Datasets:

dataseek
/

ptbr-academic

license: cc-by-4.0
language:
  - pt
pretty_name: PT-BR Academic Corpus (CAPES theses + SciELO abstracts/books)
size_categories:
  - 1M<n<10M
task_categories:
  - text-generation
tags:
  - pt-br
  - brazilian-portuguese
  - academic
  - theses
  - abstracts
  - pretraining
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet

PT-BR Academic Corpus (CAPES theses + SciELO abstracts/books)

Part of the MagTina350m pretrain corpus release by Dataseek under the Magestic.ai brand. This is one of nine silver-layer datasets that fed dataseek/magtina350m-base.

Summary

1.86 M Brazilian-Portuguese academic documents combining CAPES thesis and dissertation abstracts, SciELO article abstracts and SciELO open-access books. Complements the fulltext SciELO articles corpus with broader coverage at shorter document lengths.

Source and collection method

Sources: chenghao/scielo_books, eduagarcia/scielo_abstracts, eduagarcia/capes_teses_dissertacoes (all HuggingFace) → NFKD → SHA-1(text[:512]) dedup → per-row source tag preserved.

ETL script (in the MagTina1B repository): scripts/etl/17_academic_pt_v1.py (public release of the ETL scripts is on the roadmap; until then the data card below documents the recipe in full).

Filters and deduplication

The following filters were applied before this dataset reached its silver (release-ready) state:

alpha_ratio ≥ 0.65
len(text) ≥ 200 chars
SHA-1 dedup across all three upstream sources

Global URL-normalised deduplication was applied across all web-derived corpora (webpages, news, blogs) so the same article does not appear twice across those three datasets.

Schema

Column	Type	Description
`source`	`string`	Upstream identifier.
`text`	`string`	Document text.
`n_chars`	`int32`	Character count.
`n_words`	`int32`	Word count.
`meta_json`	`string`	JSON-encoded source-specific metadata.

Columns dropped at export (kept private as ETL internals): none

Size statistics

Metric	Value
Rows	1.86 M (1,862,793)
Characters	4.45 B (4,449,658,548)
Estimated tokens (PT-BR, chars / 4.5)	988.81 M
Compressed Parquet on disk	~2.49 GB

Used in MagTina350m pretrain: 0.989 B tokens (5.7 % of MagTina350m's 17.39 B-token pretrain budget).

How to load

from datasets import load_dataset

ds = load_dataset("dataseek/ptbr-academic", split="train", streaming=True)
for row in ds.take(5):
    print(row["text"][:200])

Streaming is recommended for the larger configs. For the smaller datasets (ptbr-dou, ptbr-books-publicos) eager loading is fine.

Licensing

Mixed CC-BY 4.0 (SciELO subset) and CC-BY-SA / institutional-academic licensing for CAPES theses & dissertations. The conservative interpretation is to treat the aggregate as CC-BY 4.0 with attribution to the per-row source. See meta_json for upstream identifiers.

Upstream attribution: chenghao/scielo_books, eduagarcia/scielo_abstracts, eduagarcia/capes_teses_dissertacoes (HuggingFace)

Citation

If you use this dataset, please cite both the upstream source and MagTina350m:

@misc{magtina350m_pretrain_2026,
  title  = {MagTina350m pretrain corpus — PT-BR Academic Corpus (CAPES theses + SciELO abstracts/books)},
  author = {Frasson, Ricardo and {Dataseek Team}},
  year   = 2026,
  publisher = {Hugging Face},
  url    = {https://huggingface.co/datasets/dataseek/ptbr-academic}
}

Please also honour the upstream license terms — for CC-BY-derived data, attribution to the upstream creators is mandatory; for CC-BY-SA, downstream derivatives must remain CC-BY-SA-compatible.

Intended use

Pre-training, continued pre-training, or domain-adapting of Brazilian Portuguese language models.
PT-BR NLP research where statistically representative public-web / academic / legal / encyclopedic data is needed.
Reproducing or improving on the MagTina350m result.

Known limitations and PII statement

Text was NOT PII-scrubbed. URLs, emails, phone numbers and personal names that occurred in the source data may still be present. We strip zero-width characters and normalise Unicode but we do not run an NER pass.
Crawled data carries upstream biases of CommonCrawl, Wikipedia, news outlets and academic institutions present in the source. We have not audited these.
No safety filtering beyond langid and basic alpha-ratio gates. Hate-speech, spam and adult content present in the source remain unless caught incidentally.
Provenance preserved at row level. Every row has either a url, source or doc_id column that points back to upstream — this is intentional, so consumers can re-license, redact or filter.

Related releases

Model: dataseek/magtina350m-base (354.6 M params, pretrained on this corpus + 8 sibling datasets)
Instruct model: dataseek/magtina350m-instruct
Sibling datasets: see dataseek/ptbr-* for all nine corpora

License

cc-by-4.0