electricsheepafrica/africa-cross-gender-ties

Cross Gender Ties

Publisher: AI for Good at Meta · Source: HDX · License: cc-by · Updated: 2026-02-04

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Abstract

Data from the paper "Cross-Gender Social Ties Around the World", which is available here.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-02-04. Geographic scope: AFG, ALB, DZA, AND, AGO, ARG, ARM, AUS, and 173 others.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	178
Columns	16 (10 numeric, 6 categorical, 0 datetime)
Train split	142 rows
Test split	35 rows
Geographic scope	AFG, ALB, DZA, AND, AGO, ARG, ARM, AUS, and 173 others
Publisher	AI for Good at Meta
HDX last updated	2026-02-04

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Variables

Geographic — region_id (JM, RO, AM), region_name (JM, RO, AM), country (JM, RO, AM).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-05).

Other — level (country), cgfr_5 (range 0.0661–0.8291), cgfr_10 (range 0.0653–0.8507), cgfr_25 (range 0.0661–0.9263), cgfr_50 (range 0.0682–1.0636) and 6 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cross-gender-ties")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
region_id	object	0.6%	JM, RO, AM
region_name	object	0.6%	JM, RO, AM
country	object	0.6%	JM, RO, AM
level	object	0.0%	country
cgfr_5	float64	0.0%	0.0661 – 0.8291 (mean 0.541)
cgfr_10	float64	0.0%	0.0653 – 0.8507 (mean 0.5278)
cgfr_25	float64	0.0%	0.0661 – 0.9263 (mean 0.5344)
cgfr_50	float64	0.0%	0.0682 – 1.0636 (mean 0.5598)
cgfr_75	float64	0.0%	0.0695 – 1.1722 (mean 0.5818)
cgfr_100	float64	0.0%	0.0704 – 1.2563 (mean 0.6002)
cgfr_125	float64	0.0%	0.0712 – 1.3226 (mean 0.616)
cgfr_150	float64	0.0%	0.0718 – 1.3758 (mean 0.6296)
cgfr_175	float64	0.0%	0.0723 – 1.4198 (mean 0.6415)
cgfr_200	float64	0.0%	0.0728 – 1.4564 (mean 0.6521)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-05

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Numeric Summary

Column	Min	Max	Mean	Median
cgfr_5	0.0661	0.8291	0.541	0.5595
cgfr_10	0.0653	0.8507	0.5278	0.543
cgfr_25	0.0661	0.9263	0.5344	0.5373
cgfr_50	0.0682	1.0636	0.5598	0.5555
cgfr_75	0.0695	1.1722	0.5818	0.5695
cgfr_100	0.0704	1.2563	0.6002	0.5891
cgfr_125	0.0712	1.3226	0.616	0.6032
cgfr_150	0.0718	1.3758	0.6296	0.6164
cgfr_175	0.0723	1.4198	0.6415	0.6271
cgfr_200	0.0728	1.4564	0.6521	0.6363

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from AI for Good at Meta and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 181 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_cross_gender_ties,
  title     = {Cross Gender Ties},
  author    = {AI for Good at Meta},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/cross-gender-ties},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.