---
name: Acm Mm Artifact Evaluation
slug: acm-mm-artifact-evaluation
category: Writing
description: Acm Mm Artifact Evaluation helps package ACM Multimedia code, models, datasets, or media for the right track and review process. It is used to choose blinding, licensing, and the separation between anonymous review and public release.
github: "https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-MM-Skills/skills/acmmm-artifact-evaluation"
language: Stata
stars: 974
forks: 125
install: "npx degit https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-MM-Skills/skills/acmmm-artifact-evaluation ~/.claude/skills/acmmm-artifact-evaluation"
installs_to: ~/.claude/skills/acmmm-artifact-evaluation
source_path: ACM-MM-Skills/skills/acmmm-artifact-evaluation/SKILL.md
collection_size: 53
category_size: 1012
collection_url: "https://dirskills.com/collections/brycewang-stanford/Awesome-Journal-Skills"
added: 2026-08-12T04:43:39.281Z
last_synced: 2026-08-12T04:43:39.281Z
canonical_url: "https://dirskills.com/skills/acm-mm-artifact-evaluation"
---

# Acm Mm Artifact Evaluation

Acm Mm Artifact Evaluation helps package ACM Multimedia code, models, datasets, or media for the right track and review process. It is used to choose blinding, licensing, and the separation between anonymous review and public release.

**Install:**

```bash
npx degit https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-MM-Skills/skills/acmmm-artifact-evaluation ~/.claude/skills/acmmm-artifact-evaluation
```

## README

# ACM MM Artifact Evaluation

Use this to turn an ACM Multimedia project's code, models, media, and data into the *right*
artifact for the *right* track. ACM MM has a track economy around artifacts, and the choice
determines blinding, format, and what reviewers judge.

## Which track is the artifact?

| Artifact is primarily... | Route to | Blinding | Judged on |
|---|---|---|---|
| A reusable software system/framework | Open Source Software Competition | Single-blind | Adoption, quality, license, docs |
| A new dataset/benchmark | Dataset track | Single-blind | Scale, quality, ethics, usefulness |
| A reproduction of published results | Reproducibility track | Single-blind | Whether results rebuild; ACM badges |
| Supporting evidence for a method paper | Main-track supplement | Double-blind | Whether it backs the paper's claims |

The named single-blind tracks exist *because* the artifact's identity cannot be hidden; a
main-track method paper's artifact, by contrast, must be **anonymous** through review.

## Two artifacts, two audiences

Plan both from the start:

- **Anonymous review artifact** — what reviewers see during double-blind review: an
  anonymized repository, an anonymous data mirror, stripped media metadata, and a README that
  reveals no author identity.
- **Public release artifact** — what ships at/after camera-ready: the de-anonymized
  repository, a permanent archive (DOI), the license, and the final dataset/model.

```text
review/    -> anonymous repo, anon data mirror, no names in code/media, run instructions
release/   -> public repo + DOI, LICENSE, model weights, dataset card, citation
```

## Open Source Software Competition

- The bar is a system others will *use*: clear install, documentation, examples, an
  OSI-approved license, and evidence of quality or adoption.
- Reference models and reproducible examples matter more than a single benchmark number —
  this is the lane exemplified by community frameworks and portable libraries.

## Dataset track

- Ship a **dataset card**: collection method, size, splits, license, consent, and known
  biases or limitations.
- Address ethics and rights explicitly, especially for user-generated or scraped media; a
  dataset a reviewer cannot legally use is not a contribution.

## Licensing and rights decisions

- Choose a code license (permissive vs. copyleft) and a **data** license separately; they are
  not the same choice.
- For media, confirm you have the right to redistribute; where you cannot, provide a
  retrieval script or agreement path instead of the raw files.
- Record third-party asset licenses so the release is clean.

## Ethics and consent for media artifacts

Multimedia artifacts carry people's faces, voices, and content, so the ethics review is not a
formality:

- **Consent and rights** — confirm you may redistribute the media; user-generated content often
  cannot be re-hosted, so ship a retrieval script or agreement path instead.
- **Privacy** — remove or justify identifiable individuals who did not consent; a dataset of
  scraped faces is a rejection risk regardless of its scale.
- **Documentation** — a dataset card that states collection method, consent, license, and known
  biases is part of the contribution, not paperwork.

## Timeline: review artifact, then release

```text
before paper deadline:  anonymous review artifact ready (repo + data mirror, no identity)
during review:          reviewers/AC access the anonymous artifact
on acceptance:          build the public release (de-anonymized repo + DOI + license)
by camera-ready:        release replaces the anonymous mirror; dataset/model final
```

Plan the public release early even though it ships late — a scramble at camera-ready is how
projects end up with a broken anonymous link and no working public archive.

## Output format

```text
[Track] Open Source / Dataset / Reproducibility / main-track supplement
[Blinding] correct for track / mismatch
[Review artifact] anonymous + runnable / gaps: <list>
[Release artifact] archived + licensed / gaps: <list>
[Rights] code+data+media licenses set / open questions: <list>
[Top fixes] <ordered>
```