After many years spent in research, the scientific process—from idea to publication—becomes second nature. However, this intuition, though invaluable, deserves to be structured. The desire to describe this workflow stems not only from a need to better understand my own work but also from the desire to create a map that can help others navigate this complex terrain.
One inspiration was a humorous but accurate list from the book “We Have No Idea: A Guide to the Unknown Universe” by Jorge Cham and Daniel Whiteson:
- Organize what you know
- Look for patterns
- Ask questions
- Buy a tweed jacket with elbow patches
However, scientific work is, above all, the art of asking the right questions. It’s not about “beating the baseline” but about understanding a phenomenon. The question “why?” is a researcher’s compass. In turn, understanding often means the ability to reconstruct a mechanism (e.g., by implementing code or a formal proof), although in some areas of mathematics, a complete, verifiable line of reasoning is sufficient.
I have noticed that whether I am writing an empirical paper in Natural Language Processing (NLP) or a systematic review with a meta-analysis, a common skeleton lies beneath the surface. The result of these observations is the working framework below, which attempts to visualize this skeleton.