Skill UI

Provides comprehensive guidelines for statistical analysis and reporting in biological manuscripts, particularly in cancer research. It teaches researchers how to correctly define 'n' (distinguishing biological from technical replicates), select appropriate statistical tests (ANOVA, survival analysis, etc.), and manage multiple comparisons, ensuring high scientific rigor and reproducibility in published data.

This skill acts as a comprehensive router for writing high-impact scientific manuscripts, such as those intended for Cell Press journals. It does not perform specialized analysis but guides the user through the optimal sequence of steps—from initial scope assessment and study design to statistical rigor, figure presentation, and final revision. Use this when diagnosing manuscript bottlenecks or determining the next logical writing stage.

This skill is a comprehensive guide for authors targeting the prestigious Cell journal. It meticulously analyzes the scientific fit, the required depth of mechanistic evidence, and the necessary adherence to publication standards like STAR Methods. It helps authors structure their work to meet Cell's high bar for conceptually transformative molecular and cellular biology discoveries.

This guide outlines the standardized requirements for declaring data and code availability in major scientific publications. It details mandatory best practices, including depositing raw data (e.g., sequencing, structures) and analysis code into approved, citable public repositories (such as GEO, PDB, Mendeley Data, and Zenodo). It also provides the required standardized three-bullet statement format for ensuring reproducibility.

Outlines the mandatory and highly structured STAR Methods reporting format required by top-tier scientific journals. This guide details the five critical sections: Key Resources Table (KRT), Resource Availability, Experimental Model Details, Method Details, and Quantification & Statistical Analysis (QSA). Adherence to this structure ensures transparent, reproducible, and accessible scientific reporting.

This guide details how to move beyond simple mediation analysis to establish robust, theory-driven causal mechanisms. It outlines advanced econometric strategies, such as exclusion methods, pre-specified heterogeneity testing, and temporal analysis, required to distinguish a primary effect from competing channels, meeting high academic standards.

A comprehensive guide for correcting bias in Difference-in-Differences (DID) analysis conducted with staggered adoption timing or heterogeneous treatment effects. This methodology replaces the biased Two-Way Fixed Effects (TWFE) approach with modern estimators such as Callaway–Sant'Anna, de Chaisemartin–D'Haultfœuille, and Sun–Abraham, ensuring compliance with advanced econometric standards.

This detailed framework guides researchers in conducting rigorous and academically sound heterogeneity analysis for finance and economics manuscripts. It covers dimension prioritization, theoretical justification, sample size requirements, and statistical significance testing protocols (e.g., Chow test). Use this guide to strengthen the robustness of main findings and deepen the paper's contribution by identifying the specific conditions under which the effect operates.

A comprehensive guide for conducting rigorous mechanism analysis in empirical finance and economics papers. It covers three main testing paths (Mediation, Replacing Y, Sub-samples), best practices for causal inference (e.g., recommending Bootstrap over Sobel), and the academic structure required for high-impact journals. Focuses on time consistency, theory grounding, and excluding competing explanations.

A comprehensive guide for academic researchers on designing, auditing, and reporting rigorous studies (experiments, field deployments, surveys, etc.) for Human-Computer Interaction (HCI) papers. It details best practices for quantitative power analysis, qualitative thematic analysis, ethical reporting, and mixed-methods integration to ensure research claims are strongly supported by evidence.

A comprehensive guide detailing best practices for achieving research transparency and reproducibility, especially for HCI and related scientific studies. It outlines three critical layers of documentation—Protocol, Analysis, and Data—to ensure that studies can be reliably verified and built upon by the academic community.

This guide outlines the scope and submission requirements for the journal China Sport Science and Technology. It focuses specifically on application-oriented exercise science, covering competitive training, physical conditioning, physiological monitoring, technical analysis, and sports rehabilitation. It helps researchers determine the best fit for their work, distinguishing between purely theoretical research and practice-guided, evidence-based studies for athletic development.