Skill UI

Provides comprehensive guidelines for packaging and documenting complex robotics research artifacts (code, simulations, hardware designs, datasets, etc.) for scholarly review. It outlines best practices for ensuring reproducibility, adopting the "five-minute-skeptic standard," and managing the full lifecycle of artifacts from review submission to public acceptance.

This comprehensive guide provides best practices for designing, running, and reporting computational experiments for academic journals. It teaches authors how to make performance claims scientifically defensible by addressing potential referee criticisms (e.g., tuning artifacts, seed luck). It covers advanced statistical techniques (performance profiles, nonparametric tests) and details the full structure required for a complete, verifiable GitHub data deposit.

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 package ensures full reproducibility for international macroeconomics research. It is designed for projects utilizing complex, multi-source, and often restricted data (such as IMF or central bank files). It mandates capturing the entire data lineage, pinning the execution environment (R, Python, Stata), and documenting access instructions, allowing any authorized replicator to rebuild the analysis from scratch.

A comprehensive toolkit designed for international macroeconomics research, ensuring that headline estimates are stable and reliable. It systematically tests the robustness of findings against common methodological threats, such as leaving out individual countries, changing sample periods, controlling for global common shocks, or varying estimation specifications. Essential for submitting high-impact academic papers.

A comprehensive framework for strengthening International Relations (IR) manuscripts, particularly for top-tier journals. It guides researchers through rigorous design requirements, including causal identification in dyadic/network data, structured case selection and process tracing, experimental planning (surveys/conjoint), and formal-empirical linkages. The focus is on ruling out rival explanations and establishing robust empirical claims.

A guide for designing and structuring empirical tables and figures for submissions to International Organizations (IOs). Focuses on meeting strict word limits, ensuring exhibits are self-contained, reproducible, and clearly separating core arguments (main text) from supporting details (supplementary material). Best practices include explicit unit labeling, prioritizing effect plots over dense tables, and guaranteeing that all displayed numbers match the original analysis code.

This guide helps researchers determine the primary focus of their work—whether it lies in Information Processing (algorithms, estimation) or Sensor Platforms, Tools, and Design Methods (hardware, deployment). It provides a structured methodology for selecting the most appropriate academic venue (e.g., SenSys, MobiSys, INFOCOM) based on the project's technical contribution and maturity.

A comprehensive methodological guide for researchers writing Information Systems Research (ISR) papers. It details how to establish rigorous evidence across empirical (DiD, IV, RDD), analytical (proofs, comparative statics), and design-science claims. Learn to structure your analysis, handle common-method bias, and ensure full reproducibility using modern econometric techniques.

This guide ensures that the chosen research methodology genres (e.g., behavioral empirical, analytical modeling, design science) are correctly aligned with the core research claim or phenomenon. It helps researchers identify the necessary design rigor, appropriate identification strategies (like DiD or IV), and the level of analysis required to ensure the study can genuinely support its contribution in the IS domain.

A comprehensive methodological guide detailing how to design rigorous, publishable empirical or analytical studies for top-tier accounting and economics journals (like JAE). It focuses heavily on achieving causal identification by mastering techniques such as Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RDD), and event studies, ensuring the study design credibly supports the economic prediction.