Skill UI

This guide provides comprehensive standards for packaging ubiquitous computing sensing artifacts and datasets for academic publication. It covers best practices for ensuring reproducibility, emphasizing cross-subject evaluation (LOSO), necessary de-identification protocols for human-subjects data, and structuring the submission package for rigorous peer review.

A comprehensive guide to understanding the Public Opinion Quarterly (POQ) peer review process. It helps researchers critically assess their manuscripts against high academic standards, including double-blind review protocols, rigorous survey methodology checks (Total Survey Error, AAPOR compliance), and anticipating reviewer demands for code and data. Use this skill to stress-test your paper's robustness and maximize its chances of successful publication.

A comprehensive checklist and guide for authors preparing manuscripts for scholarly submission, specifically for Public Opinion Quarterly (POQ) via ScholarOne. It ensures compliance with double-blind anonymity, specific word count caps, mandatory AAPOR disclosure elements (Appendix A), and proper data archiving protocols (Dataverse). Use this guide as the final critical check before clicking 'Submit'.

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.

This skill guides the planning of psychological studies to meet the highest academic standards. It provides comprehensive methodologies for performing power analysis, justifying sample sizes, and implementing preregistration protocols. Use this when initiating a new research project or developing a pre-analysis plan to ensure the study design is robust and transparent.

A comprehensive preflight checklist designed for authors submitting to the Quarterly Journal of Economics (QJE). This guide ensures manuscripts comply with strict academic standards, including single-PDF formatting, double-blind anonymization protocols, and correct author-date referencing, saving time and improving submission quality.

A comprehensive guide detailing best practices for achieving high reproducibility in recommender system research. It covers critical areas such as dataset version pinning, defining symmetric tuning protocols, selecting full-ranking metrics, handling non-determinism via multiple seeds, and structuring rigorous divergence analysis for academic publications.

A comprehensive guide for revising academic papers in recommendation systems (RecSys). It covers best practices for structuring the first page, ensuring rigorous evaluation protocols (e.g., temporal splits, equal-budget baselines), and avoiding methodological overclaims, helping authors meet the standards of top-tier AI conferences.

Provides a comprehensive guide to the rigorous editorial standards and submission protocols for the Review of Finance. It covers double-blind review requirements, the two-round decision philosophy, common desk screening patterns, and critical timing expectations (Regular vs. Fast-Track). This resource is essential for authors aiming to publish in top-tier finance journals by adhering strictly to academic integrity and compliance rules.

A rigorous framework for designing reproducible and scientifically sound experiments in robotics and AI, specifically for academic publication (e.g., RSS). It guides users to shift focus from simple system demonstration to testing a falsifiable scientific claim. Key areas covered include deriving conditions from a claim, establishing hardware trial protocols, managing simulation vs. real-world evidence splits, and performing detailed failure attribution analysis.

This guide outlines essential strategies for ensuring that complex physical or scientific measurements are fully reproducible by external parties, long after the original testbed is decommissioned. It mandates capturing detailed provenance data, including energy measurement methods, hardware specifications, firmware toolchains, environmental conditions, and ground-truth protocols, thus transforming raw data into verifiable, sharable scientific artifacts.

A comprehensive guide for organizing academic conference papers. It dictates how to keep the main body focused on the core argument while moving detailed protocols, full derivations, and secondary data into unlimited appendices. It also provides best practices for anonymity and managing reviewer responses.