Lab Automation & Reproducibility.

The Reproducibility Problem

Over 70% of researchers have tried and failed to reproduce another scientist's experiments. Over 50% have failed to reproduce their own. The reproducibility crisis is not primarily a fraud problem -- it is an infrastructure problem. Analyses depend on specific software versions, undocumented preprocessing steps, manual parameter tuning, and "I ran this script but tweaked a few things" decisions that are never recorded. AI-assisted automation addresses this directly. When your analysis is a pipeline (not a sequence of manual steps), every decision is recorded, every parameter is logged, and any researcher can re-run the entire workflow with a single command.

Building Reproducible Pipelines

A reproducible pipeline is a script that takes raw data as input and produces every result, figure, and table in your paper as output. AI helps you build this from your ad-hoc analysis code. ```python PIPELINE_PROMPT = """ I have the following analysis scripts that I ran manually during my research: {list_of_scripts_with_descriptions} Convert these into a single reproducible pipeline (Makefile or Snakemake) that: 1. Downloads/loads raw data from {data_source} 2. Runs preprocessing with logged parameters 3. Executes each analysis with fixed random seeds 4. Generates all figures (saved to figures/) 5. Generates all tables (saved to tables/) 6. Compiles a results summary (saved to results/) 7. Checks that outputs match expected values (regression tests) Include: - requirements.txt with pinned versions - Dockerfile for environment reproducibility - README with one-command instructions - Makefile with targets: setup, run, test, clean """ ``` Example Makefile structure: ```makefile # Makefile for reproducible analysis PYTHON = python3 DATA_DIR = data RESULTS_DIR = results FIGURES_DIR = figures .PHONY: all setup run test clean all: setup run test setup: pip install -r requirements.txt mkdir -p $(RESULTS_DIR) $(FIGURES_DIR) data/raw.csv: $(PYTHON) scripts/download_data.py --output $(DATA_DIR)/raw.csv data/processed.csv: data/raw.csv $(PYTHON) scripts/preprocess.py \ --input $(DATA_DIR)/raw.csv \ --output $(DATA_DIR)/processed.csv \ --seed 42 results/analysis.json: data/processed.csv $(PYTHON) scripts/analyze.py \ --input $(DATA_DIR)/processed.csv \ --output $(RESULTS_DIR)/analysis.json \ --seed 42 figures/figure1.pdf: results/analysis.json $(PYTHON) scripts/plot_figure1.py \ --input $(RESULTS_DIR)/analysis.json \ --output $(FIGURES_DIR)/figure1.pdf run: results/analysis.json figures/figure1.pdf test: $(PYTHON) scripts/regression_test.py --results $(RESULTS_DIR) clean: rm -rf $(RESULTS_DIR) $(FIGURES_DIR) $(DATA_DIR)/processed.csv ```