Skill UI

This skill guides researchers on the mandatory, rigorous evaluation of complex systems models (e.g., AgSy). It details best practices for model validation, emphasizing the comparison of observed vs. simulated data using independent, held-out datasets. Key components covered include standard fit statistics (RMSE, NSE), sensitivity analysis (identifying core drivers), uncertainty propagation, and multi-objective trade-off/scenario analysis to ensure scientific credibility.

A comprehensive guide for authors preparing an artifact appendix (ae.tex) for major conferences like ASPLOS. Learn how to structure your submission to achieve Available, Functional, and Reproducible badges. Covers managing complex dependencies (hardware, software, data), structuring the repository for evaluators, and optimizing collaboration protocols to ensure successful post-acceptance validation.

Comprehensive guidelines for conducting rigorous empirical validation in financial forecasting and econometrics manuscripts. This framework covers essential steps such as ensuring data provenance, performing robust out-of-sample testing (rolling windows), establishing multi-layered benchmark models, and conducting significance tests (e.g., DM/MCS). It helps authors meet the high academic standards required for top-tier journals.

This skill provides a comprehensive fit and structural review tool for manuscripts targeting 'Field Crops Research.' It assesses whether an agronomy or crop-physiology paper meets the journal's high standard: rigorous, adequately replicated, multi-environment field experimentation. It guides authors on key topics like resource-use efficiency, crop modeling validation, and required methodological depth (e.g., mixed models, full metadata reporting).

This guide details best practices for submitting research artifacts to academic conferences, following the ACM Artifact Review and Badging scheme. It covers everything from required packaging plans (Docker, README, data provenance) to achieving various levels of validation (Functional, Reusable, Reproduced). Learn how to structure your submission to ensure maximum reproducibility and pass rigorous evaluator checks on a clean machine.

A comprehensive guide for transforming an accepted academic conference paper into its final, publishable "camera-ready" format for IEEE Xplore. Covers mandatory steps like PDF eXpress validation, completing the electronic copyright form, integrating reviewer feedback, confirming metadata, and preparing for both physical presentation and open-access publication.

This comprehensive guide details the critical steps required after conference acceptance to prepare a camera-ready manuscript for IEEE Xplore publication. It covers mandatory procedures such as IEEE eCopyright completion, running through PDF-eXpress validation, adhering to the double-column template, integrating required reviewer changes, and releasing the final licensed artifacts and data.

This skill guides authors to evaluate if their mechanistic immunology study meets the high standards of the journal Immunity. It serves as a comprehensive pre-submission checklist, detailing required evidence (in vivo validation, single-cell genomics, multi-omic data), necessary mechanistic depth, structural requirements, and strict submission guidelines to ensure the core advance is the immune mechanism.

A comprehensive guide for structuring and validating empirical research designs (Survey/SEM, Secondary Data, Experiments, Meta-analysis) intended for high-impact journals. It addresses critical methodological issues such as construct validity, endogeneity control, robust identification strategies, and ensuring overall rigor across diverse academic genres.

A comprehensive guide for executing and reporting the full empirical robustness battery required by top accounting journals. It covers modern econometric techniques like standard error clustering, advanced Difference-in-Differences (DiD) estimators for staggered timing, Regression Discontinuity (RD) diagnostics, and Instrumental Variables (IV/2SLS). The guide emphasizes rigorous validation of accounting constructs, executing multiple sensitivity tests, and ensuring full data-and-code reproducibility for academic publication.

A comprehensive guide for advanced quantitative research, focusing on demonstrating the validity of econometric methods. It outlines best practices for combining rigorous Monte Carlo simulations (stress-testing DGPs, baselines) with substantive real-world data applications, essential for publication in high-impact economics and statistics journals.

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.