Skill UI

A comprehensive guide for authors submitting revised manuscripts, specifically addressing the complex requirements of Population and Development Reviews (PDR). Learn how to structure a rebuttal that satisfies cross-field experts (demographers and policy scholars), reconcile conflicting comments, protect the core scientific contribution, and write a cohesive, persuasive cover letter to the editor.

Provides detailed guidelines for submitting empirical papers to POPL. It teaches authors how to correctly match specific claims (e.g., soundness, precision) with appropriate evidence types (proofs, case studies, effort data). The core principle emphasized is that evidence must demonstrate realizability but never substitute for the formal theorem.

This guide helps researchers determine the optimal venue (like POPL, PLDI, OOPSLA) for their academic papers on programming languages. It advises authors on whether their core contribution is theoretical (proofs, semantics), implementation-focused (compilers, benchmarks), or empirical (user studies), ensuring their work reaches the most appropriate specialized reviewer community.

This guide outlines the rigorous data and transparency requirements for submitting academic manuscripts, particularly for social science journals. It details two core components: methodological disclosure via 'Appendix A: Disclosure Elements' and building a complete replication package. Authors must ensure all published tables and figures can be reproduced exactly using deposited data and code in a permanent archive (Dataverse) before the paper advances to typesetting.

A comprehensive guide for researchers preparing submissions to specialized academic journals, such as Perspectives on Psychological Science (PoPS). This guide details the strategic process of writing a proposal, differentiating between stand-alone, cooperative, and miscellaneous contributions. It teaches authors how to structure the core argument (the 'spine') and articulate the field's current need, ensuring submissions are timely, focused, and methodologically sound.

This guide outlines the comprehensive workflow for academic authors after their manuscript is accepted for publication in PR&AI. It covers all critical stages, including final draft revisions, recovering author information, detailed proofreading of figures, formulas, references, and adhering to the core principle of making only error corrections, not content modifications. Essential for a smooth transition to print.

This guide assists researchers in determining the topic suitability, appropriate format (research paper, review, special issue), and correct academic placement for manuscripts targeting 'Pattern Recognition and Artificial Intelligence' (PR&AI). It outlines the journal's focused scope across core AI domains like ML, CV, and NLP, helping authors avoid common submission pitfalls and ensuring their contribution has sufficient originality and academic depth.

A comprehensive guide for positioning computer science manuscripts for the Privacy Enhancing Technologies Symposium (PETS). It helps authors assess if their research matches the venue's core focus (e.g., differential privacy, anonymity) and provides a detailed framework for structuring empirical evidence against a clear adversary model to maximize acceptance chances.

A comprehensive guide for authors writing for high-impact journals like Physical Review Letters (PRL). It addresses the strict 'deductible budget' model where figures, equations, references, and text all count against a single limit. Provides systematic steps to triage content—such as moving derivations to Supplemental Material, compressing figures, and tightening prose—to ensure the core scientific claims remain clear and within publication guidelines.

This skill guides researchers through the rigorous process of determining if a scientific finding meets the 'Importance' and 'Broad Interest' criteria required for top-tier physics journals, such as Physical Review Letters (PRL). It diagnoses venue fit, helping authors frame their work to maximize visibility and impact, without altering the core scientific result. Useful for optimizing manuscript submissions and developing a clear narrative for non-specialist audiences.

This guide provides authors with best practices for structuring scientific papers, specifically focusing on the division of content between the main body (The Letter) and the Supplemental Material (SM). It emphasizes the 'stand-alone' rule, ensuring the core argument remains fully understandable without consulting the SM, while effectively organizing detailed derivations, extended datasets, and secondary controls in the appendix.

This guide outlines the rigorous methodology for developing theoretical arguments in psychological review papers. Instead of presenting raw data, the focus is on deriving specific, falsifiable predictions from a model's core assumptions. Techniques involve confronting these predictions against existing published evidence and rigorously comparing them head-to-head with rival theoretical models, ensuring logical and quantitative soundness.