Skill UI

This guide outlines best practices for packaging research artifacts (prototypes, datasets, instruments, code) for academic review and permanent public release, particularly in HCI venues like CHI. It covers strategies for creating highly reviewable, anonymous submissions (e.g., claims maps, minimal demos) and establishing robust, licensed, permanent archives (e.g., DOIs, OSF), ensuring research reproducibility and auditability.

A comprehensive guide detailing how to structure and populate the supplementary material section of technical research papers, especially in NLP and Machine Learning. It distinguishes between load-bearing elements (main text) and audit trails (appendix), covering prompt verbatim, decoding configurations, full result grids, and code anonymization protocols to ensure high reproducibility and academic rigor.

This tool guides researchers in selecting and rigorously stress-testing appropriate methodologies (quantitative, qualitative, or normative) for public administration manuscripts. It ensures methodological alignment with the core research question, providing detailed checks for causal inference techniques (DID, IV, RDD) and qualitative rigor, thereby strengthening the overall academic argument.

This guide provides comprehensive standards for achieving auditability and transparency in Computer-Supported Cooperative Work (CSCW) research. It teaches researchers how to document non-shareable evidence, such as field interviews, log traces, and system deployments, by focusing on detailed analysis trails, decision logs, and rigorous process documentation, thus strengthening academic rigor.

A comprehensive guide for authors submitting to Computer Science journals, detailing how to achieve full experimental reproducibility. It systematically covers locking environments (dependencies, hardware, seeds), preventing data leakage, structuring the submission package, and creating a clear audit trail so reviewers can independently reproduce all main results from raw data.

A comprehensive guide for structuring, auditing, and writing mathematically rigorous proofs for advanced academic papers, particularly in econometrics and statistics. It covers everything from defining assumptions and stating self-contained theorems to applying advanced proof techniques (e.g., fixed-point theorems, CLT). Use this when your central result requires impeccable rigor and completeness for top-tier publication.

This comprehensive guide helps researchers audit their papers before submission to the EDBT conference. It covers critical steps including selecting the correct submission cycle and paper shape (Regular, Vision, Experiments & Analysis), adhering to strict formatting standards, managing the 12-month resubmission ban, ensuring double-blind compliance, and providing verifiable, reproducible artifacts. It is essential for academic publishing compliance.

This skill guides users through strengthening the causal identification strategy of empirical economic manuscripts (DiD, IV, RDD, Experiments). It helps audit research designs against general academic credibility standards, ensuring that assumptions, potential biases, and the link between data variation and causal objects are explicitly stated and robustly defended for diverse expert audiences.

This comprehensive guide outlines the rigorous standards for designing, auditing, and reporting Natural Language Processing (NLP) experiments, specifically tailored for top-tier academic conferences like EMNLP. It covers critical areas including baseline fairness checks, data contamination audits, statistical significance testing, prompt sensitivity analysis, and structured human evaluation protocols, ensuring reported claims are robust, reproducible, and methodologically sound.

A comprehensive methodological guide for auditing experimental economic designs. Use this when the causal claim hinges on strict experimental control, addressing issues like incentive compatibility, randomization robustness, and adherence to the no-deception norm. It stress-tests the underlying methodology, verifying identification through rigorous design checks.

A comprehensive guide for researchers in Human Relations to structure and report analyses, ensuring the direct, auditable link between raw evidence and theoretical claims. It covers best practices for qualitative coding (Gioia-style), critical interpretation, and advanced quantitative modeling, ensuring the final contribution is substantive and transparent.

This guide helps structure, audit, and refine the exhibits (tables and figures) for academic manuscripts, particularly in Human Relations. It ensures that the inference path from raw data to theoretical claims is visible. It covers best practices for both qualitative exhibits (data-structure models, evidence tables) and quantitative exhibits (regression models, interaction plots), while adhering to submission constraints like anonymization and word limits.