Do annotations matter to researchers?
Why annotations are essential to research — from medieval monks marking manuscripts to modern researchers highlighting insights. How Synthetic Users brings annotation into AI-powered research.
In the olden days, monks would annotate in the margins of manuscripts. The interesting aspect of doing it wasn’t the memory, but the fact that it would start conversations with other monks who read the same books. It was like an asynchronous chat and an early form of academic discourse before there was academia.
This exchange of ideas and interpretations not only enriched their individual understanding but also served as a foundation for a collective wisdom that transcended the confines of their monastic cells. Each note in the margin became a thread in a larger tapestry of knowledge, spanning topics from theology to the natural sciences, revealing the interconnectedness of all fields of inquiry.
As a researcher, you rely heavily on codebooks. You’re looking for patterns and annotating is the process that will create those codes (or tags) so you may identify those very patterns. These come as themes, behaviors, or user needs.
This meticulous process of coding and annotation is akin to the intricate art of cartography, where each code you apply is a landmark, guiding you through the vast landscape of qualitative data. The act of annotating, then, becomes an exploration, a way to chart the unknown, making the invisible visible, and uncovering the rich, nuanced layers of human experience embedded within your data.
Qualitative data is rich and rather unwieldy. Synthetic qualitative data is a little more structured, but it will still benefit from your annotations. The next step for us is to take those annotations and ensure the final report — when all interviews are summarized, takes your annotations into account.
This ensures that the essence of each conversation, the subtleties of every response, and the nuances of every interaction are not lost in translation. Through this synthesis, your annotations become the narrative glue that binds the data together, offering insights that are both profound and actionable.
In the meantime we’ve given you three colours. It’s not exactly the rainbow but scarcity will keep you focused. And focus will deliver the results you need. Enjoy.
Releated Articles
More articles for you
Synthetic users, an intro
A quick video walkthrough introducing the Synthetic Users platform — covering key navigation and core features for new users getting started.
Introducing Iris
Introducing Iris, Synthetic Users' research agent. A tutorial on how to work alongside Iris to define study parameters, run interviews, and get precisely structured insight reports.
Multi-study planner: plan and run multiple studies with different audiences
PRISMA is Synthetic Users' multi-study planner — a single interface to design, manage, and run multiple studies with different audiences simultaneously, without juggling separate projects.
Enrich your Synthetic Users with your data. RAG tutorial.
Learn how to enrich Synthetic Users with your own data using Retrieval-Augmented Generation (RAG) to make AI participants more context-aware and accurate.
Synthetic Users Leadership Webinar: Mapping the Synthetic Research Industry
Recap of the Synthetic Users Leadership Webinar featuring Wikipedia founder Jimmy Wales — covering the state of AI, synthetic research risks, differentiators, and the road ahead.
Help! How do I go beyond the average with Synthetic Users?
How to move past generic insights with Synthetic Users by changing your research mindset, framing better goals, and probing deeper — just like you would with organic participants.
Features: Knowledge Graph, Cloning Research, Exporting Annotations, Searching inside History...
A roundup of four new Synthetic Users features: Knowledge Graph for visualising interview themes, Research Cloning, Annotation Exporting, and searching inside your research history.
What's new? 4 new features
A look at four new Synthetic Users features: a Research Assistant UI, expanded language support, and more — designed to help teams run better research faster.
Generating, Running and Sharing Synthetic Research. Really?
A step-by-step walkthrough of how to generate your Synthetic User panel, run interviews, and share your research — from setup to insights report
What to do when you feel your Synthetic Users are being too generalist
Three practical steps to get more specific, nuanced insights from Synthetic Users — including how to probe deeper, ask better questions, and when to complement with organic research.
Which interview type should I pick?
A guide to Synthetic Users' three interview types — dynamic script, custom script, and concept testing — and how to choose the right one based on your research goal.
Do annotations matter to researchers?
Why annotations are essential to research — from medieval monks marking manuscripts to modern researchers highlighting insights. How Synthetic Users brings annotation into AI-powered research.
The transition to Continuous Insight and where we excel
A look at four new Synthetic Users features: a Research Assistant UI, expanded language support, and more — designed to help teams run better research faster.
Don’t fall into the: “It’s not real. It’s just programming.“ fallacy.
Why dismissing AI-generated feedback as "just programming" is a mistake. The value of synthetic research lies in the insights it generates — not in the origin of the participant.
Products creating products
A reflection on how AI is transforming product development — and how Synthetic Users fits into a future where products help create better products through real-time synthetic feedback loops.
Synthetic Users: Merging Qualitative and Quantitative Research, in seconds.
How Synthetic Users blurs the line between qualitative and quantitative research — enabling teams to get the depth of qual at the scale and speed of quant, in seconds.