Skill UI

A comprehensive guide for authors receiving a revise-and-resubmit decision. It outlines the strategic planning, required new analyses (robustness, identification tests), and structured writing process needed to draft a rigorous, point-by-point rebuttal letter, ensuring data integrity and compliance with journal standards.

A comprehensive checklist detailing all mandatory requirements for submitting academic manuscripts (e.g., Contemporary Accounting Research). It verifies compliance on key areas including length limits, abstract word count, separated title pages, blind manuscripts, ethics approval (IRB/REB), data integrity statements, and correct payment proof. Use this before the final upload to ensure maximum readiness and avoid editorial rejection.

This guide provides comprehensive standards for designing, quantifying, and narrating scientific figures and tables for high-impact publications. It details best practices for maintaining image integrity (e.g., uncropped blots, linear adjustments), ensuring all data points are quantified across replicates, and structuring the visual evidence to advance a clear, mechanistic argument step-by-step.

A comprehensive guide for creating publication-quality scientific figures, adhering to high-impact journal standards. Covers critical topics such as optimal sizing for print widths, displaying underlying raw data (dot plots, replicates), ensuring colorblind accessibility, structuring stand-alone legends, and maintaining image integrity. Essential reading for researchers preparing manuscripts.

This master preflight checklist guides authors through every necessary detail before submitting a high-impact manuscript. It covers complex academic requirements, including narrative structure, figures integrity, data transparency (DOIs, accession numbers), required metadata (CRediT, Lead Contact), and high-stakes cover letter strategy, ensuring the work is both scientifically sound and submission-ready.

This comprehensive guide provides a meticulous audit checklist for authors preparing a computer science manuscript before submitting it to a high-tier academic journal. It covers all required elements, including the 'Manuscript Six Components,' formatting standards (e.g., GB/T 7714), ethical declarations, structural integrity (e.g., report-style abstract), and institutional submission protocols. Use this checklist to significantly reduce the risk of rejection due to procedural or structural deficiencies.

A comprehensive playbook for academic authors detailing the two distinct tasks of responding to journal feedback. It differentiates between the private, pre-acceptance revise-and-resubmit letter (R&R) and the public, scholarly CA✩ Reply published alongside the article. It guides authors on strategic rebuttal, synthesizing themes, maintaining academic integrity, and ethically managing sources.

This comprehensive guide outlines the critical steps required to prepare a camera-ready version of an NLP conference paper (e.g., EACL). It covers mandatory submissions after acceptance, including content expansion, de-anonymization (restoring author names, citations, and metadata), addressing meta-review required changes, ensuring correct ACL Anthology metadata, and managing public artifact release and presentation logistics. Essential for ensuring academic integrity and compliance.

A comprehensive guide for authors submitting computer vision papers (e.g., to ECCV). It outlines best practices for ensuring the full reproducibility of AI research, covering everything from detailed training recipes and hyperparameter logging to pinning foundation model dependencies and reporting variance honesty. Essential for academic publication integrity.

This guide outlines the rigorous standards required for submitting econometrics papers to top-tier academic journals. It focuses on ensuring the integrity of identification assumptions, linking theoretical claims to empirical data, structuring proofs correctly, and anticipating referee critiques (e.g., object drift, assumption opacity). Essential for advanced academic researchers writing methods papers.

A comprehensive guide for researchers submitting NLP papers, detailing best practices for achieving high reproducibility. It instructs users to pin model versions, log data provenance, report compute costs, and adhere rigorously to the Responsible NLP checklist. Essential for ensuring academic integrity and enabling rigorous verification of ML experiments.

A comprehensive guide for authors writing Experimental Economics manuscripts. This skill helps you anticipate and proactively address the rigorous, double-blind objections typically raised by academic referees—such as concerns over deception, statistical power, omitted confounds, or impure treatment contrasts—before submission, ensuring methodological integrity and strengthening the manuscript.