Skill UI

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 detailing the mandatory replication and data policy required by journals like JDE. It outlines how to build a self-contained, reproducible package containing master scripts, documented data provenance, pinned environments, and strategies for handling restricted or proprietary data, ensuring readiness for review and acceptance.

A comprehensive guide for submitting research artifacts to the MobiSys Artifact Evaluation Committee (AEC). It details the process for achieving three independent ACM badges (Available, Functional, Results Reproduced). Emphasis is placed on creating fully reproducible, script-first workflows, designing for hardware-optional environments, and maintaining strict anonymity during review.

A comprehensive guide for researchers detailing how to establish rigorous provenance for on-device performance measurements (e.g., energy, latency, accuracy). It mandates recording all necessary context—including device model, SoC, OS build, framework versions, power instrument setups, and thermal conditions—to ensure results are verifiable and reproducible across different hardware or environments for academic publication.

A comprehensive guide for researchers to ensure network security findings are reproducible and verifiable. It covers techniques for freezing volatile environments (targets, networks, toolchains), anonymizing sensitive data traces, maintaining a detailed claims ledger, and ethically stating limitations.

A comprehensive guide to ensuring the quantitative rigor and full reproducibility of econometrics manuscripts, particularly for submissions to high-tier journals like QE. Covers structural estimation (GMM, MSM), moment construction, data cleaning best practices, and comprehensive validation checks (e.g., multi-start, Monte Carlo recovery). It ensures that all results can be regenerated from raw data using pinned environments and documented scripts.

A comprehensive guide for submitting networking research artifacts for SIGCOMM conferences. It dictates how to package code, downscale topologies, substitute traces, and pin environments so that reviewers can independently verify and reproduce the paper's headline results, establishing verifiable scientific claims.

A comprehensive methodology for rigorous scientific research in operating systems and complex systems. This guide details how to pin environments (OS, firmware, hardware), automate data capture, and script all figures to ensure that experimental results are auditable and fully reproducible, protecting findings against review challenges.

A comprehensive guide for researchers submitting academic papers, detailing the standards required for scientific reproducibility. It covers fixing experimental environments (toolchains, datasets, hardware), differentiating result sources (simulation vs. physical measurement), statistical reporting best practices, and providing clear guidelines for code and data availability statements.

This comprehensive guide outlines best practices for designing and auditing empirical evaluations of formal verification tools and algorithms. It stresses the use of shared, community-accepted benchmarks, ensuring fair comparison against the strongest baselines with equal resources, meticulous reporting of all metrics, validating results for soundness, and guaranteeing full reproducibility on clean virtual environments.

This guide details advanced best practices for achieving scientific reproducibility in probabilistic modeling and deep learning research. It mandates rigorous disclosure of seeds, hyperparameters, diagnostic traces (e.g., R-hat, ELBO), and compute environments to ensure that all claims made in academic papers are verifiably backed by traceable evidence. It elevates reproducibility from a simple code run to a comprehensive audit.

A comprehensive guide to vcpkg, a native C/C++ package manager. It covers project setup, dependency management, and cross-compiling for robust builds. The guide details best practices for Manifest mode, CMake integration, and Visual Studio setup, ensuring reproducible and isolated build environments.