Skill UI

Run OpenMM-powered molecular dynamics simulations and MDAnalysis trajectory workflows to build protein or small-molecule systems, minimize energy, equilibrate conditions, and characterize RMSD/RMSF, contacts, and free-energy surfaces for structural biology or drug studies.

This comprehensive skill guides researchers through the submission process for the Chinese Journal of Management Science. It covers the journal's core academic positioning (operations research, decision analysis, complex systems), required methodological rigor (model setting, algorithms, proofs), structural guidelines, and essential formatting checks. It helps authors assess manuscript fit and identify common rejection risks before official submission.

A comprehensive guide for strengthening the methodological design of anthropological manuscripts. It provides frameworks for defending evidence across multiple modes—including ethnographic fieldwork, archival analysis, and quantitative biological data—by anticipating and structurally refuting potential expert critiques. Essential for rigorous qualitative and mixed-methods research.

A comprehensive guide for executing and reporting robust statistical analyses for manuscripts in Global Change Biology (GCB). This skill ensures the analysis meets the high standards of reproducibility required for publicly archived data (DOI). It covers advanced methods including mixed/hierarchical modeling, time-series and spatial analysis, meta-analysis, and rigorous uncertainty quantification (parameter, structural, and scenario). It emphasizes statistical rigor over result fabrication, guiding the use of appropriate inferential machinery based on data structure.

Provides rigorous guidelines for analyzing experimental game data and constructing numerical examples for high-level theoretical journals. Emphasizes that analysis must be tightly linked to theoretical claims, such as matching observed outcomes to predicted equilibria, rather than serving as general causal evidence. Covers session-level clustering, structural fitting, and ensuring computational reproducibility.

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 self-auditing guide designed for authors preparing manuscripts for submission to top-tier academic journals. It systematically addresses recurring, structural, and critical flaws across theory contribution, econometric identification, mechanism explanation, heterogeneity analysis, and writing conventions. This tool helps authors proactively identify and correct potential weaknesses before facing peer review.

This skill adapts scholarly drafts to the specific, rigorous style of the Journal of Financial and Quantitative Analysis (JFQA). It enforces quantitative prose, strict formatting (e.g., 100-word abstract, TNR 12pt, double-spacing), and structural rules necessary for high-impact finance submissions. Use it to polish content, ensure precision, and guarantee compliance with top academic journal standards.

This guide provides comprehensive methodological frameworks for conducting rigorous empirical analyses in international trade and macroeconomics, particularly tailored to the standards of top-tier journals like the JIE. It covers assembling bilateral trade and cross-country panels, estimating gravity models using PPML, implementing local projections, and calibrating structural open-economy models. It ensures analyses are robust, replicable, and address common referee criticisms.

A comprehensive guide for conducting rigorous, publication-quality statistical analyses required by top-tier management journals. It covers advanced techniques including Structural Equation Modeling (SEM)/CFA, Hierarchical Linear Modeling (HLM), complex regression, and meta-analysis, ensuring reports include necessary effect sizes, confidence intervals, and robustness checks to meet demanding academic standards.

This guide assists researchers in aligning their study design—whether behavioral (lab/field experiments) or modeling-econometric (IV/DiD/RDD)—with the rigorous standards required by the Journal of Marketing Research (JMR). It covers best practices for causal identification, power analysis, structural estimation, and meeting journal-level expectations like replication and Web Appendix planning.

A detailed guide for researchers tackling advanced financial-mathematics problems. It outlines the stringent criteria for suitability in top-tier journals, emphasizing methodological novelty, mathematical rigor, and deep theoretical contribution (e.g., stochastic analysis, structural theorems) over routine data computation. Use this to refine and scope your research proposal.