Skill UI

A comprehensive guide for achieving experimental reproducibility and verifiability in academic writing, specifically targeting top-tier journals. It covers crucial aspects like fixing computing environments, managing random seeds, ensuring data provenance, preventing data leakage, and structuring scripts for external review, transforming research rigor into actionable steps.

A comprehensive guide for translating complex, real-world management settings into formal, rigorous mathematical models suitable for high-level academic journals. It provides a systematic framework (Situational Element $\rightarrow$ Model Element Mapping) for developing optimization, game, and data-driven models, ensuring that the model structure, assumptions, and novelty are explicitly defensible against academic critiques.

This guide provides a rigorous framework for designing and interpreting quantitative case studies in academic manuscripts. It instructs authors on justifying parameter sources (real data, literature, policy), structuring comprehensive experiment matrices (effectiveness, sensitivity, boundaries, context), and moving beyond mere result description to deep mechanism-based analysis.

This tool serves as an orchestrator for authors preparing submissions to Cognitive Psychology (Elsevier). It guides users through the entire research lifecycle—from initial topic selection and theoretical formalization to study design, data analysis, and open science practices. It emphasizes structuring the contribution as a model-driven, multi-experiment program, ensuring all necessary steps, including code transparency, are addressed.

A comprehensive guide for authors submitting to Computer Science journals, detailing how to achieve full experimental reproducibility. It systematically covers locking environments (dependencies, hardware, seeds), preventing data leakage, structuring the submission package, and creating a clear audit trail so reviewers can independently reproduce all main results from raw data.

Comprehensive guidelines for structuring and submitting reproducible AI/ML research papers for top-tier conferences. It details two modes: Mode 1 for theoretical/KR work (requiring full, explicit proofs in the supplement), and Mode 2 for empirical/ML work (requiring fixed seeds, cached outputs, and rigorous provenance pinning for datasets, models, and environments). The guide also covers best practices for maintaining double-blind anonymity and properly dividing content between the main body and the supplementary material.

This guide offers a rigorous framework for structuring advanced econometric theory papers. It details how to move beyond simple identification by defining minimal, defensible assumptions, proving advanced limit theorems (e.g., FCLT, high-dimensional limits), and ensuring the result's generality across various data regimes (stationary, nonstationary, semiparametric). Use this when theoretical rigor is the primary challenge.

This guide outlines the rigorous standards required for submitting econometrics papers to top-tier academic journals. It focuses on ensuring the integrity of identification assumptions, linking theoretical claims to empirical data, structuring proofs correctly, and anticipating referee critiques (e.g., object drift, assumption opacity). Essential for advanced academic researchers writing methods papers.

This guide outlines best practices for achieving high levels of research reproducibility for academic papers, particularly in database and systems research. It details methods for pinning dataset versions, logging system environments, mapping empirical claims to verifiable evidence, and structuring availability statements. Adherence ensures the work is robust, transparent, and fully verifiable by the scientific community.

A comprehensive guide for researchers on packaging and documenting NLP artifacts, such as datasets, prompts, and evaluation code, for academic publication. It details best practices for ensuring reproducibility, managing provenance, adhering to licensing standards, and structuring evidence for top-tier conferences like EMNLP.

A comprehensive guide detailing the empirical writing style required for EMNLP submissions. It emphasizes scoping claims to specific datasets and languages, ensuring examples are load-bearing, structuring the argument phenomenon-first, and accurately calibrating hedging language based on evidence strength.

A comprehensive guide for academic researchers on structuring the 'Data and Sample' section of manuscripts for high-impact journals. It details requirements for database naming, variable definition using calculation formulas, descriptive statistics reporting (including winzorization and quantiles), and establishing full, reproducible sample selection trails. Essential for ensuring methodological rigor and passing peer review.