# How to Create a Reproducibility Plan When You Have No Time By the end of this guide you will have a one-page reproducibility plan for your current project, a version-controlled code folder, a pinned software environment, and a 15-minute weekly checklist that keeps all of it current. You need a laptop with Git installed (confirm with `which git` on macOS and Linux, or `where git` on Windows), Python 3.10 or newer or R 4.0 or newer, a plain-text editor, and about 90 minutes split into 15-minute blocks across one week. Nothing here requires new purchases, server access, or anyone's permission. Reproducible research means that another person, or you in six months, can take your raw data and your code and regenerate every number and figure without emailing you. Replicability is different. It means a new experiment, run by new hands, reaches the same conclusion. The first is a tooling and paperwork problem you can solve this week. The second becomes possible to attempt only after the first is done. The pressure is real. Begley and Ellis reported in Nature in 2012 that their industry team could confirm only a small fraction of published preclinical cancer findings, and later surveys across fields found the same pattern. Funders and journals responded. The NIH [Data Management](/blog/guides/data-management-basics-principles-processes-and-best-practices) and Sharing Policy, in effect since January 25, 2023, requires NIH-funded projects to submit a written plan for managing and sharing scientific data. Many journals now ask for a data and code availability statement at submission. This guide doubles as a free reproducibility plan template for busy researchers, built from paste-ready commands, so meeting those requirements costs minutes instead of weekends. ## What a Reproducibility Plan Actually Is A reproducibility plan is a short document, plus a folder structure and a few habits, that records how every result in a project was produced and how a stranger could produce it again. It sits one level below a data management plan. A data management plan is the funder-facing document about storage, sharing, and compliance. The reproducibility plan is working-level. It answers a practical question: what would a new person need to install, touch, and type to get my results? Every plan covers the same six things: data, code, the software environment, documentation, outputs, and sharing. Each maps to a numbered section in the template below. Three published standards shape the content. The FAIR principles, published by Wilkinson and colleagues in Scientific Data in 2016, describe data that is Findable, Accessible, Interoperable, and Reusable. For computational work, Sandve and colleagues published Ten Simple Rules for Reproducible Computational Research in PLOS [Computational Biology](/knowledge/bioinformatics/vaccinomics-and-the-future-of-personalized-vaccines) in 2013, and their first rule, for every result keep track of how it was produced, is the sentence this whole plan implements. For animal work, the ARRIVE 2.0 guidelines list ten essential reporting items, including [sample size justification](/knowledge/diagnostics/research-methods/how-to-justify-sample-size-in-grant-proposals-and-manuscripts-a-reviewer-s-perspective), randomization, and blinding, and a good plan records those details where a reviewer can find them. ## The Time Budget: Six 15-Minute Blocks ![Timeline of six 15-minute setup blocks, from building folders to running the fresh-clone test and scheduling the weekly checklist.](/article-images/illustrations/reproducibility-plan-template-for-busy-researchers-six-blocks.webp) You do not need a free afternoon. You need six blocks. 1. **Block 1 (15 min).** Create the folder skeleton and the empty plan file. 2. **Block 2 (15 min).** Fingerprint and lock the raw data. 3. **Block 3 (15 min).** Start version control and make the first commit. 4. **Block 4 (15 min).** Pin the software environment and record the random seeds. 5. **Block 5 (15 min).** Wire up the one-command run and write the README. 6. **Block 6 (15 min).** Run the fresh-clone test and schedule the weekly checklist. That is 90 minutes of setup for a project a stranger can rerun. After that, maintenance costs one block per week. ## A Free Reproducibility Plan Template for Busy Researchers Copy the block below into a file named REPRODUCIBILITY.md at the top of your project folder and fill the brackets. Leave a section blank rather than deleting it, so the structure survives when the project changes. The whole thing fits on one printed page. ```markdown # Reproducibility Plan: [short project name] Last updated: [YYYY-MM-DD] Owner: [name, email] ## 1. Question and scope [One sentence: what this project measures or tests, and in what system.] ## 2. Data - Raw data location: [data/raw, never edited after collection] - How it was produced: [instrument, kit, protocol name and version] - Backup: [location, frequency] - Access