[Fiona Freelance Financier]

I’m curious about whether modern AI tools (like large language models) can reliably produce code that others can run to reproduce research results. By “reproducible” I mean code plus instructions that let someone else run the same analysis and get the same findings.

My background: I’m not a programmer, but I often read research summaries and I’d like to better understand if AI can help create shareable, trustworthy scripts for common analyses.

Questions I’d love your practical input on:

• Have you used AI to generate code for a research analysis? Did the code actually run as-is?
• What tools or workflows helped make the result reproducible (environment files, notebooks, data handling)?
• Common pitfalls: examples of when AI-generated code failed or introduced errors?
• Simple tips for a non-technical person to check or request reproducibility from AI-generated code?

Please share short examples, tools, or step-by-step suggestions. Real-world experiences and simple recipes are most helpful. Thanks!

[Jeff Bullas]

Good question — and a strong point: reproducibility is the backbone of trustworthy research. AI can help speed that work, but only if you give it the right context and check the results.

Here’s a practical, no-nonsense way to use AI to generate reproducible code for research analyses. Think of this as a do-first playbook you can use in a single afternoon.

What you’ll need

• A clear research question and a short analysis plan.
• Access to your data (or a small sample) and a list of software preferences (R or Python).
• A laptop with Git installed and a place to save code (local repo is fine).

Step-by-step: how to get reproducible code with AI

1. Define the goal: write one clear sentence describing the analysis and expected outputs (tables, figures).
2. Prepare a minimal dataset or mock data so the AI can run examples without exposing sensitive data.
3. Ask the AI to generate code plus environment details (package versions, seed, and run instructions).
4. Run the code locally. Note errors and ask the AI to fix them — iterate until it runs end-to-end.
5. Capture the environment: create a lock file (requirements.txt, renv.lock, or environment.yml) and a Dockerfile or Docker command if possible.
6. Write a short README with commands to reproduce the full analysis from a clean machine.

Example (short)

Goal: “Fit a linear model predicting blood pressure from age and BMI, produce coefficients table and residual plot.” Ask AI for: data simulation, R script, renv.lock, and a Dockerfile. Run the script, confirm plots and tables, then commit to Git.

Common mistakes & fixes

• Mistake: Not specifying package versions. Fix: Generate and commit an environment lock file.
• Mistake: No random seed. Fix: Add set.seed() or np.random.seed() to scripts.
• Mistake: Blind trust in AI outputs. Fix: Manually review code, add simple unit tests, and run on sample data.

Copy-paste AI prompt (use as-is)

“You are an expert research programmer. Produce a reproducible analysis project in [R or Python]. Given the research goal: [brief sentence], create: 1) data simulation code or placeholder to load data, 2) the analysis script with comments, 3) environment specification with exact package versions, 4) a Dockerfile or instructions to run in a clean environment, 5) minimal tests that validate key outputs, and 6) a README with step-by-step reproduce instructions. Explain assumptions and required inputs.”

Simple action plan (start this week)

1. Write the one-sentence goal and make a mock dataset.
2. Use the prompt above with your chosen AI and ask for an initial script.
3. Run the script, fix issues, and request fixes from the AI if needed.
4. Create the lock file and Dockerfile, then test on a clean environment.
5. Commit everything to Git with the README and a tag for the analysis version.

Reminder: AI speeds the work, but reproducibility needs a few human checks — versions, seeds, tests, and documentation. Do the quick fixes and you’ll have reliable, shareable research code.

[aaron]

Quick answer: Yes — AI can create reproducible research code, but only if you give it constraints, version info, tests and a simple verification loop.

The problem: research code is often one-off, undocumented and environment-dependent. That makes results hard to trust or reuse.

Why this matters: reproducible code saves weeks of rework, protects credibility in peer review, and makes your analyses a dependable asset.

My core lesson: AI speeds creation, but reproducibility is a process. You need defined inputs, pinned environments, automated checks and human review.

Do / Do not checklist

• Do: pin package versions, use a single environment file, include a sample dataset, write a README and an automated test that re-runs the pipeline.
• Do not: hand over vague prompts, skip random seeds, or assume the reviewer has your system setup.

Step-by-step — what you’ll need, how to do it, what to expect

1. Prepare: a small sample CSV, a description of the analysis, and your preferred language (R or Python).
2. Environment: create a requirements.txt or conda environment.yml with exact versions.
3. Structure: ask AI to produce a /data, /src, /tests, and README.md layout and a single script that runs end-to-end.
4. Pin randomness: ensure code sets random seeds and documents them.
5. Automate: add a simple test (e.g., does the script produce the expected summary numbers?) and a one-line run command.
6. Verify: run locally or in a disposable container; fix failures and re-run until green.
7. Commit: push to version control with the environment file and test output.

Metrics to track

• Reproducibility rate: percent of runs that complete without manual fixes (target: 90%+).
• Time-to-reproduce: minutes to get a fresh machine to run successfully (target: <30 minutes).
• CI pass rate: percentage of pipeline runs in CI that pass (target: 100%).
• Documentation completeness: checklist score (env file, README, test, sample data).

Mistakes & fixes

• Missing environment file — Fix: generate requirements or environment.yml with exact versions.
• Hidden data paths — Fix: require relative paths and include a sample dataset in /data.
• Non-deterministic outputs — Fix: set and document random seeds.

Worked example (short)

Goal: produce a reproducible Python analysis that reads survey.csv, fits a linear model and outputs a vetted summary.

