Skill UI

This guide details the USENIX FAST artifact evaluation process, providing systematic advice for researchers submitting system papers. It explains the requirements for earning the three USENIX badges (Available, Functional, Reproduced). Key topics include managing hardware dependencies in storage systems, packaging best practices (e.g., Docker containers, raw logs, comprehensive READMEs), and ensuring the final artifact is maximally reproducible for external evaluation.

This guide details best practices for submitting research artifacts to academic conferences, following the ACM Artifact Review and Badging scheme. It covers everything from required packaging plans (Docker, README, data provenance) to achieving various levels of validation (Functional, Reusable, Reproduced). Learn how to structure your submission to ensure maximum reproducibility and pass rigorous evaluator checks on a clean machine.

This guide details the comprehensive process for preparing security research artifacts for post-acceptance evaluation at major conferences like IEEE S&P. It covers the three types of evaluation badges (Available, Functional, Results Reproduced), strict handling constraints for exploits and malware, and best practices for packaging the submission (e.g., Dockerfiles, README structure) to ensure external evaluators can reliably reproduce findings within a limited time frame.

This guide outlines the rigorous requirements for building a comprehensive replication package and proof appendix for manuscripts submitted to journals following the International Economic Review (IER) style. It details what must be included in the data deposit (datasets, programs, README) and how to structure a self-contained proof appendix, ensuring the work meets the highest standards of reproducibility and scientific rigor required for publication.

This guide outlines the mandatory data and code deposit policy for articles published in the Journal of Applied Econometrics (JAE). Authors must submit a complete, non-confidential set of input data (plain ASCII/CSV), programs that replicate every result, and a detailed README file. It emphasizes strict adherence to archival format rules, ensuring the research is fully reproducible for the academic community.

This guide assists researchers in assembling a comprehensive, reproducible data and code supplement for submissions to statistical journals, adhering to the American Statistical Association (ASA) data-sharing policies. It outlines the required structure (README, code, data), provenance tracking, environment pinning, and necessary steps (like setting random seeds and drafting data availability statements) to ensure that published results can be fully regenerated and verified by reviewers.

This guide provides best practices for preparing code, data, and supporting artifacts for academic submissions, focusing on achieving reproducibility for peer review (especially in double-blind settings). It covers crucial steps including artifact anonymization, establishing clear data availability statements, structuring the repository (README, env, data, src), ensuring licensing compliance, and planning for permanent post-acceptance archiving.

A comprehensive guide for assembling the required data and code replication package for manuscripts submitted to the Journal of the European Economic Association (JEEA). This package ensures compliance with the DCAS standard, enabling the journal's Data Editor to verify the reproducibility of empirical, simulation, or experimental results before formal acceptance. It outlines the mandatory structure, README contents, and execution steps.

A comprehensive guide and package structure for preparing empirical law and economics manuscripts for journal submission, ensuring full data and code reproducibility prior to publication. It mandates the assembly of data files, transformation scripts, a master execution script, and detailed README documentation, explicitly addressing the unique challenges of restricted or proprietary legal data sources (e.g., sealed court records).

This skill guides authors on meeting stringent academic requirements for manuscript submission, specifically addressing the mandatory replicability standard set by The Journal of Politics (JOP). Acceptance is strictly contingent on providing a complete, documented, and executable package. The required materials include the dataset(s), master code, codebook, and a comprehensive readme file, ensuring that any published finding can be independently verified and reproduced by a journal analyst. It also provides guidelines for handling restricted data.

This guide helps authors prepare manuscripts for high-impact journals, specifically detailing the mandatory requirements for data and code transparency (e.g., JPART). It outlines how to build a complete, reproducible package, including master scripts, documentation (README), data availability statements, and managing potential data restrictions, ensuring publication acceptance is not stalled by materials.

Provides comprehensive guidelines for creating a fully reproducible replication package for submissions to the Journal of Political Economy (JPE). It details the required structure, components (README, master script, data), and best practices needed to pass the JPE Data Editor's reproducibility check, ensuring research claims are verifiable and transparent.