rules: [who may read it, any privacy or embargo limits] ## 3. Code - Repository: [local path and remote URL] - Language and versions: [for example R 4.3.2 or Python 3.11] - Entry point: [the single command that runs the full analysis] ## 4. Environment - Recreate with: [environment.yml, requirements.txt, renv.lock, or Dockerfile] - Random seeds: [every seed value and the file where it is set] ## 5. Outputs - Tables: [results/, one row per statistical test] - Figures: [figures/, generated by code, never edited by hand] ## 6. Documentation - README.md: [what the project is, how to run it, who to contact] - Data dictionary: [docs/data_dictionary.csv, every column with units] ## 7. Sharing and archiving - Code archive: [GitHub or GitLab repo, DOI minted at release] - Data archive: [repository name, accession or DOI] - Licenses: [code license, data license] ## 8. Verification - Fresh-run test: [date last passed, how outputs were compared] - Second runner: [who else has executed the pipeline end to end] ``` ## Reproducibility Plan Format Rules Four format rules keep the template useful after the novelty wears off. 1. Use ISO 8601 dates, meaning year, month, day, for example 2026-11-11, everywhere. Dates written as 11/07/26 mean different things to different readers and will eventually corrupt a timeline. 2. Update the Last updated line every time you touch the plan. A stale plan is worse than none, because it looks authoritative. 3. Keep it to one page. If a section grows past a few lines, the detail belongs in the README or the data dictionary, not the plan. 4. Record locations as paths relative to the project root, for example `data/raw` rather than `/home/arivera/drugx-qpcr/data/raw`. Relative paths survive moves between laptops, a collaborator's machine, or a cluster. ## A Filled-In Reproducibility Plan Example: qPCR Dose-Response Here is the same template filled in for a small wet-lab project with an R analysis, the situation most biology labs face. ```markdown # Reproducibility Plan: Drug X effects on GAPDH mRNA Last updated: 2026-11-11 Owner: A. Rivera, arivera@example.edu ## 1. Question and scope Does 24-hour exposure to Drug X change GAPDH mRNA in HEK293 cells, measured by RT-qPCR at 0, 1, and 10 micromolar with 3 biological replicates per dose? ## 2. Data - Raw data location: data/raw (one CSV of Ct values per plate, exported from the core facility qPCR instrument, never edited after export) - How it was produced: [qPCR protocol](/knowledge/diagnostics/molecular/troubleshooting-qpcr-interpreting-amplification-curves-and-melting-peaks) v2.3, SYBR Green master mix lot 2411A, plate maps in docs/plate_maps.pdf - Backup: nightly sync to lab server, quarterly copy to external drive - Access rules: lab members only, no human-subject data ## 3. Code - Repository: ~/projects/drugx-qpcr, remote github.com/arivera/drugx-qpcr - Language and versions: R 4.3.2, package versions pinned by renv - Entry point: Rscript run_all.R ## 4. Environment - Recreate with: renv.lock, then renv::restore() - Random seeds: set.seed(2024) in src/02_analysis.R (bootstrap [confidence intervals](/blog/data-analysis/confidence-interval-formula-examples), that is, intervals from repeated resampling) ## 5. Outputs - Tables: results/fold_change.csv, results/stats_summary.csv - Figures: figures/dose_response.png, generated by src/03_plot.R ## 6. Documentation - README.md: purpose, run instructions, contact - Data dictionary: docs/data_dictionary.csv (well, gene, dose_uM, ct, replicate) ## 7. Sharing and archiving - Code archive: Zenodo DOI minted from the GitHub repo at tag v1.0 on manuscript acceptance - Data archive: supplementary files with the manuscript - Licenses: MIT for code, CC BY 4.0 for data ## 8. Verification - Fresh-run test: 2026-11-04, clean clone, output checksums matched - Second runner: lab mate ran renv::restore() and run_all.R cleanly ``` ## The 90-Minute Setup, Step by Step Each step corresponds to one time block. Commands are shown for macOS and Linux, with Windows equivalents noted where they differ. Expected output follows each command block so you can check your result. ### Step 1: Build the folder skeleton (Block 1) ```bash mkdir -p drugx-qpcr/data/raw drugx-qpcr/data/processed mkdir -p drugx-qpcr/src drugx-qpcr/results drugx-qpcr/figures drugx-qpcr/docs cd drugx-qpcr ls ``` Expected output: ``` data docs figures results src ``` The `-p` flag creates nested folders in one shot and stays silent if a folder already exists, which makes the command safe to rerun. `data/raw` holds raw data, meaning files exactly as the instrument or survey produced them. `data/processed` holds files your code generates from raw data. `src` holds scripts, `results` holds tables, `figures` holds plots, and `docs` holds the plan, README, and data dictionary. Drop your raw data files into `data/raw` now, then create the empty REPRODUCIBILITY.md from the template. ### Step 2: Fingerprint and