Skill UI

A comprehensive guide for researchers submitting academic papers, detailing the standards required for scientific reproducibility. It covers fixing experimental environments (toolchains, datasets, hardware), differentiating result sources (simulation vs. physical measurement), statistical reporting best practices, and providing clear guidelines for code and data availability statements.

This guide outlines the essential principles for structuring scientific manuscripts submitted to academic journals. It details how to correctly allocate core evidence, lengthy proofs, additional experiments, and multimedia content across the main text, appendices, and supplementary materials. The core principle emphasized is that all evidence crucial for judging the paper's scientific contribution must reside within the main body text.

A comprehensive, end-to-end guide for researchers aiming to publish high-impact manuscripts in top-tier journals like SCI. It meticulously maps the entire journey, from initial topic brainstorming and framework building to rigorous experimentation, submission auditing, revision, and final publication preparation. This skill minimizes common publication pitfalls and guides authors through the entire academic life cycle.

This guide clarifies the role of computation in theoretical computer science submissions, particularly for venues that prioritize theorems over empirical data. It distinguishes between legitimate forms of evidence—such as certified proof steps, constructive object discoveries, and illustrative examples—and inappropriate performance benchmarking. It helps researchers structure their work to align with theorem-based research, ensuring computational elements enhance the core theoretical contribution.

This comprehensive guide outlines best practices for designing and auditing empirical evaluations of formal verification tools and algorithms. It stresses the use of shared, community-accepted benchmarks, ensuring fair comparison against the strongest baselines with equal resources, meticulous reporting of all metrics, validating results for soundness, and guaranteeing full reproducibility on clean virtual environments.

A comprehensive methodological guide for designing rigorous academic studies in accounting. It details advanced econometric techniques—including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and experimental setups—to establish credible causal identification. Use this guide when your research challenge is solving endogeneity or isolating a specific economic channel from complex, non-random variables.

This guide instructs researchers on how to build robust, theoretically grounded economic mechanisms and derive testable predictions for accounting and finance research. It covers the methodologies required for archival, analytical, and experimental papers, ensuring claims move beyond mere correlation to establish clear causal economic channels.

A comprehensive guide designed to help authors position, frame, and refine academic manuscripts for submission to top-tier accounting journals like The Accounting Review (TAR). It assesses method rigor, theoretical contribution, scope fit, and common desk-reject triggers across archival, experimental, and analytical designs.

This comprehensive guide helps authors determine if their microbial ecology manuscript is suitable for The ISME Journal. It details the journal's focus on ecological and evolutionary mechanisms, outlining expectations for multi-omics integration, rigorous experimental design, and high-level evidence. Use this skill for venue selection, manuscript re-framing, and understanding the required standards for publication.

This tool helps authors determine if their food science work is best suited for a critical review or trend analysis within the scope of this specific journal. It guides authors to focus on synthesizing existing literature, identifying research gaps, and charting future directions, rather than presenting original primary experimental data.

A comprehensive guide for designing, auditing, and reporting advanced statistical and causal inference experiments. It emphasizes moving beyond simple accuracy metrics to quantify uncertainty, model robustness, and causal mechanisms. Covers metrics like coverage, KL divergence, and regret, and outlines best practices for seeding, compute fairness, and creating standardized reporting blocks (e.g., UAI standards).

Provides comprehensive guidelines for designing rigorous technical evaluations for interactive systems, specifically tailored for top-tier venues like UIST. It emphasizes matching evaluation methods to specific artifact claims, reporting distributions over means, closing the interaction loop, and performing thorough ablations against real-world baselines. This framework ensures the scientific rigor and credibility of submitted research.