Skill UI

Provides a comprehensive guide for authors responding to "Revise and Resubmit" notices from high-impact academic journals. It teaches structured academic writing, how to systematically address every reviewer comment, handle conflicting demands, integrate new experiments, and maintain a professional, non-defensive tone to maximize acceptance chances.

This comprehensive guide provides best practices for structuring appendices and supplementary materials for high-level academic journals. It helps authors manage the balance between main text conciseness and academic completeness by clearly defining what belongs in the core narrative (main findings) versus what should be reserved for the supplementary section (detailed proofs, extended experiments, raw data, and multimedia).

This skill provides a comprehensive, end-to-end workflow guide for academic journal submissions, specifically tailored for Acta Electronica Sinica (AES). It maps the entire publishing journey—from topic selection and research design to submission, review tracking, revision, and final publication—into a structured timeline. It helps authors manage complex decision points, adhere to strict deadlines (like the 6-month response window), and avoid common pitfalls, ensuring a systematic approach to high-stakes scholarly publishing.

A comprehensive guide detailing the rigorous formatting standards for tables and figures required for submission to the China Rural Economy journal. It covers three-line table style, strict column limits, variable definition protocols, mandatory data source citation, and specific figure aesthetic requirements (e.g., DPI, color usage), ensuring research output meets high academic standards.

A comprehensive guide for revising visualization research papers for top-tier conferences and journals (e.g., IEEE VIS/TVCG). It mandates shifting focus from aesthetic design to rigorous, task-grounded contributions. Users learn how to justify every visual encoding and interaction based on the analytical task, ensure evaluation methods match the contribution type, and structure the narrative around the data-task problem.