Skill UI

A meticulous style guide for authors submitting papers to the International Conference on Database Theory (ICDT). It mandates a rigorous theorem-proof structure, emphasizing precise statement of data models, query languages, and complexity measures. The guide teaches how to structure the introduction to lead with the core result and its data-management implications, adhering to top-tier academic standards.

This guide provides essential instructions for authors submitting research to the ECAI conference, focusing on creating credible and reproducible artifacts. It strongly cautions authors about misconceptions regarding separate artifact badges, emphasizing that credibility must be built into the paper and supplement for direct reviewer judgment. Guidelines are provided for matching the artifact (code, proofs, data) to the specific contribution type (Theory, ML, Optimization).

This skill guides authors through the crucial decision of selecting the correct JACS format (Article or Communication). It provides detailed strategies for rightsizing the manuscript, determining what essential evidence belongs in the main text, and what detailed data should be moved to the Supplementary Information (SI), ensuring compliance with rigorous journal norms.

Comprehensive guidelines for preparing and reporting complex empirical evidence across various methodologies (SEM, econometrics, design science, qualitative). This guide ensures results meet the high academic standards of top-tier journals by emphasizing theoretical traceability, statistical rigor, and required data transparency, including full correlation/covariance matrices.

A comprehensive guide for authors preparing medical manuscripts for high-impact journals. It outlines rigorous standards for statistical reporting, emphasizing the use of effect sizes with 95% Confidence Intervals (CIs) over p-values alone. Key topics include pre-specification, intention-to-treat analysis, multiplicity control, handling missing data through imputation, and reporting both relative and absolute effects to ensure maximum statistical rigor and reproducibility.

A comprehensive preflight checklist designed for submitting manuscripts to the Journal of Business & Economic Statistics (JBES). This guide ensures authors have verified all necessary components—including the data/code supplement, conflict disclosures, and cover letter targeting—against the latest academic standards. It functions as a critical final review step before formal submission, emphasizing the need to cross-reference live journal guidelines.

This guide outlines a systematic methodology for gathering, reading, and synthesizing large bodies of academic research for comprehensive economic surveys (JEL style). It moves beyond simple summarization by enforcing structured data collection into a detailed evidence matrix. The process ensures maximum coverage through advanced search techniques (e.g., snowballing, JEL code sweeps) and focuses on synthesizing connections, identifying consensus, and articulating scholarly tensions across different studies.

This guide details the rigorous statistical reporting standards required for submitting manuscripts to the Journal of Applied Psychology (JAP). It covers advanced techniques such as SEM, Multilevel Models (HLM), mediation, and meta-analysis, emphasizing the reporting of effect sizes with confidence intervals, model fit indices, and full analytical transparency.

A comprehensive guide detailing the specific review standards and submission requirements for the Journal of Labor Economics (JOLE). This resource covers the single-blind review protocol, the non-refundable submission fee structure, and the expectations of labor-focused referees, emphasizing credible identification, disciplined data work, and replicability.

This comprehensive skill guides authors through the specific submission requirements and editorial culture of the Journal of Machine Learning Research (JMLR). It helps authors determine if their ML method, theory paper, or dataset is appropriate for JMLR's high standards—emphasizing rigor, complete proofs, open code, and lasting scientific contribution over novelty signaling. Use this when targeting JMLR or comparing it against other top-tier ML venues.

Provides comprehensive methodological guidelines for designing rigorous studies in public policy and marketing. The framework covers both prospective (e.g., realistic experiments) and retrospective (e.g., DiD, RDD) evaluation designs, emphasizing the necessity of establishing a strong counterfactual basis and ensuring policy relevance in data collection and analysis.

A detailed guide on establishing and reporting the necessary robustness and sensitivity analyses for manuscripts in quantitative economics and econometrics. It clarifies that the required robustness differs significantly from causal inference studies, emphasizing testing against variations in method choice, parameters, data scope, and sample periods. Provides structured methodologies for generating sensitivity matrices and discussing model stability.