Skill UI

A comprehensive guide for designing, auditing, and reporting reliable robotic experiments, particularly for major conferences like IROS. It moves beyond simple success rates, detailing how to map every claim to verifiable evidence (trials, simulations, field data). Key areas covered include defining pre-set success criteria, reporting failure taxonomies, ensuring fair baselines, and quantifying the critical sim-to-real gap using advanced small-n statistical methods.

This guide provides comprehensive guidelines for designing, conducting, and auditing empirical studies in software engineering. It emphasizes achieving scientific rigor by matching every claim to specific, measured evidence, ensuring fair baselining (equal budget), and applying appropriate advanced statistics (non-parametric tests, effect sizes). Essential reading for researchers submitting papers based on performance evaluations.

A comprehensive guide for calibrating the balance between coverage and emphasis when conducting literature surveys (JEL surveys). It provides guidelines for structuring the corpus by tiers, ensuring fair representation of all academic schools and controversies, and avoiding self-promotion bias.

This comprehensive guide is designed for authors submitting full-length manuscripts to Journal of Geophysical Research: Atmospheres (JGR-Atmospheres). It helps assess the fit, required depth, and methodological rigor of atmospheric science studies. The skill covers essential topics like aerosol interaction, cloud physics, atmospheric dynamics, data requirements (FAIR), and the specific structural expectations (e.g., Plain Language Summary) needed to meet the journal's high standard for quantitative, definitive scientific advances.

This guide provides a structured framework for authors responding to academic peer review decisions (R&R or Rejoinder). It transforms scattered reviewer objections into a cohesive, point-by-point revision plan. The process emphasizes engaging fairly with all criticism, strengthening claims with new data or predictions, and openly reconciling conflicting academic viewpoints.

This guide provides an expert methodology for designing and auditing Natural Language Processing (NLP) experiments for top-tier academic submissions. It instructs researchers on how to map meta-review claims to measurable evidence, ensure budget-fair comparisons, and systematically address potential reviewer probes (scope, fairness, contamination, etc.) to maximize publication rigor.

A comprehensive self-check tool for authors targeting Nature Water. It guides the framing, scope, methodological rigor, and structural requirements necessary for submitting high-impact research in water science, hydrology, and environmental engineering. Focuses on connecting technical findings to broad societal challenges and ensuring FAIR data compliance.

This skill provides comprehensive guidance for submitting research to npj Computational Materials. It defines the journal's scope, requiring rigorous computational methods (DFT, MD, ML, high-throughput screening) that yield generalizable materials insights or predictive capabilities. It educates authors on the required standards, including methodological rigor, reproducibility, and adherence to FAIR data principles, helping authors re-frame their work for a high-impact venue.

A comprehensive guide for designing and auditing rigorous empirical evaluations for sensing systems, particularly Human Activity Recognition (HAR). It emphasizes best practices such as leave-one-subject-out cross-validation, using real human subjects under diverse conditions, reporting event-level metrics (e.g., Macro-F1) over raw accuracy, establishing fair baselines, and mitigating data leakage (e.g., subject or session leakage). This ensures that reported results are robust, generalizable, and defensible against academic skepticism.

This skill guides authors on adapting philosophical arguments for public concern, specifically targeting venues like Philosophy & Public Affairs. It details the required academic rigor, scope (e.g., justice, bioethics, democracy), and structural elements needed for a manuscript to succeed. It serves as a fit check, helping authors sharpen their thesis, engage with opposing viewpoints, and maintain a strong connection between abstract theory and real-world public problems.

This guide details the strict requirements for data transparency and reproducibility when submitting manuscripts to the Population and Development Review (PDR). It covers creating data availability statements, depositing materials in FAIR repositories, documenting code with versions, and managing restricted data access, ensuring adherence to modern research integrity standards.

This guide provides a comprehensive checklist for designing, executing, and presenting empirical evidence for academic papers in Pattern Recognition and AI. It covers critical areas like establishing fair baselines, ensuring statistical rigor (mean ± std dev, significance testing), conducting sufficient ablation studies, preventing data leakage, and reporting computational costs. It helps authors make their findings withstand rigorous external review.