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ACM IMC Artifact Evaluation

brycewang-stanfordbybrycewang-stanford
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Learning & Documentation

Prepares research artifacts, measurement datasets, and code releases for ACM Internet Measurement Conference (IMC) shepherding and award evaluation.

The ACM IMC Artifact Evaluation skill guides researchers through the complete lifecycle of artifact preparation for the ACM Internet Measurement Conference. It standardizes the workflow across double-blind review anonymization, post-acceptance availability shepherding, and long-term public archiving in DOI-issuing repositories like Zenodo and Software Heritage. By focusing on Internet measurement reproducibility—including vantage point documentation, probe configurations, ethical data anonymization, and figure generation scripts—it helps authors satisfy availability declarations and qualify for the ACM IMC Community Contribution Award.

Key Features

01Ensures compliance with ethical data sharing, rate-limiting disclosure, and open licensing
02Optimizes dataset and code packaging for ACM IMC Community Contribution Award eligibility
03Differentiates between anonymized submission artifacts and de-anonymized public releases
04Structures measurement provenance including vantage points, capture timelines, and tool schemas
051,079 GitHub stars
06Validates script-to-figure reproducibility for dynamically shifting Internet datasets

Use Cases

01Packaging longitudinal network scan datasets for DOI archiving on Zenodo or figshare
02Completing post-acceptance shepherding requirements before the camera-ready deadline
03Preparing an anonymized data and code repository for double-blind submission to ACM IMC