Skill UI

This skill utilizes hyperbolic space embedding (Poincare ball model) to represent complex data structures with inherent hierarchy, such as dependency trees, ontologies, or module taxonomies. Unlike standard Euclidean embeddings, hyperbolic space preserves hierarchical distances efficiently, allowing for accurate distance calculations (geodesic distance) that grow logarithmically with tree depth. It is ideal for advanced knowledge graph analysis and code architecture mapping.

This comprehensive guide provides authors with best practices for preparing supplementary artifacts for AAAI submissions. It outlines how to structure technical appendices, package reproducible code and data ZIPs, maintain strict anonymity (by removing author metadata and paths), and maximize the evidence for reproducibility. The goal is to ensure that external reviewers can easily verify the research claims without specialized or private tools.

This comprehensive checklist guides authors through the final pre-submission compliance checks for the American Economic Journal: Macroeconomics (AEJ: Macro). It covers critical academic standards, including formatting rules (PDF, Abstract length, JEL codes), fee structures, author disclosure statements, and essential data/code reproducibility policies, ensuring the manuscript meets all journal requirements before submission.

This guide outlines the comprehensive package structure required for submitting manuscripts to American Economic Review: Insights (AER: Insights). It ensures authors meet the AEA Data and Code Availability Policy, providing all necessary code, raw data access, and documentation (including a master script and exhibit-to-code map) to allow the data editor to verify the reproducibility of every result before publication. It is crucial for passing the final publication gate.

This guide outlines the stringent reproducibility and data requirements for submitting manuscripts to Agricultural Systems (AgSy). Because AgSy treats code, models, and algorithms as research data, authors must learn how to deposit all materials in a recognized repository (e.g., Zenodo, OSF), cite them permanently, and document the model structure, parameters, and versions. It provides a checklist for building a reproducible research workflow.

This guide provides comprehensive best practices for preparing supplementary materials for major academic conferences like AISTATS. It details the proper structure and handling of appendices, mathematical proofs, code archives, and additional data tables, ensuring that the main paper remains fully understandable even if the supplementary material is ignored by reviewers. Essential for publishing high-quality, robust research.

This guide outlines the standardized requirements for declaring data and code availability in major scientific publications. It details mandatory best practices, including depositing raw data (e.g., sequencing, structures) and analysis code into approved, citable public repositories (such as GEO, PDB, Mendeley Data, and Zenodo). It also provides the required standardized three-bullet statement format for ensuring reproducibility.

This skill provides a comprehensive guide for authors targeting top-tier Chinese academic journals, such as the Chinese Review of Financial Studies. It analyzes the journal's specific academic scope (finance theory, capital markets, etc.), required manuscript structure, submission formatting (abstract length, JEL codes, reference rules), and common pitfalls. Use it pre-submission to ensure your paper meets the high academic standards required for CSSCI-level publication.

A comprehensive guide detailing how to structure and populate the supplementary material section of technical research papers, especially in NLP and Machine Learning. It distinguishes between load-bearing elements (main text) and audit trails (appendix), covering prompt verbatim, decoding configurations, full result grids, and code anonymization protocols to ensure high reproducibility and academic rigor.

This guide provides a detailed strategy for managing and releasing research artifacts (code, models, data) associated with a vision paper submitted to ECCV. It covers two critical phases: the sealed, anonymous archival submission during review, and the structured public release runway post-acceptance, ensuring reproducibility and adherence to conference policies.

This comprehensive guide outlines the strict requirements for preparing replication and data packages for submission to The Econometrics Journal (EctJ). It covers everything from README structure and detailed code inclusion to ensuring Monte Carlo determinism and managing restricted data, ensuring the method's credibility for reviewers.

This guide provides best practices for ensuring scientific reproducibility in Earth and Planetary Science Letters (EPSL) manuscripts. It details how to structure supplementary materials, data tables, and analytical code to comply with modern research data policies (FAIR principles). It directs authors on depositing data into recognized community repositories (e.g., PANGAEA, Zenodo, EarthChem), ensuring that the paper's findings can be independently validated by any reader.