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.

A comprehensive pre-writing guide for researchers determining the optimal venue and track for a data science contribution. It outlines a two-stage decision process: first, assessing if the work aligns with the KDD focus on data regimes (scale, drift, heterogeneity); and second, selecting the correct track (Research, ADS, Datasets & Benchmarks, etc.) based on the strongest, most honest evidence available—especially emphasizing real-world deployment outcomes.

This comprehensive guide instructs researchers on how to design rigorous and credible evaluations for Machine Learning systems. It covers critical areas such as workload selection, baseline comparison discipline, reporting four key coordinates (throughput, latency tails, memory, cost), structuring ablations, and generating scaling evidence. It ensures measurements meet academic benchmarking standards.

Provides comprehensive guidelines for achieving high reproducibility in Machine Learning Systems (MLSys) performance claims. It details how to rigorously control sources of non-determinism (e.g., thermal noise, random seeds) and pin the entire technical stack—from hardware and drivers to frameworks and serving policies—to ensure results can be validated by third parties.

A comprehensive guide for authors submitting biomedical engineering research. It assesses the manuscript's fit, biomedical significance, and translational relevance against Nature's rigorous standards. Covers scope, validation requirements (benchtop vs. clinical), structural guidelines, and official submission checklists for devices, biomaterials, and engineered systems.

This comprehensive skill guides authors on whether their enabling technology, platform, or method is suitable for Nature Biotechnology. It analyses the journal's specific focus on translational potential, broad applied impact, and industrial relevance. It covers best practices for advanced topics including genome editing, synthetic biology, biomanufacturing, and AI applications, emphasizing the requirement for robust in vivo proof and comprehensive benchmarking.

A comprehensive guide for authors submitting new methods, tools, or technologies to Nature Methods. It details the journal's specific requirements, including the mandatory criteria for generalizability, rigorous benchmarking against state-of-the-art alternatives, and the required structure and house style for achieving publication success in life sciences.

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 comprehensive guide outlines best practices for designing rigorous and convincing technical evaluations for networked systems. It structures the process by requiring experiments to answer specific questions, advising authors to climb the evidence ladder from synthetic benchmarks to real-world deployment. Key concepts include reporting tail latencies (p99), establishing strong baselines, and maintaining meticulous provenance records to ensure reproducibility.

This guide provides a framework for designing and auditing the experimental rigor of academic papers, particularly in systems and programming languages. It emphasizes the crucial link between a claim type (e.g., 'Faster') and the required evidence type (e.g., benchmarks, user studies, formal proofs). Learn how to establish robust baselines, define clear workload rules, size experiments for conference cycles, and ensure statistical compliance for maximum impact.

This guide helps researchers evaluate whether their high-impact optics or photonics manuscript meets the stringent criteria of Optica. It covers ideal scope (e.g., nanophotonics, quantum optics), required experimental rigor (quantitative measurements, state-of-the-art benchmarking), article structure (concise, load-bearing figures), and official submission guidelines, ensuring the work is framed for broad scientific interest.

A comprehensive guide for designing and reporting rigorous computational studies in Operations Research (OR) manuscripts. It covers best practices for selecting benchmark instances and strong baselines, applying statistical methods to stochastic output (e.g., confidence intervals), and adhering to mandatory code and data deposit standards (ORJournal workflow) to ensure full reproducibility.