Skill UI

A comprehensive guide for researchers preparing their academic artifacts (code, data, results) for the SIGMOD Availability & Reproducibility Initiative (ARI). This document details the criteria for achieving reproducibility badges (Available, Evaluated, Results Reproduced), focusing on coverage, ease of reproduction, flexibility, and portability. It provides best practices for structuring artifact repositories and ensuring hardware honesty for verifiable research claims.

A comprehensive guide for authors submitting to top-tier database conferences (like SIGMOD) on how to position their research. It covers deep-history diligence, managing concurrent work, and structuring the related-work section using contrast-form writing to establish clear novelty claims against multiple literature lines, including industrial systems and theoretical boundaries.

A comprehensive guide for addressing endogeneity, selection bias, and reverse causality in empirical research, specifically tailored for top-tier journals. It details advanced econometric methods (DID, IV, PSM, Heckman) and outlines best practices for conducting rigorous robustness checks, ensuring that identified effects are genuinely causal, not merely correlated.

This guide provides expert best practices for structuring and refining empirical tables and figures for top-tier academic journals. It teaches authors how to make exhibits self-contained, report complex econometric results (like DID and identification checks), and visualize nuanced mechanisms, ensuring maximum scholarly impact.

This guide details how to construct the critical real-data section of a sociological methods paper. It emphasizes that the illustration must demonstrate that the proposed method substantively changes a conclusion compared to the standard/incumbent approach, thereby proving the method's necessity and theoretical stake.

A comprehensive guide for designing rigorous Monte Carlo simulation studies, particularly for methods-heavy academic submissions (e.g., SMR). This guide covers specifying the Data Generating Process (DGP) space, selecting non-negotiable competitor methods, choosing appropriate performance metrics (e.g., power, bias, coverage), and critically reporting the limitations and failure regimes of the proposed method to ensure maximum credibility and reproducibility.

Provides expert guidelines for creating high-rigor methodological exhibits for the Sociological Methods & Research (SMR) journal. Focuses on constructing self-contained simulation grids, method-comparison tables, and diagnostic figures that clearly communicate methodological arguments, ensuring reviewers can audit claims directly without relying on surrounding prose.

This guide helps researchers determine if their paper constitutes a genuine methodological contribution suitable for SMR. It teaches authors to distinguish between purely applied analyses and theoretical breakthroughs by mastering three modes: Developing new methods, Evaluating existing methods, or Critically assessing assumptions. The core goal is ensuring the method itself is the primary takeaway, not the substantive finding.

This guide helps researchers determine the ideal venue for their cloud computing research paper. It outlines the architectural boundaries between pure systems (OSDI/SOSP), pure data management (SIGMOD/VLDB), and the systems-data intersection (SoCC). It provides crucial tests, such as the model-swap and re-label tests, and advises on necessary evidence maturity (measurement studies, experience papers) to ensure a high chance of acceptance.

A comprehensive guide for executing and reporting complex social psychological analyses required for expert peer review. This skill covers norms for reporting uncertainty, assessing measurement quality, ensuring robustness checks, selecting appropriate statistical inference (e.g., multilevel models, clustering), and linking statistical findings back to the theoretical structure. It emphasizes methodological rigor and reproducibility.

A comprehensive guide for authors submitting to SCI journals, detailing best practices for making research code and data available. It addresses how to proactively provide reproducible materials, structured documentation, and honest availability statements, even when formal 'Artifact Evaluation' badges are not mandatory. It helps authors improve the robustness and impact of their scientific findings.

A comprehensive guide for researchers submitting academic papers, detailing the standards required for scientific reproducibility. It covers fixing experimental environments (toolchains, datasets, hardware), differentiating result sources (simulation vs. physical measurement), statistical reporting best practices, and providing clear guidelines for code and data availability statements.