Skill UI

This comprehensive guide serves as a preflight checklist for authors submitting manuscripts to the Journal of Financial and Quantitative Analysis (JFQA). It details stringent requirements, including double-anonymous formatting, specific layout standards (8.5x11, 12pt Times New Roman, double-spaced), the 100-word abstract limit, and mandatory disclosure rules regarding prior rejections and code-sharing. Use this checklist before submitting via Editorial Manager to ensure compliance and avoid desk rejection.

This comprehensive checklist guides authors through the rigorous pre-submission process for the Journal of Financial Research. It covers critical academic elements such as adhering to specific word count limits (~20k), abstract formatting, JEL codes, and proper citation conventions. It also emphasizes maintaining double-blind anonymity and verifying structural consistency, ensuring the manuscript meets the journal's demanding standards before online submission.

A comprehensive guide for assembling reproducible research packages using restricted health data, such as Medicare claims or EHR data. This package focuses not on running the analysis, but on establishing a credible data access path, creating detailed Data Availability Statements (DAS), and ensuring code is shared and reproducible, which is crucial for publishing in health economics journals.

This comprehensive guide details the strict replication and data deposit policies required by the Journal of International Economics (JIE). It ensures that all components—including raw data, cleaning scripts, structural solver code, and master scripts—are properly deposited in a secure repository (like Mendeley Data) before publication acceptance, guaranteeing full research reproducibility.

A comprehensive guide and package structure for preparing empirical law and economics manuscripts for journal submission, ensuring full data and code reproducibility prior to publication. It mandates the assembly of data files, transformation scripts, a master execution script, and detailed README documentation, explicitly addressing the unique challenges of restricted or proprietary legal data sources (e.g., sealed court records).

This skill guides researchers through drafting a robust and credible response to major academic revisions (R&R) from top-tier journals, particularly in institutional economics. It teaches how to systematically decode editor feedback, separate core conceptual challenges from technical gaps, and structure a point-by-point reply that addresses deal-breakers effectively, ensuring the response is substantive and highly responsive.

A comprehensive guide for authors submitting empirical or quantitative manuscripts to the Journal of Monetary Economics (JME). It details mandatory requirements for data deposition, model code (Dynare, MATLAB, Stata, R), master scripting, and adherence to Elsevier's generative AI declaration, ensuring maximum reproducibility and meeting high academic standards.

A comprehensive final checklist for authors submitting manuscripts to top academic journals (e.g., JAP) via Editorial Manager. It ensures compliance with APA 7th edition style, full author anonymization for masked review, adherence to open science (TOP) standards (data/code availability), and statistical rigor. This guide helps prevent common submission failures before final upload.

Provides comprehensive guidelines for authors submitting to the Journal of Econometrics (JoE). It details the required best practices for data citation (using the Elsevier `[dataset]` format), ensuring methodological reproducibility (e.g., Monte Carlo simulations), and preparing estimators as runnable code packages (R, Python, etc.). This guide is essential for meeting rigorous academic publication standards and passing peer review.

This comprehensive guide outlines the mandatory standards for data and code submission for academic papers published in the Journal of Labor Economics (JOLE). It instructs researchers on how to build a complete, reproducible package, detailing the requirements for documenting the full data chain (raw to final), master scripts, environment pinning, and handling proprietary data for journal review.

This comprehensive skill guides authors through the specific submission requirements and editorial culture of the Journal of Machine Learning Research (JMLR). It helps authors determine if their ML method, theory paper, or dataset is appropriate for JMLR's high standards—emphasizing rigor, complete proofs, open code, and lasting scientific contribution over novelty signaling. Use this when targeting JMLR or comparing it against other top-tier ML venues.

This skill is a comprehensive guide for authors targeting the Journal of the American Mathematical Society (JAMS). It outlines the journal's extremely high standards for pure and applied mathematics, covering required levels of mathematical rigor, novelty, general significance, and specific formatting guidelines (AMS LaTeX, MSC codes, arXiv). Use this tool to assess the suitability of your manuscript for top-tier academic publication.