electricsheepasia/asia-education-schools-in-syria-2018-edition-4

Schools in Syria 2018 - Edition 4

Publisher: Assistance Coordination Unit · Source: HDX · License: cc-by-igo · Updated: 2025-04-29

Abstract

The ACU’s Information Management Unit conducted the 4th version of its annual research “Schools in Syria”, to highlight the impact of the Syrian conflict on education and the needs of students and school supplies. This is the most representative, nuanced iteration of this study to date, covering 4,079 schools within 99 sub-districts across 10 governorates, building upon 35,925 data e-forms with 31,846 forms on perception surveys. It has significant increase in the number of the functional schools addressed over its first version to the current one by 2,572 schools.

Please note to be more specific as possible on the following when you request the data:

Why are you requesting the data? What is the intent to use the data? What is your role? Who is the organization that you represent for?

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-04-29. Geographic scope: SYR.

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

Dataset Characteristics

Variables

Geographic — syrian_arab_republic_schools_in_syria_edition_04_2018_issued_by_imu_acu (Idleb, Aleppo, Ar-Raqqa).

Identifier / Metadata — unnamed_1 (SY07, SY02, SY11), unnamed_2 (Al Mara, Tell Abiad, Idleb), unnamed_3 (SY0702, SY1102, SY0700), unnamed_4 (Ein Issa, Ma'arrat An Nu'man, Sarin), unnamed_5 (SY110202, SY070200, SY020602) and 58 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-education-all")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snakecase. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 3 column(s) with >80% missing values were removed: `unnamed29, unnamed30`, `unnamed64`. 1 exact duplicate rows were removed. 47 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 Assistance Coordination Unit and has not been independently validated by ESA.
• —Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• —The following columns have >20% missing values and should be treated with caution in modelling: unnamed_28.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_education_all,
  title     = {Schools in Syria 2018 - Edition 4},
  author    = {Assistance Coordination Unit},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/schools-in-syria-2018-edition-4},
  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.