Skill UI

This guide helps researchers evaluate whether a finance research topic meets the general-interest and intellectual standards of top-tier academic journals. It provides critical frameworks, such as testing for a "first-order question," "generality," and "top-three standards," ensuring the idea has broad appeal beyond a niche field. Ideal for pre-submission vetting and refining academic arguments.

A rigorous framework for designing reproducible and scientifically sound experiments in robotics and AI, specifically for academic publication (e.g., RSS). It guides users to shift focus from simple system demonstration to testing a falsifiable scientific claim. Key areas covered include deriving conditions from a claim, establishing hardware trial protocols, managing simulation vs. real-world evidence splits, and performing detailed failure attribution analysis.

This tool acts as a comprehensive workflow router for manuscripts aiming for top-tier journals like Science. It doesn't write the content but guides the user through essential stages—from initial significance testing to final submission and rebuttal. It diagnoses bottlenecks and suggests the next specialized skill needed, ensuring the manuscript meets high standards for general interest and structure.

This guide provides a comprehensive framework for authors responding to peer reviews in major NLP/IR conferences. It covers strategies for active rebuttal periods, pre-emptive revisions when no window exists, and planning resubmissions after rejection. Key advice includes technical rebuttal tactics (e.g., addressing baselines, significance testing), maintaining professional tone, and efficiently using existing computational evidence to maximize acceptance chances.

This comprehensive guide details the rigorous methodology for designing, executing, and reporting experimental results in Information Retrieval (IR). It covers best practices for selecting appropriate test collections, choosing defensible metrics, performing proper statistical significance testing (e.g., paired t-tests), ensuring baseline fairness, and proactively addressing modern pitfalls like LLM contamination. Essential for publishing high-quality, auditable research in IR/NLP.

A comprehensive guide for researchers developing systems or algorithms, detailing the necessary level of evidence required for performance claims (e.g., bounds, real-world performance, learning guarantees). It emphasizes scientific rigor, statistical reporting (confidence intervals), assumption testing, and data provenance to ensure findings are robust and reproducible in academic settings.

A comprehensive guide for conducting and reporting rigorous empirical analysis in academic research, particularly in finance and accounting. It covers selecting appropriate estimators (e.g., DiD, 2SLS, RDD), handling fixed effects, ensuring statistical robustness through placebo and alternative testing, and fulfilling strict data-authenticity and code-access requirements required by top-tier journals.

A comprehensive guide detailing the standards for scientific reproducibility in visualization research, particularly for IEEE VIS submissions. It outlines the Graphics Replicability Stamp Initiative (GRSI) requirements, covering how independent volunteers must reproduce results using provided code, data, and environments (e.g., Docker containers). Learn how to package your work—including determinism aids, mapping tables, and licensed data—to pass rigorous, clean-machine testing.

A comprehensive guide for designing and presenting highly rigorous technical evaluations for database or system papers. It covers establishing baseline necessity, proving mechanism causality through ablation, testing scalability across data sizes, and mandating the disclosure of loss cases to satisfy skeptical academic reviewers.