Skill UI

A comprehensive guide for authors submitting to top-tier journals, detailing how to structure policy implication sections for open economies. It emphasizes shifting the focus from generic domestic policy lists to specific, evidence-based recommendations concerning international mechanisms such as trade tariffs, exchange rate elasticity, capital account management, and global value chains (GVCs).

This guide helps researchers elevate vague global trends into rigorous, open-economy causal questions suitable for high-impact journals. It teaches methodology for identifying clear treatments, measuring international exposure (e.g., GVC, trade dependency), and defining precise counterfactuals within a general equilibrium framework.

This advanced analytical skill focuses on rigorously identifying and testing the specific transmission channels through which external shocks affect an economy. Essential for high-impact international economics research, it moves beyond descriptive mechanism narratives by focusing on testable proxies across trade, investment, finance, and expectation channels. It requires advanced econometrics (e.g., mediation analysis, sample splitting) and careful identification strategy to ensure causal claims are robust.

A comprehensive guide detailing the strategy and structure for author responses to peer reviews (rebuttals) in top-tier academic conferences like KDD. It covers the unique constraints, such as the prohibition of hyperlinks, how to anchor claims using specific coordinates (e.g., "Table 3, column 4"), and structuring responses to address multiple reviewer concerns while targeting the Area Chair's decision.

A comprehensive guide for authors targeting Kidney International, the flagship journal of the International Society of Nephrology (ISN). This skill helps gauge the fit of nephrology, hypertension, or kidney translational studies by checking against the journal's global scope, international clinical relevance, strict methodological bar (e.g., CONSORT, STROBE), and required structure. It is a venue-selection aid, not clinical advice.

A mandatory skill for original research articles, designed to create The Lancet's signature 'Research in context' panel. This panel forces authors to synthesize their findings within a systematic evidence base by detailing what was known previously (with documented search strategies), outlining the specific added value of the current study, and discussing the collective implications for clinical practice and public health policy.

This guide enforces rigorous standards for reporting clinical trial statistics, moving beyond simple P values. It mandates the use of confidence intervals, absolute and relative effect sizes, clear designation of the primary analysis population (ITT), and proper handling of multiplicity and missing data, aligning with standards set by top-tier medical journals.

A comprehensive guide for authors to assess if their high-impact photonics research meets the broad significance and methodological rigor required by journals like Light: Science & Applications. It helps authors re-frame specialized technical results into a Nature-style narrative, focusing on conceptual advances and quantitative evidence.

This guide provides expert advice on positioning, framing, and validating medical imaging AI manuscripts for the MICCAI conference. It helps authors tailor their research to meet the venue's specific requirements, focusing on clinical-task grounding, robust validation, and alignment with core scopes like computational anatomy and image-guided intervention. Essential for optimizing conference submission strategy.

This skill is a comprehensive guide for researchers to determine if their Information Systems (IS) research manuscript meets the high academic standards of MIS Quarterly (MISQ). It evaluates the theoretical contribution, methodological rigor (behavioral, design-science, economics), and overall fit, providing crucial guidance on re-framing the work or suggesting alternative academic venues before submission.

This comprehensive guide instructs researchers on how to design rigorous and credible evaluations for Machine Learning systems. It covers critical areas such as workload selection, baseline comparison discipline, reporting four key coordinates (throughput, latency tails, memory, cost), structuring ablations, and generating scaling evidence. It ensures measurements meet academic benchmarking standards.

This guide walks authors through the critical steps of transitioning an accepted academic paper from an anonymous submission to the final, published, double-column camera-ready version. It covers de-anonymization (restoring author details, links), fulfilling all review promises, completing ACM metadata and rights, coordinating artifact badges, and planning the in-person conference presentation.