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oercommons-v1-optimized

Tasks:
Text Generation
Image-Text-to-Text
Modalities:
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Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
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text
image
multimodal
education
open-educational-resources
document-understanding
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metadata 
pretty_name: OERCommons v1 Optimized
task_categories:
  - text-generation
  - image-text-to-text
language:
  - en
license: other
size_categories:
  - n<10K
tags:
  - text
  - image
  - multimodal
  - education
  - open-educational-resources
  - document-understanding
  - markdown
  - provenance
  - data-quality
  - datasets
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet

OERCommons v1 Optimized

Authors: Junjie Wang and Yuhan Sun
Hosted by: PIN Team
Dataset: pin-team/oercommons-v1-optimized

OERCommons v1 Optimized is a provenance-preserving multimodal pretraining corpus built on the OERCommons subset of The Common Pile v0.1, which serves as its upstream data and licensing baseline. We extend it with full-page recovery, canonical Markdown, ordered image/PDF/link metadata, conservative corrections, and integrity evidence.

At a glance

Item Result
Upstream Common Pile rows traced 9,339
Unique lesson records 6,251
Canonical text from live lesson pages / HF snapshots 6,221 / 30
Records with accepted changes / accepted patches 240 / 418
Image / PDF / non-PDF link occurrences 21,282 / 3,460 / 7,573
Pathful image / PDF occurrences 19,445 / 3,444
Unique packaged image / PDF files 16,709 / 3,255
Split and configuration one train split, one default config

The distributed Parquet has exactly seven top-level fields and 6,251 unique IDs. quality_signals.ppl is null for every row because no frozen PPL scorer was run.

What we built

  • Audited all 9,339 immutable upstream rows and consolidated duplicates into 6,251 stable lesson IDs without discarding provenance.
  • Recovered valid article.lesson pages and used the immutable Hugging Face snapshot only when live lesson content was unavailable.
  • Rendered headings, lists, tables, links, formulas, authored questions, and image positions as canonical Markdown instead of flat excerpts.
  • Aligned text with ordered image, PDF, and link objects carrying paths, source URLs, hashes, byte sizes, retrieval state, and license evidence.
  • Limited GLM-4.7 to narrow semantic windows; accepted patches must replay exactly and preserve numbers, URLs, Markdown, and media ordering.
  • Validated schema, patch replay, quality counts, Parquet read-back, media paths, MIME types, sizes, and SHA-256 hashes deterministically.
Common Pile: 9,339 rows
          │ audit + deduplicate
          ▼
   6,251 stable lessons
      ┌───┴────────────┐
      ▼                ▼
live pages: 6,221   HF snapshots: 30
      └───┬────────────┘
          ▼
Markdown + ordered media + provenance
          ▼
conservative patches → Parquet + media/

Dataset structure 

record
├── content
│   ├── original
│   └── optimized
├── content_media
│   ├── images ──> media/images/...
│   ├── pdfs   ──> media/pdfs/...
│   └── links  ──> source URLs
├── meta + license
├── quality_signals
└── optimization + replayable patches
Field Meaning
id Stable oercommons:lesson:<lesson_id> identifier.
meta Language, dates, canonical URL, source details, and lesson metadata.
license Normalized record-level license; media may have separate rights.
content Canonical Markdown before and after accepted localized patches.
content_media Ordered images, PDFs, and links with provenance and state.
quality_signals Character counts, source gaps, optional PPL, and flags.
optimization Content source, semantic window, status, and replayable patches.

content.original is canonical Markdown, not raw HTML or flattened Common Pile text. content.optimized differs only where a localized correction passed all acceptance checks.

Quick start 

from datasets import load_dataset
from huggingface_hub import hf_hub_download

repo_id = "pin-team/oercommons-v1-optimized"
dataset = load_dataset(repo_id, split="train")

row = dataset[0]
text = row["content"]["optimized"]
changed = row["content"]["original"] != text
print(row["id"], changed, text[:300])

def download_first_pathful(items):
    item = next((item for item in items if item["file_path"]), None)
    if item is None:
        return None
    return hf_hub_download(
        repo_id=repo_id,
        repo_type="dataset",
        filename=item["file_path"],
    )

local_image = download_first_pathful(row["content_media"]["images"])
local_pdf = download_first_pathful(row["content_media"]["pdfs"])

load_dataset() reads the Parquet main table; it does not automatically download the entire media/ tree. Text-only users can remove standalone <img src="/datasets/pin-team/oercommons-v1-optimized/resolve/main/media/images/..." /> lines. Multimodal users can resolve those paths on demand and verify them against the same-row metadata.

