Skill UI

A comprehensive guide for researchers preparing robust, reproducible, and ethically sound artifacts for major security conferences. It details how to claim 'Available,' 'Functional,' and 'Reproduced' badges, structure the submission package, and responsibly gate dangerous exploit code while adhering to disclosure embargoes.

This guide details the rigorous process of converting an accepted NDSS paper into the final open-access, camera-ready version. It covers mandatory steps such as fulfilling revision conditions, deliberately de-anonymizing the work (restoring vendor names, DOIs, and disclosure specifics), adding earned artifact badges, and completing a comprehensive production checklist to ensure publication quality for the Internet Society proceedings.

This comprehensive guide provides a structured workflow for academic submissions to the NDSS security symposium. It maps out the distinct summer and fall submission cycles, detailing critical milestones from the initial paper deadline through early-reject, rebuttal, notification, and final camera-ready stages. It emphasizes strategic backward planning, managing the sensitive disclosure clock for vulnerabilities, and positioning research within the broader security research lifecycle.

This guide outlines the rigorous data and transparency requirements for submitting academic manuscripts, particularly for social science journals. It details two core components: methodological disclosure via 'Appendix A: Disclosure Elements' and building a complete replication package. Authors must ensure all published tables and figures can be reproduced exactly using deposited data and code in a permanent archive (Dataverse) before the paper advances to typesetting.

A comprehensive guide detailing the rigorous data analysis and reporting standards required for top-tier psychological science journals. It emphasizes moving beyond p-values to report effect sizes with confidence intervals, ensuring full disclosure of exclusions, and maintaining a clear separation between confirmatory and exploratory findings to enhance research credibility and reproducibility.

This skill helps authors assess the suitability of their accounting manuscripts for prestigious academic journals like Review of Accounting Studies (RAST). It analyzes the paper's fit, rigor, methodological strength, and structure against top-tier academic standards in areas like valuation, disclosure, and information economics. It provides actionable feedback on potential rejection risks and alternative venues.

This comprehensive preflight checklist guides authors through the mandatory submission process for the Review of Finance (RoF). It covers crucial details including fee structures (Regular vs. Fast-Track), manuscript length (60-page cap), abstract word limits, formatting standards, double-blind anonymity requirements, Chicago citation style, and necessary ethical disclosures. Use this guide before uploading to prevent desk rejection.

A comprehensive audit checklist designed for authors submitting papers to the SIGIR conference. It guides users through critical pre-submission checks, covering adherence to specific full/short paper formats, mandatory anonymization techniques (e.g., stripping system names, log provenance), cross-track submission rules, AI disclosure requirements, and common pitfalls that lead to desk rejection.

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.

A comprehensive preflight checklist designed for submitting academic manuscripts to major journals. It ensures strict compliance with publishing standards, covering double-blind anonymization, required AI disclosure statements, ORCID linking, adherence to specific formatting rules (e.g., Chicago style, page limits), and data authenticity requirements for research integrity.

This guide details advanced best practices for achieving scientific reproducibility in probabilistic modeling and deep learning research. It mandates rigorous disclosure of seeds, hyperparameters, diagnostic traces (e.g., R-hat, ELBO), and compute environments to ensure that all claims made in academic papers are verifiably backed by traceable evidence. It elevates reproducibility from a simple code run to a comprehensive audit.

This guide provides comprehensive instructions for authors responding to peer reviews and engaging with a shepherd during major security symposium cycles (like USENIX Security). It details the technical mechanics of writing effective rebuttals, addressing compliance gaps (e.g., ethics/disclosure), and managing limited revisions during the post-acceptance shepherding phase.