Skill UI

A comprehensive guide for authors submitting to top-tier electronics journals. It provides detailed methodologies on structuring experimental chapters, ensuring comparison fairness, achieving statistical rigor, and presenting reproducible results to withstand demanding peer review scrutiny.

This guide provides expert principles for designing and auditing system-level experiments in computer architecture (e.g., for ASPLOS-level conferences). It details the proper usage of various instruments—from real silicon and FPGAs to cycle-level simulators and analytical models—ensuring that every claim is matched to a valid, stated error model. It emphasizes workload selection, establishing strong baselines, performing rigorous ablation studies, and honestly reporting measurement noise and limitations to maintain scientific credibility.

This guide outlines the rigorous standards for evaluating systems research, particularly for top-tier conferences like ATC. It provides detailed instructions on designing reproducible experiments, establishing fair baselines, utilizing realistic workloads, and reporting critical metrics such as tail latency, variance, and resource cost, ensuring the highest level of scientific rigor.

Provides rigorous guidelines for conducting robust, verifiable, and reproducible evaluations in networking research. It emphasizes moving beyond simple simulations by demanding the use of real target platforms, documenting full system provenance (traces and configs), establishing honestly tuned baselines, and reporting detailed measurement statistics and uncertainty. Essential for authors aiming for top-tier conference submissions.

This guide provides a comprehensive checklist for evaluating Electronic Design Automation (EDA) research manuscripts for top-tier conferences like DAC. It emphasizes the necessity of using standard, recognized benchmark suites (e.g., ISPD, EPFL, TAU), comparing results against the true State-of-the-Art (SOTA), reporting full PPA metrics and runtime, and implementing ablation studies to ensure fairness, reproducibility, and generalizability, especially for ML-based methods.

This guide provides comprehensive standards for designing, auditing, and reporting empirical evaluations in database systems research. It emphasizes the principle that evidence must proportionally match the claim, requiring the use of real-world workloads, fair and tuned baselines, thorough measurement of variance and state (warm/cold), and fully reproducible evaluation harnesses. It also outlines the higher bar for 'Experiments & Analysis' papers.

This guide assists in structuring the design of Environmental Science & Technology (ES&T) studies, including lab, mesocosm, field sampling, or modeling experiments. It ensures methodological rigor by planning for environmental relevance, necessary controls (e.g., abiotic loss, sorption), proper replication, mass/energy balance, and a comprehensive QA/QC plan from the outset, helping the study withstand expert peer review.

A comprehensive guide for auditing and improving the scientific rigor of hardware architectural evaluations (e.g., HPCA style). It emphasizes establishing a clear 'fidelity contract' defining tools, scope, and configurations. It requires that all performance claims are backed by credible instruments, mandates capturing full machine state provenance, ensuring honest baseline tuning, and reporting results via per-workload distributions rather than single means.

A comprehensive guide for designing and auditing empirical experiments in signal processing, audio, and speech domains. It emphasizes matching standard metrics (e.g., WER, SI-SDR, PSNR) to specific tasks, establishing current strong baselines, and systematically sweeping operating conditions (like SNR or reverberation time) to ensure robust, reliable, and publication-ready results.

This guide provides comprehensive standards for designing, executing, and reporting systems experiments for top-tier academic venues (like ICDE). It emphasizes methodological rigor, including mandatory ablation studies, reporting variance and tail latency (p99), demonstrating scaling trends (curves over points), disclosing cost trade-offs, and ensuring full hardware and software reproducibility to withstand adversarial review.

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.

A comprehensive methodology guide for designing and auditing empirical studies in software engineering, specifically focused on maintenance, evolution, and change impact. It outlines best practices for ensuring evidence is proportional to the claim, managing data provenance (SHAs, criteria), handling statistical confounds (survivorship, effect sizes), and integrating rigorous LLM evaluation protocols.