randomization

Professional research tools for PhD students and academics

Systematically identifies and prioritizes research gaps across knowledge, methodological, theoretical, practice, and evidence quality domains.

Designs methodologically rigorous experiments with clear hypotheses, appropriate controls, randomization, and pre-specified statistical analysis.

Transforms complex datasets into interactive, insightful charts, graphs, and dashboards using Python's leading visualization libraries.

Performs statistical power analysis to determine sample size, detect effect sizes, and validate experimental design for research studies.

Enables interactive Ruby (IRB) protocol interactions for AI-driven scientific computing and code execution within the AI Scientist framework.

Generates comprehensive research synthesis matrices by integrating cross-disciplinary literature, identifying patterns, and mapping knowledge relationships

Automatically formats academic citations across multiple styles (APA, MLA, Chicago) with precision and handles complex reference types.

Generates visual database schema diagrams from Prisma schema files, facilitating database design and documentation.

Interprets and analyzes experimental results, extracting key insights, statistical significance, and potential implications for scientific research.