lock the raw data (Block 2) A checksum is a short fingerprint computed from a file's bytes. Change one byte and the fingerprint changes, so checksums prove later that a file is untouched. ```bash sha256sum data/raw/*.csv > data/raw/checksums.txt chmod a-w data/raw/*.csv sha256sum -c data/raw/checksums.txt ``` Expected output: ``` data/raw/plate_001.csv: OK data/raw/plate_002.csv: OK ``` `sha256sum` writes a SHA-256 fingerprint for each CSV into checksums.txt. `chmod a-w` removes write permission for every user, which turns the raw folder into a read-only record. The `-c` flag rechecks the files against the saved fingerprints and prints OK for each match. On macOS, use `shasum -a 256` in place of `sha256sum`. On Windows PowerShell, use `Get-FileHash -Algorithm SHA256 data/raw/plate_001.csv`. When new data arrives later, unlock it with `chmod u+w`, add it, then relock and regenerate the checksum file. ### Step 3: Start version control (Block 3) Version control records every saved change to your files with a note about why. Git is the standard tool. A repository is the folder Git watches, and a commit is one saved snapshot with a message. ```bash git init ``` Expected output: ``` Initialized empty Git repository in /home/arivera/drugx-qpcr/.git/ ``` Create a file named `.gitignore` so Git skips raw data and machine-generated clutter: ``` data/raw/ .Rhistory .RData __pycache__/ *.pyc .DS_Store ``` Raw data stays out of Git on purpose. Git is for text that changes often, not for large binary files that must never change, and private data should not sit on a code-sharing site. Then make the first commit: ```bash git add .gitignore REPRODUCIBILITY.md src/ git commit -m "Add plan, pipeline skeleton, raw data lockfile" ``` Expected output: ``` [main (root-commit) 3f2c9a1] Add plan, pipeline skeleton, raw data lockfile 3 files changed, 18 insertions(+) create mode 100644 .gitignore ``` The short hash `3f2c9a1` identifies this snapshot and will differ on your machine. Check the result with `git log`, which should list the commit you just made. From now on, commit whenever you finish a working change, with a message that says what changed and why. ### Step 4: Pin the environment and record the seeds (Block 4) The environment is the exact set of software versions your analysis depends on. Pinning means writing those versions into a lockfile, a machine-readable list another machine can reproduce. Pick the path that matches your language. For R, use renv, a package that tracks project-specific package versions: ```r install.packages("renv") renv::init() renv::snapshot() ``` Expected output, with wording that varies slightly by version: ``` * Discovering package dependencies ... Done! The following package(s) have been updated in the lockfile: - renv [1.0.7] ``` This creates `renv.lock`. A short excerpt looks like this: ```json { "R": { "Version": "4.3.2", "Repositories": [ { "Name": "CRAN", "URL": "https://cloud.r-project.org" } ] }, "Packages": { "ggplot2": { "Package": "ggplot2", "Version": "3.4.4" } } } ``` Commit the lockfile. Also run `sessionInfo()` at the end of each script and save the text with the results, because it records the R version and attached packages in human-readable form. For Python, use conda, a tool that manages isolated Python installations: ```bash conda create -n drugx python=3.11 pandas scipy matplotlib conda activate drugx conda env export > environment.yml ``` The `-n` flag names the environment, here `drugx`. The saved file looks like this, and your versions will differ: ```yaml name: drugx dependencies: - python=3.11 - pandas=2.1.4 - scipy=1.11.4 - matplotlib=3.8.2 ``` If you do not use conda, activate any virtual environment and run `pip freeze > requirements.txt` instead. Last, record every random seed. A seed is the integer that starts a random number generator, and the same seed with the same code produces the same random draws: ```r set.seed(2024) # R, at the top of the script that uses randomness ``` ```python import random import numpy as np random.seed(2024) np.random.seed(2024) # Python has separate generators, set both ``` Put each seed at the top of the script that needs it and list every seed in section 4 of the plan. ### Step 5: Wire up the one-command run (Block 5, first half) A pipeline is the chain of scripts from raw data to final figure. The goal is a single command that runs the whole chain. The simplest version is a runner script: ```r # run_all.R source("src/01_import.R") source("src/02_analysis.R") source("src/03_plot.R") ``` Run it with `Rscript run_all.R`. For larger projects, use Make, a tool that reruns only the parts affected by a change: ```make all: results/fold_change.csv figures/dose_response.png results/fold_change.csv: src/02_analysis.R data/raw/plate_001.csv Rscript src/02_analysis.R figures/dose_response.png: src/03_plot.R results/fold_change.csv Rscript src/03_plot.R ``` Each line under a target is a recipe, and