Skill UI

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.

A comprehensive guide to understanding the Public Opinion Quarterly (POQ) peer review process. It helps researchers critically assess their manuscripts against high academic standards, including double-blind review protocols, rigorous survey methodology checks (Total Survey Error, AAPOR compliance), and anticipating reviewer demands for code and data. Use this skill to stress-test your paper's robustness and maximize its chances of successful publication.

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 outlines the rigorous standards for creating publication-grade tables and figures for top-tier academic journals, such as The Review of Economic Studies. It emphasizes the principles of elegance, economy, and self-containment. Authors must ensure that every exhibit carries one single, clear result, presents interpretable magnitudes (not just stars), and is generated programmatically from code to guarantee reproducibility and consistency.

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 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.

A comprehensive guide for authors preparing methodological papers for submission to SMR. It outlines strict standards for creating a usable, reproducible package, including structured code, master scripts with fixed seeds, and formal data/code availability statements, ensuring the method can be run by others.

This comprehensive preflight checklist guides authors preparing manuscripts for submission to Sociological Methods & Research (SMR) via ScholarOne. It covers critical modern academic requirements, including double-anonymization techniques, specific formatting rules (ASA style, abstract limits), data and code availability statements, and generative AI disclosure, ensuring submissions meet the latest journal standards.

This comprehensive guide outlines the mandatory steps for transforming an accepted, anonymized academic paper into the final ACM Digital Library Camera-Ready format. It covers de-anonymization, adhering to ACM metadata standards (DOI, ORCID, CCS concepts), integrating reviewer feedback, and ensuring permanent reproducibility of code and traces.

A comprehensive guide for researchers submitting academic papers, detailing the standards required for scientific reproducibility. It covers fixing experimental environments (toolchains, datasets, hardware), differentiating result sources (simulation vs. physical measurement), statistical reporting best practices, and providing clear guidelines for code and data availability statements.

A detailed guide for authors submitting theoretical computer science papers, outlining the required evidence objects (proofs, certificates, code) and best practices for preparing the public, full version of the work. It emphasizes structuring arguments to meet the standards of top-tier academic conferences like STOC.