Skill UI

This guide outlines best practices for submitting theoretical econometrics manuscripts to ET. It addresses how to handle extensive proofs, auxiliary lemmas, and simulation results that exceed page limits. The core focus is utilizing the voluntary Supplementary Material policy to ensure full reproducibility and transparency without requiring mandatory data or code archives.

A comprehensive guide detailing the rigorous standards for presenting statistical exhibits in high-level econometric theory journals. It covers best practices for Monte Carlo simulations, constructing diagnostic plots (size, power, coverage, bias), ensuring legibility at print reduction, and adhering to specific file formats (TIFF/EPS/PDF).

This router guides the entire lifecycle for writing complex, theory-driven academic manuscripts, particularly in fields like econometrics. It provides a structured roadmap spanning from initial topic selection and literature positioning through rigorous proof development, data simulation, drafting style, and the final submission/rebuttal phases. Use this resource when you are unsure of the correct sequential step or need a systematic check of all necessary components before finalizing your work.

This guide provides comprehensive standards for conducting and reporting rigorous empirical and simulation evidence in econometrics. It covers designing Monte Carlo simulations, creating minimum evidence maps, ensuring reproducibility through detailed logging, and linking theoretical claims directly to finite-sample evidence. Essential for high-level statistical research.

This guide provides strict guidelines for authors submitting econometrics papers, detailing how to strategically compress, optimize, and structure academic exhibits (tables, figures, simulations). It ensures that critical results and diagnostic information are presented clearly within tight page constraints, maximizing impact while adhering to journal standards.

This guide outlines the mandatory structure for creating a reproducible research package, required by academic journals like the European Economic Review (EER). It details how to properly assemble raw data, master analysis scripts, environmental specifications, and comprehensive documentation (README) to ensure that reported results can be independently verified by others. It is essential for any paper involving empirical analysis or simulations.

This central routing skill guides authors through the complex process of journal selection for engineering and technology manuscripts. It does not replace a single journal submission but first classifies the content based on the engineering sub-discipline, contribution type (e.g., method, system, device), validation shape (e.g., simulation, field experiment), and intended audience breadth. It then routes the paper to the most specialized and appropriate journal skill for accurate re-framing and submission.

This comprehensive routing skill guides researchers in selecting the optimal journal venue for natural-science, clinical, or physical manuscripts. It classifies the work based on discipline, advance type (e.g., mechanism, discovery), evidence shape (e.g., clinical trial, simulation), and audience breadth. It helps optimize the manuscript's fit and submission chances across hundreds of leading scientific journals.

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 outlines the rigorous methodology for ensuring that computational research results, especially those involving hardware architecture and simulation, are fully reproducible. It emphasizes pinning the entire result chain—from simulator commits and configuration files to workloads, sampling recipes, and statistics scripts—and mandates creating a manifest for every figure. Key practices include recording machine state for silicon runs and containerizing the environment to prevent data drift.

This guide provides rigorous structural and stylistic guidelines for writing top-tier academic papers in computer architecture (specifically for HPCA). It emphasizes building arguments around a single, measurable mechanism-to-behavior claim, quantifying the motivation with measured costs, ensuring all proposed mechanisms are checkable, and making sure every performance number is anchored to a specific instrument or simulation environment. It details the essential 'first-page arc' structure.

Provides comprehensive guidelines for packaging and documenting complex robotics research artifacts (code, simulations, hardware designs, datasets, etc.) for scholarly review. It outlines best practices for ensuring reproducibility, adopting the "five-minute-skeptic standard," and managing the full lifecycle of artifacts from review submission to public acceptance.