Skill UI

This comprehensive guide outlines the mandatory standards for data and code submission for academic papers published in the Journal of Labor Economics (JOLE). It instructs researchers on how to build a complete, reproducible package, detailing the requirements for documenting the full data chain (raw to final), master scripts, environment pinning, and handling proprietary data for journal review.

This skill guides authors on meeting stringent academic requirements for manuscript submission, specifically addressing the mandatory replicability standard set by The Journal of Politics (JOP). Acceptance is strictly contingent on providing a complete, documented, and executable package. The required materials include the dataset(s), master code, codebook, and a comprehensive readme file, ensuring that any published finding can be independently verified and reproduced by a journal analyst. It also provides guidelines for handling restricted data.

Provides comprehensive guidelines for creating a fully reproducible replication package for submissions to the Journal of Political Economy (JPE). It details the required structure, components (README, master script, data), and best practices needed to pass the JPE Data Editor's reproducibility check, ensuring research claims are verifiable and transparent.

A detailed guide for researchers preparing manuscripts for public economics journals (e.g., JPubE). It outlines best practices for creating a fully auditable, reproducible research package, focusing on data-availability statements, handling restricted administrative data (like tax or health records), and adhering to journal Option C requirements.

A comprehensive guide for assembling a fully transparent and reproducible research package required for academic submissions (e.g., JRU/Springer). It structures the necessary components—including full experimental instructions, source code (z-Tree/Qualtrics), estimation scripts, and de-identified data—to allow any external party to replicate the study's findings end-to-end. Focuses on both procedural reproducibility and structural econometrics re-runnability.

A comprehensive guide detailing the mandatory replication package requirements for submitting empirical papers to the Journal of Urban Economics (JUE). It covers not only standard data and code deposits but also specialized techniques for handling complex geospatial data, geocoding pipelines, and proprietary/confidential restricted data paths to ensure maximum reproducibility.

This comprehensive guide outlines the mandatory replication and data policy for mathematical finance manuscripts. It provides authors with templates for Data Availability Statements (covering no data, code only, or public data) and details rigorous best practices for ensuring numerical exhibits are fully reproducible. It covers creating exhibit provenance ledgers and managing computational dependencies to maintain academic rigor.

This guide outlines the rigorous process for estimating, validating, and interpreting structural models in Marketing Science manuscripts. It covers advanced econometric techniques (GMM, MLE, Bayesian), demonstrating empirical identification, assessing model fit (in-sample/out-of-sample), and computing robust counterfactuals. It ensures the final results are statistically sound, economically plausible, and prepared for replication.

A comprehensive checklist and guide for authors submitting a Marketing Science manuscript via ScholarOne. It details critical steps including manuscript anonymization for double-anonymous review, selecting the correct article track (Regular, Frontiers, Practice), adherence to strict formatting rules, assembling a robust replication package (code and data access), and managing all submission requirements.

A comprehensive guide detailing the methodological standards for conducting high-impact research in Manufacturing and Service Operations Management. It covers both analytical modeling (proofs, sensitivity) and empirical studies (identification, robustness), emphasizing the necessity of full data and code replicability for published findings.

A comprehensive guide for authors assessing if their specialized plant science research meets the broad significance and high standards required by Nature Plants. It covers scope fit, evidence bar (e.g., convergent evidence, replication), structural requirements, and submission best practices for cross-disciplinary impact.

A comprehensive guide for authors submitting to machine learning conferences like NeurIPS. It details the rigorous standards for data splitting, hyperparameter reporting, random-seed protocols, and compute transparency required for a main-track paper. It also helps authors strategically decide between the main track and the MLRC/TMLR routes based on their core contribution (replication vs. novelty).