Skill UI

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.

This guide details the necessary workflow for organizing supplementary academic content, such as code, extensive methodologies, and raw data, across various conference submission phases. It emphasizes placing only core arguments in the main body, keeping sensitive materials anonymized in the mirror, and preparing a staging shelf for efficient revision.

A comprehensive guide for authors structuring signal processing manuscripts for top-tier venues like IEEE Transactions on Signal Processing. It details the necessary theoretical depth, the requirement for rigorous analysis (e.g., convergence, bounds), correct baseline selection, and adherence to specific academic formatting. This tool helps authors reframe general machine learning applications into rigorous, theory-driven scientific methods.

A comprehensive guide to designing rigorous, reproducible, and fair experimental protocols for computational science research, particularly for top-tier academic journals. It covers critical elements such as selecting standard benchmark instances, establishing strong baselines, ensuring fair parameter tuning, reporting hardware constraints, and performing robust statistical validation, helping researchers avoid common pitfalls and maximize the scientific validity of their claims.

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 guide outlines best practices for ensuring the scientific reproducibility and artifact availability of network measurement research. It distinguishes between analysis reproducibility (recreating figures from released data) and method reproducibility (re-running the measurement). It mandates thorough documentation of provenance, including vantage points, timing, and tool versions, ensuring research claims are verifiable and robust.

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 heuristic tool and guide for authors targeting JAMA. It assesses manuscript fit, methodological rigor (including adherence to CONSORT, PRISMA, STROBE guidelines), structured abstract formatting, and the latest AMA house style. Use this before submitting a high-impact clinical or scientific manuscript to ensure compliance with top-tier journal standards.

This guide provides a rigorous, step-by-step framework for authors responding to major journal critiques, such as those received after a JAMA revision decision. It emphasizes maintaining scientific integrity by structuring point-by-point replies, accurately citing revisions, and addressing statistical and methodological concerns without fabricating new findings or overstating results.

This comprehensive aid helps researchers determine the suitability of their surgical trials, outcomes studies, or perioperative research for JAMA Surgery. It details required scientific rigor, including comparative, patient-centered outcomes, robust risk adjustment, and advanced statistical methods. It also covers specific reporting standards (CONSORT, STROBE, PRISMA) and the current JAMA Network house style, ensuring manuscripts are submission-ready.

A comprehensive guide for authors on structuring professional and scientifically sound rebuttals to major journal peer reviews (e.g., JEEM). It teaches how to triage demanding feedback, address core methodological critiques (spatial confounding, leakage, valuation bias), and maintain a collegial, evidence-based tone to strengthen the manuscript.