Skill UI

This guide outlines the mandatory data and code deposit policy for articles published in the Journal of Applied Econometrics (JAE). Authors must submit a complete, non-confidential set of input data (plain ASCII/CSV), programs that replicate every result, and a detailed README file. It emphasizes strict adherence to archival format rules, ensuring the research is fully reproducible for the academic community.

This tool guides researchers through the critical initial phase of academic manuscript preparation. It tests whether a research question has genuine accounting relevance, a credible source of identifying variation (e.g., policy shock, rule change), clear economic stakes, and sufficient marginal contribution for top-tier accounting journals like JAR. It serves as a methodological fit check before any data collection begins.

This guide assists researchers in assembling a comprehensive, reproducible data and code supplement for submissions to statistical journals, adhering to the American Statistical Association (ASA) data-sharing policies. It outlines the required structure (README, code, data), provenance tracking, environment pinning, and necessary steps (like setting random seeds and drafting data availability statements) to ensure that published results can be fully regenerated and verified by reviewers.

This framework guides authors in transforming raw empirical findings into compelling academic contributions suitable for top-tier finance journals. It teaches how to sharpen the focus from merely presenting 'better data' to articulating the underlying financial mechanism, institutional friction, or policy implication, ensuring the research answers the critical question of 'why it matters.'

This comprehensive guide outlines the best practices for preparing a complete and reproducible research package for submission to the Journal of Banking & Finance (JBF) following Elsevier Option C guidelines. It details requirements for data availability statements, proper structuring of code and data access materials, and best methods for managing proprietary or restricted datasets (such as CRSP or Compustat), ensuring research transparency and verifiability.

A comprehensive guide for authors preparing artifacts (code, data, models, etc.) for submission to the Journal of Computer-Aided Design & Computer Graphics (JCAD&CG). It details best practices for ensuring research reproducibility, writing honest availability statements, and managing artifacts during the double-blind review process, focusing on ethical standards regardless of specific journal policy.

A comprehensive guide for authors submitting to the Journal of Corporate Finance (JCF). It details how to comply with Elsevier's Option C data availability requirements, focusing on drafting the data availability statement and structuring the research archive. It provides best practices for handling licensed, proprietary data (e.g., Compustat, CRSP) by recommending the sharing of construction code and access instructions, rather than raw data deposits.

A comprehensive guide detailing the mandatory replication and data policy required by journals like JDE. It outlines how to build a self-contained, reproducible package containing master scripts, documented data provenance, pinned environments, and strategies for handling restricted or proprietary data, ensuring readiness for review and acceptance.

A comprehensive guide for academics writing in environmental economics, detailing how to articulate the marginal contribution of a manuscript. It teaches authors to move beyond simply presenting new data or methods, focusing instead on identifying the specific environmental-economic gap, policy parameter, or mechanism that the field currently lacks.

Provides comprehensive guidelines for authors submitting empirical, quantitative, or theoretical growth papers. It details how to package data sources, code pipelines, calibration files, and historical/geospatial artifacts to meet stringent reproducibility standards for publishing journals.

This comprehensive guide details how to build a robust, credible, and shareable replication package for a Journal of Economic Geography (JEG) manuscript. It focuses heavily on documenting spatial data provenance, handling version control for boundaries and projections, and scripting complex spatial joins to preempt reproducibility concerns, even though JEG's policy is post-acceptance.

Provides comprehensive guidelines for authors submitting to the Journal of Economic Theory (JET). It details mandatory data sharing (Elsevier Option C), ensuring computational reproducibility for numerical examples, and proper declaration of generative AI usage. This playbook helps authors structure their submissions to meet high academic standards of verifiable evidence and transparency.