Iβve been watching the language around ResearchOps change.
New phrases appear in articles, product announcements, etc. Some describe genuinely new practices. Some rename established ideas. Others compress several different activities into one attractive label.
βAI-assisted researchβ is the clearest example.
It might refer to transcription, moderation, analysis, synthetic participants, research repositories, evidence tracing, or governance. Saying that a study used AI tells us very little about what the system didβor what people remained responsible for judging.
I created ResearchOps.md to begin mapping this emerging language.
The current version contains 27 concepts connected to 21 public sources. Each concept has a working definition and links back to the material that prompted it. Entries are available as readable webpages and raw Markdown, making the collection usable by both people and agents.
The goal is not to declare a finished taxonomy.
The goal is to make the language inspectable while it is still forming.
That means distinguishing between terms that are established, redefined, or newly coined. It means preserving attribution. It also means keeping definitions close to their sources so labels do not travel farther than their evidence.
I want this to become a useful public reference for ResearchOps professionals, researchers, and UX leaders trying to understand how the field is changing.
So Iβm opening the question:
What terms are you hearing that deserve a clearer definition?
Add the term in the comments and include a publicly published webpage where it appears. Iβll review nominations for the candidate list.
Dedicated to those who care about how the work of ResearchOps matures.