Skill UI

A comprehensive guide for authors assessing whether their genomics or genetics research meets the rigorous standards of Nature Genetics. It details the journal's scope (GWAS, functional genomics, rare variants), the required methodology and evidence bar, and essential house style guidelines for submission.

This skill helps assess whether a manuscript in solid-earth, ocean, atmospheric, or planetary sciences meets the high conceptual and systemic standards required for top-tier geoscience journals like Nature Geoscience. It guides authors to re-frame local findings into global, Earth-system processes, focusing on cross-disciplinary coupling, mechanistic advances, and robust data documentation. It is a conceptual fit tool, not a submission guide.

A comprehensive skill guide for authors targeting Nature Human Behaviour. It assesses the fit of cross-disciplinary behavioral science manuscripts across psychology, neuroscience, economics, and sociology. The guide emphasizes open science practices, including preregistration, large-N designs, causal inference, and mandatory data transparency, ensuring submissions meet the journal's rigorous standards.

This skill provides comprehensive guidance for authors submitting manuscripts to Nature Medicine. It assesses the fit of research that bridges basic biological mechanisms to clear clinical applications (translational medicine). It details highly rigorous requirements, including the need for convergent evidence, specific reporting standards (CONSORT, TRIPOD-AI), and structural guidelines necessary for publishing high-impact translational science.

This guide details the scope, structure, and standards for submitting a 'Primer' to Nature Reviews Methods Primers. It is designed for authors seeking to author an authoritative, cross-disciplinary 'how-to' account of a method (experimental, computational, or analytical). It covers the expected evidence bar, full method lifecycle coverage, reproducibility requirements, and house style, helping authors determine if their topic fits the journal's mandate.

A comprehensive academic guide for validating survey instruments. This resource details required standards for reliability (Cronbach's α, Composite Reliability), validity (AVE, Fornell-Larcker, HTMT), and diagnosing common method bias (CMV) in self-report studies. It outlines best practices for cross-cultural adaptation, pre-testing, and advanced statistical reporting necessary for high-impact academic publications.

A comprehensive checklist for confirming clinical ethics and research integrity required for academic submissions, adhering to standards set by the Declaration of Helsinki and ICMJE guidelines. It guides authors on securing IRB approval, obtaining informed consent, managing conflicts of interest, defining authorship roles, and ensuring data transparency through mandatory data-sharing statements. Essential for preparing manuscripts for high-impact medical journals.

This comprehensive guide outlines best practices for creating key clinical display items, including Table 1 (baseline characteristics), Kaplan-Meier curves, Forest Plots, and CONSORT diagrams. It emphasizes statistical rigor (e.g., using standardized differences instead of P values for baseline), ensuring numbers-at-risk, and maintaining consistency across all reporting elements for high-impact medical publications.

This skill rigorously stress-tests a clinical study's potential impact against the standards of top-tier medical journals (e.g., NEJM, Lancet, JAMA). It evaluates three critical gates: clinical importance (patient outcomes), methodological rigor (study design), and generalizability (broader applicability). Use this to determine if a study is merely incremental or if it genuinely sets a new standard of care, helping authors decide the best publication venue.

This guide details best practices for rigorous statistical reporting in clinical trials, following guidelines from top medical journals like NEJM. It emphasizes using confidence intervals (CIs) over bare P-values, defining primary analysis populations (ITT), controlling for multiplicity, and reporting absolute risks (NNT). It helps researchers ensure their findings are statistically robust, transparent, and defensible.

This tool provides a comprehensive checklist and rigor check for writing clinical studies for top-tier journals like NEJM. It emphasizes the mandatory requirements for prospective trial registration (ICMJE), pre-specification of Statistical Analysis Plans (SAP), and adherence to design principles (e.g., randomization, blinding, ITT). It helps authors identify critical methodological gaps before submission.

A comprehensive guide for authors submitting to machine learning conferences like NeurIPS. It details the rigorous standards for data splitting, hyperparameter reporting, random-seed protocols, and compute transparency required for a main-track paper. It also helps authors strategically decide between the main track and the MLRC/TMLR routes based on their core contribution (replication vs. novelty).