Skill UI

This guide outlines the rigorous process of formalizing cognitive theories for academic manuscripts. It teaches researchers how to structure a computational or mathematical model, define interpretable parameters, and derive discriminating predictions that differentiate their theory from rival accounts. Essential for moving from a verbal hypothesis to a testable, falsifiable scientific model.

This tool serves as an orchestrator for authors preparing submissions to Cognitive Psychology (Elsevier). It guides users through the entire research lifecycle—from initial topic selection and theoretical formalization to study design, data analysis, and open science practices. It emphasizes structuring the contribution as a model-driven, multi-experiment program, ensuring all necessary steps, including code transparency, are addressed.

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.

Provides comprehensive guidelines for executing and reporting advanced statistical analyses (e.g., ANOVA, SEM, multilevel models) for academic manuscripts. It mandates adherence to rigorous APA reporting standards, including reporting effect sizes, confidence intervals, and ensuring methodological transparency and reproducibility to withstand expert scientific scrutiny.

A comprehensive guide to strengthening the quantitative research design for communication manuscripts. It covers best practices and methodological rigor for experiments (lab/online), panel surveys, and content analysis. Learn how to pre-register designs, control for common confounds (e.g., common-method variance), and rigorously establish causality, helping you defend your work against skeptical reviewers.

A comprehensive guide for developing scientifically rigorous, APA-formatted tables and figures for quantitative communication research. This skill emphasizes that exhibits must be self-contained, displaying effect sizes and uncertainty (not just significance stars), and following strict reporting standards for maximum clarity for reviewers.

This skill acts as an academic positioning tool for targeting high-level journals focused on institutional and economic comparison, such as the Comparative Economic and Social Systems journal. It helps users assess if their manuscript possesses the necessary comparative scope, institutional logic, and generalizable theoretical contribution required for acceptance, guiding manuscript reframing and submission strategy.

This comprehensive guide outlines the strict analytical and reporting standards required for top-tier conservation biology manuscripts. It covers everything from selecting appropriate statistical models (e.g., hierarchical, spatial) to honestly quantifying uncertainty, ensuring full reproducibility, and translating complex findings into actionable, decision-relevant conservation insights. Use this skill when performing main analyses or preparing manuscripts for expert peer review.

A comprehensive guide for creating self-contained, accessible, and budget-compliant scientific exhibits (figures, tables, and maps) for manuscripts, particularly in conservation biology. It covers best practices for data visualization, adhering to journal standards, ensuring accessibility (e.g., grayscale compatibility), and masking sensitive geographical data.

This guide provides a framework for rigorously defending the methodological design of scientific manuscripts in conservation biology. It covers advanced topics including detection modeling (occupancy, capture-recapture), addressing spatial structure and pseudoreplication, quasi-experimental designs (BACI, difference-in-differences), and systematic review protocols (PRISMA). The goal is to ensure that the evidence presented directly and credibly supports transferable, decision-relevant conservation conclusions.

This skill guides authors in assessing the suitability and optimal framing of their machine learning research in the health domain for top-tier academic conferences like CHIL. It ensures the manuscript meets the specific evidence standards, emphasizing clinical grounding, fairness, robustness, and deployment constraints, transforming general CS narratives into specialized health ML contributions.

A comprehensive guide helping authors position their research for the Conference on Learning Theory (COLT). It covers core topics like formal learning theory, statistical learning, optimization, and lower bounds. Use this tool to evaluate venue fit, structure the manuscript as 'theorem-first,' and adhere to specific academic conference submission standards.