Skill UI

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.

This guide provides comprehensive standards for achieving statistical rigor in scientific research reporting. It mandates that every quantitative claim must include effect sizes, uncertainty, sample size (n), and justified statistical tests. Key areas covered include distinguishing biological vs. technical replication, conducting power analyses, implementing multiple comparison corrections, and utilizing Registered Reports to prevent publication bias, ensuring maximum transparency and reliability in scientific findings.

This guide directs authors on how to structure the related work section for academic papers in database theory (e.g., for venues like PODS, ICDT). It emphasizes a 'delta-first' approach, requiring authors to compare their specific *results* (theorems, bounds, dichotomies) directly against the nearest prior *results*, rather than simply cataloging related citations. It also covers complex rules regarding self-citation, anonymity, and overlap declaration with concurrent or prior-version work.

This guide details the rigorous and multi-stage academic review process for submissions to the PODS conference. It moves beyond simple accept/reject decisions, explaining the importance of multi-cycle revisions, shepherded rounds, and the strict focus on mathematical proofs and theoretical correctness. It helps authors understand how to strategically strengthen their work for high-impact, theory-based venues.

A comprehensive guide for researchers to determine the optimal academic venue (e.g., PODS, SIGMOD, VLDB, ICDT) for their paper. The guide helps distinguish between contributions that are foundational theorems (theory) and those that are performance-measured systems artifacts, ensuring proper scope definition before submission.

A comprehensive guide for structuring the analysis and results section of academic papers in Production and Operations Management (POM). It covers rigorous theoretical proofs, advanced empirical identification methods (e.g., DiD, IV), and data science validation techniques. The core focus is bridging statistical significance with actionable, operational managerial magnitude, ensuring research findings are decision-relevant.

This guide assists researchers in selecting the most appropriate and rigorous methodology for Production and Operations Management (POM) manuscripts. It covers various research tracks, including analytical modeling, stochastic process theory, game theory, causal empirical identification (DiD, IV, RD), behavioral experiments, simulation, and operations data science (ML/forecasting). It ensures the research method is tightly linked to the core operational question and possesses significant practical value.

A comprehensive guide for designing and presenting empirical evidence (tables and figures) in Production and Operations Management (POM) manuscripts. It helps authors manage the limited 'exhibit budget' across the main paper and the online e-companion, ensuring that all visuals are decision-legible, operationally relevant, and adhere to journal standards.

This skill guides the execution and reporting standards for advanced demographic analysis, particularly for Population and Development Reviews (PDR). It ensures that demographic rates, life tables, and projections are not only statistically rigorous (handling uncertainty, denominators, and complex inference) but also clearly interpreted in the context of development, policy, and socio-economic meaning. It focuses on reproducibility and methodological transparency.

This skill guides the rigorous defense of research design for demographic studies, particularly those linking population dynamics to development outcomes. It assists in selecting and justifying appropriate methods—such as life tables, decomposition, event-history modeling, and age-period-cohort analysis—and critically addresses the identification of causal links between population structure and development progress. It focuses on strengthening the argument by confronting rival hypotheses and establishing methodological rigor, without writing code.

Provides comprehensive guidelines for constructing self-contained, highly legible tables and figures for population and development research manuscripts. It emphasizes that exhibits must communicate not only raw demographic patterns but also their broader development and policy significance to a wide, non-specialist readership (e.g., economists, environmental scholars). Key principles include showing uncertainty, using appropriate specialized graphics (e.g., population pyramids, survival curves), and maintaining data reproducibility.

This guide details the strict requirements for data transparency and reproducibility when submitting manuscripts to the Population and Development Review (PDR). It covers creating data availability statements, depositing materials in FAIR repositories, documenting code with versions, and managing restricted data access, ensuring adherence to modern research integrity standards.