Skill UI

This guide provides comprehensive methodological standards for conducting high-quality human-subjects studies in Human-Robot Interaction (HRI). It covers the entire research lifecycle, including choosing optimal designs (within/between-subjects), ensuring statistical power, reporting effect sizes, and adhering to ethical guidelines (e.g., Wizard-of-Oz disclosure). It ensures research meets the rigorous academic standards required for top-tier publications.

Provides comprehensive guidelines for selecting and rigorously justifying research designs across various academic paradigms: qualitative/ethnographic, critical, quantitative, and mixed-methods. This guide ensures the chosen methodology aligns perfectly with the theoretical question, emphasizing rigor, transparency, and advanced methodological considerations for academic manuscripts in Human Relations.

Provides comprehensive guidelines for packaging and documenting complex robotics research artifacts (code, simulations, hardware designs, datasets, etc.) for scholarly review. It outlines best practices for ensuring reproducibility, adopting the "five-minute-skeptic standard," and managing the full lifecycle of artifacts from review submission to public acceptance.

A comprehensive guide for designing and reporting rigorous experimental results in robotics research. It details the 'evidence ladder' (from simulation to real-world deployment), statistical best practices (e.g., sample size, confidence intervals), ensuring baseline fairness, and quantifying sim-to-real transfer gaps. Essential reading for researchers submitting to top-tier robotics conferences.

This guide provides comprehensive standards for designing, auditing, and documenting AI experiments for major conferences like IJCAI. It covers establishing strong baselines, conducting ablation studies, and reporting meticulous details on hyperparameters, computational infrastructure, and ethical risks, ensuring maximum reproducibility and academic credibility for AI research.

This framework provides a comprehensive methodology for designing, auditing, and reporting empirical measurements about large-scale systems like the Internet. It guides users to ensure measurements are scientifically rigorous, by matching claims to evidence, quantifying coverage bias, designing for temporal changes, and adhering to strict ethical guidelines and data provenance standards.

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 detailing the specific conventions required for submitting high-quality, data-driven papers in computer architecture to top-tier conferences like ISCA. It covers structuring the core argument around a single insight, developing measured motivation sections, describing mechanisms with checkable precision, and calibrating quantitative claims with empirical data and instruments. Essential for academic research communication in hardware design.

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.

Comprehensive guidelines for preparing and reporting complex empirical evidence across various methodologies (SEM, econometrics, design science, qualitative). This guide ensures results meet the high academic standards of top-tier journals by emphasizing theoretical traceability, statistical rigor, and required data transparency, including full correlation/covariance matrices.

A comprehensive guide designed to help researchers assess the suitability and structure of clinical neurology studies for submission to JAMA Neurology. It covers scope fit, evidence requirements (e.g., validated endpoints, adequate power), mandated reporting guidelines (CONSORT, STROBE, PRISMA), and specific journal house style and submission checklists. This is a venue-fit aid, not clinical advice.

A comprehensive preflight checklist designed for submitting manuscripts to the Journal of Business & Economic Statistics (JBES). This guide ensures authors have verified all necessary components—including the data/code supplement, conflict disclosures, and cover letter targeting—against the latest academic standards. It functions as a critical final review step before formal submission, emphasizing the need to cross-reference live journal guidelines.