Skill UI

Provides detailed guidelines for creating high-standard clinical figures and tables, modeled after leading medical journals (e.g., The Lancet). It covers essential display items including Table 1 (baseline characteristics), Kaplan-Meier survival curves (with numbers-at-risk), Forest plots for meta-analyses, CONSORT/PRISMA flow diagrams, and global maps. The guide emphasizes the rigorous reporting of 95% confidence intervals (CIs) and proper statistical methods to ensure scientific accuracy.

This guide enforces rigorous standards for reporting clinical trial statistics, moving beyond simple P values. It mandates the use of confidence intervals, absolute and relative effect sizes, clear designation of the primary analysis population (ITT), and proper handling of multiplicity and missing data, aligning with standards set by top-tier medical journals.

This guide details best practices for rigorous statistical reporting in clinical trials, following guidelines from top medical journals like NEJM. It emphasizes using confidence intervals (CIs) over bare P-values, defining primary analysis populations (ITT), controlling for multiplicity, and reporting absolute risks (NNT). It helps researchers ensure their findings are statistically robust, transparent, and defensible.

A comprehensive guide for designing and reporting rigorous computational studies in Operations Research (OR) manuscripts. It covers best practices for selecting benchmark instances and strong baselines, applying statistical methods to stochastic output (e.g., confidence intervals), and adhering to mandatory code and data deposit standards (ORJournal workflow) to ensure full reproducibility.

This guide addresses the stringent requirements for empirical evaluation in compiler and runtime systems, aligning with SIGPLAN's guidelines. It covers critical aspects such as proper warmup discipline, reporting variance and confidence intervals, selecting principled benchmarks, pinning toolchains, and ensuring cross-platform validity. It provides a protocol for generating verifiable measurement logs for academic publications.

A comprehensive guide based on high-impact journal requirements (like PNAS), detailing how to report quantitative scientific claims rigorously. It emphasizes moving beyond simple P values to include effect sizes, confidence intervals, sample size justification, distinction between biological and technical replicates, and ensuring full code and data reproducibility.

A comprehensive guide detailing the rigorous data analysis and reporting standards required for top-tier psychological science journals. It emphasizes moving beyond p-values to report effect sizes with confidence intervals, ensuring full disclosure of exclusions, and maintaining a clear separation between confirmatory and exploratory findings to enhance research credibility and reproducibility.

This guide details best practices for creating high-impact tables and figures for psychological science manuscripts, adhering to APA 7th edition guidelines. It emphasizes showing effect sizes, uncertainty (like confidence intervals), and embedding visuals directly within the main text near their first discussion, moving beyond traditional end-of-document placement. It covers data visualization principles and manuscript triage.

Provides rigorous statistical guidance for conducting meta-analyses in academic settings. This skill covers the calculation of standardized effect sizes (e.g., Hedges' g, log odds ratio), selecting appropriate models (defaulting to random-effects or multilevel models), and accurately quantifying between-study heterogeneity (Q, I², τ², and prediction intervals). It ensures the analysis meets state-of-the-art standards for high-impact psychological journals.

A comprehensive guide for executing and reporting quantitative analyses for high-stakes public policy research. This skill emphasizes methodological rigor, demanding transparency in uncertainty reporting (effect size, intervals), robust checks (heterogeneity, alternative specifications), and ensuring full reproducibility through master scripts and pinned versions for journal submission.

A comprehensive guide for researchers developing systems or algorithms, detailing the necessary level of evidence required for performance claims (e.g., bounds, real-world performance, learning guarantees). It emphasizes scientific rigor, statistical reporting (confidence intervals), assumption testing, and data provenance to ensure findings are robust and reproducible in academic settings.

This skill applies consistent date shifting to clinical text, ensuring that absolute dates are obscured while critically preserving the temporal intervals between medical events. It is essential for longitudinal research or survival analysis where time-to-event data must be preserved under HIPAA Safe Harbor rules. It maintains the integrity of time differences (e.g., length of stay, visit gaps) by applying a stable, per-patient offset.