electricsheepafrica/africa-science-technology-south-africa

South Africa - Science and Technology

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27

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Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Technological innovation, often fueled by governments, drives industrial growth and helps raise living standards. Data here aims to shed light on countries technology base: research and development, scientific and technical journal articles, high-technology exports, royalty and license fees, and patents and trademarks. Sources include the UNESCO Institute for Statistics, the U.S. National Science Board, the UN Statistics Division, the International Monetary Fund, and the World Intellectual Property Organization.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: ZAF.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	336
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	268 rows
Test split	67 rows
Geographic scope	ZAF
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (South Africa), country_iso3 (ZAF), year (range 1960.0–2024.0).

Outcome / Measurement — value (range 0.5269–2861275747.0).

Identifier / Metadata — indicator_name (Charges for the use of intellectual property, payments (BoP, current US$), Charges for the use of intellectual property, receipts (BoP, current US$), Patent applications, nonresidents), indicator_code (BM.GSR.ROYL.CD, BX.GSR.ROYL.CD, IP.PAT.NRES), esa_source (HDX), esa_processed (2026-04-27).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-science-technology-south-africa")
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
country_name	object	0.0%	South Africa
country_iso3	object	0.0%	ZAF
year	int64	0.0%	1960.0 – 2024.0 (mean 2001.5655)
indicator_name	object	0.0%	Charges for the use of intellectual property, payments (BoP, current US$), Charges for the use of intellectual property, receipts (BoP, current US$), Patent applications, nonresidents
indicator_code	object	0.0%	BM.GSR.ROYL.CD, BX.GSR.ROYL.CD, IP.PAT.NRES
value	float64	0.0%	0.5269 – 2861275747.0 (mean 254206710.2039)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-27

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2024.0	2001.5655	2006.0
value	0.5269	2861275747.0	254206710.2039	6744.5

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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 World Bank Group and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_science_technology_south_africa,
  title     = {South Africa - Science and Technology},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-south-africa},
  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.