Skill UI

A comprehensive guide to assessing whether a mental health or psychiatry study is suitable for publication in The Lancet Psychiatry. It details the journal's high standards, focusing on large-scale population/epidemiological analyses, policy relevance, and rigorous methodology (e.g., RCTs, cohort studies). The skill helps authors re-frame their research question to meet global health and system-level impact requirements, covering reporting guidelines (CONSORT/STROBE/PRISMA) and submission structure.

This guide assists researchers in determining the suitability of their study for The Lancet Public Health. It outlines the scope, emphasizing population-level interventions, global epidemiology, and health policy. It details stringent requirements for methodology, reporting guidelines (STROBE, CONSORT, PRISMA), addressing biases, and adhering to specialized journal house styles and submission protocols.

This skill provides comprehensive guidance for submitting high-impact clinical and population research to The Lancet Respiratory Medicine. It details the journal's specialized scope, which favors practice-changing randomized trials and major prospective cohorts in respiratory and critical care. The guide highlights the rigorous evidence bar, including expected reporting guidelines (CONSORT, STROBE, PRISMA), statistical rigor, and structural house style, serving as a crucial fit and framing aid for authors.

A comprehensive guide for structuring and positioning computer science manuscripts for The Web Conference (WWW). It advises authors on leveraging open-web data, focusing on web systems, web mining, and responsible AI, while adhering to the latest venue-specific standards and evidence requirements.

This guide helps researchers determine the most appropriate conference (UAI, AISTATS, NeurIPS, ICML, etc.) for their work, particularly when the core contribution revolves around uncertainty representation, probabilistic reasoning, or causal inference. It provides detailed filters and comparison points to refine the paper's scope and target audience.

A comprehensive guide for authors submitting to venues focused on probabilistic AI, including UAI. It emphasizes achieving maximum formal rigor by enforcing strict notational discipline, clearly separating conditioning from intervention, explicitly stating assumptions, structuring the main body for maximum impact, and calibrating claims to match empirical evidence. Essential for papers involving graphical models, causality, and Bayesian methods.

This skill enables rigorous tracking of physical units and the systematic propagation of measurement uncertainty in scientific calculations. It supports unit conversions, building comprehensive GUM uncertainty budgets, and performing dimensional and physical plausibility checks. Use it whenever a calculation requires adherence to strict metrological standards and accurate error reporting.

A comprehensive audit checklist for designing and evaluating security experiments, adhering to the highest academic standards (e.g., USENIX Security). It guides users through verifying adaptive attacker models, calculating precision at realistic base rates, ensuring statistical honesty for stochastic results, and maintaining ethical compliance when working with live systems.

This guide covers the mandatory Open Science appendix for high-level security research. It instructs authors on enumerating all artifacts (code, datasets, configurations), documenting data withholding decisions, and implementing engineering controls (like environment pinning and version tracking) to ensure that published results are independently and reliably reproducible.

A comprehensive guide detailing the standards for scientific reproducibility in visualization research, particularly for IEEE VIS submissions. It outlines the Graphics Replicability Stamp Initiative (GRSI) requirements, covering how independent volunteers must reproduce results using provided code, data, and environments (e.g., Docker containers). Learn how to package your work—including determinism aids, mapping tables, and licensed data—to pass rigorous, clean-machine testing.

A comprehensive guide for designing and auditing visualization experiments for high-stakes academic submissions (e.g., IEEE VIS). It details how to match evidence—such as controlled studies, power analyses, and benchmarks—to the specific type of claim (perceptual, algorithmic, system). Learn best practices for statistical rigor, CVD-safe encoding, and maintaining full provenance to ensure your findings are scientifically sound and trustworthy.

A comprehensive guide for authors submitting visualization papers to major conferences like IEEE VIS. It outlines the strict rules for partitioning content, ensuring the core argument remains judgeable from the main body while leveraging supplemental materials (video, code, full data) to demonstrate interaction and reproducibility. Essential reading for academic researchers in the field.