CoolFace
Datasetpublic

electricsheepeurope/europe-owid-affected-by-disasters-vs-gdp

Affected By Disasters Vs Gdp | Europe (Our World in Data) 🇪🇺 1,024 observations · 41 Europe countries · 2000–2024 · Repackaged by Electric Sheep Europe TL;DR This dataset contains 1,024 observations of Affected By Disasters Vs Gdp data across 41 Europe countries, spanning 2000–2024. About the source Source: Our World in Data Publisher: Our World in Data License: cc-by-4.0 Topic: Affected By Disasters Vs Gdp Geographic coverage… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepeurope/europe-owid-affected-by-disasters-vs-gdp.

sourceHugging Facecc-by-4.0updated 4mo agoView on Hugging Face
0likes15downloads
Dataset Card

Affected By Disasters Vs Gdp | Europe (Our World in Data)

🇪🇺 1,024 observations · 41 Europe countries · 2000–2024 · Repackaged by [Electric Sheep Europe](https://huggingface.co/electricsheepeurope)

rows countries years license

TL;DR

This dataset contains 1,024 observations of Affected By Disasters Vs Gdp data across 41 Europe countries, spanning 2000–2024.

About the source

Geographic coverage

41 Europe countries · top rows shown below, sorted by row count:

CountryRowsFirst yearLast year
ALB2520002024
AND2520002024
AUT2520002024
BEL2520002024
BGR2520002024
BIH2520002024
BLR2520002024
CHE2520002024
CZE2520002024
DEU2520002024
DNK2520002024
ESP2520002024
EST2520002024
FIN2520002024
FRA2520002024
...26 more countries

Schema

ColumnTypeDescriptionExample
country_namestring—Albania
country_iso3string—ALB
yearint64—2000
People affected by disastersfloat64—6122.469
GDP per capitafloat64—6582.0166
World region according to OWIDstring—Europe

Data quality & caveats

  • —People affected by disasters column has 51.5% null values (filtered to non-null in this dataset).

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepeurope/europe-owid-affected-by-disasters-vs-gdp")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python
germany = df[df["country_iso3"] == "DEU"]

Time-series for a single indicator

python
sample = df.sort_values("year")
sample.plot(x="year", y="People affected by disasters")

Citation

bibtex
@misc{europe_owid_affected_by_disasters_vs_gdp_2024,
  title        = {Affected By Disasters Vs Gdp | Europe (Our World in Data)},
  author       = {Our World in Data},
  year         = {2024},
  url          = {https://ourworldindata.org/grapher/affected-by-disasters-vs-gdp},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Europe},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepeurope/europe-owid-affected-by-disasters-vs-gdp}}
}

License

Released under cc-by-4.0.

Original data © Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Europe repackaging.

About Electric Sheep

Electric Sheep Europe is part of the Electric Sheep mission: a unified, ML-ready data layer for Europe on HuggingFace. 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/electricsheepeurope


Provenance: ingested 2026-06-02 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/affected-by-disasters-vs-gdp