| --- |
| license: odc-by |
| language: |
| - en |
| task_categories: |
| - text-generation |
| size_categories: |
| - 1B<n<10B |
| tags: |
| - scientific |
| - pretraining |
| - web-corpus |
| - stem |
| - common-crawl |
| --- |
| |
| # HainaWeb-Sci Sample (Anonymous Submission) |
|
|
| 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. |
|
|
| ## Curation Summary |
|
|
| Documents are sourced from Common Crawl snapshots (2013–March 2026) and |
| DCLM-Pool, then processed by the data-centric curation pipeline described |
| in Section 3 of the paper: |
|
|
| 1. Heuristic filtering with discipline-aware differential rules. |
| 2. Two-tier content-driven discipline routing. |
| 3. Rubric-based quality scoring with asymmetric penalties. |
| 4. Intra-dump fuzzy + intra-disciplinary exact deduplication. |
|
|
| ## 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](https://commoncrawl.org/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. |
|
|
