# allenai/s2orc

S2ORC: The Semantic Scholar Open Research Corpus:  https://www.aclweb.org/anthology/2020.acl-main.447/

Repository: https://github.com/allenai/s2orc
Canonical: https://ross.abutalabs.com/products/s2orc
Language: Python
License Family: other
Last push: 2024-04-26T20:45:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2494, "days_push": 859, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1082, forks 79 (observed 2026-08-28T04:03:31.026000+00:00)

## What it is
S2ORC is a large-scale open corpus of over 136 million scientific paper records with full text for roughly 12 million papers, built for NLP and text mining research. It is now distributed as a continuously updated bulk dataset via the Semantic Scholar Public API, with tooling like s2orc-doc2 for parsing PDFs and LaTeX into JSON.

## Use cases
- train language models on scientific full text
- mine citations and citation contexts from research papers
- build search or recommendation systems over scholarly literature
- extract structured text from PDFs and LaTeX sources
- run NLP experiments on academic corpora
- analyze trends across millions of scientific papers

## When to choose
- you need large-scale full-text scientific paper data for NLP or text mining
- you want citation-linked metadata and parsed paper text in a consistent schema
- you want a continuously updated corpus via the Semantic Scholar API

## When to avoid
- you need a small curated dataset rather than hundreds of gigabytes of data
- you require a permissive software license for the repo code (the corpus is ODC-By 1.0 and the repo has no license)
- you need hands-on support for the legacy 2020 release, which is no longer supported

## Facets
- artifact type: dataset
- maturity: maintenance
- function: nlp, data-science, parser
- domain: data-science, big-data
- platform: python, cross-platform
- tags: scientific-papers, research-corpus, text-mining, open-research-corpus, semantic-scholar, pdf-parsing, latex-parsing, natural-language-processing

## Member repositories
- allenai/s2orc (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:31.026000+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:51:14.378589+00:00, confidence not recorded.
  - readme: https://github.com/allenai/s2orc (fetched 2026-08-28T04:03:31.026000+00:00, sha 885ed1b78569)
- Data as of 2026-08-30T08:39:29.467469+00:00.