ACM CHI Reproducibility & Research Transparency

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Strengthens research transparency and methodological reproducibility for ACM CHI conference paper submissions to pass ADR-Method desk-reject screening.

chi-reproducibility helps HCI researchers and authors prepare transparent, replicable ACM CHI papers by standardizing protocols, analysis pipelines, and human-subjects data availability. It guides authors through a three-layer framework covering study protocols (interview guides, stimuli, task scripts), analysis transparency (codebooks, pinned-environment analysis pipelines, qualitative audit trails), and ethical data sharing under human-subjects constraints. This skill is critical for navigating ACM CHI's ADR-Method screening to prevent desk rejects, verifying cold-start pipeline execution, and drafting robust, anonymized data availability statements for peer review.

Key Features

01ADR-Method screening risk assessment and automated transparency claim verification
02Qualitative audit trail guidance for interview guides, coding decisions, and reflexive theme development
03Quantitative pipeline verification including environment pinning, exclusions, and cold-start execution checks
041,074 GitHub stars
05Three-layer framework audits covering study protocols, analysis pipelines, and ethical data sharing
06Human-subjects data availability ladder for balancing open science with participant privacy and consent

Use Cases

01Drafting human-subjects-compliant data availability statements and preregistration links
02Preparing supplementary materials and anonymized repository links prior to ACM CHI submission
03Auditing qualitative and quantitative research pipelines to satisfy CHI transparency requirements