Skill UI

A comprehensive guide to help researchers determine if their Additive Manufacturing (AM) manuscript meets the high scientific rigor required by leading journals. It focuses on shifting the narrative from 'part printing' to process-structure-property relationships, process physics, and advanced qualification methods. This tool helps authors refine their work for optimal journal targeting and submission readiness.

This comprehensive skill provides in-depth guidance for authors targeting Advanced Materials. It covers journal positioning, scope fit (e.g., soft matter, 2D materials, energy), the required depth of evidence (methodology and characterization), and specific house style rules. Use this to re-frame your manuscript and ensure it meets the high standards for performance claims and scientific rigor expected by top-tier materials journals.

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.

This guide outlines the rigorous methodology for ensuring that computational research results, especially those involving hardware architecture and simulation, are fully reproducible. It emphasizes pinning the entire result chain—from simulator commits and configuration files to workloads, sampling recipes, and statistics scripts—and mandates creating a manifest for every figure. Key practices include recording machine state for silicon runs and containerizing the environment to prevent data drift.

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 methodology for rigorous scientific research in operating systems and complex systems. This guide details how to pin environments (OS, firmware, hardware), automate data capture, and script all figures to ensure that experimental results are auditable and fully reproducible, protecting findings against review challenges.