Skill UI

A comprehensive guide detailing how to package all necessary supplementary evidence for academic submissions in AI, statistics, and machine learning domains. It covers best practices for ensuring reproducibility, including managing proofs, simulation scripts, benchmark data, random seeds, and minimizing metadata risks for reviewers.

This guide outlines best practices for packaging research artifacts (prototypes, datasets, instruments, code) for academic review and permanent public release, particularly in HCI venues like CHI. It covers strategies for creating highly reviewable, anonymous submissions (e.g., claims maps, minimal demos) and establishing robust, licensed, permanent archives (e.g., DOIs, OSF), ensuring research reproducibility and auditability.

A comprehensive guide detailing the methodology for packaging, documenting, and staging research artifacts (including code, datasets, knowledge graphs, and demo systems) for CIKM conference submission. It covers best practices for licensing, provenance tracking, versioning, and transitioning artifacts from anonymous review status to citable public releases, ensuring reproducibility and adoptability.

Provides comprehensive guidelines for researchers packaging and releasing artifacts (data, code, instruments) derived from real communities in CSCW research. It emphasizes ethical constraints, de-identification techniques (such as quote search and join attacks), and best practices for protecting studied communities' privacy, ensuring responsible sharing.

A comprehensive guide for researchers on packaging and documenting NLP artifacts, such as datasets, prompts, and evaluation code, for academic publication. It details best practices for ensuring reproducibility, managing provenance, adhering to licensing standards, and structuring evidence for top-tier conferences like EMNLP.

This comprehensive guide outlines the process for packaging artifacts from Human-Robot Interaction (HRI) research. Recognizing that HRI artifacts are complex, involving human subjects, robot behaviors, and extensive study materials, this skill provides guidelines for honest, ethical, and reproducible dissemination. It covers structuring de-identified data, analysis code, and behavioral specifications while adhering to participant consent and academic archiving standards.

A comprehensive guide for generating and packaging reproducible evidence for signal processing submissions to ICASSP. It details what reviewers prioritize (scoring scripts, dataset splits, logs) and outlines best practices to ensure that headline metrics are easily regenerated, thereby maximizing review credibility and post-publication impact.

Provides comprehensive guidelines for packaging research artifacts (code, data, logs) for submission to data mining conferences like ICDM. The focus is on ensuring the artifact is completely anonymous for triple-blind review (scrubbing Git history, paths, and internal names) and maximizing reproducibility, allowing reviewers to verify results with minimal effort.

This guide details the process of packaging artifacts for academic conferences (ICLR), ensuring that research claims are fully reproducible and reviewable. It emphasizes providing minimal reproduction paths, maintaining data provenance, rigorously anonymizing metadata to protect authors' identities, and creating robust, permanent records for the scholarly community.

Provides comprehensive guidelines for packaging and documenting complex robotics research artifacts (code, simulations, hardware designs, datasets, etc.) for scholarly review. It outlines best practices for ensuring reproducibility, adopting the "five-minute-skeptic standard," and managing the full lifecycle of artifacts from review submission to public acceptance.

A comprehensive guide for authors packaging their accepted MLSys research artifacts. It details strategies for achieving Availability, Functional, and Reproducible badges by correctly structuring the submission, handling hardware dependencies, and writing the detailed Artifact Appendix. Essential for ensuring scientific claims are independently verifiable.

This guide provides comprehensive best practices for packaging and documenting NLP research artifacts (datasets, models, prompts, etc.) for academic submission (e.g., NAACL/ARR). It emphasizes rigorous provenance tracing, ensuring reproducibility, and ethically handling sensitive data, particularly community-owned or indigenous language resources.