Skill UI

This guide outlines the rigorous data and transparency requirements for submitting academic manuscripts, particularly for social science journals. It details two core components: methodological disclosure via 'Appendix A: Disclosure Elements' and building a complete replication package. Authors must ensure all published tables and figures can be reproduced exactly using deposited data and code in a permanent archive (Dataverse) before the paper advances to typesetting.

This guide details the rigorous methodology for conducting systematic literature reviews and meta-science pieces, particularly for integrative reviews in psychology (PoPS style). It instructs researchers on how to systematically gather evidence across multiple sub-areas by utilizing techniques like forward/backward snowballing, comprehensive database sweeps (e.g., PsycINFO), and developing detailed evidence matrices. The focus is on synthesizing findings and establishing replicable search protocols to ensure academic rigor and comprehensiveness, moving beyond simple summarization.

A comprehensive guide for constructing academic exhibits, such as conceptual framework diagrams, 'who-found-what' summary tables, and meta-science plots, for literature review and perspective pieces. It ensures adherence to APA style, emphasizes comparing like-with-like constructs, and mandates systematic data sourcing to maintain scientific credibility and reproducibility.

A comprehensive guide detailing the open science and reproducibility standards required for academic publications, especially those utilizing original data or conducting meta-science. It outlines critical steps such as data repository deposit, protocol preregistration, and reporting inter-rater reliability to ensure research methods are fully auditable.

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 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 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.

A comprehensive guide for structuring and positioning computer science manuscripts for The Web Conference (WWW). It advises authors on leveraging open-web data, focusing on web systems, web mining, and responsible AI, while adhering to the latest venue-specific standards and evidence requirements.

This guide covers the mandatory Open Science appendix for high-level security research. It instructs authors on enumerating all artifacts (code, datasets, configurations), documenting data withholding decisions, and implementing engineering controls (like environment pinning and version tracking) to ensure that published results are independently and reliably reproducible.