Skill UI

This comprehensive checklist guides authors through the final preflight stages of submitting a commissioned article to Annual Reviews of Economics (ARE). It ensures compliance with the publisher's specific requirements, including conflict-of-interest disclosure, data reproducibility for meta-analyses, systematic coverage documentation, and adherence to the unique house style. It is crucial for authors delivering an invited review, not for unsolicited submissions.

A guide for creating high-impact academic exhibits for literature reviews (e.g., Annual Reviews of Economics). It teaches how to synthesize findings into structured formats, including 'Who-found-what' summary tables, conceptual framework diagrams, and meta-evidence plots. The focus is on synthesizing existing knowledge across studies, not presenting original estimates. Essential for communicating complex, cross-field research findings to non-specialists.

This guide assists researchers in compiling and documenting all necessary ethical approvals, biosafety statements, and data/code availability declarations required for high-impact scientific manuscripts (e.g., Cancer Cell). It ensures compliance with guidelines covering animal protocols (IACUC), human subject ethics (IRB/Consent), and data sharing standards (GEO, Zenodo).

A comprehensive checklist and audit guide for submitting papers to ACM CCS. This resource covers mandatory requirements such as abstract registration deadlines, adherence to the strict two-column sigconf template, rigorous anonymization protocols, dedicated ethics sections, and avoiding desk-reject triggers. Essential for authors preparing for top-tier security conferences.

A comprehensive guide for creating publication-quality scientific figures, adhering to high-impact journal standards. Covers critical topics such as optimal sizing for print widths, displaying underlying raw data (dot plots, replicates), ensuring colorblind accessibility, structuring stand-alone legends, and maintaining image integrity. Essential reading for researchers preparing manuscripts.

This guide helps users transform weak research ideas, correlations, or policy descriptions into strong, internationally generalizable, and causally identifiable research questions suitable for top-tier academic journals. It emphasizes establishing exogenous variation, clear underlying mechanisms, and structural modeling potential to elevate research quality.

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.

This guide outlines the rigorous academic requirements for submissions to the China Sport Science journal. It covers both natural sciences (physiology, biomechanics, exercise medicine) and social sciences (sports sociology, policy, management). Submissions must address core sport science ontology problems with high methodological rigor, ensuring the contribution is central to the field, rather than merely applying other disciplines.

A comprehensive guide for finalizing empirical tables and figures for high-level academic journals. It mandates moving beyond mere statistical significance by requiring deep economic interpretation (magnitude analysis). Covers three-line table conventions, proper event-study plot construction (CI and time markers), and precise variable sourcing (formula and database name) to ensure publication readiness.

This comprehensive guide provides expert protocols for writing the 'Related Work' section in Machine Learning papers. It teaches authors how to maintain 'versioned honesty' by meticulously triaging the literature into distinct lanes (e.g., method lineage, model artifacts, evaluation infrastructure). It covers advanced topics like managing concurrent work, citing arXiv status accurately, and avoiding self-citation violations, ensuring the paper is positioned rigorously within the rapidly evolving academic field.

A detailed guide for authors submitting theoretical machine learning papers (like COLT) regarding the appropriate inclusion and design of numerical simulations. It emphasizes that numerics should only visualize proved bounds, phase transitions, or separations, maintaining the focus on theoretical proofs rather than empirical data. Provides rules for plotting, reporting variability, and maintaining academic honesty.