This repository hosts a representative sample of the HainaWeb-Sci scientific web corpus, released anonymously for double-blind peer review at NeurIPS 2026.

The full 1.09T-token corpus, finalised license, classifier checkpoints, and long-term maintenance repository will be released at the non-anonymous location at camera-ready time, pending internal release review.

Sample Composition

The sample (~3.46 GB compressed) is uniformly distributed across all 14 STEM disciplines covered by HainaWeb-Sci:

AerospaceEngineering, Agriculture, Astronomy, Biology, Chemistry, CivilEngineering, ComputerScience, EarthScience, ElectricalElectronicEngineering, MaterialsScience, Mathematics, MechanicalEngineering, Medicine, Physics.

Each discipline lives under its own top-level folder; documents are stored as gzip-compressed JSONL shards (HainaWeb-Sci_<discipline>_<id>.jsonl.gz).

Data Schema

Each line is a JSON object. Refer to the paper's Appendix A (Datasheet) for the full field-level specification. Key fields:

text: main document text after the full curation pipeline.
metadata: original WARC fields (URI, content-type, crawl date, etc.).
websci_meta.language: detected language and confidence.
websci_meta.discipline: fine-grained discipline distribution from the two-tier discipline router (top-4 disciplines with confidence).
websci_meta.model_quality_score: content-driven fastText quality score.
websci_meta.sci_quality_score: 0–5 scientific value score from the rubric-based quality classifier.

Data Curation Pipeline

The HainaWeb-Sci corpus is constructed from DCLM-Pool and DCLM-Pool-aligned Common Crawl data (2013–March 2026). We process the raw data through a four-stage data-centric curation pipeline tailored to scientific web data:

Stage 1 (Data Preparation) transforms raw web data into a consistent textual foundation through WARC parsing, text extraction, URL filtering, and language identification.
Stage 2 (Quality Filtering) focuses on science-aware content refinement by combining rule-based and model-based filtering. Unlike general-purpose pipelines, both heuristic rules and learned quality models are customized to better preserve scientifically meaningful content, particularly symbolic expressions and structured reasoning.
Stage 3 (Deduplication) removes redundant content at scale to improve corpus diversity and training efficiency.
Stage 4 (Discipline-Specific Curation) introduces fine-grained discipline classification and scientific value scoring, enabling structured organization of scientific knowledge across 14 disciplines and providing quantitative signals for data selection.

PII Handling

Personally identifiable information has been masked using the standard Datatrove PII formatter: email addresses are replaced with placeholders (user@domain.org); public IPs are replaced with the RFC 5737 TEST-NET address (198.51.100.1).

License

This sample is released under the Open Data Commons Attribution License (ODC-By) v1.0. Use of the data is also subject to the original Common Crawl terms of use.

Intended Use

This anonymous sample is provided solely for reviewer evaluation of the NeurIPS 2026 submission. Redistribution prior to camera-ready is not permitted. The full corpus, code, classifier checkpoints, and Datasheet will be released at the non-anonymous repository at camera-ready.

Limitations

English-only; humanities and social sciences are out of scope.
Code and multimodal content are not included.
Sample size is intentionally limited for reviewer inspection; quality metrics reported in the paper are computed on the full corpus, not this sample.

Reporting Issues

Anonymous review channel — please contact the authors via the OpenReview submission discussion thread.

Downloads last month: 33

Total file size:

3.46 GB