Standard record and real example

Every full row follows the seven-field structure above. This is a compact, selected-field view of the real Parquet record oercommons:lesson:122659; values are unchanged, while unshown fields remain in the dataset.

{
  "id": "oercommons:lesson:122659",
  "license": "CC-BY-4.0",
  "content_media": {
    "images": [{
      "id": "img_7ec2274d41ac82a24e1b",
      "file_path": "media/images/7e/img_7ec2274d41ac82a24e1b.jpg",
      "mime_type": "image/jpeg",
      "redistribution_status": "downloaded_local"
    }],
    "pdfs": [{
      "id": "119075",
      "file_path": "media/pdfs/68/68ca95cb814e898f9ecbdf0d4d5a61684a582120433ed4c61a195d08483e52a6.pdf",
      "mime_type": "application/pdf",
      "redistribution_status": "downloaded_local"
    }]
  },
  "quality_signals": {
    "char_count": {"original": 799, "optimized": 796},
    "missing_image_source_count": 0,
    "unavailable_resource_count": 0,
    "ppl": null
  },
  "optimization": {
    "status": "accepted",
    "content_source": "live_page",
    "patches": [
      {"before": "Informatation", "after": "Information", "category": "typo"},
      {"before": "science ,", "after": "science,", "category": "spacing"}
    ]
  }
}

The record's Markdown contains <img src="/datasets/pin-team/oercommons-v1-optimized/resolve/main/media/images/7e/img_7ec2274d41ac82a24e1b.jpg" /> at the source position, matching content_media.images[0].file_path. Its linked PDF is a separate, hash-addressed media object. This ordered text-media relationship is the multimodal unit; provenance and rights status remain explicit per object.

Validation, licensing, and limitations

The local all-media package passed structural acceptance:

  • Parquet read-back: 6,251 rows and 6,251 unique IDs.
  • Missing required top-level fields: 0.
  • Patch replay, media path/hash, missing-file, and orphan-file failures: 0.
  • Source gaps remain explicit: 530 image occurrences lack a recoverable source URL, and 19 resources are unavailable across 13 records.
Artifact Bytes SHA-256
data/train-00000-of-00001.parquet 43,642,237 07cd4920de8952d5e6f6c581d4d66ccabccc899cf8f96d709c3d98fa15af4ff6

Media states are intentionally distinct:

  • downloaded_local: byte-verified local research copy; rights not independently cleared.
  • source_only: source provenance retained, with no distributed local path.
  • source_missing_url: the page contains an image occurrence but no recoverable URL.
  • unavailable: the resource could not be retrieved or verified.

The current all-media acceptance reports hub_upload_ready=false: 22,889 media occurrences are downloaded_local, and none are bundled. Uploading those binaries is therefore a publisher policy decision, not evidence of cleared rights. The mixed corpus keeps license: other; inspect license, meta.ori_meta.license_url, and media-level status before use or redistribution.

Other limitations are concise but important: OER Commons pages and links can change; 30 records depend on frozen snapshots; source metadata can be empty; the corpus is English-dominant rather than guaranteed English-only; and the localized optimizer does not certify pedagogical quality, factual correctness, accessibility, or currentness.

Citation and provenance

Please cite this dataset release first:

@misc{wang2026oercommonsv1optimized,
  title        = {OERCommons v1 Optimized},
  author       = {Wang, Junjie and Sun, Yuhan},
  year         = {2026},
  publisher    = {PIN Team},
  howpublished = {Hugging Face dataset},
  url          = {https://huggingface.co/datasets/pin-team/oercommons-v1-optimized}
}

For upstream provenance, also cite the Common Pile paper:

@article{kandpal2025common,
  title   = {The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text},
  author  = {Kandpal, Nikhil and others},
  journal = {arXiv preprint arXiv:2506.05209},
  year    = {2025}
}

Primary provenance links: