Skill UI

This skill provides a comprehensive pre-submission checklist for manuscripts targeting Atmospheric Chemistry and Physics (ACP), an EGU journal. It details the journal's scope—covering aerosols, clouds, chemistry, and dynamics—and outlines the rigorous standards required, including open-access policies, mandatory FAIR data practices, detailed methodological transparency, and preparation for interactive public peer review. Use this guide to assess the fit and completeness of your scientific work.

A comprehensive guide for executing and reporting quantitative, computational, or experimental analyses for top-tier academic journals (e.g., BJPS). It outlines stringent norms for ensuring methodological rigor, transparency, and reproducibility, covering topics like uncertainty reporting, robustness testing, and measurement equivalence. This skill is essential when structuring the results section of a manuscript, particularly when facing expert peer review or preparing for data deposition.

A comprehensive guide detailing the selection of appropriate statistical estimators (e.g., DiD, Panel FE, ANOVA) for various research designs. It also outlines rigorous best practices for ensuring data integrity, conducting robustness checks, and adhering to journal code-sharing policies, ensuring highly reproducible empirical research.

This guide provides strict guidelines for compiling and formatting scholarly exhibits (tables, figures, and appendices) for top-tier accounting journals, such as CAR. It ensures compliance with academic style guides, requiring variable definitions, clustered standard errors, and self-contained presentation so that the results are immediately interpretable by reviewers. It is crucial for transforming raw analysis output into submission-ready academic materials.

This guide provides comprehensive standards for designing, quantifying, and narrating scientific figures and tables for high-impact publications. It details best practices for maintaining image integrity (e.g., uncropped blots, linear adjustments), ensuring all data points are quantified across replicates, and structuring the visual evidence to advance a clear, mechanistic argument step-by-step.

A comprehensive guide for planning and auditing highly rigorous biological experimental protocols, particularly for cancer mechanism studies. It emphasizes orthogonal validation across independent systems (in vitro, in vivo, human), detailing requirements for controls, replicates, sample size justification, randomization, and blinding to ensure scientific rigor.

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.

A detailed guide for scrutinizing statistical inference in high-level econometric manuscripts, designed to withstand rigorous technical peer review. It covers critical areas such as selecting appropriate clustering levels, handling weak instrumental variables (IV), correcting for multiple hypothesis testing, and ensuring accurate standard error reporting for panel data and causal inference models.

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.

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 finalizing academic manuscripts in finance and economics. It enforces strict formatting rules for regression tables (three-line style, standard error presentation), requires detailed variable definition tables with calculation formulas, mandates specific data source citation, and ensures high-resolution, professionally designed figures, moving beyond simple copy-pasting of results.