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Provides comprehensive guidelines for authors submitting papers to Acta Automatica Sinica (AAS). It details how to organize and present anonymous, reproducible code, data, and experimental artifacts to enhance the credibility and rigor of research in control, automation, and pattern recognition, supporting the double-blind review process.

A comprehensive guide for researchers in electronics and signal processing preparing submissions to Acta Electronica Sinica (AES). It outlines best practices for proactively structuring, documenting, and disclosing research artifacts—including code, datasets, and hardware designs—to maximize the paper's credibility and chances of acceptance, even without mandatory journal badges. Covers anonymization, reproducibility standards, and proper disclosure in the manuscript.

This guide details the process of packaging and evaluating academic artifacts for ACM CCS conferences. It outlines the criteria for earning specific ACM badges (e.g., Functional, Reusable, Results Reproduced) by ensuring that supporting evidence—such as exploits, defenses, and measurement pipelines—is turnkey, reproducible, and rigorously documented for the review committee. It covers best practices for artifact planning, handling sensitive material, and justifying any withheld components.

This guide outlines the process for converting accepted academic papers into optional ACM reproducibility badges. It walks authors through the rigorous evaluation cycle, covering everything from initial opt-in before submission to providing detailed one-page artifact descriptions. The goal is to prove that the research results are available, functionally sound, highly reusable, and accurately reproducible by independent evaluators.

Provides guidelines for creating robust, verifiable, and reusable Electronic Design Automation (EDA) artifacts for submission to academic conferences like DAC. The focus shifts from chasing conference badges to ensuring community trust and scientific reproducibility. Key elements include pinning the toolchain, using standard benchmarks, providing clear setup instructions, and archiving the release in a DOI-issuing repository.

A comprehensive guide for researchers preparing academic artifacts for voluntary, post-acceptance evaluation (e.g., HPCA). It outlines best practices for structuring submissions for cold-start evaluators, defining reproducibility tiers (full reproduction, regeneration from logs, inspection), and managing computational budgets to successfully earn IEEE reproducibility badges.

This comprehensive guide provides best practices for packaging supplemental materials and artifacts for top-tier academic conferences like ICDE. It instructs authors on how to build a robust, reproducible evidence chain, including parameterized workload generators, configuration scripts, and detailed logs. The goal is to ensure that reviewers can quickly and accurately validate all performance claims and technical contributions, making the work 'turnkey' and verifiable.

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.

Provides a comprehensive guide for packaging complex research artifacts, integrating hardware, firmware, and datasets. It outlines best practices for achieving academic badges (e.g., Available, Functional, Reusable) and winning the Best Research Artifact Award. Learn how to structure analysis paths, manage raw traces, and ensure reproducibility across different evaluation environments.

A comprehensive guide for structuring the evidence bundle required for robotics research submissions. It details the critical components—including hardware ledgers, time-stamped logs, configuration files, code, and data—necessary to ensure that scientific claims are fully auditable and reproducible, addressing the needs of skeptical reviewers.

This guide provides rigorous guidelines for structuring and validating experimental results in computer architecture papers. It covers essential aspects like declaring simulation methodology contracts, ensuring result reproducibility through manifest files, selecting representative workloads, and performing detailed attribution and sensitivity analyses to distinguish true architectural gains from modeling artifacts.

A comprehensive guide for authors preparing artifacts (code, data, models, etc.) for submission to the Journal of Computer-Aided Design & Computer Graphics (JCAD&CG). It details best practices for ensuring research reproducibility, writing honest availability statements, and managing artifacts during the double-blind review process, focusing on ethical standards regardless of specific journal policy.