Add dataset card README

README.md

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+---
+dataset_info:
+  features:
+    - name: text
+      dtype: string
+    - name: corpus_id
+      dtype: int64
+    - name: external_ids
+      dtype: string
+    - name: source
+      dtype: string
+    - name: title
+      dtype: string
+    - name: authors
+      sequence: string
+    - name: abstract
+      dtype: string
+    - name: sections
+      sequence: string
+    - name: figures
+      sequence: string
+    - name: references
+      sequence: string
+  splits:
+    - name: train
+      num_examples: 14515649
+size_categories:
+  - 10M<n<100M
+license: odc-by
+task_categories:
+  - text-generation
+  - feature-extraction
+  - text-classification
+language:
+  - en
+tags:
+  - scientific-papers
+  - academic
+  - s2orc
+  - semantic-scholar
+  - nlp
+  - full-text
+pretty_name: S2ORC Full (Semantic Scholar Open Research Corpus)
+---
+
+# S2ORC Full — Semantic Scholar Open Research Corpus
+
+A complete redistribution of the [S2ORC](https://github.com/allenai/s2orc) dataset in Parquet format on Hugging Face, containing **14.5 million** academic papers with full text, structured metadata, and citation information.
+
+## Dataset Description
+
+S2ORC (Semantic Scholar Open Research Corpus) is a general-purpose corpus for NLP and text mining research over scientific papers, originally developed by the [Allen Institute for AI](https://allenai.org/). This version provides the full dataset in a convenient, ready-to-use format via the Hugging Face Hub.
+
+The corpus spans all major academic disciplines — computer science, biomedicine, physics, mathematics, social sciences, and more — making it suitable for large-scale scientific language modeling, information extraction, citation analysis, and document understanding tasks.
+
+| | |
+|---|---|
+| **Papers** | 14,515,649 |
+| **Format** | 2,690 Parquet shards |
+| **Total Size** | ~1.4 TB |
+| **Language** | English |
+| **License** | [ODC-By 1.0](https://opendatacommons.org/licenses/by/1-0/) |
+
+## Schema
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `text` | `string` | Full text of the paper (4 – 856k chars) |
+| `corpus_id` | `int64` | Semantic Scholar corpus identifier |
+| `external_ids` | `dict` | External identifiers: ArXiv, MAG, ACL, PubMed, PubMedCentral, DBLP, DOI |
+| `source` | `dict` | Source PDF URL, content hash, and open-access status |
+| `title` | `string` | Paper title |
+| `authors` | `list` | List of author names |
+| `abstract` | `string` | Paper abstract |
+| `sections` | `list` | Structured full-text sections |
+| `figures` | `list` | Figure and table metadata |
+| `references` | `list` | Cited references |
+
+## Quick Start
+
+### Stream (no full download required)
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("AlgorithmicResearchGroup/s2orc_full", split="train", streaming=True)
+
+for paper in ds:
+    print(paper["title"])
+    print(paper["abstract"][:200])
+    break
+```
+
+### Load with Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+# Full dataset (~1.4 TB) — make sure you have enough disk space
+ds = load_dataset("AlgorithmicResearchGroup/s2orc_full", split="train")
+```
+
+### Load with Dask (for distributed processing)
+
+```python
+import dask.dataframe as dd
+
+df = dd.read_parquet("hf://datasets/AlgorithmicResearchGroup/s2orc_full/data/")
+```
+
+### Load with Polars
+
+```python
+import polars as pl
+
+df = pl.read_parquet("hf://datasets/AlgorithmicResearchGroup/s2orc_full/data/train-00000-of-02690.parquet")
+```
+
+## Use Cases
+
+- **Scientific language modeling** — Pre-train or fine-tune language models on academic text
+- **Citation analysis** — Study citation networks and reference patterns across disciplines
+- **Information extraction** — Extract entities, relations, and structured knowledge from papers
+- **Document summarization** — Train abstractive or extractive summarization models on paper/abstract pairs
+- **Bibliometric research** — Analyze publication trends, authorship patterns, and cross-disciplinary connections
+- **Retrieval-augmented generation** — Build retrieval systems over the scientific literature
+
+## Citation
+
+If you use this dataset, please cite the original S2ORC paper:
+
+```bibtex
+@inproceedings{lo-etal-2020-s2orc,
+    title = "{S2ORC}: The Semantic Scholar Open Research Corpus",
+    author = "Lo, Kyle and Wang, Lucy Lu and Neumann, Mark and Kinney, Rodney and Weld, Daniel",
+    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.acl-main.447",
+    doi = "10.18653/v1/2020.acl-main.447",
+    pages = "4969--4983"
+}
+```
+
+## License
+
+This dataset is released under the [Open Data Commons Attribution License (ODC-By 1.0)](https://opendatacommons.org/licenses/by/1-0/). You are free to share and adapt the data, provided you give appropriate attribution.