Skill UI

A comprehensive guide for designing and auditing empirical research in CSCW. It emphasizes methodological pluralism, defining distinct standards of rigor for various techniques like ethnography, trace analysis, surveys, and mixed methods. The guide mandates pre-design planning, explicitly addressing ethical concerns, unit of analysis, and aggregation logic before data collection.

Provides comprehensive guidelines for developing and reporting high-stakes academic analyses, particularly in the social sciences and anthropology. It teaches authors how to ground every interpretive claim in specific evidence, proactively address alternative readings, integrate disconfirming data, and maintain methodological transparency to ensure the work survives rigorous expert peer review.

A comprehensive guide for strengthening the methodological design of anthropological manuscripts. It provides frameworks for defending evidence across multiple modes—including ethnographic fieldwork, archival analysis, and quantitative biological data—by anticipating and structurally refuting potential expert critiques. Essential for rigorous qualitative and mixed-methods research.

This tool serves as the central orchestrator for developing and submitting manuscripts to Current Anthropology (CA). It guides authors through the entire academic lifecycle, helping them determine the correct article type (Major Article, Report, Forum, etc.), appropriate subfield, and the necessary current stage (e.g., theory building, data analysis, ethics review). It ensures submissions are robust enough to withstand the rigorous CA✩ commentary and reply process.

This comprehensive guide details the rigorous standards for conducting and reporting advanced demographic analyses. It covers essential techniques, including constructing accurate rates, performing decomposition, generating life tables, and handling event history/survival data. The core focus is on methodological rigor, ensuring the reproducibility of results, and honestly reporting uncertainty (e.g., confidence and credible intervals) to meet the standards of expert peer review.

This tool serves as the comprehensive orchestrator for manuscripts submitted to Demography (PAA). It guides authors through the entire population science publishing lifecycle. By assessing the manuscript's current stage (e.g., topic selection, research design, data analysis, rebuttal) and determining the appropriate article type (Research Article, Note, or Commentary), it routes the user to the next specialized skill or writing phase required for successful submission.

A comprehensive guide for analyzing and reporting longitudinal data in Developmental Psychology manuscripts, ensuring compliance with high academic standards. It emphasizes modeling developmental change (e.g., growth curves, multilevel models, SEM), establishing measurement invariance (configural, metric, scalar) before interpretation, and rigorously reporting effect sizes and confidence intervals (CIs) using JARS guidelines. It guides the distinction between confirmatory and exploratory findings.

This comprehensive guide helps authors assess the suitability of their diabetes or metabolic manuscript for *Diabetes Care* (ADA). It details the required clinical scope, evidence strength (e.g., RCTs, observational studies), necessary data analysis standards, and specific structural requirements, ensuring the work aligns with high-impact clinical practice guidelines and the ADA Standards of Care.

This skill guides authors on submitting original, high-quality, and reusable Earth System datasets (observations, reanalysis, model outputs) as primary publications. It assesses whether the work fits the criteria of a 'data paper,' requiring open deposition in a FAIR repository with a persistent DOI, detailed provenance documentation, and rigorous quality control. It serves as a venue-selection and manuscript re-framing tool.

This guide outlines the creation of a complete, verifiable research package for economic policy manuscripts. It ensures that all empirical, experimental, or simulation results are fully reproducible by providing raw data guides, cleaning scripts, analysis code, and a single 'master' script that runs end-to-end. This process is critical for academic acceptance, allowing reviewers and discussants to independently verify every figure, table, and key number reported in the paper.

This guide details the principles of conducting rigorous Monte Carlo simulations for econometric theory papers. It instructs users on designing simulations that test finite-sample behavior, ensuring that asymptotic approximations are useful and accurate across boundary cases (e.g., weak identification, heavy tails). Emphasis is placed on reproducibility, comprehensive metrics (bias, coverage, power), and ensuring the simulation supports the core theory.

This guide provides comprehensive standards for conducting and reporting rigorous empirical and simulation evidence in econometrics. It covers designing Monte Carlo simulations, creating minimum evidence maps, ensuring reproducibility through detailed logging, and linking theoretical claims directly to finite-sample evidence. Essential for high-level statistical research.