Hugging Face's logo

Datasets:
Nacryos
/
ancient-scripts-datasets

Tasks:
Text Classification
Token Classification
Modalities:
Tabular
Text
Formats:
parquet
Languages:
Multiple languages
Size:
10M - 100M
ArXiv:
Tags:
linguistics
historical-linguistics
cognate-detection
phylogenetics
ancient-languages
IPA
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
ancient-scripts-datasets / docs /changelog /INDEX.md
Alvin
Fix Linear B Wiktionary tr= extraction bug causing 5 wrong IPA values
105ea5a
preview code
|
raw
history blame contribute delete
2.45 kB

Dataset Changelog Index

All changes to the Nacryos/ancient-scripts-datasets HuggingFace dataset are logged here in reverse chronological order. Each entry is a self-contained document explaining what changed, why, how (scripts, sources, methodology), and how it was validated.

Changelog Entries

Date Entry Summary
2026-03-19 008_linear_b_dataset.md Linear B (Mycenaean Greek) dataset: 211 signs, 74 IPA mappings, 2,478 words from Unicode/Shannon/Wiktionary/IE-CoR
2026-03-19 007_phono_quality_flagging.md Added Phono_Quality column (strong/moderate/weak/none/unscored), downgraded 27K Robbeets cross-family to "contested", fixed 2.9K Sino-Tibetan doubt markers
2026-03-19 006_tier1_cldf_ingestion.md +573K expert cognate pairs from IE-CoR, Robbeets, Savelyev — 31 new ancient languages, 4-agent adversarial audit
2026-03-15 005_parquet_conversion.md Added Parquet files + YAML dataset card for HF datasets library integration
2026-03-14 004_phylo_enrichment.md Added phylogenetic metadata (phylo_pairs.tsv) derived from Glottolog CLDF
2026-03-13 003_cognate_pairs_v2.md Rebuilt all cognate pairs from scratch (v2), fixing 6 critical pipeline bugs
2026-03-12 002_database_rectification.md IPA pipeline fixes, ancient language expansion, adversarial audits
2026-03-08 001_initial_dataset.md Initial dataset creation: 1,178 languages, lexicons, cognate pairs v1, validation sets

Log Standard

Every changelog entry MUST include:

  1. Objective — Why was the data changed?
  2. Scripts — What scripts were written/run? What does each do?
  3. Sources — What external data sources were used?
  4. Source Reputability — Academic credentials, peer-review status, citation counts, cross-referencing
  5. Methodology — Algorithms, formulas, thresholds, and academic references
  6. Tests Performed — Automated tests, known-answer checks, random sampling
  7. Cross-Referencing — How were random samples verified against original sources?
  8. Output Summary — Row counts, file sizes, schema changes
  9. Commits — Git commit hashes for traceability