sensitivity-analysis

Professional research tools for PhD students and academics

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

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

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.

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

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

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

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

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