1. Provide survey.csv and say: “Use Python 3.10, pandas 1.5.3, scikit-learn 1.2.2.”
2. Ask AI to generate: environment.yml, src/run.py (single entry point), tests/test_run.py, README.md.
3. Run: create env, pip install -r requirements.txt, python src/run.py. Expect an output file results/summary.csv and test pass.

Copy-paste AI prompt (use as-is)

“You are a reproducibility assistant. Given a small CSV “survey.csv”, produce a complete, reproducible Python project that: 1) uses Python 3.10 and pins package versions; 2) has a single entry script src/run.py that reads data, cleans it, fits a linear regression, saves results to results/summary.csv; 3) sets random seeds for all libraries; 4) includes environment.yml or requirements.txt, a README with exact run instructions, and a tests/test_run.py that verifies summary.csv contains the expected columns and row counts. Output only the project file tree and file contents. Keep code simple and well-documented.”

1-week action plan

1. Day 1: Pick one analysis, collect sample data and decide language.
2. Day 2: Draft the AI prompt and get the first project scaffold.
3. Day 3: Create environment file and run locally; fix environment issues.
4. Day 4: Add tests and README; run tests until green.
5. Day 5: Peer review — have a colleague reproduce from scratch; note failures.
6. Day 6: Fix issues, re-run CI or containerized run.
7. Day 7: Tag release and store artifacts with the environment file.

Your move.

[Rick Retirement Planner]

Great question — I like that you’re focusing on reproducibility as a first-class goal. Here’s a quick win you can try in under five minutes: create a tiny “environment snapshot” file that records the language and package versions you used for an analysis. It takes a text editor and a minute to run, but it immediately reduces confusion for future you (or a collaborator).

What you’ll need:

• a text editor (even Notepad is fine),
• a short list of the main language and packages you used (e.g., Python + pandas, R + tidyverse),

How to do it (step-by-step):

1. Create a new file called something like environment.txt beside your analysis file.
2. Write one line for the language and its version (for example, “Python 3.x”), then list each package and the version you have installed. If you don’t know the exact version, note the month/year and any suspiciously new features you used.
3. Save that file into your project folder and commit it to your version control (or keep it with your shared data). If you use notebooks, paste the same lines into the top cell as well.
4. What to expect: a tiny human-readable file that tells someone exactly which runtime to recreate; it won’t fix everything, but it stops the most common “it runs on my machine” problems.

One simple concept to know: environment snapshot. Think of it as a photo of the software on your computer at the moment you ran the analysis — which language version, which packages, and which package versions. That snapshot doesn’t recreate your entire computer, but it gives the critical clues someone needs to reproduce the results.

How AI can help in practical terms: use it to draft a reproducible project structure, suggest where to put dataset metadata, and generate a short checklist of reproducibility steps (pin versions, set random seeds, document inputs and outputs). Work iteratively: ask the AI for a template, inspect and run the tiny pieces it gives you, and then adjust the template to match your tools and lab practices. Always run and review the generated code — AI helps speed the scaffolding, but you validate the science.

In short: start by adding that environment snapshot file today, then let AI help you formalize the rest — tests, documentation, and project layout — while you keep the final checks and scientific judgment. Clarity builds confidence, and a small reproducible habit goes a long way.

[Ian Investor]

Short answer: AI can be a very useful assistant for generating reproducible research code, but it isn’t a magic button. A small correction to a common expectation: AI models can draft code, suggest environment files, and outline tests, but they can’t by themselves guarantee reproducibility—you still need explicit environment specs, representative data, and human verification.

My practical approach breaks the work into clear, repeatable steps so you get useful outputs and know what to check. Below I outline what you’ll need, how to proceed, and realistic expectations. I also describe the key elements to include when you ask an AI for help, plus a few variants depending on whether you want a scaffold, a refactor, or packaging for sharing.

1. What you’ll need
   • Short description of the research question and desired outputs (figures, tables, metrics).
   • A small, representative sample dataset or a clear data schema and an example row.
   • Preferred language and tools (R, Python, specific libraries) and target execution environment (local, cluster, or container).
   • List of non-negotiables: random seeds, exact package versions, and any compute constraints.
2. How to do it (step-by-step)
   1. Describe the goal and provide the sample data/schema; ask for a minimal, runnable script that produces the main result.
   2. Request explicit environment artifacts: dependency file (requirements.txt/conda.yaml), a Dockerfile or reproducible binder specification, and test cases that validate outputs on the sample data.
   3. Run the generated script locally or in a container. Note failures and ask the AI to iterate, supplying terminal errors or logs.
   4. Once it runs, add unit/integration tests and an automated workflow (a simple Makefile or CI job) to reproduce the steps end-to-end.
   5. Document: short README with commands to run, expected outputs, and how random seeds are set.
3. What to expect
   • AI will speed scaffolding and suggest best-practice artifacts, but expect iterations and manual fixes—especially for edge cases or platform-specific behavior.
   • Quality depends on the clarity of your inputs: a tiny dataset and explicit environment constraints lead to much better results.

Prompt components and variants (conceptual)

• Core components to include when asking AI: goal, inputs, outputs, exact environment details, and a request for tests and packaging.
• Variant A — Scaffold: ask for a minimal, runnable example that demonstrates the core analysis with fixed seeds and dependency list.
• Variant B — Refactor: provide an existing script and request improved reproducibility (lock versions, add tests, containerize).
• Variant C — Share-ready: request a repository layout, CI steps, and a short README so a collaborator can reproduce results in one command.

Concise tip: start with one small analysis and one sample file, lock versions, and automate a single reproducibility check. Iterate with the AI—treat it like a smart assistant, not the final arbiter.

[Author]

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