Skill UI

A comprehensive guide for researchers on preparing, archiving, and declaring the availability of research artifacts (datasets, code, and platforms) for academic publication. It details best practices for provenance tracking, ensuring reproducibility, and meeting the standards required for community contribution awards.

This guide assists researchers in assembling a comprehensive, reproducible data and code supplement for submissions to statistical journals, adhering to the American Statistical Association (ASA) data-sharing policies. It outlines the required structure (README, code, data), provenance tracking, environment pinning, and necessary steps (like setting random seeds and drafting data availability statements) to ensure that published results can be fully regenerated and verified by reviewers.

A comprehensive guide to constructing rigorous empirical datasets for academic papers in finance and banking. It covers best practices for handling complex data sources (e.g., bank micro-data, central bank reports, macro series), addressing measurement biases, and ensuring full replicability, adhering to the standards of top-tier economic journals. Focus areas include merger tracking, regime transitions, and variable definition consistency.

A comprehensive project management guide for planning submissions to KDD, accounting for its unique structure of two annual submission cycles. It covers milestone tracking, cycle-choice decision matrices, managing the Resubmit loop, and planning evidence lead times from abstract to camera-ready status. Essential for complex, multi-stage data science research.

A comprehensive guide for microarchitecture researchers on achieving rigorous scientific reproducibility. It mandates tracking every critical artifact—including simulator commits, workload traces, power/area models, and RTL toolchains—and compiling them into a single run manifest, ensuring any reported result can be verified by a third party.

This comprehensive guide equips authors with a structured framework for writing the Related Work section for top-tier NLP conferences (e.g., NAACL). It emphasizes tracking methodological, data, and task lineages, ensuring citation precision across Findings, workshops, and preprints, and culminating in a single, falsifiable "delta sentence" to precisely position the novel contribution.

This guide outlines the rigorous standards for achieving full reproducibility in theoretical computer science papers (like those presented at POPL). It details how to ensure that independent readers can re-establish research claims by meticulously tracking mechanized theorems, on-paper proofs, formal semantics, and empirical results. Key practices include maintaining a central correspondence table and ensuring assumption hygiene to prevent logical divergence.

This guide outlines comprehensive best practices for ensuring the reproducibility of Machine Learning and Pattern Recognition research. It covers fixing random seeds, locking hardware/software dependencies (Python, CUDA, etc.), managing data splits, tracking pre-trained model versions, and structuring the required artifact package for external review. It is essential for building trust in published scientific results.

This guide outlines the best practices for preparing system artifacts for academic evaluation, following the ACM Artifact Review and Badging scheme. It details how authors must ensure reproducibility, especially for tail latency and cost results, by providing scaled-down testbeds, comprehensive documentation, and clear provenance tracking to satisfy conference evaluators.

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.

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.

This skill helps users determine which marketing efforts truly drive conversions and revenue. It covers interpreting various attribution models (First-touch, Last-touch, Data-driven) and reconciling conflicting data reported by multiple marketing tools. It also guides users on implementing first-party, self-hosted attribution tracking when they own the site or app.