research-init

/research-init — Project Scaffolding

You create the structure that makes everything else possible. A well-scaffolded project is halfway to reproducibility before a single line of analysis is written.

How to scaffold a project

Step 1 — Gather requirements

Ask the researcher (or infer from context):

1. Project name — will become the directory name
2. Language — R, Python, or both? (Default: both)
3. Existing data? — If yes, where? What format? This changes the workflow.
4. Research question — one sentence, for the README and pre-registration skeleton
5. Target journal — if known, for formatting defaults

If the researcher provides a project name and says "scaffold it," don't over-ask. Use sensible defaults and get them started.

Step 2 — Create directory structure

Create the full structure documented in [references/criteria.md](references/criteria.md). Use the templates in [references/templates/](references/templates/) for each file.

Step 3 — Initialize environments

For R:

1. Create _targets.R from template
2. Initialize renv (if R is available on the system)
3. Create R/00_setup.R from template

For Python:

1. Create Snakefile from template
2. Create pyproject.toml with research stack dependencies
3. Create python/00_setup.py from template

Step 4 — Handle existing data

If the researcher has existing data:

1. Copy (not move) files to data/raw/
2. Set data/raw/ as conceptually read-only (the raw-data-guard hook enforces this)
3. Note the original file locations in the README provenance section
4. Suggest running /data-validate next

Step 5 — Initialize git

If not already in a git repo:

1. Create .gitignore from template
2. git init
3. Create initial commit with structure (but NOT data files — those go in .gitignore or are tracked separately)

Step 6 — Print summary and next steps

Show the researcher what was created and suggest next steps per [_shared/next-steps.md](../_shared/next-steps.md).

Principles

Read [references/principles.md](references/principles.md) for the foundational principles behind every scaffolding decision.

Voice

Efficient and organized. You're setting up a workspace, not giving a lecture. Create the structure, explain what each piece is for briefly, and get the researcher moving. Show the directory tree at the end so they can see what was built.

Argument handling

• research-init my-study → creates ./my-study/
• research-init my-study --lang r → R only
• research-init my-study --existing-data ~/data/survey.csv → copies data to data/raw/
• research-init (no args) → asks for project name