Skill UI

This guide outlines the rigorous execution and reporting standards required for quantitative research intended for top-tier academic journals, such as APSR. It covers best practices for ensuring reproducibility, assessing robustness, reporting uncertainty honestly (using credible intervals), and correctly applying advanced statistical inference (e.g., clustering, randomization inference). It is essential for researchers conducting empirical work in political science, economics, or social sciences.

A comprehensive guide for authors submitting to the American Political Science Review (APSR). It outlines best practices for creating self-contained, accessible, and statistically rigorous tables and figures. The resource emphasizes principles like maintaining word-count compliance, ensuring reproducibility, and presenting complex results (like treatment effects) using appropriate visualizations.

A comprehensive guide for authors submitting manuscripts to the American Sociological Review (ASR). It outlines best practices for creating self-contained, highly readable, and reproducible tables and figures (exhibits). Learn how to present complex empirical data, quantitative findings, and qualitative evidence while adhering to ASA style and meeting rigorous reviewer expectations.

A comprehensive guide detailing the selection of appropriate statistical estimators (e.g., DiD, Panel FE, ANOVA) for various research designs. It also outlines rigorous best practices for ensuring data integrity, conducting robustness checks, and adhering to journal code-sharing policies, ensuring highly reproducible empirical research.

This guide provides comprehensive standards for designing, quantifying, and narrating scientific figures and tables for high-impact publications. It details best practices for maintaining image integrity (e.g., uncropped blots, linear adjustments), ensuring all data points are quantified across replicates, and structuring the visual evidence to advance a clear, mechanistic argument step-by-step.

This guide outlines the standardized requirements for declaring data and code availability in major scientific publications. It details mandatory best practices, including depositing raw data (e.g., sequencing, structures) and analysis code into approved, citable public repositories (such as GEO, PDB, Mendeley Data, and Zenodo). It also provides the required standardized three-bullet statement format for ensuring reproducibility.

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 best practices for packaging research artifacts (prototypes, datasets, instruments, code) for academic review and permanent public release, particularly in HCI venues like CHI. It covers strategies for creating highly reviewable, anonymous submissions (e.g., claims maps, minimal demos) and establishing robust, licensed, permanent archives (e.g., DOIs, OSF), ensuring research reproducibility and auditability.

A comprehensive guide detailing best practices for achieving research transparency and reproducibility, especially for HCI and related scientific studies. It outlines three critical layers of documentation—Protocol, Analysis, and Data—to ensure that studies can be reliably verified and built upon by the academic community.

This guide outlines the scope and submission requirements for the journal China Sport Science and Technology. It focuses specifically on application-oriented exercise science, covering competitive training, physical conditioning, physiological monitoring, technical analysis, and sports rehabilitation. It helps researchers determine the best fit for their work, distinguishing between purely theoretical research and practice-guided, evidence-based studies for athletic development.

A comprehensive guide for advanced econometric identification strategies, detailing best practices for conducting Difference-in-Differences (DID), event studies, and staggered treatment analysis. It provides stringent requirements for ensuring causal claims are robust and methodologically sound in academic research.

A comprehensive guide detailing the methodology for packaging, documenting, and staging research artifacts (including code, datasets, knowledge graphs, and demo systems) for CIKM conference submission. It covers best practices for licensing, provenance tracking, versioning, and transitioning artifacts from anonymous review status to citable public releases, ensuring reproducibility and adoptability.