Skill UI

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.

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.

A comprehensive style guide for revising research papers to meet the specific, high standards of KDD conferences. It details the expected structure of the first page, emphasizing data regimes, named mechanisms, and quantifiable evidence (scale, metrics). It also provides rules for differentiating research vs. applied data science (ADS) voices and optimizing content for two-column, page-limited formats.

A detailed guide for researchers tackling advanced financial-mathematics problems. It outlines the stringent criteria for suitability in top-tier journals, emphasizing methodological novelty, mathematical rigor, and deep theoretical contribution (e.g., stochastic analysis, structural theorems) over routine data computation. Use this to refine and scope your research proposal.

A comprehensive guide detailing the methodological standards for conducting high-impact research in Manufacturing and Service Operations Management. It covers both analytical modeling (proofs, sensitivity) and empirical studies (identification, robustness), emphasizing the necessity of full data and code replicability for published findings.

This skill serves as a detailed guide for authors submitting theoretical articles in syntax or morphology to Natural Language & Linguistic Theory (NLLT). It outlines the journal's specific expectations, emphasizing that formal analyses must be deeply grounded and rigorously tested using rich, representative, and often cross-linguistic empirical data. Use this tool to assess the methodological rigor, scope fit, and structural requirements of your manuscript against NLLT's high academic standards.

This guide provides comprehensive standards for packaging ubiquitous computing sensing artifacts and datasets for academic publication. It covers best practices for ensuring reproducibility, emphasizing cross-subject evaluation (LOSO), necessary de-identification protocols for human-subjects data, and structuring the submission package for rigorous peer review.

Provides a comprehensive, milestone-based project management guide for submitting a paper to the IEEE PerCom conference. It outlines the entire workflow, from initial venue fit and securing human-subjects data collection to final camera-ready submission, emphasizing the single annual deadline and the critical importance of backward planning for successful academic publishing.

This comprehensive checklist guides researchers through finalizing scientific figures and tables for submission to PNAS. It enforces rigorous standards covering optimal column sizing, minimum font size, displaying underlying data (points and 'n'), colorblind accessibility, and maintaining data integrity. Use this guide before submission to ensure your visualizations meet high journal standards.

A comprehensive guide for maintaining consistency and completeness of references for PNAS Nexus submissions. It details the critical differences between the format-neutral requirements at submission time and the final numbered style required for publication, emphasizing the proper citation of datasets, software, and preprints.

This guide ensures authors meet the rigorous standards for submitting figures and tables to PNAS Nexus. It covers critical aspects including managing the page budget (graphical-element count), optimizing sizing for print legibility, visualizing raw data points (vs. means), ensuring colorblind-safe palettes, and maintaining complete data integrity by retaining raw, unprocessed source images.

Provides comprehensive guidelines for conducting highly rigorous, reproducible statistical analysis for complex survey data (e.g., weights, strata, clusters). This guide ensures that analyses meet the standards of top academic journals, emphasizing design-based inference, honest uncertainty reporting (CIs, MOE), and thorough robustness checks. Use this when you need to build a master script that regenerates every published table and figure.