the indent must be a real tab character, which is the source of the most common Make error (see the fixes section below). ### Step 6: Write the README and the data dictionary (Block 5, second half) A README is the plain-text front door of a project, the first file a stranger opens. Keep it under one screen: ```markdown # Drug X effects on GAPDH mRNA (RT-qPCR) Run everything with one command: Rscript run_all.R First time on a new machine: install.packages("renv") renv::restore() Raw data is read-only in data/raw. Questions: arivera@example.edu ``` A data dictionary is a table describing every column in your data, with units. Keep it as a small CSV in docs: ```csv column,unit,description well,,"Plate well position, for example A1" gene,,"Target gene symbol, for example GAPDH" dose_uM,uM,"Drug X concentration in micromolar" ct,Cq,"Raw cycle threshold value from the instrument" replicate,,"Biological replicate number, 1 to 3" ``` ### Step 7: Run the fresh-clone test (Block 6, first half) This is the test that tells you the plan works. Clone your own repository into a sibling folder, which simulates a stranger downloading it: ```bash cd .. git clone drugx-qpcr drugx-qpcr-test cd drugx-qpcr-test Rscript -e 'install.packages("renv")' Rscript -e 'renv::restore()' Rscript run_all.R sha256sum results/fold_change.csv ``` Then compute the same fingerprint in the original folder and compare: ```bash cd ../drugx-qpcr sha256sum results/fold_change.csv ``` Expected output, twice, with identical values (your value will differ): ``` e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 results/fold_change.csv ``` Identical fingerprints mean the fresh clone regenerates the same table. If they differ, check three things in order: the seed is set in every script that uses randomness, the lockfile was restored before running, and no script reads files by absolute path. If you work in Jupyter notebooks, the equivalent test is Kernel, then Restart and Run All. A notebook that errors or produces different numbers on a clean restart was running cells out of order. ### Step 8: Tag, back up, and share (Block 6, second half) A tag is a named marker on a commit, useful for milestones. Push your repository to GitHub or GitLab, then tag it: ```bash git remote add origin https://github.com/arivera/drugx-qpcr.git git push origin main git tag v1.0 git push origin v1.0 ``` Two sharing moves finish the job. First, connect the GitHub repository to Zenodo, a service that archives a release and mints a DOI, a permanent citable link, for it. Second, if you prefer not to make code public before publication, the Open Science Framework (OSF) hosts private project spaces that can be made public with one click later. Journals that follow the TOP guidelines for transparency (Nosek and colleagues, Science, 2015) ask for availability statements, and a tagged, archived repository writes that statement for you. The natural next step after this is a container, a package that bundles operating system libraries together with your code, built from a Dockerfile so the whole stack rebuilds on any machine. ## Common Errors and Exact Fixes Each error below is one you will hit. The messages appear as the system prints them. 1. **`git: command not found`.** Git is not installed. Install it from git-scm.com, then confirm with `which git` on macOS and Linux or `where git` on Windows. 2. **`fatal: not a git repository (or any of the parent directories): .git`**. You ran a Git command outside the project. Change into the folder that contains `.git`, or run `git init` if the project is brand new. 3. **`CondaError: Run 'conda init' before 'conda activate'`**. Your shell has not been set up for conda. Run `conda init bash` (or `conda init zsh`), close the terminal, and open a new one. 4. **`ModuleNotFoundError: No module named 'pandas'`**. The script ran outside the pinned environment. Run `conda activate drugx` and try again, or run `pip install -r requirements.txt` inside the active environment. 5. **`Makefile:3: *** missing separator. Stop.`** A recipe line starts with spaces instead of a tab. Delete the leading spaces and press the Tab key. 6. **`Error in library(tidyverse) : there is no package called 'tidyverse'`**. The lockfile was never restored on this machine. Run `renv::restore()` from the project root. 7. **`sha256sum: WARNING: 1 computed checksum did NOT match`**. A raw file changed after you fingerprinted it. Find out why before touching anything. If a new data batch was added on purpose, regenerate the checksum file and note the change in the plan. 8. **`! [rejected] main -> main (fetch first)`**. The remote has commits your machine lacks. Run `git pull`, resolve any conflicts, commit, and push again. ## The Weekly Reproducibility Checklist Fifteen minutes, ideally in the same slot every week, keeps the plan alive. - [ ] Run the pipeline from the top with the single entry-point command. Fix or note anything that errors today. - [ ] Commit code changes with a message that says what changed and why. - [ ] Verify raw data with `sha256sum -c data/raw/checksums.txt`. - [ ] Update the README if the run instructions changed. - [ ] Update the Last updated line in REPRODUCIBILITY.md if anything in the plan changed. - [ ] Confirm the off-laptop backup actually ran. At milestones (manuscript submission, a new aim, a new analyst), add three items: tag a version, mint or refresh the archive DOI, and ask a second person to run the fresh-clone test. That last item catches the assumptions only you can read past. ## Common Mistakes and Limitations 1. **Treating reproducibility as end-of-project cleanup.