license: cc-by-sa-4.0
language:
- en
task_categories:
- tabular-classification
- tabular-regression
multilinguality: monolingual
size_categories:
- n<1K
tags:
- tabular
- xlsx
- africa
- libya
- official-statistics
- open-data
- education
configs:
- config_name: default
data_files:
- split: train
path: data/train-00000-of-00001.parquet
pretty_name: Education in Danger Data | Africa (Libya official open data)
Education in Danger Data | Africa (Libya official open data)
28 rows - 1 Africa country - 2021-2023 - Repackaged by Electric Sheep Africa
TL;DR
This dataset packages one official XLSX resource from Libya as
ML-ready Parquet. The source file is the provenance boundary; all usable
indicators or tabular columns from the resource stay together in this repo.
About the source
- Source: Education in Danger Data
- Publisher: Insecurity Insight
- Resource: 2021-2023 Drone Attacks on Education Data.xlsx
- Format:
XLSX - License: CC BY-SA
- Packaging mode:
tabular_resource
Geographic coverage
1 Africa country:
| Country | Rows | First year | Last year | Name |
|---|---|---|---|---|
LBY |
28 | 2021 | 2023 | Libya |
Indicators or Resource Contents
- This source file is packaged as a normalized tabular resource.
Schema
| Column | Type | Description | Example |
|---|---|---|---|
source_record_id |
string |
Stable row identifier for tabular resources. | 3603cf01-a00f-4909-9327-9babcc7c2b17:2021-2023-drone-attacks-on-educ:0 |
country_iso3 |
category |
ISO3 country code. | LBY |
country_name |
category |
Country name. | Libya |
source_sheet |
string |
Workbook sheet name, when the source is a spreadsheet. | 2021-2023 Drone Attacks on Educ |
date |
string |
Observation date. | #date +occurred |
country |
string |
Source column. | #country |
country_iso |
string |
Source column. | #country_ISO |
admin_1 |
string |
Source column. | #admin1 |
latitude |
float64 |
Source column. | `` |
longitude |
float64 |
Source column. | `` |
geo_precision |
string |
Source column. | `` |
launch_type |
string |
Source column. | #weapons |
reported_perpetrator |
string |
Source column. | #group +perp |
reported_perpetrator_name |
string |
Source column. | #group +perp +name |
location |
string |
Source column. | `` |
location_of_event |
string |
Source column. | #loc |
education_infrastructure_damaged_or_destroyed_by_explosi |
string |
Source column. | `` |
sind_event_id |
float64 |
Source column. | `` |
source_period_start_year |
Int64 |
First year inferred from source resource metadata. | 2021 |
source_period_end_year |
Int64 |
Last year inferred from source resource metadata. | 2023 |
source_period_label |
category |
Human-readable period inferred from source resource metadata. | 2021-2023 |
source_provider |
category |
Publishing organization. | Insecurity Insight |
source_dataset |
category |
Source package title. | Education in Danger Data |
source_resource |
category |
Source resource title. | 2021-2023 Drone Attacks on Education Data.xlsx |
source_package_id |
category |
CKAN package UUID. | c7db52ae-13c2-4bf5-ae71-a9243a68194e |
source_resource_id |
category |
CKAN resource UUID. | 3603cf01-a00f-4909-9327-9babcc7c2b17 |
source_url |
category |
Original source resource URL. | https://data.humdata.org/dataset/c7db52ae-13c2-4bf5-ae71-a9243a68194e/re |
license_id |
category |
Source license identifier. | cc-by-sa |
retrieved_at |
category |
UTC retrieval timestamp. | 2026-08-17T13:13:43Z |
data_notes |
string |
Source column. | `` |
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-libya-education-in-danger-data-8ab879bb")
df = ds["train"].to_pandas()
print(df.head())
Filter to one country
sample_country = df[df["country_iso3"] == "LBY"]
Work with indicators
if "indicator_id" in df.columns:
print(df["indicator_id"].value_counts().head())
sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])
Citation
@misc{electric_sheep_africa_africa_libya_education_in_danger_data_8ab879bb_2023,
title = {Education in Danger Data | Africa (Libya official open data)},
author = {Insecurity Insight},
year = {2023},
url = {https://data.humdata.org/dataset/sind-education-in-danger-monthly-news-briefs-dataset},
publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-libya-education-in-danger-data-8ab879bb}}
}
License
Released under CC BY-SA.
Original data (c) Insecurity Insight. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.
About Electric Sheep
Electric Sheep Africa is part of the Electric Sheep mission: a unified,
ML-ready data layer for Africa on Hugging Face. 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/electricsheepafrica
Provenance: ingested 2026-08-17 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/c7db52ae-13c2-4bf5-ae71-a9243a68194e/resource/3603cf01-a00f-4909-9327-9babcc7c2b17/download/2021-2023-drone-attacks-on-education-data.xlsx