Skill UI

A comprehensive self-check tool for authors targeting Nature Water. It guides the framing, scope, methodological rigor, and structural requirements necessary for submitting high-impact research in water science, hydrology, and environmental engineering. Focuses on connecting technical findings to broad societal challenges and ensuring FAIR data compliance.

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.

This guide provides a detailed model of the PerCom conference review process, covering double-blind review, the three-TPC-member evaluation model, the early-rejection gate, and the bounded rebuttal stage. It clarifies how authors should structure their submissions to succeed in this single-round, non-revisionary academic environment, differentiating it sharply from journal cycles.

Provides comprehensive guidelines for constructing self-contained, highly legible tables and figures for population and development research manuscripts. It emphasizes that exhibits must communicate not only raw demographic patterns but also their broader development and policy significance to a wide, non-specialist readership (e.g., economists, environmental scholars). Key principles include showing uncertainty, using appropriate specialized graphics (e.g., population pyramids, survival curves), and maintaining data reproducibility.

A comprehensive guide to help researchers pressure-test if their project aligns with the scope of Population and Development Review (PDR). It teaches how to frame research by connecting demographic processes (e.g., fertility, aging) to broader social, economic, or environmental development issues, ensuring the contribution has broad policy interest beyond narrow estimation.

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 authors submitting manuscripts to the RAND Journal of Economics (RJE). It details how to build a robust, reproducible replication package, focusing on creating a master script, pinning the computational environment, and properly documenting restricted datasets. It specifically addresses RJE's unique policy, which does not mandate formal data or code deposits, while ensuring academic rigor is maintained for reviewers.

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 outlines essential strategies for ensuring that complex physical or scientific measurements are fully reproducible by external parties, long after the original testbed is decommissioned. It mandates capturing detailed provenance data, including energy measurement methods, hardware specifications, firmware toolchains, environmental conditions, and ground-truth protocols, thus transforming raw data into verifiable, sharable scientific artifacts.

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.