Skill UI

A comprehensive audit checklist for preparing research submissions for FSE. This guide covers critical steps including meeting two-stage deadlines (registration and submission), adhering to strict ACM formatting and page budgets, ensuring heavy double-anonymous anonymity by scrubbing all metadata leaks, and fulfilling open science requirements with Data Availability statements. Essential for authors ensuring their manuscript meets high academic standards.

A comprehensive tool for authors aiming to submit high-significance, novel research to journals like Geology (GSA). It guides authors on compressing broad geoscientific findings into concise letters, ensuring the work meets strict standards for novelty, general significance, and adherence to specific journal house style and data deposition policies.

This guide outlines the best practices and norms for executing and reporting empirical analyses in governance and comparative political science. It covers essential methods including cross-national inference, clustering uncertainty, robustness checks, multi-method triangulation, and ensuring reproducibility, guiding researchers to conduct rigorous, publication-ready studies.

This guide details the necessary steps for achieving and reporting reproducibility in Human-Robot Interaction (HRI) studies. Since HRI involves unique elements like human participants and physical environments, reproducibility focuses on making the study protocol, robot behavior specifications, analysis code, and de-identified data transparent. Learn how to properly assemble a comprehensive replication package and ethically disclose limitations to enhance academic credibility in top-tier HRI publications.

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 detailing the academic standards for ensuring research reproducibility at ICSE. Covers open-science policies, mandatory data availability statements, techniques for pinning provenance (especially for mining and LLMs), and best practices for maintaining verifiability while ensuring anonymity. Essential for academic submissions.

A comprehensive post-acceptance checklist detailing the rigorous steps needed to transform an accepted research paper into the final camera-ready version for IEEE Xplore publication. This includes de-anonymization, adherence to the IEEEtran format, completing metadata and rights forms, integrating reviewer feedback faithfully, and permanentizing data availability links for open science.

A comprehensive pre-submission audit checklist for IEEE ICSME conference papers. Covers critical lifecycle steps including the abstract-then-paper two-step deadline, adherence to IEEE two-column formatting and page budgets, maintaining double-anonymous identity, and incorporating open science requirements like data availability statements and artifact links.

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 guide provides comprehensive best practices for authors to enhance the reproducibility of technical research papers (especially in networking and computer science). It covers documenting proofs, pinning simulation setups, reporting parameters, and best practices for releasing code and datasets, transforming a paper from a claim into a verifiable scientific artifact.

A comprehensive methodological guide for researchers writing Information Systems Research (ISR) papers. It details how to establish rigorous evidence across empirical (DiD, IV, RDD), analytical (proofs, comparative statics), and design-science claims. Learn to structure your analysis, handle common-method bias, and ensure full reproducibility using modern econometric techniques.

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.