Skill UI

This skill provides comprehensive guidance for authors considering submitting a large-scale, monograph-length review to Physics Reports. It covers journal positioning, required scope across all physics subfields, the rigorous evidence bar, house style, and submission checklist. Use this tool to help structure and refine an article intended to become a definitive, self-contained academic reference.

This guide addresses the stringent requirements for empirical evaluation in compiler and runtime systems, aligning with SIGPLAN's guidelines. It covers critical aspects such as proper warmup discipline, reporting variance and confidence intervals, selecting principled benchmarks, pinning toolchains, and ensuring cross-platform validity. It provides a protocol for generating verifiable measurement logs for academic publications.

This skill guides authors on assessing the suitability of their general biology manuscript for PLOS Biology. It covers the journal's high standards for scientific rigor, data availability, mandatory reporting guidelines (e.g., ARRIVE, CONSORT), and the structure of a plain-language Author Summary. Use this tool for venue selection and optimizing your submission to maximize publication chances.

This guide details the rigorous process of revising an academic essay after receiving multiple substantive reader reports and an Editorial Board decision, specifically for PMLA submissions. It outlines strategies for addressing every reader comment, reconciling conflicts openly, protecting the core argument, and maintaining academic anonymity throughout the revision process.

A comprehensive guide based on high-impact journal requirements (like PNAS), detailing how to report quantitative scientific claims rigorously. It emphasizes moving beyond simple P values to include effect sizes, confidence intervals, sample size justification, distinction between biological and technical replicates, and ensuring full code and data reproducibility.

This guide teaches how to write a mandatory Significance Statement for scientific reports (like PNAS Nexus). Unlike an abstract, this statement must explain, in plain language, why the research matters to science and society—the 'so what?'. It adheres to strict length guidelines (50-120 words) and is written for a broad, non-specialist audience.

This guide provides comprehensive standards for achieving statistical rigor in scientific research reporting. It mandates that every quantitative claim must include effect sizes, uncertainty, sample size (n), and justified statistical tests. Key areas covered include distinguishing biological vs. technical replication, conducting power analyses, implementing multiple comparison corrections, and utilizing Registered Reports to prevent publication bias, ensuring maximum transparency and reliability in scientific findings.

A comprehensive guide for authors structuring and formatting scientific manuscripts for PNAS Nexus. It details selecting the appropriate article type (e.g., Research Report, Review), managing page length budgets, ensuring the Materials and Methods section remains in the main text, and organizing results logically by argument, not chronology. Essential for successful scientific publishing.

A comprehensive guide for structuring the analysis and results section of academic papers in Production and Operations Management (POM). It covers rigorous theoretical proofs, advanced empirical identification methods (e.g., DiD, IV), and data science validation techniques. The core focus is bridging statistical significance with actionable, operational managerial magnitude, ensuring research findings are decision-relevant.

This skill guides the execution and reporting standards for advanced demographic analysis, particularly for Population and Development Reviews (PDR). It ensures that demographic rates, life tables, and projections are not only statistically rigorous (handling uncertainty, denominators, and complex inference) but also clearly interpreted in the context of development, policy, and socio-economic meaning. It focuses on reproducibility and methodological transparency.

Provides comprehensive guidelines for conducting highly rigorous, reproducible statistical analysis for complex survey data (e.g., weights, strata, clusters). This guide ensures that analyses meet the standards of top academic journals, emphasizing design-based inference, honest uncertainty reporting (CIs, MOE), and thorough robustness checks. Use this when you need to build a master script that regenerates every published table and figure.

This skill guides the creation of rigorous, self-contained tables and figures (exhibits) for academic journals like Public Opinion Quarterly (POQ). It ensures that all visual data accurately report design-based uncertainty, weighting adjustments, and the base N, meeting the high standards required for empirical survey research.