Update README.md

README.md

@@ -22,7 +22,8 @@ configs:
   data_files:
   - split: train
     path: train-*.parquet
-license: cc-by-nc-4.0
+license: other
+license_name: pubmed-ocr-multiple-cc-licenses
 task_categories:
 - image-to-text
 - image-text-to-text
@@ -38,6 +39,187 @@ size_categories:
 - 1M<n<10M
 ---
 
-# pubmed-ocr
+# PubMed-OCR: PMC Open Access OCR Annotations
 
-Processed output from PubMed OCR pipeline (CC-BY-NC).
+PubMed-OCR is an OCR-centric corpus of scientific articles derived from PubMed Central Open Access PDFs. Each **page** is rendered to an image and annotated with **Google Cloud Vision OCR**, released in a compact JSON schema with **word-, line-, and paragraph-level** bounding boxes.
+
+**Scale (release):**
+- **209.5K** articles
+- **~1.5M** pages
+- **~1.3B** words (OCR tokens)
+
+This dataset is intended to support layout-aware modeling, coordinate-grounded QA, and evaluation of OCR-dependent pipelines on scientific documents.
+
+## Dataset Details
+
+### Dataset Description
+
+- **Curated by:** Roots.ai
+- **Point of contact:** ai-ml@roots.ai
+- **Language:** English (primarily; see limitations)
+- **Data unit:** **1 row = 1 PDF page** (unique by `{basename, page}`)
+- **License:** See **Licensing** section (source-article licenses; per-row `license` field)
+
+### Dataset Sources
+
+- **Repository:** https://huggingface.co/datasets/rootsautomation/pubmed-ocr
+- **Paper:** Coming Soon
+- **Source corpus:** PubMed Central Open Access (PMCOA)
+
+## Uses
+
+### Direct Use
+
+PubMed-OCR is suitable for:
+- Training/evaluating **OCR-aware** or **layout-aware** document models
+- Testing robustness of pipelines that depend on OCR (parsing, retrieval, extraction)
+- Building tasks that require **coordinate-grounded evidence** (e.g., quote-and-locate, region attribution)
+- Benchmark curation for scientific PDFs (tables, formulas, captions, references)
+
+### Out-of-Scope Use
+
+- Do **not** treat OCR output as gold text; it contains recognition errors.
+- Not intended for clinical/medical decision-making.
+- Not intended for learning copyrighted content outside the applicable license terms.
+- Not intended as a reading-order ground truth dataset.
+
+## Dataset Structure
+
+### Data Instances
+
+Each row corresponds to a single page. Key identifiers:
+- `basename`: page group identifier (article-level)
+- `page`: page index within the article
+
+`ocr_json` is a JSON string containing OCR outputs with bounding boxes in **pixel coordinates** for the rendered page image.
+
+Example (schema sketch; fields may include additional metadata):
+
+```json
+{
+  "image": {"width": 1275, "height": 1650, "dpi": 150},
+  "text": {
+    "words": [{"text": "Introduction", "bbox": [74, 132, 210, 156]}],
+    "lines": [{"text": "Introduction", "bbox": [74, 130, 612, 160]}],
+    "paragraphs": [{"text": "…", "bbox": [70, 120, 1180, 420]}]
+  }
+}
+```
+
+### Data Fields
+
+* `basename` *(string)*: article/page group identifier.
+* `page` *(int32)*: page index within the PDF/article.
+* `license` *(string)*: the **source article’s license** (e.g., `cc-by-4.0`, `cc-by-nc-4.0`, …).
+* `pmid` *(string)*: PubMed ID when available.
+* `accession_id` *(string)*: accession identifier (e.g., PMCID or internal ID).
+* `article_citation` *(string)*: a citation string for the source article.
+* `pdf_bytes` *(binary)*: raw PDF bytes **when redistribution is permitted**; may be empty/null otherwise.
+* `ocr_json` *(string)*: OCR output JSON (see above).
+
+### Splits
+
+This release is provided as a single split (`train`) because it is primarily a **corpus**.
+For benchmarking, consider constructing evaluation splits that reduce leakage, e.g.:
+
+* **Journal-level splits** (hold out entire journals)
+* **Time-based splits** (hold out by publication year)
+* **PMID/PMCID disjoint splits** (article-level separation)
+
