Skill UI

This guide provides rigorous guidelines for structuring theoretical papers submitted to the International Conference on Database Theory (ICDT). It emphasizes that proofs and formal evidence are the primary contribution, not benchmarks or experiments. It teaches authors how to match their specific theoretical claims (e.g., complexity, expressiveness) to the appropriate academic evidence, ensuring that any proposed experiment is proportional, honest, and merely supplements the core theoretical results.

A comprehensive audit guide for authors preparing submissions for the International Conference on Database Theory (ICDT). This guide covers critical aspects including selecting the correct submission cycle, adhering to the two-step abstract-then-paper deadline, meeting the LIPIcs formatting standards (15 pages excluding references), ensuring complete and verifiable proofs, and maintaining strict anonymity since 2024. Use this before uploading to Microsoft CMT.

A comprehensive self-auditing guide designed for authors preparing manuscripts for submission to top-tier academic journals. It systematically addresses recurring, structural, and critical flaws across theory contribution, econometric identification, mechanism explanation, heterogeneity analysis, and writing conventions. This tool helps authors proactively identify and correct potential weaknesses before facing peer review.

A comprehensive guide detailing the academic standards for ensuring research reproducibility at ICSE. Covers open-science policies, mandatory data availability statements, techniques for pinning provenance (especially for mining and LLMs), and best practices for maintaining verifiability while ensuring anonymity. Essential for academic submissions.

A comprehensive guide for researchers submitting academic papers to ensure high standards of reproducibility and verifiable evidence. It covers critical areas such as data-availability statements, artifact management, provenance pinning (for LLMs and mining), and mapping claims directly to evidence, adhering to rigorous academic standards.

A comprehensive guide modeling the specific, single-round, double-anonymous review pipeline for IEEE ICSME submissions. It details the crucial early-decision cut (Accept, Reject, Response Recommended) and emphasizes that unlike journal submissions, there are no Major Revision rounds. Use this to strategically plan your research submission and response artifact.

A comprehensive guide for positioning visualization and visual analytics manuscripts for the IEEE Pacific Visualization Symposium (PacificVis). This skill helps authors refine their scope, evidence presentation, and overall narrative to match the unique requirements and review expectations of this top-tier conference. It acts as a fit/framing tool for conference submission strategy, ensuring the work is appropriately targeted for the Asia-Pacific community focus.

This guide provides comprehensive strategic advice for authors targeting the IEEE Visualization Conference (IEEE VIS). It covers conference positioning, scope fitting, required evidence (visual encoding, evaluation), and helps authors re-frame their work from journal or arXiv formats into a selective, high-impact conference narrative. Use it when determining if a research manuscript is appropriate for IEEE VIS.

This comprehensive guide provides rigorous econometric strategies for establishing parameter identification, crucial for structural modeling and causal inference. It details how to prove that model parameters are uniquely determined by the observed data, moving beyond mere estimation convergence or external calibration. Essential for advanced empirical research and addressing stringent peer review standards.

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.

This guide provides best practices for creating clear and legible computational exhibits for academic manuscripts, particularly for INFORMS journals. It covers advanced techniques like performance profiling, visualizing scaling behavior on log axes, and presenting stochastic results with full dispersion. The goal is to ensure that the presented figures and tables strongly support the computational claims while maintaining rigorous methodological standards and reproducibility.

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.