Skill UI

Outlines the mandatory and highly structured STAR Methods reporting format required by top-tier scientific journals. This guide details the five critical sections: Key Resources Table (KRT), Resource Availability, Experimental Model Details, Method Details, and Quantification & Statistical Analysis (QSA). Adherence to this structure ensures transparent, reproducible, and accessible scientific reporting.

A comprehensive guideline for researchers developing robot-learning papers. It details best practices for experimental design, emphasizing statistical rigor, handling stochasticity (training seeds, initial states), bridging the sim-to-real gap, and ensuring fairness across baseline policy families (BC/RL/VLA). This resource helps ensure claims are supported by credible evidence.

This resource provides a comprehensive fit-check and framing guide for submitting research to Critical Care Medicine, the flagship journal of SCCM. It outlines the high academic and clinical bar required for studies on the critically ill, covering necessary focus on clinically important advances, adherence to specific reporting guidelines (e.g., CONSORT, STROBE), and rigorous methodological expectations for ICU-related research.

This guide details the rigorous review standards and criteria for developmental psychology manuscripts, covering masked peer review, developmental significance, methodological rigor (age/cohort effects, invariance, attrition), JARS reporting, and TOP transparency. Use this resource to stress-test your paper before submission, understand expected revisions, or interpret complex decision letters.

This router guides the entire lifecycle for writing complex, theory-driven academic manuscripts, particularly in fields like econometrics. It provides a structured roadmap spanning from initial topic selection and literature positioning through rigorous proof development, data simulation, drafting style, and the final submission/rebuttal phases. Use this resource when you are unsure of the correct sequential step or need a systematic check of all necessary components before finalizing your work.

A comprehensive guide modeling the USENIX FAST conference submission review pipeline. It details the double-blind review process, the author-response period, and clarifies the four distinct outcomes (Accept, Shepherding, One-shot Revision, Reject). This resource helps authors understand the strategic implications of the review stages, especially how a one-shot revision differs from standard journal revisions, providing actionable advice for maximizing submission chances.

This skill provides a comprehensive fit and structural review tool for manuscripts targeting 'Field Crops Research.' It assesses whether an agronomy or crop-physiology paper meets the journal's high standard: rigorous, adequately replicated, multi-environment field experimentation. It guides authors on key topics like resource-use efficiency, crop modeling validation, and required methodological depth (e.g., mixed models, full metadata reporting).

This resource details the rigorous evaluation process for Global Change Biology (GCB) submissions. It covers the criteria for high-bar desk rejection (scope, significance) and the depth of expert peer review. It is designed to guide authors on strengthening their manuscripts by pre-empting reviewer concerns, ensuring data reproducibility, and optimizing scope fit.

This guide determines if a scholarly manuscript aligns with the scope of *History and Theory*, a leading venue for the philosophy of history and historical theory. It emphasizes that accepted submissions must contain an original theoretical or methodological argument, rather than merely being an empirical case study. Use this resource to frame, re-check, or select the appropriate academic venue for your advanced research.

This guide helps researchers structure their academic arguments for Human Resource Management (HRM) journals. It mandates articulating a dual contribution: a rigorous scholarly advance (theoretical, empirical, or methodological) AND a clear, actionable implication for workforce policy or HRM practice. Mastering this framing is crucial to avoid being labeled as 'incremental' research.

This guide provides comprehensive guidelines for advanced statistical and qualitative data analysis in Human Resource Management (HRM) research. It details the rigorous application of methods like Multilevel Modeling (HLM), Structural Equation Modeling (SEM), mediation testing, and fixed-effects models. It emphasizes methodological rigor, transparency, addressing endogeneity, and reporting effect sizes to ensure publication-quality empirical findings.

This guide assists researchers in precisely positioning their Human Resource Management (HRM) manuscripts within specific scholarly conversations. It teaches how to differentiate the work from similar fields (like Personnel Psychology or AMJ) and move beyond general topic reviews, ensuring the contribution is seen as an extension, qualification, or overturning of existing anchor works. Essential for academic writing and journal submission.