Skill UI

A comprehensive checklist and guide for authors submitting a Marketing Science manuscript via ScholarOne. It details critical steps including manuscript anonymization for double-anonymous review, selecting the correct article track (Regular, Frontiers, Practice), adherence to strict formatting rules, assembling a robust replication package (code and data access), and managing all submission requirements.

Provides comprehensive guidelines for packaging and presenting research artifacts (code, data, models, benchmarks) submitted to NeurIPS. It details requirements for anonymous review packages, public release, and ensures scientific claims are reproducible by providing necessary metadata, licenses, and execution details.

A comprehensive audit tool designed to ensure academic paper compliance before submitting to major AI conferences like NeurIPS. It systematically checks adherence to the CFP, double-blind anonymity across all assets (PDF, code, data, supplement), correct track selection, and avoids common desk-rejection pitfalls, ensuring the submission is ready for OpenReview.

A comprehensive guide on structuring research papers for top-tier AI conferences (like NeurIPS). It teaches authors how to strategically allocate contributions, proofs, data details, and code artifacts across the main body, text appendices, and separate ZIP files. Use this skill to ensure reviewers can easily identify the core contribution and assess the work's validity without missing crucial supporting details.

This guide outlines best practices for Nature Geoscience authors, detailing how to separate essential narrative (Main Text) from comprehensive, reproducible documentation (Online Methods). It emphasizes grounding every quantitative claim, quantifying uncertainty, and ensuring full data and code availability to meet high academic standards and pass rigorous peer review.

A comprehensive guide for authors submitting to Nature Geoscience. This checklist covers all necessary pre-submission steps, including verifying word counts, ensuring consistent formatting, completing critical compliance files (data/code availability, reporting summary), and auditing metadata, ensuring your manuscript is fully prepared for editorial review.

A comprehensive guide to interpreting the structural mechanics of large-scale, multi-deadline academic conferences (like NSDI). It details the difference between acceptance, one-shot revision, and rejection, explaining the strategic implications, associated bans, and the required scope for resubmission. Learn how to decode reviewers' feedback, identify critical baselines, and manage your research workflow across multiple cycles.

This guide provides a comprehensive framework for authors responding to major journal revisions. It teaches how to structure a technical, point-by-point rebuttal, systematically closing proof gaps, strengthening assumptions, incorporating missing baselines, and preparing the entire codebase and data for professional reproducibility review.

This guide details the strict requirements for data transparency and reproducibility when submitting manuscripts to the Population and Development Review (PDR). It covers creating data availability statements, depositing materials in FAIR repositories, documenting code with versions, and managing restricted data access, ensuring adherence to modern research integrity standards.

A comprehensive guide to understanding the Public Opinion Quarterly (POQ) peer review process. It helps researchers critically assess their manuscripts against high academic standards, including double-blind review protocols, rigorous survey methodology checks (Total Survey Error, AAPOR compliance), and anticipating reviewer demands for code and data. Use this skill to stress-test your paper's robustness and maximize its chances of successful publication.

A comprehensive guide for authors submitting to PR&AI, detailing how to truthfully declare and provide verifiable artifacts (code, data, models) for peer review. It emphasizes honesty, consistency, and structuring a reproducible repository to enhance reviewer trust.

This comprehensive checklist guides authors preparing theoretical manuscripts for submission to Psychological Review. It ensures meticulous adherence to APA 7th style, proper manuscript masking, accurate citation practices, and the correct presentation of computational model code, ensuring the paper meets the journal's stringent theory-focused scope.