I Made the largest real Japanese People dataset with 37 attributes, 231k rows. (name, gender , age, occupations, height and more)
This dataset has all entry from wikidata who has Japanese citizenship and a wiki article, 231k in total. The dataset is on Kaggle.
A dataset like this can only be built from data that's already public, and Wikipedia is the largest source of that kind of public personal information. There are structured people datasets out there, but none cover as many Japanese entries as this one, which makes this the largest dataset of its kind.
I Queried via SPARQL against the QLever Wikidata endpoint, then cleaned/normalized with Python (name cleaning/splitting, BMI calculation, etc). dataset is in CSV format, 65MB. with multiple entry column separated with "|"
Sample rows (two people picked so together they cover every column):
qid: Q160847
kanji: 東條 英機
hiragana: とうじょう ひでき
description: 日本の陸軍軍人、政治家、第40代内閣総理大臣(1884-1948)
gender: M
birth_year: 1884
death_year: 1948
age: 64
death_age: 64
label: 東條英機
name_en: Hideki Tojo
is_japanese_name: True
readings: とうじょう ひでき
occupations: 士官|政治家|外交官
birth_places: 麹町区
alma_maters: 東京陸軍幼年学校|陸軍大学校|陸軍中央幼年学校|陸軍士官学校
positions_held: 内務大臣|内閣総理大臣|外務大臣|軍需省|陸軍省|文部省|参謀本部|農商務卿
death_places: 巣鴨拘置所
death_causes: 縊死
death_manners: 死刑
fathers: 東條英教
mothers: 東條千歳
spouses: 東條かつ子
children: 東條敏夫|東條輝雄|東條満喜枝
awards: 大礼記念章|チュラチョームクラーオ勲章|...(18 total)
parties: 大政翼賛会
notable_works: 東條英機の遺言|大詔を拝し奉りて
military_branches: 大日本帝国陸軍|関東軍
military_ranks: 陸軍大将
wiki_url: https://ja.wikipedia.org/wiki/東條英機
image_url: http://commons.wikimedia.org/wiki/Special:FilePath/Hideki%20Tojo.jpg
qid: Q11467665
kanji: 山咲 トオル
hiragana: やまざき とおる
description: 日本の漫画家、タレント
gender: M
birth_year: 1969
age: 57
label: 山咲トオル
name_en: Tōru Yamazaki
is_japanese_name: True
readings: やまざき とおる
occupations: タレント|日本の漫画家
birth_places: 東京都
genres: ホラー漫画
height_cm: 170.0
weight_kg: 57.0
bmi: 19.7
siblings: 中沢初絵
wiki_url: https://ja.wikipedia.org/wiki/山咲トオル
Potential Use Cases & As a Dataset
| Dataset Angle | Task | Columns |
|---|---|---|
| Japanese Name with Gender | Gender inference from name | kanji, hiragana, gender |
| Kanji, Hiragana Name Pairs | Reading (furigana) prediction | kanji, hiragana, readings |
| Family Relations | Genealogy / kinship network analysis | fathers, mothers, spouses, siblings, children |
| Portrait Images | Gender/age/occupation estimation | image_url, gender, birth_year |
| Athlete / Model Physique | Body-composition trend analysis by occupation | height_cm, weight_kg, bmi, occupations |
| Politicians | Politician attribute & career analysis | parties, positions_held, birth_places, alma_maters |
| Awards | Field/attribute analysis of award recipients | awards, occupations, gender |
| Cause & Manner of Death | Statistical analysis of death cause and age | death_causes, death_manners, death_age |
| Birthplace / Alma Mater | Geographic distribution and education-career correlation | birth_places, alma_maters, occupations |
| Age | Age-based demographic analysis | birth_year, death_year, death_age |
| Writers / Artists Database | Classification of writers/artists by notable works and genre | notable_works, genres, occupations |
| Military | Historical figures database | military_branches, military_ranks, birth_year |
limitations: The least-filled column, military_ranks, has only ~3k rows filled, while the average filled-column count per row is 15.25 out of 37 (41.2%). No rows are dropped to keep this the full Wiki-derived dataset. But I added is_japanese_name column so you can reliably excludes non-Japanese names (virtually no false negatives, ~0.1% false positives). About 1% of rows have a reversed family/given order in the hiragana column.
Crawler code : Kaggle notebook / GitHub. This dataset will be updated automatically with the crawler.
Posted Sep 8, 2026, 11:37 AM UTC