Skill UI

This guide provides strict guidelines for designing, auditing, and reporting empirical or simulation experiments, particularly for theoretical machine learning and statistics papers. It emphasizes mapping every empirical claim to specific evidence, ensuring statistical rigor (e.g., reporting uncertainty, baselines, seeds), and validating theoretical assumptions rather than optimizing for leaderboards. Essential for research reproducibility.

This guide provides comprehensive methodological frameworks for designing rigorous empirical studies suitable for top-tier academic journals. It helps researchers match theoretical claims (e.g., causal effects, cross-level dynamics) to appropriate designs (experiments, panel data, qualitative analysis). It details how to proactively address major threats like Common-Method Bias, Endogeneity, and inconsistent levels of analysis, ensuring high internal and external validity.

This guide provides comprehensive guidelines for selecting, validating, and defending rigorous research designs for Contemporary Accounting Research (CAR) manuscripts. It covers various approaches—including panel archival, controlled experiments, analytical models, field studies, and surveys—while stressing critical rigor checks like identification strategy, internal validity, and model discipline. It also mandates adherence to ethical review processes and data integrity policies.

This guide helps academics determine if their manuscript aligns with the scope of China Industrial Economics, or if it should be redirected to more specialized journals (e.g., Economic Research, Management World). It provides criteria based on empirical rigor, policy quasi-experiments, and specific research focus (industry, enterprise, environment).

A comprehensive guide for designing rigorous and robust empirical programs in Information Retrieval, Data Mining, and Knowledge Management. It outlines how to meet the stringent evidence requirements for top-tier conferences, ensuring claims are substantiated across multiple evaluation dimensions (retrieval, mechanism, scalability) and incorporating best practices for reproducibility and handling modern challenges like LLM contamination.

This comprehensive guide provides best practices for designing and presenting scientific experiments for high-impact academic journals. It covers structuring research around clear Research Questions (RQs), selecting strong and fair baselines, ensuring statistical rigor (mean ± standard deviation, significance testing), performing detailed ablation studies, and preventing common issues like data leakage and evaluation pollution. It is essential for authors aiming to validate complex computational findings.

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.

A comprehensive guide to strengthening the quantitative research design for communication manuscripts. It covers best practices and methodological rigor for experiments (lab/online), panel surveys, and content analysis. Learn how to pre-register designs, control for common confounds (e.g., common-method variance), and rigorously establish causality, helping you defend your work against skeptical reviewers.

Provides a comprehensive framework for defending the methodological rigor of comparative social science research. This skill guides users through establishing comparative leverage, whether in cross-national panel data, case-based comparisons, or natural experiments. It emphasizes techniques like causal inference, process tracing, and ruling out rival theoretical accounts to strengthen the study's claims.

This comprehensive guide helps researchers audit their papers before submission to the EDBT conference. It covers critical steps including selecting the correct submission cycle and paper shape (Regular, Vision, Experiments & Analysis), adhering to strict formatting standards, managing the 12-month resubmission ban, ensuring double-blind compliance, and providing verifiable, reproducible artifacts. It is essential for academic publishing compliance.

This skill guides users through strengthening the causal identification strategy of empirical economic manuscripts (DiD, IV, RDD, Experiments). It helps audit research designs against general academic credibility standards, ensuring that assumptions, potential biases, and the link between data variation and causal objects are explicitly stated and robustly defended for diverse expert audiences.

This comprehensive guide outlines the rigorous standards for designing, auditing, and reporting Natural Language Processing (NLP) experiments, specifically tailored for top-tier academic conferences like EMNLP. It covers critical areas including baseline fairness checks, data contamination audits, statistical significance testing, prompt sensitivity analysis, and structured human evaluation protocols, ensuring reported claims are robust, reproducible, and methodologically sound.