data-visualization

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.

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

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.

Streamlines participant recruitment and data collection for clinical trials by automating consent, screening, and enrollment workflows.

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

Generates reproducible random numbers, samples, and distributions with advanced statistical controls for scientific simulations and experimental design

Generates structured, targeted research questions and hypotheses based on scientific literature, research goals, and domain-specific contexts.