Skill UI

A comprehensive audit guide for authors preparing submissions for the ECAI conference. It details critical steps including selecting the correct submission regime (standalone vs. joint), managing page budgets (7-page body limit), ensuring double-blind anonymity, and adhering to strict deadlines for abstract and full paper submissions. Essential reading before uploading to the conference portal.

A detailed auditing guide for submitting to major academic conferences, such as ECCV. It covers critical aspects including multi-stage submission deadlines (CET timezone), strict LNCS formatting rules (14 pages single-column), policy adherence (dual-submission, embargo rules), and pre-upload checks to mitigate rejection risks. Essential for researchers managing academic publishing workflows.

This guide provides comprehensive instructions for preparing and submitting supplementary material for computer vision conferences like ECCV. It details how the supplement should support the main paper's claims without adding new results, covering topics such as qualitative result visualization, detailed ablation studies, code archive scrubbing, and ensuring full anonymity across all uploaded assets.

This guide provides comprehensive instructions for creating a robust, reproducible artifact package for database systems research. It details best practices including containerization, pinning workloads, generating a permanent DOI-archived record, and designing the package to be successfully built and demonstrated on a reviewer's clean machine within a tight time budget. The goal is to strengthen scientific rigor and ensure permanent, open-access citation.

This guide provides comprehensive standards for designing, auditing, and reporting empirical evaluations in database systems research. It emphasizes the principle that evidence must proportionally match the claim, requiring the use of real-world workloads, fair and tuned baselines, thorough measurement of variance and state (warm/cold), and fully reproducible evaluation harnesses. It also outlines the higher bar for 'Experiments & Analysis' papers.

This guide outlines best practices for structuring advanced NLP/AI research papers for academic submission. It details the critical differences between core content pages, mandatory limitations sections, discretionary appendices, and supplementary uploads, ensuring that reviewers are properly guided and that the work remains reproducible and verifiable.

A comprehensive guide detailing the empirical writing style required for EMNLP submissions. It emphasizes scoping claims to specific datasets and languages, ensuring examples are load-bearing, structuring the argument phenomenon-first, and accurately calibrating hedging language based on evidence strength.

This tool guides researchers through the final pre-submission checklist for academic journals like EPSL. It verifies critical mechanical elements—such as word count adherence, data availability statements with DOIs, declarations of competing interests, and file format compliance—before uploading to the journal's editorial manager. It ensures the manuscript passes all technical gates.

A comprehensive final checklist for submitting scientific manuscripts to ACS (American Chemical Society) via the Publishing Center. It verifies mandatory elements such as the Table of Contents (TOC) graphic, word count adherence, supporting information, data availability statements, and ethical disclosures, ensuring the submission meets all journal guidelines before final upload.

A comprehensive guide for authors submitting systems papers to the EuroSys conference. It helps authors assess the suitability of their manuscript, frame the narrative, and determine the required evidence depth (including implementation artifacts and realistic workloads). The guide specifically covers European systems research in OS, distributed systems, cloud, and security, while providing comparisons to related top-tier venues like SOSP and OSDI.

This guide provides a comprehensive framework for designing rigorous, falsifiable system evaluations for academic research papers. It details best practices across four critical evidence layers: end-to-end performance, mechanism decomposition, operational cost, and boundary testing. Learn how to ensure workload realism, maintain baseline fairness, structure your experiment matrix, and avoid common pitfalls to meet top-tier systems research standards.

A comprehensive framework for ensuring rigorous scientific reproducibility in systems papers. It details how to establish a full provenance ledger (tracking hardware, software commits, and workloads), manage performance variance through repeated runs and statistical reporting, and implement automated pipelines so that all reported figures can be regenerated from raw data.