Research Templates and Project Scaffolding

When every researcher on a team sets up their own folder structure, naming conventions, and data-loading code from scratch, two problems appear quickly: results become hard to reproduce months later, and it becomes hard for one researcher to pick up another's project without relearning its idiosyncrasies. A research template fixes this by giving every new project the same starting skeleton — standard folders for raw data, processed data, notebooks, and source code; a config file listing parameters instead of hardcoded values; a fixed random seed; and a boilerplate script for loading data and saving results in a common format. Starting from the same scaffolding every time means a new project can be understood by a teammate in minutes, and a result from six months ago can actually be rerun and reproduced.

Good scaffolding also nudges researchers toward good habits by default — for instance, separating "raw, never edited" data from "derived, can be regenerated" data, which prevents the common mistake of accidentally overwriting an original data file during cleaning.

A template also lowers the bar for review: when every project follows the same layout, a manager or peer reviewer knows exactly where to look for the assumptions, the parameter choices, and the final results, instead of having to first reverse-engineer an unfamiliar and ad hoc structure before evaluating whether the research itself is sound. Teams that adopt a shared template consistently find onboarding new researchers takes days rather than weeks, since the scaffolding itself teaches the house conventions.