Skill UI

A comprehensive guide for academic researchers on designing, auditing, and reporting rigorous studies (experiments, field deployments, surveys, etc.) for Human-Computer Interaction (HCI) papers. It details best practices for quantitative power analysis, qualitative thematic analysis, ethical reporting, and mixed-methods integration to ensure research claims are strongly supported by evidence.

This guide details best practices for structuring and writing the "Related Work" section for top-tier Human-Computer Interaction (HCI) conferences like CHI. It emphasizes moving beyond mere literature inventory to actively positioning your contribution by identifying research gaps across various disciplinary lanes (e.g., Psychology, Design, HCI family). Following these guidelines is crucial for avoiding desk rejection due to insufficient contextualization.

A comprehensive guide detailing best practices for achieving research transparency and reproducibility, especially for HCI and related scientific studies. It outlines three critical layers of documentation—Protocol, Analysis, and Data—to ensure that studies can be reliably verified and built upon by the academic community.

A detailed guide for researchers submitting papers to China Industrial Economics. It outlines the journal's specific focus on rigorous empirical evidence, policy evaluation, industrial organization, and the digital/regional economy. The guide emphasizes best practices for methodology (e.g., DID, event-study), robustness checks, mechanism testing, and ensuring actionable policy implications.

This guide outlines the scope and submission requirements for the journal China Sport Science and Technology. It focuses specifically on application-oriented exercise science, covering competitive training, physical conditioning, physiological monitoring, technical analysis, and sports rehabilitation. It helps researchers determine the best fit for their work, distinguishing between purely theoretical research and practice-guided, evidence-based studies for athletic development.

This guide provides comprehensive guidelines for formatting academic manuscripts, specifically for Critical Inquiry (CI) submissions. It strictly adheres to the Chicago Manual of Style (17th ed.), mandating the use of consecutively numbered footnotes and eliminating the need for a separate works-cited list. It also covers best practices for submitting high-resolution figures (300 ppi JPEG/TIFF) and managing citations to ensure they contribute effectively to the word count and scholarly argument.

A comprehensive guide for advanced econometric identification strategies, detailing best practices for conducting Difference-in-Differences (DID), event studies, and staggered treatment analysis. It provides stringent requirements for ensuring causal claims are robust and methodologically sound in academic research.

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 designing rigorous and robust empirical programs in Information Retrieval, Data Mining, and Knowledge Management. It outlines how to meet the stringent evidence requirements for top-tier conferences, ensuring claims are substantiated across multiple evaluation dimensions (retrieval, mechanism, scalability) and incorporating best practices for reproducibility and handling modern challenges like LLM contamination.

This comprehensive guide outlines best practices for achieving rigorous reproducibility in complex AI and knowledge graph research. It details how to pin every component—from tokenizers and KGs to LLM prompts and environment settings—ensuring that results are fully documented and verifiable by peers, even when proprietary data is involved.

This comprehensive guide provides best practices for designing and presenting scientific experiments for high-impact academic journals. It covers structuring research around clear Research Questions (RQs), selecting strong and fair baselines, ensuring statistical rigor (mean ± standard deviation, significance testing), performing detailed ablation studies, and preventing common issues like data leakage and evaluation pollution. It is essential for authors aiming to validate complex computational findings.

This skill systematizes the entire academic publishing journey for long-form papers, specifically targeting the requirements of top-tier journals like the Chinese Journal of Computers (CJC). It maps out the complete timeline from initial topic selection, through experimentation, writing, submission, multi-stage peer review, revision, and final publication, providing critical checkpoints and best practices to ensure a systematic and successful process.