Skill UI

This guide provides comprehensive guidelines for advanced statistical and qualitative data analysis in Human Resource Management (HRM) research. It details the rigorous application of methods like Multilevel Modeling (HLM), Structural Equation Modeling (SEM), mediation testing, and fixed-effects models. It emphasizes methodological rigor, transparency, addressing endogeneity, and reporting effect sizes to ensure publication-quality empirical findings.

This guide helps structure, audit, and refine the exhibits (tables and figures) for academic manuscripts, particularly in Human Relations. It ensures that the inference path from raw data to theoretical claims is visible. It covers best practices for both qualitative exhibits (data-structure models, evidence tables) and quantitative exhibits (regression models, interaction plots), while adhering to submission constraints like anonymization and word limits.

This guide details the best practices for packaging, documenting, and releasing research artifacts (code, models, data) for top-tier ML conferences. It covers two stages: preparing a non-identifiable archive for anonymous review, and managing the public release runway, focusing on reproducibility, licensing, and sustained maintenance.

A comprehensive guide for academic researchers detailing how to achieve and disclose full reproducibility for Computer Vision and deep learning models. Covers essential practices such as protocol pinning for foundation models, detailed logging of datasets, hardware specifications, and variance honesty, ensuring results remain verifiable years after publication.

This guide outlines the professional and technical process for authors responding to peer reviewers after a major conference submission (e.g., ICDT). It emphasizes that the revision must be a substantive correction—closing all identified proofs gaps, clarifying models, and documenting changes—rather than a mere argument or debate over taste. It provides a structured, point-by-point approach for maximizing the chances of acceptance.

This guide outlines the standards for ensuring the verifiability and rigor of theoretical computer science papers, particularly for venues like ICDT. It mandates complete proofs, precise pinning of models and assumptions, explicit matching of upper and lower bounds, and strict internal consistency across all claims and full versions. Use this checklist before submission to elevate academic quality.

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 skill guides the transformation of quantitative research findings (models, experiments, empirical results) into structured, actionable managerial insights. It enforces the 'Condition-Decision-Effect' framework, ensuring that every policy suggestion is directly traceable to specific data points, parameters, or experimental findings. Essential for authors aiming to publish high-impact academic papers in management science.

A comprehensive guide for drafting and revising academic papers, particularly in the field of Large Language Models (LLMs). It teaches how to adopt the measurement-forward, evidence-based register required by top-tier conferences like COLM. Focus areas include leading with findings, strictly scoping claims to tested models and versions, structuring content for tight page limits, and maintaining a measured, critical voice.

This skill provides comprehensive guidance for authors seeking to submit manuscripts to the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). It helps evaluate the fit of research in signal processing, speech, audio, and image processing. It guides the framing, required empirical evidence (mathematical models, reproducible validation), and optimal positioning of the paper to maximize acceptance chances.

This skill guides researchers on preparing manuscripts for IEEE Transactions on Wireless Communications (TWC). It provides a detailed framework for assessing whether a paper has genuine wireless specificity, requiring quantitative analysis and simulation under realistic channel models (e.g., imperfect CSI, mobility). It covers core topics like MIMO, resource allocation, and advanced networking, ensuring the work meets the rigorous standards of top-tier academic publications.

A comprehensive guide for developing robust and general economic models suitable for top-tier theoretical journals like the International Economic Review (IER). It teaches authors how to identify load-bearing assumptions, analytically prove existence and uniqueness, and structure arguments to demonstrate that results are intrinsic properties of the economy, not mere artifacts of functional form.