Building Reproducible Jupyter Workflows

For any new computational research project, set up this structure before writing analysis code:

project/
├── environment.yml          # or requirements.txt / Pipfile
├── README.md                # how to reproduce, in 3 steps or fewer
├── binder/                  # Binder config for one-click reproduction
├── data/
│   ├── raw/                 # never modified, read-only
│   └── processed/           # generated by notebooks, gitignored
├── notebooks/
│   ├── 01-data-preparation.ipynb
│   ├── 02-analysis.ipynb
│   └── 03-figures.ipynb
├── src/                     # reusable code extracted from notebooks
└── tests/                   # tests for src/, run in CI

Pin the environment immediately:

Bash
conda env export --no-builds > environment.yml
# or
pip freeze > requirements.txt

Then verify reproducibility from a clean environment before continuing:

Bash
conda env create -f environment.yml -n repro-check
conda activate repro-check
jupyter nbconvert --to notebook --execute notebooks/*.ipynb

Progress:

• Step 1: Define the environment specification (exact package versions, not ranges)
• Step 2: Organize notebooks by pipeline stage, numbered in execution order
• Step 3: Extract reusable logic from notebooks into a tested src/ package
• Step 4: Ensure notebooks execute top-to-bottom with no hidden state (restart & run all)
• Step 5: Separate data (raw/immutable) from generated outputs
• Step 6: Add a Binder/Docker config so others can run it with zero local setup
• Step 7: Strip or version-control notebook outputs sensibly (nbstripout for git)
• Step 8: Archive a DOI-citable snapshot (Zenodo + GitHub release) at publication time
• Step 9: Write a README with exact reproduction steps, tested by someone else

Step 1 — Environment specification. Never rely on "whatever is installed." Pin exact versions of Python, Jupyter, kernels, and all dependencies. Record OS if it matters (e.g., compiled extensions).

Step 2 — Notebook organization. One notebook = one stage of the pipeline (ingest, clean, model, visualize). Avoid monolithic notebooks mixing exploration and final analysis — keep exploratory work in a separate scratch/ folder excluded from the reproducible path.

Step 3 — Extract to src/. Any function used more than once, or any function requiring testing/debugging, moves out of the notebook into an importable module. Notebooks should read like a narrative calling well-tested functions, not contain the implementation.

Step 4 — No hidden state. Before considering a notebook "done," restart the kernel and run all cells in order. Out-of-order execution is the single most common reproducibility failure.

Step 5 — Data separation. Raw data is read-only and never edited in place. All derived data is regenerable from raw data + code — never hand-edited.

Step 6 — Zero-setup reproduction. Provide a binder/ directory (or Dockerfile) so a reviewer or collaborator can click a link and get a running, identical environment without local installation.

Step 7 — Output handling in git. Decide deliberately: either strip all outputs before commit (clean diffs, use nbstripout) or commit outputs intentionally (for rendered documentation) — never commit accidentally stale outputs.

Step 8 — Archival snapshot. At submission/publication, tag a release and mint a DOI via Zenodo (or similar) so the exact code state is permanently citable, independent of future repository changes.

Step 9 — README test. The README's reproduction instructions must be validated by someone other than the author, ideally on a different machine.