electricsheepafrica/africa-science-technology-lesotho

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- economics
- indicators
- lso
pretty_name: "Lesotho - Science and Technology"
dataset_info:
splits:
- name: train
num_examples: 100
- name: test
num_examples: 25
---

# Lesotho - Science and Technology

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-lesotho) · **License:** `cc-by` · **Updated:** 2026-03-27

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-lesotho) 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: **LSO**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 125 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 100 rows |
| **Test split** | 25 rows |
| **Geographic scope** | LSO |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Lesotho), `country_iso3` (LSO), `year` (range 1988.0–2024.0).

**Outcome / Measurement** — `value` (range -144976.9361–5036452.045).

**Identifier / Metadata** — `indicator_name` (Charges for the use of intellectual property, payments (BoP, current US$), Scientific and technical journal articles, Charges for the use of intellectual property, receipts (BoP, current US$)), `indicator_code` (BM.GSR.ROYL.CD, IP.JRN.ARTC.SC, BX.GSR.ROYL.CD), `esa_source` (HDX), `esa_processed` (2026-04-27).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-science-technology-lesotho")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | Lesotho |
| `country_iso3` | object | 0.0% | LSO |
| `year` | int64 | 0.0% | 1988.0 – 2024.0 (mean 2010.256) |
| `indicator_name` | object | 0.0% | Charges for the use of intellectual property, payments (BoP, current US$), Scientific and technical journal articles, Charges for the use of intellectual property, receipts (BoP, current US$) |
| `indicator_code` | object | 0.0% | BM.GSR.ROYL.CD, IP.JRN.ARTC.SC, BX.GSR.ROYL.CD |
| `value` | float64 | 0.0% | -144976.9361 – 5036452.045 (mean 656545.4233) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-27 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1988.0 | 2024.0 | 2010.256 | 2011.0 |
| `value` | -144976.9361 | 5036452.045 | 656545.4233 | 273.5127 |

---

## 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.

---

## 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](https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-lesotho) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_science_technology_lesotho,
title = {Lesotho - Science and Technology},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-science-and-technology-indicators-for-lesotho},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*