Skill UI

A comprehensive guide detailing the rigorous data analysis and reporting standards required for top-tier psychological science journals. It emphasizes moving beyond p-values to report effect sizes with confidence intervals, ensuring full disclosure of exclusions, and maintaining a clear separation between confirmatory and exploratory findings to enhance research credibility and reproducibility.

This guide details the mandatory open science requirements for modern academic publishing, particularly in psychological sciences. It covers essential practices such as depositing open data and materials with DOIs, drafting a Research Transparency Statement, linking preregistrations, and properly handling exemptions. It ensures manuscripts meet the high standards of reproducibility and transparency required by top-tier journals.

This comprehensive guide serves as the final preflight checklist for submitting research to academic journals, specifically focusing on Psychological Science standards. It verifies adherence to strict requirements, including APA 7th edition formatting, specific word count limits, mandatory author anonymization, and rigorous open science compliance (e.g., depositing data, materials, and scripts with DOIs). Use this before final submission to ensure maximum compliance.

This guide details best practices for creating high-impact tables and figures for psychological science manuscripts, adhering to APA 7th edition guidelines. It emphasizes showing effect sizes, uncertainty (like confidence intervals), and embedding visuals directly within the main text near their first discussion, moving beyond traditional end-of-document placement. It covers data visualization principles and manuscript triage.

A comprehensive guide and fit test designed to help researchers assess whether their empirical psychology project meets the high standards of top-tier journals like Psychological Science. It evaluates key criteria including broad impact, statistical robustness, open science readiness (preregistration/data sharing), and adherence to strict format limits. Use this skill to pressure-test your idea before committing resources to data collection or analysis, guiding you toward the optimal manuscript type (e.g., Research Article vs. Registered Report).

This skill guides authors in assembling a comprehensive transparency package for academic submissions (especially meta-analyses). It ensures adherence to Open Science principles by detailing requirements for protocol preregistration (PROSPERO/OSF), depositing the coded database, codebook, and analysis scripts. It is essential for proving the full reproducibility of quantitative research findings.

A comprehensive guide for academic authors adopting open science standards, based on TOP Guidelines. It outlines best practices for ensuring research reproducibility, covering data citation, data provenance documentation, data sharing via repositories (Dataverse/QDR), and managing restricted-data paths for various evidence types.

This comprehensive preflight guide ensures authors meet all technical and procedural requirements before submitting to academic journals, specifically detailing the steps for the Review of Economic Dynamics (RED). It covers mandatory steps such as confirming submission fees, adhering to strict formatting rules (abstract length, citation style), preparing data archives (Option C), and navigating the journal's specific workflow (ScienceDirect/Editorial Manager). Following this checklist minimizes rejection risks due to formatting or procedural errors.

This skill helps researchers evaluate whether their manuscript aligns with the scope and practical orientation of specific academic journals. It provides structural advice, outlines key evidence requirements (e.g., patent quality, data analysis), identifies common rejection pitfalls, and guides the user through official submission checks for highly specialized journals.

This comprehensive guide outlines the scope, required evidence, and structural standards for submitting mechanistic immunology research to Science Immunology (Sci Immunol). It details how to position studies with translational relevance, emphasizing the combination of human clinical data with in vivo mouse models. It serves as a critical tool for authors to assess the manuscript's fit, adherence to AAAS standards, and overall scientific rigor before submission.

This skill provides comprehensive guidelines for conducting and reporting high-stakes quantitative data analysis in the social sciences. It emphasizes methodological rigor, requiring honest reporting of uncertainty, probing robustness (specs that could break the result), and ensuring full reproducibility. It covers advanced topics like design-based inference, cluster corrections, and handling missing data, ensuring results withstand expert review.

This skill guides authors on strategically positioning data mining research for the SIAM International Conference on Data Mining (SDM). It helps adjust manuscript framing, emphasizing mathematical rigor, statistical depth, and algorithmic contribution suitable for top Computer Science venues. Use it for venue selection, understanding reviewer expectations, and structuring evidence to meet the high standards of SIAM-style research.