Skill UI

This guide outlines a systematic methodology for conducting comprehensive literature reviews in economics. It moves beyond simple summarizing by teaching researchers how to build an evidence matrix, ensuring systematic coverage by sweeping databases (JEL codes, keywords, working papers). It is essential for preparing high-impact academic reviews that must prove exhaustive coverage and synthesize complex, often conflicting, findings.

A comprehensive guide for authors developing systems papers for major conferences (like ATC). It details how to build a reproducible story by pinning all environments (hardware, OS, toolchain, workload, code), providing turnkey one-command execution paths, and preparing a runnable, anonymized artifact package for academic review and evaluation.

A comprehensive guide detailing the selection of appropriate statistical estimators (e.g., DiD, Panel FE, ANOVA) for various research designs. It also outlines rigorous best practices for ensuring data integrity, conducting robustness checks, and adhering to journal code-sharing policies, ensuring highly reproducible empirical research.

This guide assists researchers in compiling and documenting all necessary ethical approvals, biosafety statements, and data/code availability declarations required for high-impact scientific manuscripts (e.g., Cancer Cell). It ensures compliance with guidelines covering animal protocols (IACUC), human subject ethics (IRB/Consent), and data sharing standards (GEO, Zenodo).

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 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 strict requirements for submitting supplementary materials (video figures, code archives, data, and appendices) to major academic conferences like CHI. It emphasizes critical rules such as end-to-end anonymity, self-containment (the paper must stand alone), proper data archiving, and handling multiple submission stages (review vs. publication).

This specialized guide assists researchers in positioning and refining academic manuscripts intended for top-tier journals focusing on rural Chinese economy, agriculture, and rural development. It covers the journal's specific scope, required methodology, submission standards (e.g., open data/code), and common pitfalls to increase publication success rates.

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.

A comprehensive guide for structuring the algorithm section of academic papers. It details the crucial steps for deciding on the solution route—whether to use commercial solvers, exact algorithms, or meta-heuristics. It emphasizes the requirement for rigorous pseudo-code, property analysis, and multi-level baseline comparisons to prove algorithmic novelty and contribution.

A comprehensive guide for authors on best practices for packaging, documenting, and ensuring the reproducibility of LLM artifacts (weights, data, code, prompts, etc.). It addresses requirements for anonymous academic review and public post-acceptance release, focusing on reproducibility, licensing, and transparency.

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.