+## Dataset Creation
+
+### Curation Rationale
+
+Scientific PDFs are dense (formulas, tables, multi-column layouts). Many PMCOA datasets rely on PDF/XML alignment, which can miss scanned pages or inherit parser noise. PubMed-OCR provides OCR-native supervision directly from rendered page images, enabling OCR-dependent evaluation and layout-aware learning without PDF/XML alignment.
+
+### Source Data
+
+#### Data Collection and Processing
+
+High-level pipeline:
+
+1. Download PubMed Central Open Access PDFs (PMCOA) and filter to licenses permitting redistribution of derived artifacts.
+2. Uniformly sample 209.5K documents.
+3. Render each page at **150 DPI**.
+4. Run **Google Cloud Vision** `document_text_detection` on page images.
+5. Extract word- and paragraph-level polygons and canonicalize to axis-aligned bboxes `[x1, y1, x3, y3]`.
+6. Reconstruct **line** bboxes by clustering words with similar vertical alignment (heuristic).
+7. Emit one row per page with `ocr_json` (+ `pdf_bytes` where permitted).
+
+#### Who are the source data producers?
+
+The source texts were authored by scientific article authors and published via journals hosted in PubMed Central Open Access.
+
+### Annotations
+
+#### Annotation process
+
+Annotations are machine-generated via Google Cloud Vision OCR.
+
+* **Words / paragraphs:** provided by the OCR engine
+* **Lines:** reconstructed heuristically from word boxes (see Limitations)
+
+#### Who are the annotators?
+
+The OCR engine is the annotator. No manual annotation was performed in this release.
+
+#### Personal and Sensitive Information
+
+Scientific articles can contain author names, affiliations, acknowledgements, emails, and citations. Content is drawn from publicly available PMCOA articles; no additional anonymization is applied.
+
+## Bias, Risks, and Limitations
+
+* **Single OCR engine:** outputs reflect Google Vision’s strengths/weaknesses and may not generalize to other OCR systems.
+* **Heuristic line reconstruction:** line grouping and reading order can be imperfect, especially in multi-column layouts and around formulas/tables.
+* **Axis-aligned boxes:** original OCR polygons are simplified to rectangles.
+* **Domain skew:** PMCOA’s journal distribution is heavy-tailed (high-volume journals dominate).
+* **Non-text regions:** this dataset does not provide gold structure for tables/figures/formulas (only what OCR emits + derived lines).
+
+### Recommendations
+
+* When reporting results, specify whether you use **words**, **lines**, or **paragraphs**, and whether you re-linearize text.
+* For fair evaluation, prefer **journal-disjoint** or **article-disjoint** splits.
+* If you need table/figure structure, pair this with a layout/table dataset (or run a layout model on top).
+
+## Licensing
+
+This dataset contains content derived from PMCOA articles.
+
+* Each example inherits the **license of its source article**, recorded in the `license` field.
+* Users are responsible for complying with the license terms for any subset they use.
+* If `pdf_bytes` is present, it is provided only where redistribution is permitted.
+
+## Citation
+
+If you use PubMed-OCR, please cite:
+
+```bibtex
+@article{heidenreich2025pubmedocr,
+  title={PubMed-OCR: PMC Open Access OCR Annotations},
+  author={Heidenreich, Hunter and Getachew, Yosheb and Dinica, Olivia and Elliott, Ben},
+  journal={arXiv preprint arXiv:XXXX.XXXXX},
+  year={2025}
+}
+```
+
+## How to Load
+
+```python
+from datasets import load_dataset
+import json
+
+ds = load_dataset("rootsautomation/pubmed-ocr", split="train")
+
+row = ds[0]
+ocr = json.loads(row["ocr_json"])
+words = ocr["text"]["words"]
+```
+
+For large-scale iteration, consider streaming:
+
+```python
+ds = load_dataset("rootsautomation/pubmed-ocr", split="train", streaming=True)
+for row in ds:
+    ocr = json.loads(row["ocr_json"])
+    ...
+```