Skill UI

This skill guides authors in determining the suitability of their manuscripts for the journal Genome Biology. It details the journal's scope, which requires integrating large-scale genomic or epigenomic data with novel computational methods or statistical frameworks. It emphasizes that successful submissions must provide both methodological advances and meaningful biological insights, adhering strictly to standards like open-source code, public data deposition (GEO, SRA, ENCODE), and rigorous benchmarking.

This skill guides authors to evaluate if their ecology or biology manuscript meets the rigorous standards of Global Change Biology. It focuses on the necessity of linking research findings to explicit global-change drivers (e.g., warming, elevated CO2) and providing robust biological evidence, from molecular mechanisms to ecosystem-scale consequences.

This guide outlines the best practices and norms for executing and reporting empirical analyses in governance and comparative political science. It covers essential methods including cross-national inference, clustering uncertainty, robustness checks, multi-method triangulation, and ensuring reproducibility, guiding researchers to conduct rigorous, publication-ready studies.

A comprehensive methodological guide for structuring and defending empirical research designs in the fields of governance, institutions, and public policy. It covers advanced techniques including causal inference (DiD, IV, RDD), comparative case selection, process tracing, and mixed-methods integration. The skill emphasizes moving beyond mere description to robustly identifying contributions and ruling out rival institutional explanations.

This guide provides comprehensive principles for designing high-impact, self-contained tables and figures for academic manuscripts. It focuses on cross-national comparisons, event studies, and coefficient plots, ensuring the visual argument is clear, reproducible, and readable in grayscale, adhering to rigorous academic standards.

This guide instructs authors on creating mandatory Data Availability Statements and building comprehensive replication materials for academic manuscripts. It covers best practices for data provenance, code management, and handling restricted data, ensuring research integrity and reproducibility.

This comprehensive guide outlines the process for packaging artifacts from Human-Robot Interaction (HRI) research. Recognizing that HRI artifacts are complex, involving human subjects, robot behaviors, and extensive study materials, this skill provides guidelines for honest, ethical, and reproducible dissemination. It covers structuring de-identified data, analysis code, and behavioral specifications while adhering to participant consent and academic archiving standards.

This guide details the strategic process of writing a rebuttal for top-tier academic conferences (such as HRI). It provides a framework for triaging reviewer critiques, addressing methodological concerns (e.g., sample size, confounding), and making credible, actionable promises for the final camera-ready version, all while maintaining academic integrity and anonymity.

This guide provides comprehensive methodological standards for conducting high-quality human-subjects studies in Human-Robot Interaction (HRI). It covers the entire research lifecycle, including choosing optimal designs (within/between-subjects), ensuring statistical power, reporting effect sizes, and adhering to ethical guidelines (e.g., Wizard-of-Oz disclosure). It ensures research meets the rigorous academic standards required for top-tier publications.

This guide details the necessary steps for achieving and reporting reproducibility in Human-Robot Interaction (HRI) studies. Since HRI involves unique elements like human participants and physical environments, reproducibility focuses on making the study protocol, robot behavior specifications, analysis code, and de-identified data transparent. Learn how to properly assemble a comprehensive replication package and ethically disclose limitations to enhance academic credibility in top-tier HRI publications.

This guide provides comprehensive guidelines for advanced statistical and qualitative data analysis in Human Resource Management (HRM) research. It details the rigorous application of methods like Multilevel Modeling (HLM), Structural Equation Modeling (SEM), mediation testing, and fixed-effects models. It emphasizes methodological rigor, transparency, addressing endogeneity, and reporting effect sizes to ensure publication-quality empirical findings.

A comprehensive guide for structuring rigorous research designs in Human Resource Management. It addresses advanced methodological challenges such as common-method bias (CMB), endogeneity, cross-level data analysis, and multi-wave longitudinal modeling. Learn how to align theoretical claims with appropriate designs (e.g., field experiments, panel data, multi-source surveys) to ensure causal inference and methodological rigor.