** By then, manual edits are unrecoverable and versions are forgotten. Lock the raw data and pin the environment in week one, while the details are still on screen. 2. **Hand-editing files between steps.** One undocumented sort or find-and-replace breaks the chain from raw data to figure. Every transformation belongs in code, or in a documented, versioned step. 3. **Absolute paths.** A script that reads `/home/arivera/...` or `C:\Users\arivera\...` fails on every other machine. Use paths relative to the project root. 4. **Missing seeds.** A bootstrap interval or a train-test split cannot be regenerated without its seed. Record every seed in the plan the day you write it. 5. **Environment drift.** Upgrading a package mid-project silently changes numbers. Pin versions at the start and upgrade only at a tagged milestone, then rerun everything and compare. 6. **One copy of the data, on the same laptop as the analysis.** Follow the 3-2-1 rule of backups: three copies, on two kinds of storage, with one off site. 7. **Perfectionism.** A plan you actually run beats a beautiful one you abandon. If you only ever do two things, lock the raw data and build the one-command run. Two honest limits apply. First, computational reproducibility does not guarantee a result is correct. A bug reproduces perfectly. The fresh-clone test verifies the chain from data to figure, not the biology behind the data. Second, some elements resist exact reproduction: finite reagent lots, cells at a particular passage number, animal cohorts, instrument calibration. Record lot numbers, passage numbers, and calibration dates so the next person can match them closely. Human-subjects data adds sharing limits that your institutional review board (IRB) protocol defines. Individual projects also need domain-specific judgment, so involve your advisor, core facility, or institutional research data office for field-specific requirements no template can anticipate. ## Frequently Asked Questions ### What is a reproducibility plan in research? A reproducibility plan is a one-page document, plus a folder structure and a few habits, that records how every result in a project was produced and how someone else could produce it again. It covers data, code, the software environment, documentation, outputs, and sharing. It is the working-level companion to a funder-facing data management plan. ### How long does it take to create a reproducibility plan? About 90 minutes for a typical project, split into six 15-minute blocks across one week, plus 15 minutes a week to maintain it. Pinning the software environment is usually the largest single block. ### Is there a free reproducibility plan template for busy researchers? Yes. The eight-section template in this article is free to copy into a file named REPRODUCIBILITY.md and adapt to any project. The filled-in qPCR example shows what a completed plan looks like. ### What is the difference between reproducibility and replicability? Reproducibility means the same data and code regenerate the same results. Replicability means a new experiment reaches the same conclusion. A reproducibility plan targets the first and makes the second easier to attempt. ### Do I need version control if I already back up my files? Yes. Backups capture what your files looked like at a moment. Version control captures what changed, when, and why, and lets you return to any earlier state. For reproducibility, the history matters as much as the copies. ### What should a reproducibility plan include for a wet-lab project? Reagent lot numbers, instrument details, protocol names and versions, sample metadata, and the analysis code with its pinned environment. Sections 2, 5, and 6 of the template carry most of that weight. ### Can I make my research reproducible if I analyze data in Excel? Partly. Keep raw files untouched, remove manual edits, and document every formula and step in the data dictionary. For anything beyond simple summaries, moving the transformation into a script is the reliable path. ### When should I share my code and data? At publication at the latest, and at any milestone a manuscript or preprint cites. Archiving the repository through a service such as Zenodo gives reviewers and readers a permanent, citable snapshot.