Skill UI

This guide provides a comprehensive strategy for authors responding to a "Revise and Resubmit" (R&R) decision, particularly for high-stakes journals. It details the structure of the rebuttal letter, including handling conflicting referee comments, performing necessary reconciliation with prior literature, and integrating rigorous sensitivity tests (e.g., comparative estimation, pre-trend analysis). It focuses on strategic communication and method validation.

A guide for designing rigorous numerical experiments and simulations for academic manuscripts in management science. This methodology focuses on validating proven theoretical properties, assessing algorithm performance, exploring parameter sensitivity, and extracting actionable managerial insights. It emphasizes that simulations must complement, but never replace, formal mathematical proofs.

This skill provides a comprehensive checklist and guide for authors targeting the Journal of Hydrology. It helps authors assess the fit of their manuscript, covering scope alignment (surface/subsurface hydrology), required scientific advance, methodological rigor (validation, uncertainty), and adherence to Elsevier house style. Use it for pre-submission venue selection and quality assurance.

This guide outlines the rigorous process for estimating, validating, and interpreting structural models in Marketing Science manuscripts. It covers advanced econometric techniques (GMM, MLE, Bayesian), demonstrating empirical identification, assessing model fit (in-sample/out-of-sample), and computing robust counterfactuals. It ensures the final results are statistically sound, economically plausible, and prepared for replication.

A comprehensive, step-by-step preflight checklist designed for authors submitting manuscripts to top-tier scientific journals. It ensures thorough validation across all required components, including the mechanistic narrative, structural data integrity, statistical rigor (QSA), figure quality, and proper data/code deposition (DOIs). It serves as the ultimate gatekeeping mechanism before final submission.

A comprehensive skill for Particle Image Velocimetry (PIV) analysis. It processes pairs of images to measure 2D fluid velocity fields. Functions include cross-correlation, vector validation, outlier replacement, and calculating derived metrics like vorticity and strain rate. Essential for experimental fluid dynamics visualization and analysis.

A comprehensive guide for designing and auditing rigorous empirical evaluations for sensing systems, particularly Human Activity Recognition (HAR). It emphasizes best practices such as leave-one-subject-out cross-validation, using real human subjects under diverse conditions, reporting event-level metrics (e.g., Macro-F1) over raw accuracy, establishing fair baselines, and mitigating data leakage (e.g., subject or session leakage). This ensures that reported results are robust, generalizable, and defensible against academic skepticism.

A comprehensive guide for structuring the analysis and results section of academic papers in Production and Operations Management (POM). It covers rigorous theoretical proofs, advanced empirical identification methods (e.g., DiD, IV), and data science validation techniques. The core focus is bridging statistical significance with actionable, operational managerial magnitude, ensuring research findings are decision-relevant.

A comprehensive guide to ensuring the quantitative rigor and full reproducibility of econometrics manuscripts, particularly for submissions to high-tier journals like QE. Covers structural estimation (GMM, MSM), moment construction, data cleaning best practices, and comprehensive validation checks (e.g., multi-start, Monte Carlo recovery). It ensures that all results can be regenerated from raw data using pinned environments and documented scripts.

A comprehensive guide detailing the stringent methodological and reporting standards required for submitting medical imaging research (diagnostic, AI, interventional) to a top-tier radiology journal. It covers expectations for diagnostic accuracy (STARD), AI model validation (CLAIM), sample size justification, reproducibility, and ensuring clinical relevance, helping authors frame their work for optimal journal fit.

A comprehensive guide for executing and stress-testing empirical analyses in Industrial Organization (IO). It covers structural and reduced-form model diagnostics, advanced inference techniques (e.g., wild-cluster bootstrap), counterfactual validation, and meeting the rigorous robustness standards demanded by top-tier academic journals. Focuses on methodology, not study design.

A comprehensive guide for researchers on assessing the suitability of digital health, AI/ML, or health informatics manuscripts for The Lancet Digital Health. It outlines the journal's strict standards for clinical rigor, external validation, reporting guidelines (TRIPOD, CONSORT-AI), and evidence required for publication.