---
{
  "id": "ASD-EXP-RESEARCH-REPRODUCIBILITY-EN",
  "path": "/en/research/reproducibility",
  "title": "Reproducibility Resources and Templates",
  "title_en": "Reproducibility Resources and Templates",
  "category": "Experimental Program",
  "summary": "Download runnable comparison baselines, a notebook, input schemas, expected outputs and checksums; real observations and ASD closure remain separate requirements.",
  "template": "document",
  "version": "1.0.0",
  "updated": "2026-09-22",
  "date_published": "2026-09-22",
  "canonical": false,
  "source_tier": "published",
  "theory_status": "OPEN",
  "evidence_status": "UNTESTED",
  "locale": "en",
  "module_key": "experimental-program",
  "module_navigation": [
    {
      "path": "/en/tests",
      "label": "Program",
      "en": "Program"
    },
    {
      "path": "/en/tests/predictions",
      "label": "Registry",
      "en": "Registry"
    },
    {
      "path": "/en/tests/falsification",
      "label": "Falsification",
      "en": "Falsification"
    },
    {
      "path": "/en/tests/priority-experiments",
      "label": "Priorities",
      "en": "Priorities"
    },
    {
      "path": "/en/tests/reproducibility",
      "label": "Reproducibility",
      "en": "Reproducibility"
    }
  ],
  "canonical_scope": "Experimental program; not a new canonical physical result",
  "source_chapter": "Existing canonical module snapshots / 现有正式模块快照",
  "source_reference": "Predictions & Falsification v1.0 · review 2026-09-22",
  "source_version": "EXP-7ca61744dcd2",
  "source_sha256": "7ca61744dcd2129e7fcd6902223c71166f3ededeb84b599d544cb20dba88130a",
  "source_kind": "manifest",
  "source_manifest": "predictions-source-manifest.json",
  "source_manifest_url": "/downloads/ASD_Predictions_Source_Manifest.json",
  "last_reviewed": "2026-09-22",
  "depends_on": [],
  "related": [
    "/en/tests",
    "/en/tests/predictions",
    "/en/tests/falsification",
    "/en/tests/priority-experiments",
    "/en/tests/reproducibility"
  ],
  "open_problems": [],
  "equations": [],
  "sources": [
    {
      "label": "Predictions source and scope manifest",
      "kind": "research-program",
      "url": "/downloads/ASD_Predictions_Source_Manifest.json"
    },
    {
      "label": "Ota et al. (2022), Nature Physics 18, 1436–1440",
      "url": "https://www.nature.com/articles/s41567-022-01767-w",
      "kind": "external-reference"
    },
    {
      "label": "PDG (2024), Cosmic Rays review",
      "url": "https://pdg.lbl.gov/2024/reviews/rpp2024-rev-cosmic-rays.pdf",
      "kind": "external-reference"
    },
    {
      "label": "Wolf et al. (2006), Cold Atom Clock Test of Lorentz Invariance in the Matter Sector",
      "url": "https://arxiv.org/abs/hep-ph/0601024",
      "kind": "external-reference"
    },
    {
      "label": "BACON Collaboration (2021), optical frequency ratio network",
      "url": "https://www.nist.gov/publications/frequency-ratio-measurements-18-digit-accuracy-using-network-optical-clocks",
      "kind": "external-reference"
    }
  ],
  "outline": [
    "Download and run",
    "Reproducible workbench content",
    "Before importing real data",
    "Versions and contributions"
  ],
  "seo_title": "Reproducibility Resources and Templates | ASD Predictions & Falsification v1.0",
  "canonical_url": "https://asd-physics.org/en/research/reproducibility",
  "translation_path": "/research/reproducibility",
  "mathematical_review": "INCOMPLETE",
  "evidence_note": "No closed quantitative discriminator; planning/comparison only.",
  "translation_of": "/research/reproducibility",
  "translation_version": "1.0.0"
}
---
## Download and run

[ASD Experimental Reproducibility Kit v1.0](/downloads/ASD_Experimental_Reproducibility_Kit.zip)

After extraction, use Python 3.10 or later:

```sh
python run.py --output results
python verify.py
```

Calculations use only the standard library. The notebook calls the same core functions and requires your own Jupyter environment. The numerical script reads bundled synthetic inputs and writes JSON and CSV. Expected figures are bundled; regenerating them uses optional matplotlib 3.10.8 and figure.py. ASD outputs and predicted differences are explicitly null. Nothing automatically requests paper data or contacts authors.

## Reproducible workbench content

The clock model computes a user-selected background-axis projection. The muon model first computes standard single-path survival, then sums SYNTHETIC bin weights and acceptance. The charge model computes a normalized standard point-source field with a Gaussian response. Defaults match the website. README records units, precision, truncation and implementation version; expected outputs and a numerical summary are retained.

## Before importing real data

Four CSV schema templates cover clocks, muons, high-γ fields and S301, with units, separate covariance, observed/model/synthetic labels, zero points and reference frames. Real inputs need sources, licenses and SHA-256. Synthetic muon bins must not be treated as a production spectrum or measured counts.

## Versions and contributions

Bind each run to a Prediction ID and canonical source snapshot, retaining Python version and code checksums. Model changes require new expected outputs and ledger entries while preserving old versions. Follow the [reproducibility standard](/en/tests/reproducibility). This release provides standards and templates; the full data release remains a later phase.