Skill UI

This guide provides criteria for submitting research to the Journal of Tianjin University of Sport. The journal highly values mechanism-driven studies in exercise physiology, biochemistry, biomechanics, and sports medicine. Submissions must demonstrate rigorous experimental design, detailed measurements, statistical validity, and clear mechanistic interpretation, moving beyond simple descriptive reports.

A comprehensive guide on crafting convincing spatial exhibits for top-tier economics journals, especially those focusing on urban economics. This skill helps structure the necessary maps, regression tables, and event-study plots required to visually demonstrate the identifying variation (e.g., treatment boundaries, discontinuities). It focuses on best practices—such as reporting spatial standard errors and making maps do econometric work—rather than running the analysis itself.

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.

A comprehensive guide for executing, evaluating, and reporting the empirical core of MIS Quarterly manuscripts. It covers specialized methods for Behavioral IS (PLS-SEM), Economics of IS (causal identification), Design Science (artifact evaluation), and qualitative studies, ensuring full transparency and replicability for top-tier academic submissions.

A comprehensive guide detailing best practices for creating scientifically rigorous and publication-ready figures, particularly for molecular biology and structural studies. It covers statistical representation (n, error bars, point plots), structural integrity (uncropped blots, resolution reporting), colorblind accessibility, and multi-panel data presentation to ensure the figure genuinely proves the mechanism, rather than just summarizing it.

A comprehensive guide for conducting and reporting rigorous experimental research in social and behavioral sciences. It covers various design types (vignette, lab, field), statistical requirements (effect sizes, power analysis), quality control measures (manipulation checks, confound control), and advanced techniques for establishing causality, ensuring high internal validity for academic publication.

This guide provides comprehensive standards for analyzing and reporting results in Organization Science, a field characterized by diverse methodologies. It ensures methodological rigor and transparency whether the work is qualitative (establishing trustworthiness), quantitative (matching estimators to design), computational (reporting seeds and sensitivity), or formal/analytical. It emphasizes full reproducibility and mechanism-based inference.

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.

A comprehensive guide for designing and auditing rigorous empirical evaluations for sensing systems, particularly Human Activity Recognition (HAR). It emphasizes best practices such as leave-one-subject-out cross-validation, using real human subjects under diverse conditions, reporting event-level metrics (e.g., Macro-F1) over raw accuracy, establishing fair baselines, and mitigating data leakage (e.g., subject or session leakage). This ensures that reported results are robust, generalizable, and defensible against academic skepticism.

Provides comprehensive guidelines for conducting highly rigorous, reproducible statistical analysis for complex survey data (e.g., weights, strata, clusters). This guide ensures that analyses meet the standards of top academic journals, emphasizing design-based inference, honest uncertainty reporting (CIs, MOE), and thorough robustness checks. Use this when you need to build a master script that regenerates every published table and figure.

This skill guides the creation of rigorous, self-contained tables and figures (exhibits) for academic journals like Public Opinion Quarterly (POQ). It ensures that all visual data accurately report design-based uncertainty, weighting adjustments, and the base N, meeting the high standards required for empirical survey research.

A comprehensive skill for advanced meta-analysis in psychology, designed to assess the reliability and sources of heterogeneity. It performs moderator/subgroup analyses (meta-regression) to explain effect size variation and runs multiple publication-bias diagnostics (e.g., Funnel plot, Egger's test, p-curve, selection models). Essential for rigorous academic reporting requiring robust evidence and sensitivity checks.