Skill UI

This comprehensive guide details the entire editorial review process for quantitative economics publications, covering stages from initial desk screening through to pre-acceptance reproducibility checks. It provides critical insights into expected timelines (4-5 months), desk-reject risks, the Econometrica transfer option, and mandatory data/code preparation requirements. It is designed to set clear professional expectations for authors throughout the academic publishing cycle.

A comprehensive guide for structuring point-by-point responses to reviewers after a major academic review (RED). It emphasizes prioritizing model critiques, quantitative evidence, calibration discipline, and maintaining code/data archive updates. This skill ensures your rebuttal is structured, rigorous, and directly addresses the core concerns of quantitative reviewers.

Guides authors through the rigorous, end-to-end lifecycle of submitting a paper to the Review of Economic Dynamics (RED). It acts as an orchestration checklist, covering critical stages from initial scope definition and quantitative modeling to data archiving, manuscript formatting, fee payment, and handling revisions. It ensures authors maintain methodological rigor and code-first discipline throughout the entire academic publishing process.

This guide details how to assemble a submission-ready replication package for top academic journals, such as The Review of Economic Studies (REStud). It covers the necessary repository structure, mandatory README sections, and coding best practices (e.g., master scripts, relative paths, version pinning) to ensure that the data and code can be fully reproduced and verified by journal editors before publication.

This comprehensive guide outlines the rigorous standards for creating publication-grade tables and figures for top-tier academic journals, such as The Review of Economic Studies. It emphasizes the principles of elegance, economy, and self-containment. Authors must ensure that every exhibit carries one single, clear result, presents interpretable magnitudes (not just stars), and is generated programmatically from code to guarantee reproducibility and consistency.

This tool guides the transformation of a coded, screened corpus into a robust analytical spine for academic synthesis. It moves beyond simple annotated bibliography summaries by requiring the construction of a conceptual framework (e.g., taxonomy, theoretical model, tensions). This structure ensures the review advances theory or policy, providing a coherent, critical argument for the field.

A comprehensive guide detailing how to make systematic reviews and meta-analyses fully auditable and reproducible for academic publishing. It emphasizes adhering to standards like PRISMA and MARS, quantifying inter-coder reliability (e.g., Kappa/ICC), and sharing all open materials, including search strings, coding datasets, and analysis code (via OSF/DOI).

Provides comprehensive guidelines for creating publication-grade, self-contained tables and figures for top finance journals, such as The Review of Financial Studies (RFS). It covers best practices for rigorous statistical reporting, emphasizing standard error reporting, economic magnitude, and ensuring that all presented results are fully reproducible from the source code.

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 packaging robotic research artifacts (code, policies, ledgers, hardware specs) for peer review and public release. It details the necessary steps to ensure maximum reproducibility, auditability, and permanence, covering both anonymous review-time evidence and citable public repositories.

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.