Skill UI

Mandatory package structure for submitting accepted empirical papers to the Quarterly Journal of Economics (QJE). It ensures complete research reproducibility by organizing raw data, derived files, and analysis code. This package guides authors in creating a comprehensive, self-contained deposit that allows any investigator to regenerate every figure, table, and result published in the manuscript, adhering to strict academic data-availability policies.

A comprehensive guide detailing best practices for achieving high reproducibility in recommender system research. It covers critical areas such as dataset version pinning, defining symmetric tuning protocols, selecting full-ranking metrics, handling non-determinism via multiple seeds, and structuring rigorous divergence analysis for academic publications.

This guide outlines the rigorous standards for quantitative analysis required in academic economic research, particularly dynamic modeling. It provides a framework covering systematic model calibration, moment estimation, numerical solution discipline, and establishing fully reproducible computation workflows, ensuring results are highly auditable and verifiable.

This skill guides the execution and stress-testing of empirical analysis for innovation studies (Research Policy). It details how to build reproducible bibliometric and patent variables, select appropriate econometric models (like Poisson/Negative Binomial for counts), and establish robust evidence. The focus is on addressing methodological challenges, ensuring transparent variable construction, and performing rigorous sensitivity checks to withstand expert scrutiny.

This router guides authors through the entire lifecycle of a manuscript targeting specialized academic journals like Research Policy (RP). It determines the optimal next step—whether it's refining the theory, developing methods, performing data analysis, structuring arguments, or responding to reviewer critiques (rebuttal). It ensures the paper maintains a strong focus on innovation studies and policy implications.

A comprehensive guide for executing rigorous empirical accounting studies. This resource details best practices for advanced econometric techniques, including correct standard error clustering (firm-level, two-way clustering), executing identification strategies (DiD, RD, IV/2SLS), validating accounting constructs, and performing extensive robustness checks (falsification, placebo tests). It also emphasizes data provenance and reproducibility, critical for top-tier academic publications.

This router guides the entire workflow for developing a high-quality academic manuscript targeting the Review of Accounting Studies (RAST). It helps users determine the next necessary step—be it theory development, data analysis, refining contributions, or preparing for submission and rebuttal—ensuring methodological rigor and structural completeness throughout the writing process.

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).

A comprehensive guide for executing and stress-testing empirical analyses in Industrial Organization (IO). It covers structural and reduced-form model diagnostics, advanced inference techniques (e.g., wild-cluster bootstrap), counterfactual validation, and meeting the rigorous robustness standards demanded by top-tier academic journals. Focuses on methodology, not study design.

Comprehensive guidelines for auditing and reproducing high-stakes empirical financial research. This resource details the rigorous standards required for top-tier finance journals, covering sample construction, identifying variables, advanced econometric modeling (e.g., panel data, factor pricing), handling international cross-market data, and establishing a fully reproducible data pipeline. Essential for ensuring the robustness and credibility of finance findings.

This router guides authors through the complex, multi-stage process of preparing a manuscript for a top-tier finance journal like the Review of Finance (RoF). It diagnoses current bottlenecks—whether related to contribution framing, data analysis, or rebuttal strategy—and systematically recommends the next specialized skill to ensure a complete and high-quality submission.

A comprehensive guide to ensure scientific manuscripts meet the highest standards of reproducibility. It mandates documenting the deposition of all necessary data (e.g., GenBank, PDB, GEO), archiving custom analysis code with DOIs (e.g., Zenodo), and detailing sharing plans for unique reagents and materials. Crucial for primary data underlying research conclusions.