Skill UI

This comprehensive guide equips authors with a structured framework for writing the Related Work section for top-tier NLP conferences (e.g., NAACL). It emphasizes tracking methodological, data, and task lineages, ensuring citation precision across Findings, workshops, and preprints, and culminating in a single, falsifiable "delta sentence" to precisely position the novel contribution.

This skill provides a comprehensive framework for assessing whether an economics manuscript is suitable for submission to Nankai Economic Studies. It details the journal's scope, required structure (abstract, keywords, formatting), and outlines common rejection risks. It helps researchers refine their problem consciousness and adapt their writing style to meet the standards of high-level Chinese academic journals.

A comprehensive guide for authors targeting Nature Machine Intelligence. It outlines the high standard required for AI/ML research, emphasizing conceptual significance, real-world impact, interdisciplinary relevance, and rigorous ethical and methodological depth. Use this tool to assess fit and framework your submission for a Nature-family audience.

This guide provides a comprehensive framework for authors responding to peer reviews in major security conferences. It details how to triage reviewers' comments, structure technical responses (pointing to evidence, not arguing), handle complex objections like adaptive attacks and ethics, and effectively participate in the interactive discussion phase to refine the paper.

This guide provides a rigorous framework for addressing reviewer comments on scientific manuscripts, especially for high-impact journals. It teaches how to triage feedback into actionable buckets (Do, Do-partial, Defend, Defer), handle specialized statistical reviewer critiques with re-analyses, and draft a professional, point-by-point response letter that enhances the paper's credibility and maximizes acceptance chances.

This skill provides a comprehensive framework for authors targeting top-tier AI conferences like NeurIPS. It guides researchers in refining their manuscript's narrative, ensuring the contribution meets the venue's standards for broad, high-novelty machine learning research. It covers topic fit, evidence bar requirements, structuring the argument, handling interdisciplinary claims, and navigating the specific submission cycle rules for maximizing acceptance chances.

This framework guides researchers in transforming platform-specific findings into abstract, portable concepts or functional mechanisms. It moves beyond mere correlation by helping define the scope, definition, and underlying process (mechanism) of a digital-media phenomenon, making the argument applicable to different contexts and technologies.

This guide provides a framework for assessing whether a research topic is suitable for interdisciplinary journals focusing on digital media and society (like NM&S). It details a four-part fit test, ensuring the research has social stakes, a portable concept, interdisciplinary reach, and enduring relevance beyond the news cycle. It helps researchers reframe narrow case studies into broadly applicable scholarly arguments.

This guide provides a structured framework for authors aiming to submit manuscripts to Nature Geoscience. It details how to comply with strict journal limits on word count, display items, and references. By implementing a triage order—such as moving protocols to Online Methods and extended data to Supplementary Information—authors can ensure the main text remains focused, concise, and impactful.

This guide outlines advanced methodologies for achieving high reproducibility in networked-systems research. It details capturing crucial provenance ledgers—including topology, traffic traces, system configuration, and run details—to ensure that published results are verifiable and not dependent on ephemeral experimental conditions. It emphasizes characterizing inherent variance rather than demanding absolute determinism, providing a rigorous framework for academic publication.

This guide provides a framework for designing and auditing the experimental rigor of academic papers, particularly in systems and programming languages. It emphasizes the crucial link between a claim type (e.g., 'Faster') and the required evidence type (e.g., benchmarks, user studies, formal proofs). Learn how to establish robust baselines, define clear workload rules, size experiments for conference cycles, and ensure statistical compliance for maximum impact.

This guide assists in the rigorous development of theoretical frameworks for Organization Science manuscripts. It details the two legitimate modes—deductive (a priori) or inductive (grounded)—and emphasizes the critical need to identify the core organizational mechanism, specify the levels of analysis (micro, meso, macro), and articulate cross-level bridging logic and boundary conditions. It guides users beyond simple literature reviews to build genuine, testable theory.