Skill UI

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.

This guide helps researchers determine if their project qualifies as a visualization contribution (beyond general HCI or ML). It provides structured criteria for choosing the best conference venue (e.g., IEEE VIS, EuroVis, CHI) and mapping the work to one of the six core visualization areas based on contribution type and evidence maturity.

A comprehensive guide for revising visualization research papers for top-tier conferences and journals (e.g., IEEE VIS/TVCG). It mandates shifting focus from aesthetic design to rigorous, task-grounded contributions. Users learn how to justify every visual encoding and interaction based on the analytical task, ensure evaluation methods match the contribution type, and structure the narrative around the data-task problem.

This guide outlines the methodology for preparing research artifacts for rigorous academic reproducibility evaluations, such as those run by VLDB/SIGMOD. It details the required components—including prototype code, input data, workloads, and analysis scripts—to structure a comprehensive package. The goal is to ensure that any external evaluator can independently and flawlessly rebuild and validate all claimed results, maximizing the chances of winning reproducibility awards.

This comprehensive guide outlines the rigorous, multi-step process required for authors to finalize and submit their accepted papers for VLDB proceedings. It covers essential technical compliance points, including using the correct volume template, ensuring PDF/A compliance with embedded fonts, adhering to figure legibility rules, and detailing the permanent storage of research artifacts (DOI-backed). It also outlines the associated conference presentation obligations.

This comprehensive guide teaches researchers how to conduct an exhaustive "reinvention audit" for academic papers, specifically in the database field. It covers searching deep historical literature (pre-2000), modern conference venues (SIGMOD, ICDE), and industrial open-source systems (PostgreSQL, RocksDB) to ensure true novelty and properly position the contribution delta.

This comprehensive guide outlines the rigorous standards required for achieving experimental reproducibility in systems research papers (e.g., VLDB). It instructs authors on disclosing critical context, including hardware specifications, software configurations, data provenance, workload patterns, and handling measurement variance. Adhering to these guidelines ensures that all performance claims are fully traceable from raw data to published figures, meeting the highest standards of academic review.

A comprehensive checklist and guide for authors submitting research papers to PVLDB (Proceedings of the VLDB Endowment). This resource details critical steps, including adherence to multiple deadlines (abstract, full paper), category budget selection, single-blind formatting rules, and author co-contribution caps, ensuring submissions avoid desk rejection.

This comprehensive guide helps researchers determine if their project qualifies as a core data-management primitive, which specific PVLDB category (Regular, EA&B, Scalable Data Science, Vision) it belongs to, and whether a major data conference like VLDB or SIGMOD is the optimal submission venue. It provides a rigorous primitive test and detailed routing advice for top-tier data systems research.

This comprehensive guide manages the entire project lifecycle for academic conference submissions, such as VLDB. It provides detailed methodologies for initial volume planning, computing optimal submission timelines (month-picking), coordinating group author contributions, and managing the revision bandwidth. It is essential for maintaining rigorous scheduling and mitigating the risk of missed deadlines in fast-paced research environments.

This comprehensive guide provides researchers with structured methodologies for designing and auditing Computer Vision (CV) experiments for major conferences like WACV. It details the critical differences between the Applications and Algorithms tracks, emphasizing the need to report performance under real deployment constraints (e.g., power, latency) and ensuring fair, rigorous comparisons with baselines. Ideal for revising submissions to meet high review standards.

A comprehensive guide for structuring the 'Related Work' section in academic papers, especially in Computer Vision (CV). It teaches authors how to strategically position their research contribution by covering multiple literature shelves: vision methods, domain constraints, concurrent arXiv work, and prior art. This ensures the paper clearly defines a unique gap rather than merely listing citations, adhering to double-blind review standards.