Skill UI

A comprehensive guide outlining rigorous data analysis and reporting standards for geographic manuscripts. It covers spatial statistics, remote sensing accuracy, and qualitative interpretation, emphasizing spatial honesty, quantifying uncertainty, and ensuring results are robust across different scales and units.

A comprehensive set of guidelines for generating maps, figures, and tables for academic journals. This guide emphasizes cartographic standards, statistical rigor, and maximizing the visual argument, ensuring that all exhibits are reproducible, legible in print, and adhere to strict word count limitations. It covers everything from projection choice and data normalization to model plot interpretation and handling spatial uncertainty.

A comprehensive guide for preparing scholarly manuscripts involving spatial data. It outlines best practices for documenting data provenance (sources, projections, processing chains), ensuring reproducibility, and adhering to critical ethical standards such as geoprivacy and human-subjects protection. Essential for maintaining research integrity in academic publishing.

A comprehensive guide detailing the standards required for academic reproducibility in NLP and LLM research. It covers the 'Responsible NLP checklist' requirements, including detailed reporting for hyperparameters, model versions, exact prompts, decoding parameters, data contamination auditing, and variance reporting to ensure claims are auditable and robust.

A comprehensive strategic guide for authors targeting the ACM International Conference on Multimedia Retrieval (ICMR). This skill helps authors assess the fit of their multimedia retrieval, cross-modal search, or representation learning work for the venue. It provides specific advice on framing the contribution, defining necessary evidence (e.g., ranking metrics, baselines), and structuring the manuscript to meet ICMR's high standards.

This guide provides comprehensive econometric methodologies for establishing causality in accounting and finance research. It covers advanced techniques such as Difference-in-Differences (DID), Propensity Score Matching (PSM), Heckman selection models, and Instrumental Variables (IV), emphasizing the correct handling of regulatory changes and external shocks to meet top-tier academic standards.

A comprehensive guide for writing and verifying the institutional and accounting standard background section in academic manuscripts. This tool emphasizes the critical need for precision, requiring accurate standard names, official document numbers (文号), effective dates, and applicable scopes. Essential for empirical studies relying on regulatory changes, ensuring the manuscript meets the high standards of authoritative accounting journals.

A comprehensive guide for constructing academically robust and reproducible accounting metrics, such as discretionary accruals, real earnings management, and conservatism scores. This resource details the necessary steps—including estimation models, granularity, sample requirements, and standardized protocols—to ensure submissions meet the rigorous standards of top accounting journals.

This skill serves as a comprehensive guide for authors submitting manuscripts to highly selective pure mathematics journals like Acta Mathematica. It assesses the work's significance, intellectual fit, required level of rigor, structural integrity, and adherence to top-tier academic standards, ensuring the research is ready for the global mathematical research community.

A comprehensive guide for formatting and presenting empirical results (tables and figures) for submission to the American Economic Journal: Applied Economics (AEJ: Applied). It ensures that main causal estimates are immediately legible to referees by adhering to rigorous academic standards, including presenting standard errors, confidence intervals, and specific exhibit structures like event-study plots and balance tables.

This guide outlines the rigorous methodological standards for empirical identification in macroeconomics, crucial for submitting to top-tier journals. It covers advanced techniques such as Structural VAR (SVAR), Local Projections (LP), and handling high-frequency/micro-data shocks. It ensures authors explicitly defend the mapping from raw data to dynamic causal objects, addressing critical issues like endogeneity, anticipation, and structural breaks, and providing execution checklists for modern econometric tools (DiD, IV, RDD).

A comprehensive guide for formatting research exhibits, including Impulse Response Functions (IRFs), fan charts, and econometric tables, specifically for submissions to the American Economic Journal: Macroeconomics (AEJ: Macro). This ensures all figures are self-contained, meet rigorous standards for clarity, and adhere to the latest macro conventions, allowing reviewers to quickly assess the quantitative evidence.