Skill UI

This comprehensive guide outlines best practices for designing data systems evaluations, ensuring results are robust, reproducible, and convincing to expert reviewers. It covers workload realism, baseline fairness, necessary ablation studies, and advanced metric visualization techniques (e.g., sweeping curves, failure analysis) required for top-tier data systems research.

A comprehensive guide for authors in data systems and related fields on achieving rigorous, verifiable reproducibility for academic papers. It mandates detailing the provenance of every claimed result by pinning five critical layers: code version, configuration parameters, dataset source, workload characteristics, and hardware specifications. Best practices include reporting distributions (e.g., percentiles, variance) and maintaining a detailed 'repro debt ledger' to withstand intense peer review.

A comprehensive guide for writing the evaluation section of a systems paper for top-tier conferences. It teaches authors how to map every technical claim to verifiable experiments, establish fair state-of-the-art baselines, utilize mixed workloads (microbenchmarks, E2E applications, and traces), and honestly report critical metrics like performance tails, variance, and overheads. The goal is to maximize credibility and withstand rigorous peer review.

A comprehensive audit guide for preparing academic papers for the SOSP conference. This checklist details critical compliance points, including strict deadlines (abstract registration and full paper upload), strict formatting rules (12-page limit, two-column layout), robust anonymization techniques, and conflict declaration best practices. Use this before uploading any material to ensure high quality and avoid desk rejection.

This skill polishes the prose and style of academic accounting manuscripts for submission to top-tier journals like The Accounting Review (TAR). It enforces strict house conventions, including adherence to the Chicago Manual of Style, structuring the abstract (max 150 words), ensuring findings are front-loaded, and maintaining rigorous author anonymization throughout the text for double-blind review.

This comprehensive pre-upload audit guide ensures submissions meet the strict technical and anonymity requirements for UAI OpenReview. It covers critical aspects like the 8-page main body constraint, combining all content into a single PDF, handling file size caps (15MB/50MB), and performing an identity sweep to maintain double-blind review standards.

A comprehensive audit guide for the final pre-upload submission of UIST (User Interface Software and Technology) conference papers. This document details critical steps, including adhering to strict formatting rules (e.g., two-column layout, page limits), managing the abstract and paper deadlines, and executing thorough anonymity sweeps to prevent desk rejection. Essential reading for authors preparing for major academic conferences.

A specialized guide for assessing whether a storage systems or file system manuscript fits the USENIX FAST conference. It covers venue positioning, required evidence (realistic workloads, fault models), appropriate framing, and comparison methodologies necessary for top-tier systems conference submissions.

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 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.

This guide provides a structured framework for conducting and presenting robust checks in econometric manuscripts, particularly relevant for studies using developing-country data. It moves beyond simple appendix dumping by organizing tests around potential identification threats and specific data-quality risks (e.g., measurement error, coverage gaps). It helps ensure the core findings are load-bearing against sophisticated academic scrutiny.

This guide helps researchers move beyond decorative theory to construct 'load-bearing' conceptual or analytical frameworks for World Development (WD) manuscripts. It outlines how to transform vague ideas into structured, testable mechanisms by stating clear mechanisms, deriving discriminating predictions, mapping predictions to evidence, and defining scope conditions, ensuring the theory actively drives the empirical argument.