Coding SOP

What problem does it solve?

Coding SOP prevents research runs from becoming inconsistent, fragile, or hard to reproduce by standardizing how you execute experiments, clean data, test hypotheses, and produce publication-quality figures.

Core Features & Use Cases

• Experiment execution workflow: Hypothesize, design variables and iteration counts, implement a script template, execute safely, verify outcomes, and document contradictions without cherry-picking.
• Data processing pipeline: Clean → transform → validate with explicit inspection steps, missing-data handling rules, dtype/outlier checks, feature engineering, and saving cleaned datasets.
• Statistics and visualization standards: Choose tests via a decision tree, report required test statistics and effect sizes with assumption checks, and render figures with journal-quality settings (resolution, formats, labels, and accessibility).

Quick Start

Give the AI your hypothesis, dataset schema, and desired output (report tables + figures), then ask it to generate a reproducible experiment script that saves all outputs to workspace-relative paths and includes environment snapshots and versioned results.