Skill UI

A comprehensive guide for evaluating academic topics to determine if they constitute a 'review-worthy' theoretical essay for high-impact journals. It tests a topic's scope, intellectual contestation, current relevance (liveness), and disciplinary stakes, ensuring the work transcends mere summary or empirical reporting.

This skill helps physicists determine if their significant, cross-subfield research results are a good fit for Physical Review X (PRX). It guides authors on PRX's unique requirements, including its open-access mandate, cross-subfield importance bar, and expectations for long-form, comprehensive reporting, helping authors decide between PRX and other journal venues like PRL or subfield PRs.

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.

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 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 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 framework guides the critical appraisal of scientific literature syntheses. It helps users assess the credibility of individual studies—checking for power, replication status, and publication bias—to ensure the overall synthesis accurately reflects the best evidence. It emphasizes weighing conflicting results by construct rather than simple vote-counting, ensuring methodological balance and comprehensiveness in reporting.

A comprehensive guide detailing the open science and reproducibility standards required for academic publications, especially those utilizing original data or conducting meta-science. It outlines critical steps such as data repository deposit, protocol preregistration, and reporting inter-rater reliability to ensure research methods are fully auditable.

This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.