Skill UI

This skill provides a comprehensive guide for authors targeting the Journal of Marketing Research (JMR). It analyzes the journal's demanding standards for methodological rigor, covering quantitative modeling, behavioral experiments, and design validity. Use this tool to assess if your manuscript's contribution, structure, and evidence base meet JMR's high academic bar, or to suggest more appropriate alternative venues.

A comprehensive workflow guide designed for submitting manuscripts to The Journal of Quantitative & Technological Economics (JQTE). This router determines the optimal sequence of specialized skills (e.g., topic selection, measurement, econometrics, forecasting, rebuttal) based on the manuscript's current stage or contribution type. It helps researchers structure their work to meet the journal's specific requirements, emphasizing measurable methods and reproducibility over pure narrative causality.

A comprehensive pre-writing guide for researchers determining the optimal venue and track for a data science contribution. It outlines a two-stage decision process: first, assessing if the work aligns with the KDD focus on data regimes (scale, drift, heterogeneity); and second, selecting the correct track (Research, ADS, Datasets & Benchmarks, etc.) based on the strongest, most honest evidence available—especially emphasizing real-world deployment outcomes.

Provides detailed guidelines for creating high-standard clinical figures and tables, modeled after leading medical journals (e.g., The Lancet). It covers essential display items including Table 1 (baseline characteristics), Kaplan-Meier survival curves (with numbers-at-risk), Forest plots for meta-analyses, CONSORT/PRISMA flow diagrams, and global maps. The guide emphasizes the rigorous reporting of 95% confidence intervals (CIs) and proper statistical methods to ensure scientific accuracy.

This guide provides methodological standards for defending the empirical design of a language manuscript. It covers various subfields—including fieldwork, corpus construction, phonetics, and typological comparison—ensuring that the evidence base is transparent, rigorous, and directly supports the stated theoretical claims. Use this when justifying data collection methods or addressing reviewer concerns about evidence validity.

A comprehensive guide for structuring the theoretical core of academic papers in Management Science. It covers two main approaches: developing formal analytical models (assumptions, propositions, comparative statics) or generating robust empirical hypotheses based on explicit mechanisms. The goal is always to derive decision-relevant, cross-departmental insights.

This comprehensive guide outlines best practices for creating key clinical display items, including Table 1 (baseline characteristics), Kaplan-Meier curves, Forest Plots, and CONSORT diagrams. It emphasizes statistical rigor (e.g., using standardized differences instead of P values for baseline), ensuring numbers-at-risk, and maintaining consistency across all reporting elements for high-impact medical publications.

A comprehensive guide for stress-testing the evidence supporting ML claims before submitting to top-tier academic conferences. Covers rigorous auditing of baselines, ablation studies, robustness checks, data leakage, and calibrating claims against evidence strength, ensuring scientific reproducibility.

A comprehensive guide for authors submitting to machine learning conferences like NeurIPS. It details the rigorous standards for data splitting, hyperparameter reporting, random-seed protocols, and compute transparency required for a main-track paper. It also helps authors strategically decide between the main track and the MLRC/TMLR routes based on their core contribution (replication vs. novelty).

This comprehensive guide assists researchers in determining the suitability of their manuscript for Nucleic Acids Research (NAR). It details the journal's scope, structural demands, and rigorous standards for submissions involving DNA, RNA, bioinformatics, databases, or web tools. Use this to assess the fit and required elements before official submission.

This skill provides robust functionality to resolve free-text scientific labels into standard ontology term IDs (CURIEs) and validate existing identifiers against authoritative databases. It is essential for metadata curation, ensuring that submitted biological data (e.g., tissue types, diseases, assays) uses current, correct, and non-obsolete identifiers, thereby maintaining high data quality for large-scale scientific datasets.

This guide provides comprehensive standards for analyzing and reporting results in Organization Science, a field characterized by diverse methodologies. It ensures methodological rigor and transparency whether the work is qualitative (establishing trustworthiness), quantitative (matching estimators to design), computational (reporting seeds and sensitivity), or formal/analytical. It emphasizes full reproducibility and mechanism-based inference.