "master" database for tags. Any edits to the tags in the list are automatically applied to all of your notes.
Tag Instances
Tag Instances are the number of times your tag appears in your Analysis board. This is the number that appears to the right of your tag.
In the example below, we see this tag "Pain Point" appears on the Analysis board 14 times.
My tags show 0 instances even though I have used the tag, where are my tags?
If you have used a tag on a Data file, but have not yet pushed those highlights to your Analysis board, the instances will not change. Once you have clicked "Add to Analysis" in your data file, the instances will be updated!
Project Tags vs. Global Tags
There are two types of tags in Notably: project tags and global tags. Project tags are unique to individual projects and global tags are set on the account level. Global tags are automatically added to every new project. Global tags create rich, new insights across projects.
Project Tags are often specific and unique to the research within a project. Global tags are more high-level and refer more to the organization as a whole or the research repository.
There are two distinct approaches to tagging.
Inductive tagging uses a ground-up approach where you derive tags from the data itself. You don’t start with preconceived notions of what the tags should be, but allow patterns to emerge from the raw data itself.
Deductive tagging is a top down approach where you develop tags based on your research questions or an existing research framework or theory. Tags are predetermined ahead of analysis.
One way to think about the differences between project tags and global tags is to think of project tags as inductive and global tags as deductive. Project tags are inductive because they aren't designed or set up until the research presents itself, there is no pre-existing theory or hypothesis to inform them. Global tags on the other hand represent more general, agnostic tags that a research org desires to track across all projects.
The global taxonomy might reflect themes a business cares about, parts of an important journey, observation tracking, personas, or other unique identifiers that show up in research over time. Global tags not only help researchers start new projects faster, but they help researchers across a team tag in a consistent and helpful way.
See the example below:
Suggested Tags
Suggested tagging is an AI-powered feature in the Notably workspace that gets smarter the more you use it. With suggested tags, highlighting and tagging is fast and consistent.
Suggested tags are surfaced based on two parameters:
(1) how you highlight and tag data historically
(2) the keywords added to your tag taxonomy
When there is enough data available, suggested tags will appear at the top of the tag menu when you have data highlighted. See the example below:
To learn more about using tags during research analysis, check out this blog post recapping a live tagging workshop.
Where do the tags come from?
To help researchers code their data more quickly, we have created a suggested tagging feature. When you automatically highlight your summaries, these suggested tags are automatically applied to these summary highlights.
There are two distinct approaches to tagging.
Inductive tagging uses a ground-up approach where you derive tags from the data itself. You don’t start with preconceived notions of what the tags should be, but allow patterns to emerge from the raw data itself.
Deductive tagging is a top down approach where you develop tags based on your research questions or an existing research framework or theory. Tags are predetermined ahead of analysis.
Suggested and automatic tagging in Notably combines these approaches, but uses a mostly deductive approach out-of-the-box. You start with a series of tags and then inductively come up with codes as you analyze your project data.
Tips to enhance accuracy of Suggested Tags:
There are a couple techniques you can use to enhance the accuracy of auto-tagging. Like suggested tags, they get better over time the more you use them.
Provide tag descriptions and keywords: In the Tags section of your Notably project, fill out the description and keywords. Suggested and auto-tagging uses this content for accuracy.
Add to Analysis as you go: Once highlights and tags are added to Analysis the more effective they apply to subsequent data files. Highlight and Tag as you go, clicking “Add highlights to Analysis” to push tagged data to Analysis and accuracy will continue to improve.
In a hurry? Try this prompt for Chat GPT to generate descriptions and keywords for tags:
Assume you're a qualitative researcher at [Company & Description], tasked with coding unstructured data from sources like focus groups, interviews, and surveys. Your objective is to identify words and phrases related to [Research Topic] that participants might use, formatted as a list.
For the tags 'Tag 1,' Tag 2,' Tag 3,' and ‘Tag 4' provide a description in under 40 words. Then list 15-20 relevant, colloquial keywords for each tag.These should be in the first person and reflect a variety of regional dialects, income levels, ages, and accents. Keep each keyword to a maximum of three words and present them in a line, separated by commas. Do not include quotation marks around the keywords.
👋 Have an idea or feedback around using tags in Notably? Let us know!