Skill UI

A comprehensive project management guide for preparing and submitting advanced NLP conference papers to EMNLP. It details the entire research lifecycle, including backward planning from ARR deadlines, managing data annotation timelines, coordinating team roles, and making strategic decisions (commit, revise, withdraw) to ensure successful submission.

This skill provides comprehensive guidance for authors submitting manuscripts to Environment International. It focuses on the rigorous connection between measured environmental exposures (including the exposome, contaminants, and mixtures) and quantifiable, population-relevant human health outcomes or risks. It covers methodology requirements, epidemiological rigor, and journal-specific formatting standards to improve manuscript suitability and reduce desk rejection risk.

A comprehensive guide for authors assessing the suitability of environmental health, toxicology, or epidemiology manuscripts for Environmental Health Perspectives (EHP). It emphasizes the need for strong public-health significance, rigorous methodology, and clear policy relevance, guiding authors on framing, statistical rigor, and structure.

This comprehensive guide helps authors determine the suitability and necessary framing for research related to environmental contamination. It mandates moving beyond simple pollutant detection, requiring rigorous evidence to connect contaminants to their fate, transport, and demonstrable ecological or human health impact. Essential for method validation, QA/QC, and scientific rigor.

This guide provides detailed reporting norms for reducing and analyzing complex Earth and Planetary Science Letters (EPSL) data, specifically focusing on isotope geochemistry and geochronology. It mandates full uncertainty budgeting, proper statistical interpretation (e.g., MSWD, weighted means), and thorough model diagnostics to ensure results are scientifically defensible and reproducible.

This guide provides rigorous standards for developing figures and tables in Earth and Planetary Science Letters (EPSL). It emphasizes making complex scientific arguments legible to a cross-disciplinary audience by mandating visible uncertainty (error ellipses, error bars), providing spatial context, and adhering to strict sub-field conventions (e.g., MSWD, depth scales). It teaches how to structure exhibits so that figures carry the argument, while detailed data resides in the supplementary material.

This guide provides best practices for ensuring scientific reproducibility in Earth and Planetary Science Letters (EPSL) manuscripts. It details how to structure supplementary materials, data tables, and analytical code to comply with modern research data policies (FAIR principles). It directs authors on depositing data into recognized community repositories (e.g., PANGAEA, Zenodo, EarthChem), ensuring that the paper's findings can be independently validated by any reader.

A comprehensive guide for planning rigorous scientific studies in Earth and Planetary Sciences, covering sampling, analytical campaigns, experimental runs, and modeling. It ensures that research designs are robust, addressing critical elements like sample context, traceability to reference materials, replication levels, and falsifiability before data collection begins. Essential for high-impact scientific publishing.

A comprehensive guide for academic researchers on structuring the 'Data and Sample' section of manuscripts for high-impact journals. It details requirements for database naming, variable definition using calculation formulas, descriptive statistics reporting (including winzorization and quantiles), and establishing full, reproducible sample selection trails. Essential for ensuring methodological rigor and passing peer review.

A comprehensive guide for conducting and writing rigorous heterogeneity analysis, specifically tailored for top-tier economic research journals. It details how to select theoretical dimensions, perform statistical tests (like Chow tests), structure the narrative, and critically, link heterogeneity back to the core mechanism to strengthen empirical evidence.

A comprehensive guide for advanced econometric techniques in causal inference, covering modern approaches for quasi-experimental designs such as staggered Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity Design (RDD), and Double Machine Learning (DML). It details best practices, critical diagnostic tests (e.g., weak instruments, parallel trend checks, bias decomposition), and robust reporting standards required for high-impact economic research.

Provides authoritative guidance on conducting mechanism analysis for empirical economic manuscripts, reflecting the current paradigm shift in causal inference. It strongly advises against outdated methods like the stepwise mediation analysis (Sobel or Baron-Kenny) and instead mandates a two-step approach: empirically estimating the causal link from treatment to mechanism (D→M), followed by a rigorous theoretical argument for the mechanism's impact on the outcome (M→Y).