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This guide outlines the rigorous standards for academic paper reproducibility, particularly for theoretical venues. It mandates a complete, self-contained claim-to-proof map, ensuring every stated theorem or bound is fully provable within the submitted document, thereby preventing reliance on external or deferred proofs.

This guide details the strict requirements for data transparency and reproducibility when submitting manuscripts to the Population and Development Review (PDR). It covers creating data availability statements, depositing materials in FAIR repositories, documenting code with versions, and managing restricted data access, ensuring adherence to modern research integrity standards.

This comprehensive guide outlines the requirements for submitting computational artifacts for post-acceptance evaluation at PPoPP. It details PPoPP's unique badge policy (Functional, Reusable, Results Reproduced), addresses the challenges of ensuring results reproduce across different hardware architectures, and provides a detailed packaging plan, including containerization, README structure, and provenance documentation for maximizing reproducibility.

This guide outlines the necessary steps to ensure that parallel computing performance papers are scientifically reproducible. Since parallel results depend heavily on machine architecture, topology, and runtime conditions, proper documentation must cover everything from CPU/GPU specifications and interconnects to thread pinning policies, random seeds, and compiler flags. The focus is on proving the scaling trend, not just the absolute numbers, allowing results to hold true across different hardware platforms.

This guide details best practices for creating high-impact tables and figures for psychological science manuscripts, adhering to APA 7th edition guidelines. It emphasizes showing effect sizes, uncertainty (like confidence intervals), and embedding visuals directly within the main text near their first discussion, moving beyond traditional end-of-document placement. It covers data visualization principles and manuscript triage.

A comprehensive guide for designing and documenting systematic literature searches for meta-analyses or systematic reviews. It ensures reproducibility by covering multiple databases (e.g., PsycINFO, PubMed), controlled vocabulary, grey literature, and adhering strictly to PRISMA-S guidelines. It helps build a robust, traceable research pool.

This guide details how to structure a scholarly response document after receiving major revisions for a theoretical model. It emphasizes that the response must argue from substantive theoretical improvements—by adding derivations, simulations, or clarifying scope—rather than merely defending the original wording. It provides a rigorous, point-by-point framework for authors addressing critical academic feedback.

A comprehensive guide for academic authors adopting open science standards, based on TOP Guidelines. It outlines best practices for ensuring research reproducibility, covering data citation, data provenance documentation, data sharing via repositories (Dataverse/QDR), and managing restricted-data paths for various evidence types.

A comprehensive guide to ensuring the quantitative rigor and full reproducibility of econometrics manuscripts, particularly for submissions to high-tier journals like QE. Covers structural estimation (GMM, MSM), moment construction, data cleaning best practices, and comprehensive validation checks (e.g., multi-start, Monte Carlo recovery). It ensures that all results can be regenerated from raw data using pinned environments and documented scripts.

This guide outlines the rigorous requirements for preparing a reproducible package for Quantitative Economics (QE) journal submissions. It details the strict standards set by the Econometric Society Data Editor, covering raw data, code, documentation, and handling complex data exemptions to ensure the submission passes the pre-acceptance reproducibility check (DCAS compliant).

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.

This comprehensive guide details the rigorous methodology for conducting systematic literature reviews and meta-analyses, particularly in educational research. It ensures the reproducibility and defensibility of the evidence corpus by documenting the entire PRISMA flow, from exhaustive multi-database searching and dual screening to structured data extraction and subsequent synthesis of findings.