Skill UI

This guide offers detailed guidelines for assembling and auditing supplementary materials for top-tier AI conferences (e.g., IJCAI). It covers critical aspects like separating the technical appendix from resubmission files, ensuring anonymity across all artifacts, maintaining the self-contained nature of the core paper, and managing source code and data for maximum reproducibility.

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.

A mandatory guide for authors submitting to the Journal of Financial and Quantitative Analysis (JFQA). This policy dictates the required archive structure, ensuring that all reported findings can be fully reproduced by depositing source code, raw or pseudo datasets, and a detailed execution roadmap in the JFQA Dataverse. It guarantees the highest standards of academic transparency and reproducibility in financial research.

This comprehensive guide outlines the mandatory data and replication policies for submissions to the Journal of Human Resources (JHR). It details best practices for creating robust submission packages, including Data Availability Statements, code packages, and plans for public repository deposit. It ensures maximum reproducibility and adherence to CC0 licensing standards for academic research.

A comprehensive guide and package structure for preparing empirical law and economics manuscripts for journal submission, ensuring full data and code reproducibility prior to publication. It mandates the assembly of data files, transformation scripts, a master execution script, and detailed README documentation, explicitly addressing the unique challenges of restricted or proprietary legal data sources (e.g., sealed court records).

This guide details how to assemble a comprehensive, reproducible replication package for academic manuscripts, especially in fields relying on complex institutional data (e.g., court records, contracts). It mandates creating a one-command master script, documenting the entire data pipeline from raw sources to final exhibits, and providing detailed protocols for hand-coded variables and data provenance. The goal is to ensure that any researcher can perfectly reproduce every finding.

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.

This guide details the best practices for submitting supplementary material (like full proofs, benchmark data, and source code) for major academic conferences. It instructs authors on strategically placing content to enhance depth while strictly maintaining double-blind anonymity and ensuring perfect consistency between the main paper and its supporting appendices.

This guide establishes the rigorous policy for submitting computational and empirical papers to academic journals. It mandates that authors provide sufficient data, source code, and detailed documentation (including execution order, random seeds, and OS used) to ensure that research findings can be fully reproduced by others, adhering to global standards like Option C.

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.

Provides comprehensive guidelines for authors preparing manuscripts for high-level academic publication, focusing specifically on data availability and transparency. It guides users through drafting the required data availability statement, depositing data and code in public repositories (e.g., OSF, Zenodo), and methods for documenting restricted or qualitative sources to ensure maximum reproducibility and ethical compliance.

This guide details the comprehensive steps required after an ACM conference paper has been accepted. It covers critical stages including obtaining shepherd approval, de-anonymizing the work (restoring author names, funding, and system details), reflowing the paper using the acmart template, and submitting both the source code and the final PDF through the ACM Publishing System (TAPS). It emphasizes releasing public artifacts for reproducibility.