Skill UI

This guide establishes rigorous methodological standards for incorporating forecasting and prediction claims in academic manuscripts. It emphasizes the necessity of genuine out-of-sample evaluation, comparison against robust benchmarks (like Random Walk or AR models), and utilizing advanced statistical tests (e.g., Diebold-Mariano) to prove prediction superiority, moving beyond simple in-sample fit reporting.

This guide provides a rigorous framework for evaluating computer architecture designs. It covers critical steps including selecting the appropriate simulation instrument (from analytical models to silicon measurements), constructing robust baselines, performing ablation studies, and reporting comprehensive metrics like geomeans and full overhead accounting to ensure scientific validity.

This guide provides comprehensive best practices for packaging and documenting NLP research artifacts (datasets, models, prompts, etc.) for academic submission (e.g., NAACL/ARR). It emphasizes rigorous provenance tracing, ensuring reproducibility, and ethically handling sensitive data, particularly community-owned or indigenous language resources.

This guide outlines the stringent standards for ensuring the reproducibility of Natural Language Processing (NLP) research. It teaches authors how to treat the Responsible NLP checklist as a binding contract by meticulously 'pinning' every variable—including model identifiers, API access dates, prompt strings, random seeds, and evaluation metrics. It details creating a comprehensive reproducibility manifest to make research results verifiable and defensible.

This guide assists researchers in determining whether a manuscript's topic, methodology, and theoretical contribution are appropriate for a specific theory-building management journal. It provides detailed criteria for evaluating conceptual relationships, suitable methods (SEM, experiment, case study), and appropriate re-routing suggestions for papers that are misaligned with the target journal's scope (e.g., pure math models, macro policy evaluation).

A comprehensive guide detailing the high standards required for presenting security evidence at major conferences. It covers methodologies for proving claims—including attacks, defenses, and detection systems—across live internet measurements, controlled testbeds, and emulation. Key requirements include adaptive adversary modeling, rigorous measurement hygiene, and adherence to ethical review protocols to ensure results are reproducible and relevant to real-world networks.

Provides comprehensive guidelines for packaging and presenting research artifacts (code, data, models, benchmarks) submitted to NeurIPS. It details requirements for anonymous review packages, public release, and ensures scientific claims are reproducible by providing necessary metadata, licenses, and execution details.

This guide provides a detailed model of the PerCom conference review process, covering double-blind review, the three-TPC-member evaluation model, the early-rejection gate, and the bounded rebuttal stage. It clarifies how authors should structure their submissions to succeed in this single-round, non-revisionary academic environment, differentiating it sharply from journal cycles.

A crucial guide for authors preparing for PODC (Proofs venue). It corrects the common misconception that runnable artifacts or badges are required. Instead, it redirects focus to the three core areas of evaluation: ensuring proof completeness, establishing model rigor by declaring all assumptions, and maintaining transparency for optional simulations.

A comprehensive guide for authors submitting to PR&AI, detailing how to truthfully declare and provide verifiable artifacts (code, data, models) for peer review. It emphasizes honesty, consistency, and structuring a reproducible repository to enhance reviewer trust.

A comprehensive guide on preparing and structuring evidence packages for recommender system research, especially for academic submissions. It details how to ensure that all ranking claims, off-policy estimates, and evaluation metrics are end-to-end reproducible by providing artifacts like datasets, split scripts, model checkpoints, and propensity logs in an anonymous, verifiable repository.

This guide assists researchers in evaluating the fit of their manuscripts for the journal Scientific Decision Making. It covers journal scope, required methodologies (e.g., system modeling, policy evaluation), structural improvements, common formatting pitfalls (footnotes, Arabic numerals), and cross-journal selection criteria to maximize acceptance chances.