Skill UI

This skill guides authors needing to assess the suitability of their research on resource efficiency, waste management, or recycling for the journal Resources, Conservation & Recycling. It provides detailed expectations regarding Material Flow Analysis (MFA), Substance Flow Analysis (SFA), and the necessity of presenting quantified, measurable circularity contributions, ensuring rigorous methodological alignment with the circular economy framework.

A comprehensive guide for ensuring robust scientific reproducibility in robotics research (RSS). It structures evidence into distinct 'Replication Tiers' (e.g., Rerunnable, Auditable), distinguishing between the computational half (code, seeds) and the physical half (robot, environment, human protocol). It mandates the use of platform ledgers and raw trial logs to convert claims of trust into verifiable, auditable evidence.

This guide provides a comprehensive project management framework for submitting research to major robotics conferences, particularly RSS. It maps out the complex annual cycle, from abstract submission to final presentation, detailing critical deadlines, required milestones (like hardware trials and writing drafts), and fallback options at sibling venues (IROS, CoRL, ICRA). Essential for researchers managing time-sensitive academic pipelines.

This guide provides a comprehensive audit checklist for designing and evaluating sensor-based systems (SenSys). It mandates rigorous, real-world measurements across five core axes: energy efficiency, on-device latency distribution, ground truth accuracy, deployment realism, and embedded AI footprint. It ensures that research results are based on measured hardware performance, not simulations or idealized baselines.

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 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.

This guide provides comprehensive best practices for designing and auditing cloud system evaluations, particularly for academic submissions. It mandates that claims must be supported by measured evidence from real or realistic deployments, moving beyond simple simulations. Key metrics emphasized include tail latency (p99), operational cost modeling, and verifiable behavior under scale and multi-tenancy.

This comprehensive guide outlines best practices for achieving high levels of scientific reproducibility in systems papers, especially for cloud measurement studies. It teaches authors how to map every claim in their paper to verifiable evidence, detailing the entire testbed setup, reproducing tail latency and costs accurately, and maintaining consistency between reported numbers and artifacts.

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.

This guide assists researchers in determining the optimal venue for their formal verification project. It helps decide whether the work belongs at TACAS (ETAPS) or a sibling conference (like CAV or VMCAI), depending on the emphasis (tool vs. theory), and guides the selection among the four distinct contribution categories (Research, Tool, Case-study, Demonstration).

This comprehensive guide is designed to help authors assess the methodological and theoretical suitability of their manuscript for Transportation Research Part B. It emphasizes that the core contribution must be a generalizable method, model, or theorem (e.g., in traffic flow, network optimization, or choice modeling), rather than a simple applied case study. Use this tool for venue selection, manuscript re-framing, and ensuring rigorous analytical justification.

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.