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.