| --- |
| 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](https://huggingface.co/electricsheepafrica) |
|
|
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|
|
| ## 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](https://data.humdata.org/dataset/sind-education-in-danger-monthly-news-briefs-dataset) |
| - **Publisher:** Insecurity Insight |
| - **Resource:** [2021-2023 Drone Attacks on Education Data.xlsx](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) |
| - **Format:** `XLSX` |
| - **License:** [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) |
| - **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 |
|
|
| ```python |
| 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 |
|
|
| ```python |
| sample_country = df[df["country_iso3"] == "LBY"] |
| ``` |
|
|
| ### Work with indicators |
|
|
| ```python |
| 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 |
|
|
| ```bibtex |
| @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](https://creativecommons.org/licenses/by-sa/4.0/). |
|
|
| 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](https://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 |
|
|
