Skill UI

This guide assists authors targeting the ACM SIGMOD conference. It provides expert advice on positioning, framing, and structuring database systems papers. It emphasizes strong algorithmic or systems contributions, realistic workloads, and necessary evidence, while also advising on routing relative to similar venues like VLDB and ICDE.

A comprehensive guide for academics submitting AI and statistical research papers. It details how to map every claim (theoretical, algorithmic, or empirical) to verifiable evidence, covering necessary details like datasets, hyperparameters, random seeds, compute resources, and uncertainty estimates. It outlines the standards for achieving high levels of reproducibility, from descriptive to turnkey, ensuring scientific rigor for top-tier conferences.

This comprehensive skill guides researchers through the submission process for the Chinese Journal of Management Science. It covers the journal's core academic positioning (operations research, decision analysis, complex systems), required methodological rigor (model setting, algorithms, proofs), structural guidelines, and essential formatting checks. It helps authors assess manuscript fit and identify common rejection risks before official submission.

This comprehensive workflow guides researchers through the entire academic publication lifecycle for submissions to the Chinese Journal of Management Science. It acts as a critical router, ensuring manuscripts cover essential stages: topic selection, advanced model formulation, algorithm design, empirical validation (out-of-sample testing), and generating actionable managerial insights. It helps prevent common pitfalls like lacking empirical depth or misaligning with the journal's core focus on applied modeling.

A comprehensive guide for authors submitting data mining research to elite computer science venues like ICDM. This skill helps define the paper's fit, target the correct scope (algorithms, graph mining, etc.), and structure the manuscript to meet rigorous academic standards. It emphasizes providing sufficient evidence, rigorous baselines, and clear positioning relative to the conference's focus.

This skill helps authors evaluate whether their research—particularly in algorithms, optimization, or machine learning for Operations Research (OR)—meets the high computational standards required by the INFORMS Journal on Computing (IJOC). It acts as a fit/venue-selection tool, guiding authors on methodological rigor, computational evidence, and structuring their submission to maximize acceptance chances.

This guide helps researchers determine the primary focus of their work—whether it lies in Information Processing (algorithms, estimation) or Sensor Platforms, Tools, and Design Methods (hardware, deployment). It provides a structured methodology for selecting the most appropriate academic venue (e.g., SenSys, MobiSys, INFOCOM) based on the project's technical contribution and maturity.

This comprehensive guide details the rigorous methodological standards required for validating new statistical estimators, tests, or algorithms. It moves beyond simple causal inference, emphasizing foundational proofs of identification, regularity conditions, asymptotic consistency, and finite-sample robustness. It serves as a checklist for assuring the credibility and reliability of methods in academic statistical journals.

This guide assists authors in preparing research materials for submission to the Journal of Financial Intermediation (JFI), adhering to Elsevier Option C. It provides detailed instructions on managing, depositing, citing, and linking research data, code, and algorithms—whether the data is public, proprietary, or restricted (e.g., central bank registers). The goal is to ensure methodological rigor and reproducibility through the Data Statement.

A comprehensive framework for analyzing and presenting algorithmic contributions in academic papers. It guides users beyond mere computational speed, requiring rigorous proof of complexity (Big O), convergence (rate and conditions), or approximation bounds (rho). Essential for optimizing and validating complex computational models in scientific fields.

A guide for designing rigorous numerical experiments and simulations for academic manuscripts in management science. This methodology focuses on validating proven theoretical properties, assessing algorithm performance, exploring parameter sensitivity, and extracting actionable managerial insights. It emphasizes that simulations must complement, but never replace, formal mathematical proofs.

This guide details the rigorous requirements for publishing in top-tier quantitative management journals. It specifically targets research outputs that utilize mathematical models, theoretical proofs, and computational algorithms (e.g., operations research, decision theory). It strongly advises against purely empirical surveys or case studies, emphasizing methodological contribution over policy implication.