Skill UI

A comprehensive guide for authors submitting data mining papers to the ACM SIGKDD Conference. It helps authors assess the manuscript's fit, reframe the contribution (e.g., from journal to conference style), and strengthens the evidence bar by focusing on novelty, scale, reproducibility, and clear real-world impact required by top-tier data science venues.

This guide assists authors targeting the ACM SIGMOD conference. It provides expert advice on positioning, framing, and structuring database systems papers. It emphasizes strong algorithmic or systems contributions, realistic workloads, and necessary evidence, while also advising on routing relative to similar venues like VLDB and ICDE.

A comprehensive guide for structuring research artifacts (code, models, datasets, media) for ACM Multimedia conferences. It details the distinct requirements for anonymous review packages versus permanent public releases, offering guidelines on ethics, licensing, reproducibility, and track-specific documentation.

A comprehensive checklist for auditing academic submissions to ACM Multimedia (ACM MM). Covers thematic area selection, formatting constraints (6-8 page limit), enforcing double-blind anonymity, managing OpenReview mechanics, and identifying common desk-reject triggers. Essential for authors preparing technical papers for top-tier conferences.

This guide provides comprehensive econometric methodologies for establishing causality in accounting and finance research. It covers advanced techniques such as Difference-in-Differences (DID), Propensity Score Matching (PSM), Heckman selection models, and Instrumental Variables (IV), emphasizing the correct handling of regulatory changes and external shocks to meet top-tier academic standards.

A comprehensive guide for writing and verifying the institutional and accounting standard background section in academic manuscripts. This tool emphasizes the critical need for precision, requiring accurate standard names, official document numbers (文号), effective dates, and applicable scopes. Essential for empirical studies relying on regulatory changes, ensuring the manuscript meets the high standards of authoritative accounting journals.

A comprehensive guide for constructing academically robust and reproducible accounting metrics, such as discretionary accruals, real earnings management, and conservatism scores. This resource details the necessary steps—including estimation models, granularity, sample requirements, and standardized protocols—to ensure submissions meet the rigorous standards of top accounting journals.

A rigorous guide for academic researchers on establishing and testing the role of information mechanisms in accounting literature. It outlines how to shift focus from general pricing or financing channels to specific information channels—such as reducing information asymmetry, improving disclosure quality, or constraining earnings management. Provides advanced methodologies, including direct measurement of information variables and cross-sectional cutoff analysis.

This comprehensive guide details the methodological requirements for building a robust robustness section in accounting and empirical research manuscripts. It covers four critical pillars: substituting accounting measures, controlling for institutional/governance variables, executing placebo tests, and advanced endogeneity follow-ups. It helps authors ensure their primary findings withstand stringent academic scrutiny.

A comprehensive guide for authors preparing empirical research manuscripts (especially in accounting). It details best practices for structuring main tables (keeping them lean), transforming statistical coefficients into interpretable economic magnitudes, and properly visualizing dynamic effects using event study graphs. It emphasizes moving beyond simple significance stars to provide deep academic context.

This skill serves as a comprehensive guide for authors submitting manuscripts to highly selective pure mathematics journals like Acta Mathematica. It assesses the work's significance, intellectual fit, required level of rigor, structural integrity, and adherence to top-tier academic standards, ensuring the research is ready for the global mathematical research community.

This skill guides authors through the rigorous requirements of American Economic Journal: Economic Policy. It helps evaluate whether an applied economics manuscript meets the journal's high bar, focusing specifically on the joint criteria of sound causal identification (DiD, IV, RDD) and clear, impactful policy relevance. Use it to re-frame arguments and check against best practices for policy evaluation.