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electricsheepasia/asia-owid-representation-of-women-in-the-judiciary

Representation Of Women In The Judiciary | Asia (Our World in Data) 🌏 92 observations · 25 Asia countries · 2015–2024 · Repackaged by Electric Sheep Asia TL;DR This dataset contains 92 observations of Representation Of Women In The Judiciary data across 25 Asia countries, spanning 2015–2024. About the source Source: Our World in Data Publisher: Our World in Data License: cc-by-4.0 Topic: Representation Of Women In The Judiciary… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-owid-representation-of-women-in-the-judiciary.

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Dataset Card

Representation Of Women In The Judiciary | Asia (Our World in Data)

🌏 92 observations · 25 Asia countries · 2015–2024 · Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

rows countries years license

TL;DR

This dataset contains 92 observations of Representation Of Women In The Judiciary data across 25 Asia countries, spanning 2015–2024.

About the source

  • —Source: Our World in Data
  • —Publisher: Our World in Data
  • —License: cc-by-4.0
  • —Topic: Representation Of Women In The Judiciary

Geographic coverage

25 Asia countries · top rows shown below, sorted by row count:

CountryRowsFirst yearLast year
SGP1020152024
TUR820162023
QAT720182024
CYP720162024
ISR720162024
JPN520202024
TLS520202024
ARM420162022
ARE420152018
AZE420162022
KAZ420182022
LKA420182021
GEO420162022
PSE420202023
BHR320202023
...10 more countries

Schema

ColumnTypeDescriptionExample
country_namestring—Armenia
country_iso3string—ARM
yearint64—2016
Proportions of positions in the judiciary compared to national distributions (ratio) - All age ranges or no breaks by age - No breakdown by disability - TOTAL - Female - JUDGESfloat64—0.5

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-owid-representation-of-women-in-the-judiciary")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python
indonesia = df[df["country_iso3"] == "IDN"]

Time-series for a single indicator

python
sample = df.sort_values("year")
sample.plot(x="year", y="Proportions of positions in the judiciary compared to national distributions (ratio) - All age ranges or no breaks by age - No breakdown by disability - TOTAL - Female - JUDGES")

Citation

bibtex
@misc{asia_owid_representation_of_women_in_the_judiciary_2024,
  title        = {Representation Of Women In The Judiciary | Asia (Our World in Data)},
  author       = {Our World in Data},
  year         = {2024},
  url          = {https://ourworldindata.org/grapher/representation-of-women-in-the-judiciary},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-owid-representation-of-women-in-the-judiciary}}
}

License

Released under cc-by-4.0.

Original data © Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Asia repackaging.

About Electric Sheep

Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.

Browse the full collection: huggingface.co/electricsheepasia


Provenance: ingested 2026-06-07 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/representation-of-women-in-the-judiciary