Skill UI

Provides comprehensive guidelines for conducting rigorous networking evaluations, particularly for top-tier conferences. It mandates that every networking claim—be it optimality, performance improvement, or real-world behavior—must be matched with specific, verifiable evidence. This includes rigorous mathematical proof, seeded simulation results, or real-traffic testbed measurements. The guide emphasizes stating assumptions, using fair baselines, and ensuring statistical reproducibility across all studies.

This comprehensive guide outlines best practices for designing, evaluating, and reporting experimental results in speech processing research (ASR, TTS, Speaker Verification, etc.). It details required metrics (WER, MOS, EER), establishing robust baselines, conducting significance testing (bootstrap, CI), and ensuring comprehensive condition coverage (speakers, noise, languages) to enhance the scientific rigor of academic submissions.

A comprehensive guide for designing and auditing rigorous experiments for sensing systems and information processing claims. It emphasizes the necessity of measuring performance on real hardware (not simulation), accurately instrumenting ground truth, quantifying energy consumption, latency, and physical footprint, and establishing fair, skeptical baselines. This ensures scientific rigor and reproducibility in research submissions.

A comprehensive guide for designing, auditing, and reporting reliable robotic experiments, particularly for major conferences like IROS. It moves beyond simple success rates, detailing how to map every claim to verifiable evidence (trials, simulations, field data). Key areas covered include defining pre-set success criteria, reporting failure taxonomies, ensuring fair baselines, and quantifying the critical sim-to-real gap using advanced small-n statistical methods.

This guide provides rigorous guidelines for structuring and validating experimental results in computer architecture papers. It covers essential aspects like declaring simulation methodology contracts, ensuring result reproducibility through manifest files, selecting representative workloads, and performing detailed attribution and sensitivity analyses to distinguish true architectural gains from modeling artifacts.

This guide provides comprehensive guidelines for designing, conducting, and auditing empirical studies in software engineering. It emphasizes achieving scientific rigor by matching every claim to specific, measured evidence, ensuring fair baselining (equal budget), and applying appropriate advanced statistics (non-parametric tests, effect sizes). Essential reading for researchers submitting papers based on performance evaluations.

A guide for authors submitting to theoretical computer science venues (ITCS). It strictly defines what constitutes valid evidence for a mathematical claim, emphasizing that complete proofs are paramount. The guidelines detail how to use worked examples, separating objects, and appropriately scoped computations, while strongly advising against using empirical benchmarks or performance metrics.

A comprehensive guide for authors responding to journal reviewers' reports (e.g., JACS). It details how to systematically triage comments into data-required, clarification, or disagreement; how to structure a professional, point-by-point response letter; and how to maintain scientific rigor when performing necessary experiments.

This guide provides comprehensive methodological advice for academic submissions in Information Systems (IS), particularly for journals like JAIS. It covers selecting and rigorously defending diverse research designs—including behavioral experiments, causal inference (Economics of IS), design science, qualitative studies, and literature reviews. Use it when you need to validate the suitability of your methodology against your research claims and underlying phenomena.

A comprehensive guide for structuring and validating empirical research designs (Survey/SEM, Secondary Data, Experiments, Meta-analysis) intended for high-impact journals. It addresses critical methodological issues such as construct validity, endogeneity control, robust identification strategies, and ensuring overall rigor across diverse academic genres.

A comprehensive guide for structuring robust, peer-review-proof experiments for journals focusing on Computer Graphics and CAD. It guides users on moving from research questions to actionable evidence, covering geometric metrics (Chamfer, Hausdorff), rendering quality, ablation studies, and mandatory visual evidence presentation to ensure scientific rigor.

This comprehensive guide assists users in analyzing and reporting data for high-impact academic journals, specifically following the rigorous standards of the Journal of Consumer Research (JCR). It covers advanced statistical process evidence (mediation, moderation, effect sizes) for behavioral experiments, as well as methods for establishing trustworthiness in interpretive (CCT) research. It emphasizes transparency, replication code, and adherence to modern research reporting mandates.